Updated on 2024/04/23

写真a

 
WATADA, Junzo
 
Affiliation
Faculty of Science and Engineering
Job title
Professor Emeritus
Degree
Doctor of Engineering ( 1984.03 Osaka Prefecture University )

Research Experience

  • 2003.04
    -
    2016.03

    Professor, Waseda University   Graduate School of Production, Information, System; Faculty of Science nd Engineering;   professor   Professor

  • 2003
    -
     

    - 早稲田大学大学院、情報生産システム 研究科教授

  •  
     
     

    Waseda University

  •  
     
     

    Information, Production and Systems,

Education Background

  •  
    -
    1970

    Osaka City University   Faculty of Engineering  

  •  
    -
    1970

    Osaka City University   Faculty of Engineering   School of Electrical Engineering  

Committee Memberships

  • 2003.04
    -
    Now

    World Collaborative Innovative Innovation Center of Management Engineers  Executive Chair

  • 2003.04
    -
    Now

    International Society of Management Engineers  Executive Chair

  • 2019.01
    -
    2022.12

    Forum for Interdisciplinary Mathematics  President

  • 2013.01
    -
    2018.12

    Forum for Interdisciplinary Mathematics  Vice President

  • 2003
    -
    2005

    バイオメディカルファジィシステム学会  顧問

  • 2003
    -
    2005

    日本知能情報ファジィ学会  九州支部支部・副支部長

  • 2003
    -
     

    BMFSA  プログラム委員

  • 2003
    -
     

    日本ファジィ学会  プログラム委員

  • 2003
    -
     

    日本人間工学会  関西支部役員、関西支部副支部長、関西支部支部長、関西支部支部 顧問

  • 2001
    -
    2003

    バイオメディカルファジィシステム学会  会長

  • 2001
    -
    2003

    日本人間工学会  関西支部役員、関西支部副支部長、関西支部支部長、関西支部支部 顧問

  • 1999
    -
    2001

    龍谷大学社会科学研究所  客員研究員

  • 1998
    -
    2001

    日本人間工学会  関西支部役員、関西支部副支部長、関西支部支部長、関西支部支部 顧問

  • 1997
    -
    2001

    日本ファジィ学会  学会設立準備委員、学会誌編集委員、学会誌編集委員長、関西支部・支部長、副会長、理事、評議員

  • 1997
    -
    2000

    龍谷大学社会科学研究所  客員研究員

  • 1989
    -
    2000

    日本ファジィ学会  学会設立準備委員、学会誌編集委員、学会誌編集委員長、関西支部・支部長、副会長、理事、評議員

  • 1999
    -
     

    日本ファジィ学会  大会委員長

  • 1995
    -
    1997

    日本ファジィ学会  学会設立準備委員、学会誌編集委員、学会誌編集委員長、関西支部・支部長、副会長、理事、評議員

  • 1995
    -
     

    日本情報処理学会  査読委員

  • 1995
    -
     

    日本電気学会誌  査読委員

  • 1993
    -
    1995

    International Fuzzy Systems Association  日本支部代表

  • 1993
    -
    1995

    日本経営工学会  評議員

  • 1993
    -
    1995

    日本ファジィ学会  学会設立準備委員、学会誌編集委員、学会誌編集委員長、関西支部・支部長、副会長、理事、評議員

  • 1994
    -
     

    日本ファジィ学会  プログラム委員

  • 1986
    -
    1994

    Official Journal of International Fuzzy Systems Association  Referee of Int. Journal of Fuzzy Sets and Systems

  • 1991
    -
    1993

    日本ファジィ学会  学会設立準備委員、学会誌編集委員、学会誌編集委員長、関西支部・支部長、副会長、理事、評議員

  • 1991
    -
     

    オペレ-ションズリサーチ学会  査読委員

  • 1990
    -
     

    香港助成委員会  審査委員

  • 1990
    -
     

    香港中文大学、大学院、計算機学科、  学外学位審査委員

  • 1989
    -
     

    日本ファジィ学会  実行委員

  • 1989
    -
     

    日本人間工学会  関西支部役員、関西支部副支部長、関西支部支部長、関西支部支部 顧問

  • 1989
    -
     

    日本人間工学会  評議員

  • 1988
    -
    1989

    日本ファジィ学会  学会設立準備委員、学会誌編集委員、学会誌編集委員長、関西支部・支部長、副会長、理事、評議員

  • 1987
    -
    1989

    1990年度ODAM国際k会議  準備委員会委員

  • 1988
    -
     

    システム制御情報学会誌  査読委員

  • 1988
    -
     

    計測自動制御学会論文誌  査読委員

  • 1987
    -
     

    日本ファジィ学会誌  査読委員

  • 1987
    -
     

    日本経営工学会誌  査読委員

  • 1985
    -
     

    日本人間工学会研究部会  オーガナイゼーションデザイン研究会 委員

  • 1985
    -
     

    Official Journal of the IEEE Neural Networks Council  Referee of Int. Journal of Fuzzy Systems, IEEE

  • 1984
    -
    1985

    日本人間工学会関西支部  組織設計研究会 委員

▼display all

Professional Memberships

  •  
     
     

    IFSA

  •  
     
     

    日本感性工学会

  •  
     
     

    シミュレーションゲーミング学会

  •  
     
     

    日本ファジィ知能情報学会

  •  
     
     

    BMFSA

  •  
     
     

    龍谷大学社会科学研究所

  •  
     
     

    香港助成委員会

  •  
     
     

    計算機学科

  •  
     
     

    大学院

  •  
     
     

    香港中文大学

  •  
     
     

    日本情報処理学会

  •  
     
     

    日本電気学会誌

  •  
     
     

    オペレ-ションズリサーチ学会

  •  
     
     

    システム制御情報学会誌

  •  
     
     

    計測自動制御学会論文誌

  •  
     
     

    日本ファジィ学会誌

  •  
     
     

    日本経営工学会誌

  •  
     
     

    International Fuzzy Systems Association

  •  
     
     

    1990年度ODAM国際k会議

  •  
     
     

    日本人間工学会研究部会

  •  
     
     

    日本人間工学会関西支部

  •  
     
     

    日本知能情報ファジィ学会

  •  
     
     

    国際ファジィシステム学会

  •  
     
     

    バイオメディカルファジィシステム学会

  •  
     
     

    日本ゲーミングシミュレーション学会

  •  
     
     

    日本ファジィ学会

  •  
     
     

    計測自動制御学会

  •  
     
     

    日本人間工学会

  •  
     
     

    日本オペレーションズリサーチ学会

  •  
     
     

    日本行動計量学会

  •  
     
     

    日本経営学会

  •  
     
     

    日本経営工学会

  •  
     
     

    システム制御情報学会

  •  
     
     

    日本自動制御協会

  •  
     
     

    on Expert Systems in Engineering Applications

  •  
     
     

    at the International Conference'89

  •  
     
     

    Sponsoring Committee

  •  
     
     

    Canada

  •  
     
     

    Int.ernational Conference of North American Fuzzy Information Processing Systems (NAFIPS''90)

  •  
     
     

    at the International Symposium'89 of North American Fuzzy Information Processing Systems (NAFIPS'90)

  •  
     
     

    Editorial Board of the Proceeding

  •  
     
     

    Toronto

  •  
     
     

    at the International Conference of North American Fuzzy Information Processing Systems (NAFIPS'90)

  •  
     
     

    Program Committee

  •  
     
     

    Kyoto

  •  
     
     

    on Human Factors in Organizational Design and Management

  •  
     
     

    at the 3rd International Symposium

  •  
     
     

    Secretariat General of the Symposium

  •  
     
     

    International Fuzzy Systems Association

  •  
     
     

    President of Japan Chapter

  •  
     
     

    Brazil-Japan Joint Symposium on Fuzzy Systems

  •  
     
     

    Brazil

  •  
     
     

    Sao Paulo

  •  
     
     

    6th International Fuzzy Systems Association World Congress

  •  
     
     

    USA

  •  
     
     

    Maryland

  •  
     
     

    College Park

  •  
     
     

    ISUMA-NAFIPS'95(3rd International Symposium on Uncertainty Modeling and Analysis and Annual Conference of the North American Fuzzy Information Processing Society)

  •  
     
     

    Nagoya

  •  
     
     

    1995 IEEE/Nagoya University World Wise-person Workshop(WWW'95) on Fuzzy Logic and Neural Networks/Evolutionary Computation

  •  
     
     

    JAPAN

  •  
     
     

    Kobe

  •  
     
     

    IFCS-96(Fifth Conference of International Federation of Classification Societies)

  •  
     
     

    Local Organizing Committee

  •  
     
     

    Vietnam

  •  
     
     

    Vietnam.

  •  
     
     

    Halong Bay

  •  
     
     

    Vietnam-Japan Bilateral Symposium on Systems and Applications

  •  
     
     

    Tsukuba International Convention Center in Tsukuba Science City, Japan

  •  
     
     

    International Symposium on Theory and Applications of Soft Computing(AFSS2000)

  •  
     
     

    The Fourth Asian Fuzzy Systems Symposium

  •  
     
     

    International Program Committee

  •  
     
     

    Malaysia

  •  
     
     

    (TENCON 2000) 25th - 28th

  •  
     
     

    IEEE International Conference on Intelligent Systems and Technologies for the Next Millennium

  •  
     
     

    International Advisory Panel

  •  
     
     

    KAIST

  •  
     
     

    Korea Taejon

  •  
     
     

    2nd International Symposium on Advanced Intelligent System

  •  
     
     

    Co-chairperson for International Program Committee

  •  
     
     

    Malyasia

  •  
     
     

    Kota Kinabaru

  •  
     
     

    International Conference on Artificail Intelligence in Engineering and Technology(ICAIET-2002)

  •  
     
     

    Co-chair of International Advisory Committee for Proceedings

  •  
     
     

    Romania

  •  
     
     

    Iasi

  •  
     
     

    Co-Chair of International Advisory Committee for Second European Conference on Intelligent systems and Technologies (ECIT2002)

  •  
     
     

    Koyasa, Japan

  •  
     
     

    General chairperson for 5-th Czech-Japan Seminar

  •  
     
     

    Tsukuba, Japan

  •  
     
     

    Member of Organizing Committee for Joint International Conference on Soft Computing and Intelligent Systems at Tsukuba

  •  
     
     

    MalaysiaCommittee of AIAI

  •  
     
     

    Kuala Lumpur

  •  
     
     

    International

  •  
     
     

    International Advisory Committee

  •  
     
     

    Member of Czech-Japan Research Cooperation

  •  
     
     

    Journal of Centre for Artificial Intelligence and Robotics (CAIRO)

  •  
     
     

    Finland

  •  
     
     

    BISC Group BISC Special Interest Group In Philosophy of Soft ComputingHelsinki University

  •  
     
     

    Advisory Board

  •  
     
     

    Referee for An International Journal of Information Science

  •  
     
     

    Referee for European Journal of Operations Researches

  •  
     
     

    Referee for International Journal of Fuzzy Systems

  •  
     
     

    Official Journal of the IEEE Neural Networks Council

  •  
     
     

    Referee of Annals of the Institute of Statistical Mathematics

  •  
     
     

    Referee of Int. Journal of Information Science

  •  
     
     

    Official Journal of International Fuzzy Systems Association

  •  
     
     

    JAPONICA

  •  
     
     

    International Fuzzy Systems Association (IFSA)

  •  
     
     

    Institute of Electrical and Electronic Engineers (IEEE)

▼display all

Research Areas

  • Computer system

Research Interests

  • 経営工学

  • 人工知能

  • 人間機械係

  • システムモデル

  • 社会システム学

  • 経営学

  • Management Engineering

  • Artificial Intelligence

  • Human-Machine System

  • System Modelling

  • Engineering of Social System

  • HManagement

▼display all

Awards

  • 日本 知能情報ファジイ学会 2014年度 著述賞 (Fuzzy Stochastic Optimization, Theory and Application, Springer)

    2014  

  • KES 功労賞

    2014  

  • Outstanding Book Award, Japan Society of Intelligent Informatics and Fuzzy Theory, (Fuzzy Stochastic Optimization, Theory and Application, Springer)

    2014  

  • Outstanding Contribution to KES Award

    2014  

  • 2011年最大引用論文賞、IJICIC、、ISSN 1349-4198

    2012  

  • The most cited paper award, 2011, ICIC International ISSN 1349-4198

    2012  

  • 最優秀論文賞, KAIT2011

    2011  

  • Best Paper Award, KAIT2011

    2011  

  • 特別座長賞, KES-IDT2010

    2010  

  • Distinguished Session Chair Award, KES-IDT 2010, Balitimore,

    2010  

  • 知的技術に対する特別貢献賞

    2009  

  • Award for Distinguished Contribution of Intelligent Technologies, Guangxi Normal University, Guilin,

    2009  

  • 日本知能情報学会 フェロー

    2005  

  • グリゴール C. モイスル賞

    2005  

  • 日本知能情報ファジィ学会貢献賞

    2005  

  • ファジィシステム発展に対する貢献賞;カリフォルニア大学バークレイ校BISCファジィシステム40周年記念行事

    2005  

  • Fellor, Japan Society for Fuzzy Theory and Intelligent Informatics

    2005  

  • “Grigore C. Moisil” Award,

    2005  

  • The Contribution Award, Japan Society of Fuzzy Theory and Intellectual Informatics

    2005  

  • The Contribution Award to developing Fuzzy Systems, on behalf of BISC, Special Even 40 years of Fuzzy Systems

    2005  

  • バイオメディカル・ファジィ・システム学会 功労賞

    2004  

  • Contribution Award, Biomedical Fuzzy Systems Association

    2004  

  • 日本人間工学会人間工学専門資格

    2003  

  • Henri Coanda Medal, Inventico, Iasi, Romania

    2003  

  • 優秀論文発表賞、ソフトコンピューティングと知能システム国際会議、筑波、日本、

    2002  

  • Excelent Paper Presentation Award, Joint International Conference on Soft Computing and Intelligent Systems at Tsukuba, Tsukuba, Japan

    2002  

  • 国際会議貢献賞、第2回高度知能システム国際シンポジウム、KAIST、Taejon, Korea、

    2001  

  • International Contribution Award、2nd International Symposium on Advanced Intelligent System, Korea Taejon, KAIST

    2001  

▼display all

 

Papers

  • Knowledge acquisition from rough sets using merged decision rules

    Yoshiyuki Matsumoto, Junzo Watada

    Journal of Advanced Computational Intelligence and Intelligent Informatics   22 ( 3 ) 404 - 410  2018.05  [Refereed]

     View Summary

    Rough set theory was proposed by Z. Pawlak in 1982. This theory can mine knowledge based on a decision rule from a database, a web base, a set, and so on. The decision rule is used for data analysis as well as calculating an unknown object. We analyzed time-series data using rough sets. Economic time-series data was predicted using decision rules. However, there are cases where an excessive number of decision rules exist, from which, it is difficult to acquire knowledge. In this paper, we propose a method to reduce the number of decision rules by merging them. Similar to how it is difficult to acquire knowledge from multiple rules, it is also difficult to acquire knowledge from rules with a large number of condition attributes. We propose a method to reduce the number of condition attributes and thereby reduce the number of rules. We analyze time-series data using this proposed method and acquire knowledge for prediction using decision rules. We use TOPIX and the yen–dollar exchange rate as knowledge-acquisition data. We propose a method to facilitate knowledge acquisition by merging rules.

    DOI

  • A self-adaptive class-imbalance TSK neural network with applications to semiconductor defects detection

    Shing Chiang Tan, Shuming Wang, Junzo Watada

    INFORMATION SCIENCES   427   1 - 17  2018.02  [Refereed]

     View Summary

    This paper develops a hybrid approach integrating an adaptive artificial neural network (ANN) and a fuzzy logic system for tackling class-imbalance problems. In particular, a supervised learning ANN based on Adaptive Resonance Theory (ART) is combined with a Tagaki-Sugeno-Kang-based fuzzy inference mechanism to learn and detect defects of a real large highly imbalanced dataset collected from a semiconductor company. A benchmark study is also conducted to compare the classification performance of the proposed method with other published methods in the literature. The real dataset collected from the semiconductor company intrinsically demonstrates class overlap and data shift in a highly imbalanced data environment. The generalization ability of the proposed method in detecting semiconductor defects is evaluated and compared with other existing methods, and the results are analyzed using statistical methods. The outcomes from the empirical studies positively indicate high potentials of the proposed approach in classifying the highly imbalanced dataset posing overlap class and data shift. (C) 2017 Elsevier Inc. All rights reserved.

    DOI

  • Boosted HOG features and its application on object movement detection

    Junzo Watada, Huiming Zhang, Haydee Melo, Diqing Sun, Pandian Vasant

    Smart Innovation, Systems and Technologies   81   340 - 348  2018  [Refereed]

     View Summary

    Nowadays, traffic accidents is universally decreasing due to many advanced safety vehicle systems. To prevent the occurrence of a traffic accident, the first function that a safety vehicle system should accomplish is the detection of the objects in traffic situation. This paper presents a popular method called boosted HOG features to detect the pedestrians and vehicles in static images. We compared the differences and similarities of detecting pedestrians and vehicles, then we use boosted HOG features to get an satisfying result. In detecting pedestrians part, Histograms of Oriented Gradients (HOG) feature is applied as the basic feature due to its good performance in various kinds of background. On that basis, we create a new feature with boosting algorithm to obtain more accurate result. In detecting vehicles part, we use the shadow underneath vehicle as the feature, so we can utilize it to detect vehicles in daytime. The shadow is the important feature for vehicles in traffic scenes. The region under vehicle is usually darker than other objects or backgrounds and could be segmented by setting a threshold.

    DOI

  • SURF algorithm-based panoramic image mosaic application

    Junzo Watada, Huiming Zhang, Haydee Melo, Jiaxi Wang, Pandian Vasant

    Smart Innovation, Systems and Technologies   81   349 - 358  2018  [Refereed]

     View Summary

    Panoramic image mosaic is a technology to match a series of images which are overlapped with each other. Panoramic image mosaics can be used for different applications. Image mosaic has important values in various applications such as computer vision, remote sensing image processing, medical image analysis and computer graphics. Image mosaics also can be used in moving object detection with a dynamic camera. After getting the panoramic background of the video for detection, we can compare every frame in the video with the panoramic background, and finally detect the moving object. To build the image mosaic, SURF (Speeded Up Robust Feature) algorithm is used in feature detection and OpenCV is used in the programming. Because of special optimization in image fusion, the result becomes stable and smooth.

    DOI

  • Training method for a feed forward neural network based on meta-heuristics

    Haydee Melo, Huiming Zhang, Pandian Vasant, Junzo Watada

    Smart Innovation, Systems and Technologies   82   378 - 385  2018  [Refereed]

     View Summary

    This paper proposes a Gaussian-Cauchy Particle Swarm Optimization (PSO) algorithm to provide the optimized parameters for a Feed Forward Neural Network. The improved PSO trains the Neural Network by optimizing the network weights and bias in the Neural Network. In comparison with the Back Propagation Neural Network, the Gaussian-Cauchy PSO Neural Network converges faster and is immune to local minima.

    DOI

  • Building fuzzy variance gamma option pricing models with jump levy process

    Huiming Zhang, Junzo Watada

    Smart Innovation, Systems and Technologies   73   105 - 116  2018  [Refereed]

     View Summary

    Option pricing models are at core of financial area, and it includes various uncertain factors, such as the randomness and fuzziness. This paper constructs an jump Levy process by combining option pricing models with fuzzy theory, and it sets the drift, diffusion and trend terms as fuzzy random variable. Then, we adopts a Monte Carlo algorithm for numerical simulation, compares and analyses the variance gamma (VG) option pricing model through a simulation experiment, and determines the VG option pricing model and BS model pricing results. The results indicate that VG option pricing with fuzzy settings is feasible.

    DOI

  • Rough set-based text mining from a large data repository of experts’ diagnoses for power systems

    Junzo Watada, Shing Chiang Tan, Yoshiyuki Matsumoto, Pandian Vasant

    Smart Innovation, Systems and Technologies   73   136 - 144  2018  [Refereed]

     View Summary

    Usually it is hard to classify the situation where uncertainty of randomness and fuzziness exists simultaneously. This paper presents a rough set approach applying fuzzy random variable and statistical t-test to text-mine a large data repository of experts’ diagnoses provided by a Japanese power company. The algorithms of rough set and statistical t-test are used to distinguish whether a subset can be classified in the object set or not. The expected-value-approach is also applied to calculate the fuzzy value with probability into a scalar value.

    DOI

  • Analysis of time-series data by merging decision rules

    Yoshiyuki Matsumoto, Junzo Watada

    Journal of Advanced Computational Intelligence and Intelligent Informatics   21 ( 6 ) 1026 - 1033  2017.10  [Refereed]

     View Summary

    Rough set theory was proposed by Z. Pawlak in 1982. This theory enables the mining of knowledge granules as decision rules from a database, the web, and other sources. This decision rule set can then be used for data analysis. We can apply the decision rule set to reason, estimate, evaluate, or forecast an unknown object. In this paper, rough set theory is used for the analysis of time-series data. We propose a method to acquire rules from time-series data using regression. The trend of the regression line can be used as a condition attribute. We predict the future slope of the timeseries data as decision attributes. We also use merging rules to further analyze the time series data.

    DOI

  • Time series data analysis by rough set and merging method of decision rule

    Yoshiyuki Matsumoto, Junzo Watada

    IFSA-SCIS 2017 - Joint 17th World Congress of International Fuzzy Systems Association and 9th International Conference on Soft Computing and Intelligent Systems    2017.08  [Refereed]

     View Summary

    Rough set theory was proposed by Z. Pawlak in 1982. This theory can mine knowledge through a decision rule from a database, a web base, a set and so on. The decision rule is used for data analysis as well. And we can reason for an unknown object using the decision rule. However, there are cases where too many decision rules are found. It is difficult to acquire knowledge from too many rules. In this paper, we propose a method to reduce the number of rules by merging decision rules. It is difficult to acquire knowledge from many rules. It is also difficult to find knowledge from rules with a large number of condition attributes. We propose a method to reduce condition attributes. We think that reducing the number of rules by reducing the condition attribute. We analyze time series data using this proposed method. Acquire knowledge for prediction from time series data using decision rule. TOPIX is used as time series data. Analyze TOPIX data and acquire knowledge for prediction.

    DOI

  • Adaptive Budget-Portfolio Investment Optimization Under Risk Tolerance Ambiguity

    Shuming Wang, Bo Wang, Junzo Watada

    IEEE TRANSACTIONS ON FUZZY SYSTEMS   25 ( 2 ) 363 - 376  2017.04  [Refereed]

     View Summary

    In this study, we consider a portfolio-optimization incorporated budget investment problem under managers' risk tolerance ambiguity. In order to capture the decision dynamics driven by the risk tolerance ambiguity, a two-stage adaptive optimization model is developed. The budget allocation is the first-stage decision, which is made before knowing each manager's actual risk tolerance level, and the portfolio selection conducted by each manager is the second-stage decision, which adapts to the manager's risk tolerance. We introduce the concept of risk-neutral budget threshold (RNBT) that is modeled by a fuzzy set granule, and upon which the ambiguous risk tolerance curve is constructed, which can realistically capture the managers' risk-averse and/or risk-seeking attitudes. Due to the (realistic) nonconvex/nonconcave structure of the risk tolerance curve, and the existence of the ambiguity, the resulting problem is essentially a nonconvex adaptive optimization problem under uncertainty. To achieve a robust modeling and an efficient solution, we first restructure and robustize the information of fuzzy RNBTs and then transform the developed model into a mixed integer linear programming (MILP), which can be handled efficiently by off-the-shelf mixed integer program solvers. Leveraging the derived MILP structure, we can use the Benders decomposition to further enhance the scalability of the model. Furthermore, some model extensions on robustizing the probability estimations are discussed. Finally, computational studies are performed to demonstrate the effectiveness and insights of the model.

  • Multi-period portfolio selection with dynamic risk/expected-return level under fuzzy random uncertainty

    Bo Wang, You Li, Junzo Watada

    INFORMATION SCIENCES   385   1 - 18  2017.04  [Refereed]

     View Summary

    In this study, we discuss multi-period portfolio selection problems when security returns are described as fuzzy random variables. The main concern of this work is to apply dynamic risk tolerance and expected return levels in mathematical modeling; i.e., these two indices of each period are influenced by the investment result of the previous period as well as human risk attitudes instead of static values over the entire investment horizon. Essentially, this assumption is based on the reality that investors tend to update targets when their wealth changes. In addition, fuzzy random variables are employed here to incorporate historical data with expert knowledge when estimating security future returns. Based on the above considerations, two multi-period portfolio selection models are built in light of the different risk attitudes. We then provide property analysis on complicated nonlinear optimization problems and derive several equivalents of the models, which can be solved by the existing dynamic programming. In general situations, a fuzzy random simulation-based particle swarm optimization algorithm is developed to search for approximate optima. The performance of this research is exemplified by a real market data-based case study in which the superiority of the dynamic strategy is demonstrated by a comparison with conventional approaches. (C) 2016 Elsevier Inc. All rights reserved.

    DOI

  • Special Issue On: Optimization for Engineering, Science and Technology

    Pandian Vasant, Junzo Watada

    Intelligent Decision Technologies   11 ( 1 ) 1  2017  [Refereed]

    DOI

  • Quantum Particle Swarm Optimization for Multiobjective Combined Economic Emission Dispatch Problem Using Cubic Criterion Function

    Fahad Parvez Mahdi, Pandian Vasant, M. Abdullah-Al-Wadud, Md. Mushfiqur Rahman, Junzo Watada, Vish Kallimani

    2017 IEEE INTERNATIONAL CONFERENCE ON IMAGING, VISION & PATTERN RECOGNITION (ICIVPR)    2017  [Refereed]

     View Summary

    In this research, quantum particle swarm optimization (QPSO) is utilized to solve multiobjective combined economic emission dispatch (CEED) problem formulated using cubic criterion function considering a uni wise max/max price penalty factor. QPSO is implemented on a 6-unit power generation system and compared with Lagrangian relaxation, particle swarm optimization (PSO) and simulated annealing (SA). The obtained results verified the effectiveness and demonstrate the robustness of QPSO method. This research suggests that QPSO can be used as an effective and robust tool in other power dispatch problems.

  • Unit Commitment Optimization with Pricing Support for Ultra-Low Emissions: A Multi-Objective Approach

    Bo Wang, Min Zhou, Junzo Watada

    CONFERENCE PROCEEDINGS OF 2017 3RD IEEE INTERNATIONAL CONFERENCE ON CONTROL SCIENCE AND SYSTEMS ENGINEERING (ICCSSE)     692 - 697  2017  [Refereed]

     View Summary

    Economy, reliability and environmental friendly are primary goals when modeling modern unit commitment problems. In this study, we establish a multi-objective unit commitment model considering the above objectives. In particular, the pricing support for ultra-low emissions is addressed together with startup/shutdown, generation and environment concerns when calculating the operation cost of thermal units, which conforms the present situation of power markets, especially in China. To solve the complicated nonlinear model, a multi-objective particle swarm optimization algorithm is developed. Finally, a series of experiments were performed on a modified 26-thermal-unit test system, which demonstrates the superiority of this research.

  • Building a type-2 fuzzy regression model based on credibility theory and its application on arbitrage pricing theory

    Yicheng Wei, Junzo Watada

    IEEJ TRANSACTIONS ON ELECTRICAL AND ELECTRONIC ENGINEERING   11 ( 6 ) 720 - 729  2016.11  [Refereed]

     View Summary

    Type-2 (T2) fuzzy set was introduced to model vagueness associated with primary membership function of type-1 (T1) fuzzy set. While it was invented to handle more fuzzy information, there are only a few algorithms (models) to deal with data in the form of T2 fuzzy variables given their three-dimensional features. To solve the problem, we define the expected value of a T2 fuzzy variable using credibility theory in this paper. And by substituting the expected value for the original T2 fuzzy set, the vertical uncertainties of data are transferred to horizontal ones without much distortion of information. Calculations between three-dimensional T2 fuzzy sets are thus transferred to two-dimensional range calculations between T1 fuzzy sets. Based on that principle, we also build a T2 fuzzy expected regression model and apply it to the arbitrage pricing theory. (c) 2016 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

    DOI

  • A bertrand game-based approach to hotel yield management strategies

    Junzo Watada, Koki Yoshimura, Pandian Vasant

    Handbook of Research on Holistic Optimization Techniques in the Hospitality, Tourism, and Travel Industry     27 - 66  2016.10  [Refereed]

     View Summary

    This chapter examines hotel yield management from a game perspective using a duopoly situation of two hotels. The hotel yield management determines strategies by considering the number of available rooms in the Bertrand situation. Each hotel does not know the strategies adopted by a competitive hotel. We derive the strategy that realizes a maximum profit under a given situation and constraints. Furthermore, we validate the game-based strategy developed for hotel yield management. In the real world, a business manager adopts an optimum strategy of yield management to gain profits in the current conditions
    after the initial strategy is chosen, however, managers continuously weigh new strategies and investments. Therefore, we import the method of real option. Such maneuvers and investments are required to build new strategies amidst competition in the industry.

    DOI

  • Multi-objective unit commitment with wind penetration and emission concerns under stochastic and fuzzy uncertainties

    Bo Wang, Shuming Wang, Xianzhong Zhou, Junzo Watada

    ENERGY   111   18 - 31  2016.09  [Refereed]

     View Summary

    Recent years have witnessed the ever increasing renewable penetration in power generation systems, which entails modern unit commitment problems with modelling and computation burdens. This study aims to simulate the impacts of manifold uncertainties on system operation with emission concerns. First, probability theory and fuzzy set theory are applied to jointly represent the uncertainties such as wind generation, load fluctuation and unit outage that interleaved in unit commitment problems. Second, a Value-at-Risk-based multi-objective approach is developed as a bridge of existing stochastic and robust unit commitment optimizations, which not only captures the inherent conflict between operation cost and supply reliability, but also provides easy-to-adjust robustness against worst-case scenarios. Third, a multi-objective algorithm that integrates fuzzy simulation and particle swarm optimization is developed to achieve approximate Pareto-optimal solutions. The research effectiveness is exemplified by two case studies: The comparison between test systems with and without generation uncertainty demonstrates that this study is practicable and can suggest operational insights of generation mix systems. The sensitivity analysis on Value-at-Risk proves that our method can achieve adequate tradeoff between performance optimality and robustness, thus help system operators in making informed decisions. Finally, the model and algorithm comparisons also justify the superiority of this research. (C) 2016 Elsevier Ltd. All rights reserved.

    DOI

  • GbLN-PSO and Model-Based Particle Filter Approach for Tracking Human Movements in Large View Cases

    Zalili Musa, Mohd Zuki Salleh, Rohani Abu Bakar, Junzo Watada

    IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY   26 ( 8 ) 1433 - 1446  2016.08  [Refereed]

     View Summary

    Camera tracking systems have become a common requirement in today's society. The availability of high-quality and inexpensive video cameras and the increasing need for automated video analysis have generated a great deal of interest in numerous fields. In general, it is not easy to track human behavior in an environment with a large view. This paper aims to address four problems associated with large view in camera tracking system: 1) multiple targets in nonlinear motion; 2) relative size of the targeted object; 3) occlusion; and 4) processing time. This paper presents a new method of tracking human movements using global best local neighborhood oriented particle swarm optimization and model-based particle filter to address the above problems. The proposed method has been tested with an experimental module using several sets of video data provided by the 11th IEEE International Workshop on Performance Evaluation of Tracking and Surveillance and two other video streams of University of British Columbia (UBC) hockey and Malaysian football games. The experiment has shown that the accuracy of tracking performance has increased up to 25% compared with other reported works in the scientific literature.

    DOI

  • Fuzzy Autocorrelation Model with Fuzzy Confidence Intervals and its Evaluation

    Journal of advanced computational intelligence and intelligent informatics   20 ( 4 ) 512-520 - 520  2016.07  [Refereed]

    DOI CiNii

  • Two-Stage Multi-Objective Unit Commitment Optimization Under Hybrid Uncertainties

    Bo Wang, Shuming Wang, Xian-zhong Zhou, Junzo Watada

    IEEE TRANSACTIONS ON POWER SYSTEMS   31 ( 3 ) 2266 - 2277  2016.05  [Refereed]

     View Summary

    Unit commitment, as one of the most important control processes in power systems, has been studied extensively in the past decades. Usually, the goal of unit commitment is to reduce as much production cost as possible while guaranteeing the power supply operated with a high reliability. However, system operators encounter increasing difficulties to achieve an optimal scheduling due to the challenges in coping with uncertainties that exist in both supply and demand sides. This study develops a day-ahead two-stage multi-objective unit commitment model which optimizes both the supply reliability and the total cost with environmental concerns of thermal generation systems. To tackle the manifold uncertainties of unit commitment in a more comprehensive and realistic manner, stochastic and fuzzy set theories are utilized simultaneously, and a unified reliability measurement is then introduced to evaluate the system reliability under the uncertainties of both sudden unit outage and unforeseen load fluctuation. In addition, a cumulative probabilistic method is proposed to address the spinning reserve optimization during the scheduling. To solve this complicated model, a multi-objective particle swarm optimization algorithm is developed. Finally, a series of experiments were performed to demonstrate the effectiveness of this research; we also justify its feasibility on test systems with generation uncertainty.

    DOI

  • Design of a qualitative classification model through fuzzy support vector machine with type-2 fuzzy expected regression classifier preset

    Yicheng Wei, Junzo Watada, Witold Pedrycz

    IEEJ TRANSACTIONS ON ELECTRICAL AND ELECTRONIC ENGINEERING   11 ( 3 ) 348 - 356  2016.05  [Refereed]

     View Summary

    Methods of qualitative analysis, such as qualitative classification, have gained importance as an essential complement of existing quantitative analysis in numerous fields. Only a few models have been developed to deal with qualitative inputs in the form of type-2 fuzzy(T2F) sets properly, given that traditional defuzzification method like the Karnik-Mendel algorithm performs dimensionality reduction at the cost of loss of information. To improve the situation, we define the expected value and variance of T2F set in this paper. By using a combination of them, we transfer the vertical three-dimensional uncertainty of T2F set to horizontal range uncertainty without much distortion of information. Additionally, current classification models are unsuitable to the partial classification problem if an output is not fully assigned to a single class. We build a comprehensive qualitative classification model based on fuzzy support vector machine (FSVM) combined with type-2 fuzzy expected regression (FER) to solve the partial classification problem as mentioned. This classifier (i.e. FER-FSVM) makes it possible to achieve the discrimination of output while characterizing membership for each class in terms of multidimensional qualitative inputs (attributes) in the form of T2F sets. FER-FSVM also can self-learn the data structure and shift between FER or FSVM for classification automatically, thus largely improving the efficiency of the classification process. The new model is almost 7 times more efficient than FSVM, as shown by our empirical experiments. (c) 2016 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

    DOI

  • Analysis of the relation between health statistics and eating habits in Japanese prefectures using fuzzy robust regression model.

    Yabuuchi Y, Kawaura T, Watada J

    Computers in biology and medicine   72   256-262  2016.05  [Refereed]

    DOI

  • A unit commitment-based fuzzy bilevel electricity trading model under load uncertainty

    Bo Wang, Xian-zhong Zhou, Junzo Watada

    FUZZY OPTIMIZATION AND DECISION MAKING   15 ( 1 ) 103 - 128  2016.03  [Refereed]

     View Summary

    In this study, we establish a bilevel electricity trading model where fuzzy set theory is applied to address future load uncertainty, system reliability as well as human imprecise knowledge. From the literature, there have been some studies focused on this bilevel problem while few of them consider future load uncertainty and unit commitment optimization which handles the collaboration of generation units. Then, our study makes the following contributions: First, the future load uncertainty is characterized by fuzzy set theory, as the various factors that affect the load forecasting are often assessed with some non-statistical uncertainties. Second, the generation costs are obtained by solving complicated unit commitment problems, rather than approximate calculations used in existing studies. Third, this model copes with the optimizations of both the generation companies and the market operator, where the unexpected load risk is particularly analyzed by using fuzzy value-at-risk as a quantitative risk measurement. Forth, a mechanism to encourage the convergence of the bilevel model is proposed based on fuzzy maxmin approach, and a bilevel particle swarm optimization algorithm is developed to solve the problem in a proper runtime. To illustrate the effectiveness of this research, we provide a test system-based numerical example and discuss about the experimental results according to the principle of social welfare maximization. Finally, we also compare the model and algorithm with conventional methods.

    DOI

  • Building a Type-2 Fuzzy Random Support Vector Regression Scheme in Quantitative Investment

    Wei Yicheng, Watada Junzo

    The transactions of the Institute of Electrical Engineers of Japan.C   136 ( 4 ) 564 - 575  2016

     View Summary

    Financial markets are connected well these days. One class assets price performance is usually affected by movements of other classes of assets. However, the relationship between them is hard to trace and predict along with increase in complexity of markets behaviors these days. Nothing like stock market, money or bond market is an over-the-counter market, where assets prices are often presented in the form of classes of discrete quotations by traders subjective judgments, thus are hard to model and analyze. Given concern to this, we define the Type 2 fuzzy random variable (T2 fuzzy random variable) to quantify those bid/offer behaviors in this paper. Moreover, we build a T2 fuzzy random support vector regression (T2-FSVR)scheme to study relationships between these markets, thus form an effective trading strategy to predict the trend of market prices. We use matlab platform to implement and test the effectiveness of the new model, then train and test it with 2014 whole years price data of bond and money markets. We also compare T2-FSVRs prediction accuracy with type-2 fuzzy expected regression(T2-FER) and confidence-interval-based fuzzy random regression model(CI-FRRM). The result shows that T2-FSVR outperforms and has 98% accuracy while CI-FRRM has 81% accuracy and T2-FER has only 70% accuracy. Moreover,T2-FSVR can be developed into a automated trading strategy for practical business use, which is able to learn behaviors of different markets based on mass of available historical and real time data and earn profit automatically.

    DOI CiNii

  • Fuzzy random regression-based modeling in uncertain environment

    Nureize Arbaiy, Junzo Watada, Pei-Chun Lin

    Sustaining Power Resources through Energy Optimization and Engineering     127 - 146  2016.01  [Refereed]

     View Summary

    The parameter value determination is important to avoid the developed mathematical model is troublesome and may yield inappropriate results. However, estimating the weights of the parameter or objective functions in the mathematical model is sometimes not easy in real situations, especially when the values are unavailable or difficult to decide. Additionally, various uncertainties include in the statistical data makes common mathematical analysis is not competent to deal with. Hence, this paper presents the Fuzzy Random Regression approach to determine the coefficient whereby statistical data used contain uncertainties namely, fuzziness and randomness. The proposed methods are able to provide coefficient information in the model setting and consideration of uncertainties in the evaluation process. The assessment of coefficient value is given by Weight Absolute Percentage Error of Fuzzy Decision. It clarifies the results between fuzzy decision and non-fuzzy decision that shows the distance of different between both approaches. Finally, a real-life application of production planning models is provided to illustrate the applicability of the proposed algorithms to a practical case study.

    DOI

  • Analysis of Time-Series Data Using the Rough Set

    Yoshiyuki Matsumoto, Junzo Watada

    INNOVATION IN MEDICINE AND HEALTHCARE 2015   45   139 - 148  2016  [Refereed]

     View Summary

    Rough set theory was proposed by Z. Pawlak in 1982. This theory has high capability to mine knowledge based on decision rules from a database, a web base, a set and so on. The decision rule is widely used for data analysis as well. In this paper the decision rule is employed to reason for an unknown object. That is, the rough set theory is applied to analysis of economic time series data. An example shown in the paper indicates how to acquire knowledge from time series data. At the end we suggest its application to predictions.

    DOI

  • Gaussian-PSO with fuzzy reasoning based on structural learning for training a Neural Network

    Haydee Melo, Junzo Watada

    NEUROCOMPUTING   172   405 - 412  2016.01  [Refereed]

     View Summary

    This paper proposes Gaussian-PSO-based structural learning and fuzzy reasoning to optimize the weights and the structure of the Feed Forward Neural Network. The Neural Network is widely used for various applications; though it still has disadvantages such as learning capability and slow convergence. Back Propagation, the most used learning algorithm, has several difficulties such as the necessity for a priori specification of the network structure and sensibility to parameter settings. Recently, research studies have introduced evolutionary algorithms into the learning to improve its performance. The PSO is a population-based algorithm that has the advantage of faster convergence. However, the total number of the weights in the Neural Network determines the size of each particle, therefore the size of the network structure is computationally time consuming. The proposed method improves the learning and removes the stress by eliminating the necessity of determining a detailed network. (C) 2015 Elsevier B.V. All rights reserved.

    DOI

  • A Memetic Fuzzy ARTMAP by a Grammatical Evolution Approach

    Shing Chiang Tan, Chee Peng Lim, Junzo Watada

    INTELLIGENT DECISION TECHNOLOGIES 2016, PT I   56   447 - 456  2016  [Refereed]

     View Summary

    This paper presents a memetic fuzzy ARTMAP (mFAM) model constructed using a grammatical evolution approach. mFAM performs adaptation through a global search with particle swarm optimization (PSO) as well as a local search with the FAM training algorithm. The search and adaptation processes of mFAM are governed by a set of grammatical rules. In the memetic framework, mFAM is constructed and it evolves with a combination of PSO and FAM learning in an arbitrary sequence. A benchmark study is carried out to evaluate and compare the classification performance between mFAM and other state-of-art methods. The results show the effectiveness of mFAM in providing more accurate prediction outcomes.

    DOI

  • A Double Layer Neural Network Based on Artificial Bee Colony Algorithm for Solving Quadratic Bi-Level Programming Problem

    Junzo Watada, Haochen Ding

    INTELLIGENT DECISION TECHNOLOGIES 2016, PT I   56   437 - 446  2016  [Refereed]

     View Summary

    In this study, we formulate a double layer neural network based hybrid method to solve the quadratic bi-level programming problem. Our proposed algorithm comprises an improved artificial bee colony algorithm, a Hopfield network, and a Boltzmann machine in order to effectively and efficiently solve such problems. The improved artificial bee colony algorithm is developed for dealing with the upper level problem. The experiment results indicate that compared with other methods, the proposed double layer neural network based hybrid method is capable of achieving better optimal solutions for the quadratic bi-level programming problem.

    DOI

  • Particle Swarm Optimization Based Support Vector Machine for Human Tracking

    Zhenyuan Xu, Chao Xu, Junzo Watada

    INTELLIGENT DECISION TECHNOLOGIES 2016, PT I   56   457 - 470  2016  [Refereed]

     View Summary

    Human tracking is one of the most important researches in computer vision. It is quite useful for many applications, such as surveillance systems and smart vehicle systems. It is also an important basic step for content analysis for behavior recognition and target detection. Due to the variations in human positions, complicated backgrounds and environmental conditions, human tracking remains challenging work. In particular, difficulties caused by environment and background such as occlusion and noises should be solved. Also, real-time human tracking now seems a critical step in intelligent video surveillance systems because of its huge computational workload. In this paper we propose a Particle Swarm Optimization based Support Vector Machine (PSO- SVM) to overcome these problems. First, we finish the preliminary human tracking step in several frames based on some filters such as particle filter and kalman filter. Second, for each newly come frame need to be processed, we use the proposed PSO-SVM to process the previous frames as a regression frame work, based on this regression frame work, an estimated location of the target will be calculated out. Third, we process the newly come frame based on the particle filter and calculate out the target location as the basic target location. Finally, based on comparison analysis between basic target location and estimated target location, we can get the tracked target location. Experiment results on several videos will show the effectiveness and robustness of the proposed method.

    DOI

  • A Parsimonious Radial Basis Function-Based Neural Network for Data Classification

    Shing Chiang Tan, Chee Peng Lim, Junzo Watada

    INTELLIGENT DECISION TECHNOLOGY SUPPORT IN PRACTICE   42   49 - 60  2016  [Refereed]

     View Summary

    The radial basis function neural network trained with a dynamic decay adjustment (known as RBFNDDA) algorithm exhibits a greedy insertion behavior as a result of recruiting many hidden nodes for encoding information during its training process. In this chapter, a new variant RBFNDDA is proposed to rectify such deficiency. Specifically, the hidden nodes of RBFNDDA are re-organized through the supervised Fuzzy ARTMAP (FAM) classifier, and the parameters of these nodes are adapted using the Harmonic Means (HM) algorithm. The performance of the proposed model is evaluated empirically using three benchmark data sets. The results indicate that the proposed model is able to produce a compact network structure and, at the same time, to provide high classification performances.

    DOI

  • Summarizing Approach for Efficient Search by k-Medoids Method

    Yoshiyuki Yabuuchi, Hungming Hung, Junzo Watada

    2015 10TH ASIAN CONTROL CONFERENCE (ASCC)    2015  [Refereed]

     View Summary

    In past days, although we have focused on to collect required data, we can get required information since many data are storage and disclosed. Therefore, it has become a new task to search efficiently required information.
    Nowadays, the search engine such as Google, Bing and Baidu help us to search information in the internet. However, enormous number of search results is listed. In some cases, the number of search results can be more than a hundred million that are ranked by their relevancies to the search key words. It is difficult to find out the desired information because user's time and effort are required. In order to efficiently attain user's required information reach, although it is an effective way to rank data by their relevancies to the search key words, sometime it is better the way to summarize information. In this work, we propose summarizing approach for efficient search by k-medoids method. Without defining categories in advance, k-medoids method generates clusters with less susceptibility to noise. Experimental results verify our method's feasibility and effectiveness.

  • Studies on Eye Tracking and Brainwave Measurement

    Yung-Chin Hsiao, Hanayuki Kitagawa, Junzo Watada

    2015 10TH ASIAN CONTROL CONFERENCE (ASCC)    2015  [Refereed]

     View Summary

    Eye tracking and brainwave are becoming parts of human machine interface. In automobiles are being integrated with advanced driver assistance systems to improve the safety and assists. To reduce car accidents, vehicles should be detected by other drivers or pedestrian in any situations. Nowadays, many countries use the function of daytime running light (DRL) as a safety measure to reduce car accidents by increasing the contrast between vehicles and the background. Third brake light (TBL) also significantly increases and being legal requirement on all vehicles around the world. Light emitting diodes (LED) and their bright lights offer unique design possibilities in lighting technology. This paper surveys the effectiveness of LED in eye recognition based on for vehicles.

  • Building a Sensitivity-Based Portfolio Selection Models

    Huiming Zhang, Junzo Watada, Ye Li, You Li, Bo Wang

    INTELLIGENT DECISION TECHNOLOGIES   39   673 - 681  2015  [Refereed]

     View Summary

    Sensitivity Analysis is a method to evaluate the influence of each variable change. In portfolio selection model, it is essential to evaluate the sensitivity of each stock or security return rate in investment decision making. Investors look for selecting stable stocks or securities. For this purpose, sensitivity analysis should play a pivotal role. It is important for the decision-making to get both sensitivity and stability of each selection. This paper proposes a new portfolio-selection model (PSM) called the sensitivity-based portfolio selection models (SPSM). The SPSM model will focus on the sensitivity of the selected portfolio. In order to analyze the sensitivity of portfolio selection models, a sensitivity analysis will be introduced for calculating out insensitive stocks or securities with maximum return and minimum risk. Abstract environment.

    DOI

  • Panoramic Image mosaic based on SURF algorithm using OpenCV

    Jiaxi Wang, Junzo Watada

    2015 IEEE 9th International Symposium on Intelligent Signal Processing (WISP)     177 - 182  2015  [Refereed]

     View Summary

    Panoramic image mosaic is a technology to match a series of images which are overlapped with each other. Panoramic image mosaics can be used for different applications. Image mosaic has important values in various applications such as photogrammetry, computer vision, remote sensing image processing, medical image analysis and computer graphics. Image mosaics also can be used in moving object detection with a dynamic camera. After getting the panoramic background of the video for detection, we can compare every frame in the video with the panoramic background, and finally detect the moving object. To build the image mosaic, SURF (Speeded Up Robust Feature) algorithm is used in feature detection and OpenCV is used in the programming.

  • A fuzzy time-series prediction by GA based rough sets model

    Jing Zhao, Junzo Watada, Yoshiyuki Matsumoto

    2015 10TH ASIAN CONTROL CONFERENCE (ASCC)    2015  [Refereed]

     View Summary

    Fuzzy time-series (FTS) has been applied to handle non-linear problems, such as enrollment, weather and stock index forecasting. In the forecasting processes, fuzzy logical relation (FLR) plays a pivotal role in forecasting accuracy. Usually FTS uses an equal interval to obtain forecasting values. But in this paper, we use genetic algorithm (GA) to optimize the interval at first. Based on this, then rough set (RS) method is used to recalculate the values. In the empirical analysis, japan stock index is used as experimental data sets and one fuzzy time-series method, as a comparison model. The experimental results showed that the proposed method is more efficient than the FTS method.

  • A Bilevel Synthetic Winery System for Balancing Profits and Perishable Production Quality

    Haiyu Yu, Junzo Watada, Jingru Li

    INTELLIGENT DECISION TECHNOLOGIES   39   661 - 672  2015  [Refereed]

     View Summary

    Recent research works show an increasing trend in analyzing agriculture problem because of its good applicability and usability. Also, the rapid development of mathematical model mitigates the difficulty to solve such abstract and uncertain problem. In this paper, the questions are discussed about a wine system, which contains some grape blocks and winery together. In the paper, a bilevel model is developed for obtaining the balance between profits and production quality.

    DOI

  • Consumer and Service Characteristic Segmentations in Services Marketing Using a Biologically Systematic Computational Method

    Ikno Kim, Junzo Watada

    IEEE SYSTEMS JOURNAL   8 ( 4 ) 1227 - 1235  2014.12  [Refereed]

     View Summary

    In the field of services marketing, consumers can be segmented into separate subgroups with homogeneous service requirements in the process of consumer market segmentation. In this process, a computable number of consumers and the services that they can select are not difficult to handle. However, when there is a large consumer base and the services that the consumers can select have multiple shared and distinct characteristics, measurement and segmentation in polynomial time are extremely difficult. Therefore, in this paper, we propose a biologically systematic computational method that would be appropriate for the segmentation of consumer and service characteristics. We also demonstrate the application of this biologically systematic computational method to a services marketing problem model.

    DOI

  • Building linguistic random regression model from the perspective of type-2 fuzzy set

    Fei Song, Shinya Imai, Junzo Watada

    IEEE International Conference on Fuzzy Systems     2376 - 2383  2014.09  [Refereed]

     View Summary

    Information given in linguistic terms around real life sometimes is vague in meaning, as type-1 fuzzy set was introduced to modulate this uncertainty. Meanwhile, same word may result in various meaning to people, indicating the uncertainty also exist when associated with the membership function of a type-1 fuzzy set. Type-2 fuzzy set attempt to express the hybrid uncertainty of both primary and secondary fuzziness, in order to address regression problems, we built a type-2 Linguistic Random Regression Model based on credibility theory. Confidence intervals are constructed for fuzzy input and output, and the proposed regression model give a rise to a nonlinear programming problem focus on a well-trained model, which would be helpful and useful in linguistic assessment cases. Finally, a numerical example is provided.

    DOI

  • A Parametric Assessment Approach to Solving Facility-Location Problems with Fuzzy Demands

    Pei-Chun Lin, Junzo Watada, Berlin Wu

    IEEJ TRANSACTIONS ON ELECTRICAL AND ELECTRONIC ENGINEERING   9 ( 5 ) 484 - 493  2014.09  [Refereed]

     View Summary

    In real-world applications, sometimes randomness and fuzziness may coexist. In facility-location problems, the data expressed in natural language may contain vague information. We discuss the uncertainty included in demands in facility-location problems. The uncertain demand is called fuzzy demand in this paper. In the facility-location model, the parameters of fuzzy demand are determined by calculating the estimated expected value of the fuzzy demand, which is obtained by using the estimated parameters of the underlying probability distribution function of the fuzzy data. Moreover, we propose a defuzzification formula of the fuzzy demand called the realization of fuzzy demand. The defuzzification formula of fuzzy demand comprises the upper bound and the lower bound of the fuzzy demand. Moreover, the error of the fuzzy demand is assessed as the mean absolute percentage error of the fuzzy demand. Empirical studies show that we can solve real-life location problems by using the defuzzification formula of fuzzy demand and get higher profit in our facility-location model than by using conventional methods. (C) 2014 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

    DOI

  • Impact Evaluation of Exit Strategy in Fuzzy Portfolio-based Investment

    You Li, Bo Wang, Junzo Watada

    IEEJ TRANSACTIONS ON ELECTRICAL AND ELECTRONIC ENGINEERING   9 ( 5 ) 502 - 513  2014.09  [Refereed]

     View Summary

    This paper focuses on the impacts of exit strategy in existing fuzzy portfolio selection models. In the securities market, the term 'exit' means that the investor may sell his/her equity on account of some exogenous or endogenous incentives, especially when the security price becomes higher than his/her expectation or lower than his/her tolerance. It is a common problem that all investors need to face in the investment horizon. There have been various studies reported in the current literature employing fuzzy set theory to handle the uncertainty of portfolio selection. Nevertheless, none of these studies considers the influence caused by exit strategy which will be executed once a security price fluctuates out of the expected interval. In this paper, we first secure the future returns of each security by profit/loss exit points (prices) before rebuilding fuzzy portfolio selection models. Next, the properties of exit points are analyzed. Then a meta-heuristic method is proposed to analyze the differences between the new models' experimental results and those of previous methods. Finally, we discuss how to assign proper exit point values to different securities based on different risk attitudes, and apply our approach to a real application on the New York Stock Exchange. (C) 2014 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

    DOI

  • Fuzzy random regression based multi-attribute evaluation and its application to oil palm fruit grading

    A. Nureize, J. Watada, S. Wang

    ANNALS OF OPERATIONS RESEARCH   219 ( 1 ) 299 - 315  2014.08  [Refereed]

     View Summary

    Multi-attribute decision-making is usually concerned with weighting alternatives, thereby requiring weight information for decision attributes from a decision maker. However, the assignment of an attribute's weight is sometimes difficult, and may vary from one decision maker to another. Additionally, imprecision and vagueness may affect each judgment in the decision-making process. That is, in a real application, various statistical data may be imprecise or linguistically as well as numerically vague. Given this coexistence of random and fuzzy information, the data cannot be adequately treated by simply using the formalism of random variables. To address this problem, fuzzy random variables are introduced as an integral component of regression models. Thus, in this paper, we proposed a fuzzy random multi-attribute evaluation model with confidence intervals using expectations and variances of fuzzy random variables. The proposed model is applied to oil palm fruit grading, as the quality inspection process for fruits requires a method to ensure product quality. We include simulation results and highlight the advantage of the proposed method in handling the existence of fuzzy random information.

    DOI

  • Knowledge acquisition from time-series data with similar using the rough sets

    Matsumoto Yoshiyuki, Watada Junzo

    Proceedings of the Fuzzy System Symposium   30   87 - 90  2014

     View Summary

    Rough set theory was proposed by Z.Pawlak in 1982. This theory can mine knowledge granules through a decision rule from a database, a web base, a set and so on. The decision rule is used for data analysis as well. And we can apply the decision rule to reason, estimate, evaluate, or forecast an unknown object. In this paper, the rough set theory is used to analysis of time series data. Knowledge granules are minded from the data set of tick-wise price fluctuations. We search for the law of similarity from time-series data using the rough sets.

    DOI CiNii

  • Building a Qualitative Classification Model by Type-2 Fuzzy Regression Based Support Vector Machine

    Watada Junzo, Wei Yichen, Pedrycz Witold

    Proceedings of the Fuzzy System Symposium   30   470 - 475  2014

     View Summary

    Methods of qualitative analysis such as qualitative classification have gained importance as an essential complement of existing quantitative analysis in numerous fields, such as behavior finance, econometrics, and business management. Only a few models have been developed to deal with qualitative inputs (attributes), which appear in the form of T2F data. Additionally, classification models are unsuitable if an output point is not fully assigned to a single class. In this paper, we formulate a comprehensive qualitative classification model based on fuzzy support vector machine (FSVM in brief) combined with Type-2 fuzzy expected regression (FER in brief) to deal with T2F inputs. This classifier(FER-FSVM in brief) makes it possible to achieve discrimination of output while characterizing membership for each class in terms of multi-dimensional qualitative inputs (attributes). Moreover, FER-FSVM can self-learn the data structure and shifted between FER or FSVM for classification automatically. It will largely shorten the computing time especially for large datasets by using linear structure of FER classifier to limit the size of non-linear classification region.

    DOI CiNii

  • An estimation-of-distribution algorithm approach to redundancy allocation problem for a high-security system

    Haydee Melo, Junzo Watada

    IEEJ Journal of Industry Applications   3 ( 4 ) 358 - 367  2014  [Refereed]

     View Summary

    Reliability is an issue that has recently captivated the attention of researchers. Its goal is to develop new techniques to design more reliable systems, which can operate without failing during operation. A result of this growth in technology is an increase in the complexity and susceptibility of more complex systems. The principal objective of redundancy allocation is to maximize the availability of a system while reducing the cost, volume or weight. This paper proposes an Estimation-of-Distribution Algorithm (EDA) approach as a new meta-heuristic method to solve a redundancy allocation problem (RAP) for a high security control system.

    DOI

  • A gaussian particle swarm optimization for training a feed forward neural network

    Haydee Melo, Junzo Watada

    Advances in Intelligent Systems and Computing   293   61 - 68  2014  [Refereed]

     View Summary

    This paper proposes a Gaussian-PSO algorithm which provides the optimized parameters for Feed Forward Neural Network. Recently the Feed Forward Neural Network is widely used in various applications as a result of its advantages such as learning capability, auto-organization and auto-adaptation. However the Neural Network has the disadvantage itself to slowly converge and get easily trapped in a local minima. In this paper, Gaussian distributed random variables are used in the PSO algorithm to enhance its performance and train the weights and bias in the Neural Network. In comparison with the Back Propagation Neural Network, the Gaussian PSO-Neural Network faster converges and is immuned to the local minima.

    DOI

  • A fuzzy multi-objective portfolio selection model with piecewise linear transaction costs

    You Li, Bo Wang, Junzo Watada

    IEEJ Transactions on Electronics, Information and Systems   134 ( 6 ) 780 - 787  2014  [Refereed]

     View Summary

    In this paper, we studied a multi-objective portfolio selection problem with piecewise linear transaction costs in a fuzzy environment. Transaction costs are expenses incurred when buying or selling securities and they are a burden on investors who frequently make trades to balance their portfolio. To better evaluate portfolio performance with transaction costs, this paper extends a previous study to a new model called fuzzy multi-objective portfolio selection model with piecewise linear transaction costs. A fuzzy simulation-based particle swarm optimization algorithm is designed to solve the model considering investors' individual risk attitude. In addition, several numerical examples are provided to illustrate the effectiveness of this model and algorithm. A conclusion is drawn at the end of this paper. © 2014 The Institute of Electrical Engineers of Japan.

    DOI

  • Portfolio selection models with technical analysis-based fuzzy birandom variables

    You Li, Bo Wang, Junzo Watada

    IEICE Transactions on Information and Systems   E97-D ( 1 ) 11 - 21  2014  [Refereed]

     View Summary

    Recently, fuzzy set theory has been widely employed in building portfolio selection models where uncertainty plays a role. In these models, future security returns are generally taken for fuzzy variables and mathematical models are then built to maximize the investment profit according to a given risk level or to minimize a risk level based on a fixed profit level. Based on existing works, this paper proposes a portfolio selection model based on fuzzy birandom variables. Two original contributions are provided by the study: First, the concept of technical analysis is combined with fuzzy set theory to use the security returns as fuzzy birandom variables. Second, the fuzzy birandom Value-at-Risk (VaR) is used to build our model, which is called the fuzzy birandom VaR-based portfolio selection model (FBVaR-PSM). The VaR can directly reflect the largest loss of a selected case at a given confidence level and it is more sensitive than other models and more acceptable for general investors than conventional risk measurements. To solve the FBVaR-PSM, in some special cases when the security returns are taken for trapezoidal, triangular or Gaussian fuzzy birandom variables, several crisp equivalent models of the FBVaR-PSM are derived, which can be handled by any linear programming solver. In general, the fuzzy birandom simulation-based particle swarm optimization algorithm (FBS-PSO) is designed to find the approximate optimal solution. To illustrate the proposed model and the behavior of the FBS-PSO, two numerical examples are introduced based on investors' different risk attitudes. Finally, we analyze the experimental results and provide a discussion of some existing approaches. Copyright © 2014 The Institute of Electronics, Inf rmation and Communication Engineers.

    DOI

  • A genetic type-2 fuzzy C-means clustering approach to M-FISH segmentation

    Dzung Dinh Nguyen, Long Thanh Ngo, Junzo Watada

    JOURNAL OF INTELLIGENT & FUZZY SYSTEMS   27 ( 6 ) 3111 - 3122  2014  [Refereed]

     View Summary

    Multiplex Fluorescent In Situ Hybridization (M-FISH) is a multi-channel chromosome image generating technique that allows colors of the human chromosomes to be distinguished. In this technique, all chromosomes are labelled with 5 fluors and a fluorescent DNA stain called DAPI (4 in, 6-Diamidino-2-phenylindole) that attaches to DNA and labels all chromosomes. Therefore, a M-FISH image consists of 6 images, and each image is the response of the chromosome to a particular fluor. In this paper, we propose a genetic interval type-2 fuzzy c-means (GIT2FCM) algorithm, which is developed and applied to the segmentation and classification of M-FISH images. Chromosome pixels from the DAPI channel are segmented by GIT2FCM into two clusters, and these chromosome pixels are used as a mask for the remaining five channels. Then, the GIT2FCM algorithm is applied to classify the chromosome pixels into 24 classes, which correspond to the 22 pairs of homologous chromosomes and two sexual chromosomes. The experiments performed using the M-FISH dataset show the advantages of the proposed algorithm.

    DOI

  • Search Result Clustering Through Density Analysis Based K-Medoids Method

    Hungming Hung, Junzo Watada

    2014 IIAI 3RD INTERNATIONAL CONFERENCE ON ADVANCED APPLIED INFORMATICS (IIAI-AAI 2014)     155 - 160  2014  [Refereed]

     View Summary

    After obtaining search results through web search engine, classifying into clusters enables us to quickly browse them. Currently, famous search engines like Google, Bing and Baidu always return a long list of web pages which can be more than a hundred million that are ranked by their relevancies to the search key words. Users are forced to examine the results to look for their required information. This consumes a lot of time when the results come into so huge a number that consisting various kinds. Traditional clustering techniques are inadequate for readable descriptions. In this research, we first build a local semantic thesaurus (L.S.T) to transform natural language into two dimensional numerical points. Second, we analyze and gather different attributes of the search results so as to cluster them through on density analysis based K-Medoids method. Without defining categories in advance, K-Medoids method generates clusters with less susceptibility to noise. Experimental results verify our method's feasibility and effectiveness.

    DOI

  • Analytical Properties of Credibilistic Expectation Functions

    Shuming Wang, Bo Wang, Junzo Watada

    SCIENTIFIC WORLD JOURNAL    2014  [Refereed]

     View Summary

    The expectation function of fuzzy variable is an important and widely used criterion in fuzzy optimization, and sound properties on the expectation function may help in model analysis and solution algorithm design for the fuzzy optimization problems. The present paper deals with some analytical properties of credibilistic expectation functions of fuzzy variables that lie in three aspects. First, some continuity theorems on the continuity and semicontinuity conditions are proved for the expectation functions. Second, a differentiation formula of the expectation function is derived which tells that, under certain conditions, the derivative of the fuzzy expectation function with respect to the parameter equals the expectation of the derivative of the fuzzy function with respect to the parameter. Finally, a law of large numbers for fuzzy variable sequences is obtained leveraging on the Chebyshev Inequality of fuzzy variables. Some examples are provided to verify the results obtained.

    DOI

  • An Extended Fuzzy-kNN Approach to Solving Class-imbalanced Problems

    Zhigang Xiong, Junzo Watada, Zhenyuan Xu, Bo Wang, Shing Chiang Tan

    SMART DIGITAL FUTURES 2014   262   200 - 209  2014  [Refereed]

     View Summary

    In this paper, for solving imbalanced classification problem, more attention is placed on data points in the boundary area between two classes. The fuzzy k-nearest neighbors algorithm, which has good performance in conventional classification problems, is adapted here to solve imbalanced classification problems, where G-mean accuracy is used to evaluate our proposal method and compare it with other approaches.

    DOI

  • Optimal decision methods in two-echelon logistic models

    Junzo Watada, Thisana Waripan, Berlin Wu

    MANAGEMENT DECISION   52 ( 7 ) 1273 - 1287  2014  [Refereed]

     View Summary

    Purpose - The purpose of this paper is to investigate optimal decision methods under a cooperative situation in two-echelon logistic models.
    Design/methodology/approach - The authors propose the optimal strategies of exporters in the three types of rival game behaviors: Stackelberg, Collusion, and Cournot, each of which provides the optimal decision for the duopolistic shippers and the oligopolistic forwarders in each scenario.
    Findings - From the empirical studies the paper finds that among three scenarios, the oligopolistic treatment of forwarders' actions demonstrates that Stackelberg behavior can carry out the maximum profit, and Collusion game can achieve the maximum profit for the shippers.
    Originality/value - Proposed an optimal decision methods in two-echelon logistic models.

    DOI

  • A Combination of Genetic Algorithm-based Fuzzy C-Means with a Convex Hull-based Regression for Real-Time Fuzzy Switching Regression Analysis: Application to Industrial Intelligent Data Analysis

    Azizul Azhar Ramli, Junzo Watada, Witold Pedrycz

    IEEJ TRANSACTIONS ON ELECTRICAL AND ELECTRONIC ENGINEERING   9 ( 1 ) 71 - 82  2014.01  [Refereed]

     View Summary

    Processing an increasing volume of data, especially in industrial and manufacturing domains, calls for advanced tools of data analysis. Knowledge discovery is a process of analyzing data from different perspectives and summarizing the results into some useful and transparent findings. To address such challenges, a thorough extension and generalization of well-known techniques such as regression analysis becomes essential and highly advantageous. In this paper, we extend the concept of regression models so that they can handle hybrid data coming from various sources which quite often exhibit diverse levels of data quality. The major objective of this study is to develop a sound vehicle of a hybrid data analysis, which helps in reducing the computing time, especially in cases of real-time data processing. We propose an efficient real-time fuzzy switching regression analysis based on a genetic algorithm-based fuzzy C-means associated with a convex hull-based fuzzy regression approach. The method enables us to deal with situations when one has to deal with heterogeneous data which were derived from various database sources (distributed databases). In the proposed design, we emphasize a pivotal role of the convex hull approach, which is essential to alleviate the limitations of linear programming when being used in modeling of real-time systems. (c) 2013 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

    DOI

  • Fuzzy Robust Regression Models based on Granularity and Possibility Distribution

    Yoshiyuki Yabuuchi, Junzo Watada

    2014 JOINT 7TH INTERNATIONAL CONFERENCE ON SOFT COMPUTING AND INTELLIGENT SYSTEMS (SCIS) AND 15TH INTERNATIONAL SYMPOSIUM ON ADVANCED INTELLIGENT SYSTEMS (ISIS)     1386 - 1391  2014  [Refereed]

     View Summary

    The characteristic of the fuzzy regression model is to enwrap all the given samples. An interval of fuzzy regression model is created by considering how far a sample is from the central values. That means when samples are widely scattered the size of an interval of the fuzzy model is widened. That is, the fuzziness of the fuzzy regression model is decided by the range of sample distribution.
    Therefore, many research results on a fuzzy regression model in order to describe the possibility of the target system have been reported. We have proposed two fuzzy robust regression models which remove influences of improper data such as unusual data and outliers. In this paper, we describe the model building of our fuzzy robust regressions by removing influences of improper data.

  • A PSO based NN-SVM for Short-Term Load Forecasting

    Zhenyuan Xu, Junzo Watada, Jiliang Xue

    SMART DIGITAL FUTURES 2014   262   219 - 227  2014  [Refereed]

     View Summary

    Load forecasting has become one of the core research topics in the power system. As power load has time-variant characteristics and nonlinear characteristics, different computational intelligent techniques, neural networks (NN) in particular, are used in short-term load forecasting (STLF) to make it more effective. This study proposes a Particle Swarm Optimization (PSO)-based neural network with support vector machine (NN-SVM) model to predict the power load in short-term forecasting by using a radial-basis-function neural network (RBFNN), SVM and PSO. There are two stages in the proposed model. The first stage applies the RBFNN to predict monthly variations, and the second stage trains the SVM through hourly data to obtain the final forecast for short-term load forecasting (STLF). In the process of SVM training and NN learning, PSO is used to find the optimal parameters. The results of several experiments show that this new model performs more accurately and stably than some conventional models including RBFNN, RGA-SVM, Karman filter in STLF.

    DOI

  • Analysis using rough set of time series data including a large variation

    Yoshiyuki Matsumoto, Junzo Watada

    2014 JOINT 7TH INTERNATIONAL CONFERENCE ON SOFT COMPUTING AND INTELLIGENT SYSTEMS (SCIS) AND 15TH INTERNATIONAL SYMPOSIUM ON ADVANCED INTELLIGENT SYSTEMS (ISIS)     1378 - 1381  2014  [Refereed]

     View Summary

    Rough set theory was proposed by Z.Pawlak in 1982. This theory can mine knowledge granules through a decision rule from a database, a web base, a set and so on. The decision rule is used for data analysis as well. And we can apply the decision rule to reason, estimate, evaluate, or forecast an unknown object. In this paper, the rough set theory is used to analysis of time series data. Knowledge granules are minded from the data set of tick-wise price fluctuations. We acquire knowledge from the time-series data including large variation. And we compare the data including large variation and normal data.

  • Analysis of medical care expenditure by japanese prefecture using fuzzy robust regression model.

    Yabuuchi Y, Kawaura T, Watada J

    Studies in health technology and informatics   207   400-409  2014  [Refereed]

    DOI

  • Identifying the Distribution Difference between Two Populations of Fuzzy Data Based on a Nonparametric Statistical Method

    Pei-Chun Lin, Junzo Watada, Berlin Wu

    IEEJ TRANSACTIONS ON ELECTRICAL AND ELECTRONIC ENGINEERING   8 ( 6 ) 591 - 598  2013.11  [Refereed]

     View Summary

    Nonparametric statistical tests are a distribution-free method without any assumption that data are drawn from a particular probability distribution. In this paper, to identify the distribution difference between two populations of fuzzy data, we derive a function that can describe continuous fuzzy data. In particular, the Kolmogorov-Smirnov two-sample test is used for distinguishing two populations of fuzzy data. Empirical studies illustrate that the Kolmogorov-Smirnov two-sample test enables us to judge whether two independent samples of continuous fuzzy data are derived from the same population. The results show that the proposed function is successful in distinguishing two populations of continuous fuzzy data and useful in various applications. (c) 2013 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

    DOI

  • Risk Assessment of a Portfolio Selection Model Based on a Fuzzy Statistical Test

    Pei-Chun Lin, Junzo Watada, Berlin Wu

    IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS   E96D ( 3 ) 579 - 588  2013.03  [Refereed]

     View Summary

    The objective of our research is to build a statistical test that can evaluate different risks of a portfolio selection model with fuzzy data. The central points and radiuses of fuzzy numbers are used to determine the portfolio selection model, and we statistically evaluate the best return by a fuzzy statistical test. Empirical studies are presented to illustrate the risk evaluation of the portfolio selection model with interval values. We conclude that the fuzzy statistical test enables us to evaluate a stable expected return and low risk investment with different choices for k, which indicates the risk level. The results of numerical examples show that our method is suitable for short-term investments.

    DOI

  • Rough Sets based Knowledge Acquisition from time-series data of large fluctuations

    Matsumoto Yoshiyuki, Watada Junzo

    Proceedings of the Fuzzy System Symposium   29   91 - 91  2013

     View Summary

    Rough set theory was proposed by Z.Pawlak in 1982. This theory can mine knowledge granules through a decision rule from a database, a web base, a set and so on. The decision rule is used for data analysis as well. And we can apply the decision rule to reason, estimate, evaluate, or forecast an unknown object. In this paper, the rough set theory is used to analysis of time series data. Knowledge granules are minded from the data set of tick-wise price fluctuations. We Knowledge Acquisition If the data has changed greatly.

    DOI CiNii

  • Fuzzy Autocorrelation Model with Fuzzy Random Variables

    Yabuuchi Yoshiyuki, Watada Junzo

    Proceedings of the Fuzzy System Symposium   29   90 - 90  2013

     View Summary

    A fuzzy autocorrelation model which we proposed is a fuzzy time-series model using a fuzzy autocorrelation coefficient. A fuzzy autocorrelation coefficient obtained by the fuzzy operation tends to increase its ambiguity. On the other hand, center values of fuzzy autocorrelation coefficients and predicted values by our model are coincided with AR model. Therefore, we can understand our model describes a possibility of a time-series system by the interval of our model. In addition this, our model is able to get describe the possibility with high accuracy. However, forecasted and predicted values of our model by using fuzzified stock prices were unnatural sometimes. In this paper, we will use fuzzy random variables to solve this problem. This is a fuzzy autocorrelation model with confidence intervals of fuzzy random data.

    DOI CiNii

  • A Wavelet Transform Approach to Chaotic Short-Term Forecasting

    Yoshiyuki Matsumoto, Junzo Watada

    Intelligent Systems Reference Library   47   177 - 197  2013  [Refereed]

     View Summary

    Chaos theory is widely employed to forecast near-term future values of a time series using data that appear irregular. The chaotic short-term forecasting method is based on Takens' embedding theorem, which enables us to reconstruct an attractor in a multi-dimensional space using data that appear random but rather are deterministic and geometric in nature. It is difficult to forecast future values of such data based on chaos theory if the information that the data provide cannot be reconstructed through wavelet transformation in a sufficiently low-dimensional space. This paper proposes a method to embed data in a small-dimensional space. This method enables us to abstract the chaotic portion from the focal data and increase forecasting precision. Chaotic methods are employed to forecast near-term future values of uncertain phenomena. The method makes it possible to restructure an attractor of given timeseries data set in a multidimensional space using Takens' embedding theory. However, many types of economic time-series data are not sufficiently chaotic. In other words, it is difficult to forecast the future trend of such economic data even based on chaos theory. In this paper, time-series data are divided into wave components using a wavelet transform. Some divided components of time-series data exhibit much more chaotic behavior in the sense of correlation dimension than the original time-series data. The highly chaotic nature of the divided components enables us to precisely forecast the value or the movement of the time-series data in the near future. The up-and-down movement of the TOPICS value is shown to be well predicted by this method, with 70% accuracy.

    DOI

  • DNA Rough-Set Computing in the Development of Decision Rule Reducts

    Ikno Kim, Junzo Watada, Witold Pedrycz

    Intelligent Systems Reference Library   42   409 - 438  2013  [Refereed]

     View Summary

    Rough set methods are often employed for reducting decision rules. The specific techniques involving rough sets can be carried out in a computational manner. However, they are quite demanding when it comes computing overhead. In particular, it becomes problematic to compute all minimal length decision rules while dealing with a large number of decision rules. This results in an NP-hard problem. To address this computational challenge, in this study, we propose a method of DNA rough-set computing composed of computational DNA molecular techniques used for decision rule reducts. This method can be effectively employed to alleviate the computational complexity of the problem. © Springer-Verlag Berlin Heidelberg 2013.

    DOI

  • Building Fuzzy Autocorrelation Model and Its Application to the Analysis of Stock Price Time-Series Data

    Yoshiyuki Yabuuchi, Junzo Watada

    Intelligent Systems Reference Library   47   347 - 367  2013  [Refereed]

     View Summary

    The objective of economic analysis is to interpret the past, present or future economic state by analyzing economic data. Economic analyses are typically based on the time-series data or the cross-section data. Time-series analysis plays a pivotal role in analyzing time-series data. Nevertheless, economic systems are complex ones because they involve human behaviors and are affected by many factors. When a system includes substantial uncertainty, such as those concerning human behaviors, it is advantageous to employ a fuzzy system approach to such analysis. In this paper, we compare two fuzzy time-series models, namely a fuzzy autoregressive model proposed by Ozawa et al. and a fuzzy autocorrelation model proposed by Yabuuchi andWatada. Both models are built based on the concepts of fuzzy systems. In an analysis of the Nikkei Stock Average, we compare the effectiveness of the two models. Finally, we analyze tick-by-tick data of stock dealing by applying fuzzy autocorrelation model.

    DOI

  • Building a Rough Sets-Based Prediction Model of Tick-Wise Stock Price Fluctuations

    Yoshiyuki Matsumoto, Junzo Watada

    Intelligent Systems Reference Library   47   301 - 329  2013  [Refereed]

     View Summary

    Rough sets enable us to mine knowledge in the form of IF-THEN decision rules from a data repository, a database, a web base, and others. Decision rules are used to reason, estimate, evaluate, and forecast. The objective of this paper is to build the rough sets-based model for analysis of time series data with tick-wise price fluctuations where knowledge granules are mined from the data set of tickwise price fluctuations. We show how a method based on rough sets helps acquire the knowledge from time-series data. The method enables us to obtain IF-THEN type rules for forecasting stock prices.

    DOI

  • The Measurement of Exit Strategy Impact in Fuzzy Portfolio-based Investment

    Bo Wang, You Li, Junzo Watada

    INTELLIGENT DECISION TECHNOLOGIES   255   409 - 418  2013  [Refereed]

     View Summary

    In security market, the term exit means that the investor may sell his equity on account of some exogenous or endogenous incentives, especially when security price becomes higher than his expectation or lower than his tolerance. It is a common problem that all the deciders need to face in the investment horizon. However, there have been few studies probe into the influences caused by such strategy in fuzzy environment. Therefore, in this work, we use the exit strategy to secure security future returns and rebuild fuzzy portfolio selection models. Then, we discuss about the exit points and employ one meta-heuristic method to solve the proposed models. We also analyze the differences between the new models' experimental results and that of previous methods.

    DOI

  • Solving Imbalance Data Classification Problem by Particle Swarm Optimization Support Vector Machine

    Zhenyuan Xu, Mingnan Wu, Junzo Watada, Zuwarie Ibrahim, Marzuki Khalid

    INTELLIGENT DECISION TECHNOLOGIES   255   371 - 379  2013  [Refereed]

     View Summary

    A database has a plenty of hidden knowledge, which can be used in decision making to support commerce, research and other activities. Classification analysis performs a very important rule in pattern recognition field as one core research topics. Algorithms like support vector machine (SVM) and artificial network (ANN) have been proposed to perform binary classification according to the distribution. But these traditional classification algorithms can hardly performs the satisfied result for imbalanced dataset. In this paper, we proposed to perform a model on the basis of Particle Swarm Optimization (PSO) and support vector machine (SVM) for a large imbalanced dataset. This model is named PSOSVC (Particle Swarm Optimization support vector classification) model. Recently, PSO is proposed used as a meta heuristic frame work for the large imbalanced classification. The SVM also shows high performance in balanced binary classification, so a novel model combined both support vector classification (SVC) and PSO is introduced to improve the classification accuracy. In this paper, G-mean is used to evaluate the final result. Performance in the final part of this paper the proposed method is compared with some conventional models, the results will show the high performance for imbalanced dataset classification by using the proposed method.

    DOI

  • A Hybrid RBF-ART Model and Its Application to Medical Data Classification

    Shing Chiang Tan, Chee Peng Lim, Junzo Watada

    INTELLIGENT DECISION TECHNOLOGIES   255   21 - 30  2013  [Refereed]

     View Summary

    In this paper, a new variant of the Radial Basis Function Network with the Dynamic Decay Adjustment algorithm (i.e., RBFNDDA) to undertake data classification problems is proposed. The new network is formed by integrating the learning algorithm of the Fuzzy ARTMAP (FAM) neural network into RBFNDDA. The proposed RBFNDDA-FAM network inherits the salient features of FAM and overcomes the shortcomings of the original RBFNDDA network. The effectiveness of RBFNDDA-FAM is demonstrated using two benchmark problems. The first involves an artificial data set whereas the second uses a medical data set related to thyroid diagnosis. The results from these studies are compared, analyzed, and discussed. The outcomes positively reveal the potentials of RBFNDDA-FAM in learning information with a compact network architecture, in addition to high classification performances.

    DOI

  • Building a New Portfolio Selection Model with Technical Pattern-based Fuzzy Birandom Returns

    You Li, Bo Wang, Junzo Watada

    INTELLIGENT DECISION TECHNOLOGIES   255   84 - 93  2013  [Refereed]

     View Summary

    Fuzzy set theory has been applied to build various portfolio selection models in the past decades. Based on the knowledge of previous studies, this paper proposes a new portfolio selection model with technical pattern-based fuzzy birandom variables. There are two innovations in the work: The concept of technical pattern is combined with fuzzy set theory to use the fuzzy birandom variables as security returns; The fuzzy birandom Value-at-Risk (VaR) is introduced to build the mathematical model, named the fuzzy birandom VaR-based portfolio selection model (FBR-PSM). Then, fuzzy simulation is extended to the fuzzy birandom case to obtain a general solution to the FBR-PSM, which is called as fuzzy birandom simulation-based particle swarm optimization algorithm (FBS-PSO). To illustrate the performances of the FBR-PSM and the FBS-PSO, two numerical examples are introduced based on investors' different risk attitudes. Finally, we analyze the experimental results and provide further discussions.

    DOI

  • Building a Type-2 Fuzzy Regression Model based on Creditability Theory

    Yicheng Wei, Junzo Watada

    2013 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS (FUZZ - IEEE 2013)    2013  [Refereed]

     View Summary

    Information in real life may have linguistically vagueness. Thus, type-1 fuzzy set was introduced to model this uncertainty. Additionally, same words will mean variously to different people, which means uncertainty also exists when associated with the membership function of a type-1 fuzzy set. Type-2 fuzzy set is then invented to express the hybrid uncertainty of both primary fuzziness and secondary one of membership functions. On the one hand, type-2 fuzzy variable models the vagueness of information better. On the other hand, those variables are hard to deal with its three-dimensional feature given. To address problems in presence of such variables with hybrid fuzziness, a new class of type-2 fuzzy regression model is built based on credibility theory, and is called the T2 fuzzy expected value regression model. The new model will be developed into two forms: form-A and form-B. This paper is a further work based on our former research of type-2 fuzzy qualitative regression model.

  • GOODNESS-OF-FIT TEST FOR MEMBERSHIP FUNCTIONS WITH FUZZY DATA

    Pei-Chun Lin, Berlin Wu, Junzo Watada

    INTERNATIONAL JOURNAL OF INNOVATIVE COMPUTING INFORMATION AND CONTROL   8 ( 10B ) 7437 - 7450  2012.10  [Refereed]

     View Summary

    Conventionally, we use a chi-square test of homogeneity to determine whether the cell probabilities of a multinomial are equal. However, this process of testing hypotheses is based on the assumption of two-valued logic. If we collect questionnaire data using fuzzy logic, i.e., we record the category data with memberships instead of with a 0-1 type, then the conventional test of goodness-of-fit will not work. In this paper, we present a new method, the fuzzy chi-square test, which will enable us to analyze those fuzzy sample data. The new testing process will efficiently solve the problem for which the category data are not integers. Some related properties of the fuzzy multinomial distribution are also described.

  • A FUZZY MCDM APPROACH TO BUILDING A MODEL OF HIGH PERFORMANCE PROJECT TEAM - A CASE STUDY

    Yao Feng Chang, Junzo Watada, Hiroaki Ishii

    INTERNATIONAL JOURNAL OF INNOVATIVE COMPUTING INFORMATION AND CONTROL   8 ( 10B ) 7393 - 7404  2012.10  [Refereed]

     View Summary

    The operations of a project team play an important role in the discussion of building high performance project teams. This study focuses on building a model for high performance project teams. First, success and failure factors are evaluated across aspects of a project team that affect team effectiveness. Second, effective teams are analyzed to clarify what defines a high performance project team. Third, this analysis is combined with past results to build a model for high performance project teams. The model for high, performance project teams is evaluated by fuzzy multi-criterion decision-making (fuzzy MCDM). The results show that all of the criteria have interactions, but that team effectiveness standard is the most influential dimension. On the contrary, the team process is the least influential dimension. From the viewpoint of experts, the most important ones of the 17 evaluation criteria are performance and satisfaction.

  • BUILDING AN INTEGRATED HYBRID MODEL FOR SHORT-TERM AND MID-TERM LOAD FORECASTING WITH GENETIC OPTIMIZATION

    Jiliang Xue, Zhenyuan Xu, Junzo Watada

    INTERNATIONAL JOURNAL OF INNOVATIVE COMPUTING INFORMATION AND CONTROL   8 ( 10B ) 7381 - 7391  2012.10  [Refereed]

     View Summary

    The enhancement of load forecasting has become one of the core research topics in the energy field. Because power load has both time-variant and nonlinear characteristics, different types of methods, neural networks (NN) in particular, have been applied to power load forecasting. This study proposes a real-valued genetic algorithm (RGA)-based neural network with support vector machine (NN-SVM) model to predict the power load in both short-term and mid-term forecasting by using a radial-basis-function neural network (RBFNN), SVM and RGA. The model consists of two stages. In short-term load forecasting (STLF), the first stage applies the RBFNN to predict monthly variations, and the second stage trains the SVM through hourly data to obtain the final forecast. Similar operations are used in mid-term load forecasting (MTLF). In the process of SVM training and NN learning, RGA is used to find the optimal parameters. The results of several experiments show that this new model performs more accurately and stably than some conventional models including RBFNN, RGA-SVM, Karman filter in STLF. Also it is able to function well in MTLF.

  • A DISTANCE-BASED PSO APPROACH TO SOLVE FUZZY MOPSM WITH DISTINCT RISK MEASUREMENTS

    Bo Wang, You Li, Junzo Watada

    INTERNATIONAL JOURNAL OF INNOVATIVE COMPUTING INFORMATION AND CONTROL   8 ( 9 ) 6191 - 6203  2012.09  [Refereed]

     View Summary

    In this study, we propose an improved fuzzy multi-objective portfolio selection model (VaR-MOPSM) with distinct risk measurements. The VaR-MOPSM can precisely evaluate the investment and increase the probability of obtaining the expected return. When building the model, fuzzy Value-at-Risk (VaR), which can directly reflect the greatest loss of a selection case under a given confidence level, is used to measure the exact future risk in term of loss. Conversely, variance is utilized to make the selection more stable. In this case, the proposed VaR-MOPSM can provide investors with more significant information for decision-making. To solve this model, we designed a distance based particle swarm optimization algorithm. Finally, the proposed model and algorithm are exemplified by some numerical examples. The experimental results show that the model and algorithm are effective in solving the fuzzy VaR-MOPSM.

  • RELIABILITY ENHANCEMENT OF A TRAFFIC SIGNAL LIGHT SYSTEM USING A MEAN-VARIANCE APPROACH

    Shamshul Bahar Yaakob, Junzo Watada

    INTERNATIONAL JOURNAL OF INNOVATIVE COMPUTING INFORMATION AND CONTROL   8 ( 8 ) 5835 - 5845  2012.08  [Refereed]

     View Summary

    Traffic accidents cause tragic loss of life, property damage and substantial congestion to transportation systems. A large percentage of crashes occur at or near intersections. Therefore, traffic signals are often used to improve traffic safety and operations. The objective of this study is to present a significant and effective method of determining the optimal investment involved in retrofitting signals with light emitting diode (LED) units. In this study, the reliability and risks of each unit are evaluated using a variance-covariance matrix, and the effects and expenses of replacement are analyzed. The mean-variance analysis is formulated as a mathematical program with the objectives of minimizing the risk and maximizing the expected return. Finally, a structural learning model of a mutual connection neural network is proposed to solve problems defined by mixed-integer quadratic programming, and this model is employed in the mean-variance analysis. Our method is applied to an LED signal retrofitting problem. This method enables us to select results more effectively and enhance decision-making.

  • Rough Sets-based Prediction of Market Movements from Tick-wise Price Data

    Matsumoto Yoshiyuki, Watada Junzo

    Proceedings of the Fuzzy System Symposium   28   936 - 941  2012

     View Summary

    Rough set theory was proposed by Z.Pawlak in 1982. This theory can mine knowledge granules through a decision rule from a database, a web base, a set and so on. The decision rule is used for data analysis as well. And we can apply the decision rule to reason, estimate, evaluate, or forecast an unknown object. In this paper, the rough set theory is used to analysis of time series data. Knowledge granules are minded from the data set of tick-wise price fluctuations. We inspect whether the knowledge varies according to the difference in stocks brand.

    DOI CiNii

  • Japanese economic analysis by possibilistic regression model building through possibilitymaximization

    Yoshiyuki Yabuuchi, Junzo Watada

    Journal of Advanced Computational Intelligence and Intelligent Informatics   16 ( 5 ) 576 - 580  2012  [Refereed]

     View Summary

    A possibilistic regression model illustrates the potential possibilities inherent in the target system by including all data in the model. Tanaka and Guo employ exponential possibility distribution to build a model, while Inuiguchi et al. and Tajima are independently working on coinciding between the center of a possibility distribution and the center of a possibilistic regression model. Typically, samples influence and distort the shape of the model if they are far from the center of data. Yabuuchi and Watada have developed a model for describing the system possibility using the center of a possibilistic fuzzy regression model and an approach that mends the distortion of the model. The objective of this paper is to analyze the Japanese economy using our model, and to show the usefulness of our model by analysis results.

    DOI

  • An affective approach to developing marketing strategies of mineral water

    Junzo Watada, Le Yu, Munenori Shibata, Marzuki Khalid

    Journal of Advanced Computational Intelligence and Intelligent Informatics   16 ( 4 ) 514 - 520  2012  [Refereed]

     View Summary

    This study is concerned with the development of marketing strategies for mineral water based on consumers' taste preferences, by analyzing the taste components of mineral water. In this study, we used a twodimensional analysis to classify taste data. We conducted a correlation analysis to identify the characteristics of taste data. We applied a combination of principal component analysis and self-organizing map to classify mineral water tastes. Based on this evaluation, we identified some marketing strategies in the conclusion. According to this study, the taste of mineral water is not determined by the origin and is not influenced by the hardness of the water.

    DOI

  • PSO-particle filter-based biometric measurement for human tracking

    Zhenyuan Xu, Junzo Watada

    Journal of Advanced Computational Intelligence and Intelligent Informatics   16 ( 4 ) 533 - 539  2012  [Refereed]

     View Summary

    Today, security and surveillance systems are required not only to track the motions of humans but also, in some situations, to recognize and measure biometric features such as width and length. Few methods have been proposed for biometric height measurement in human tracking. Some studies have shown that an infrared ray technique can survey the height of a human, but the equipment required is complicated. The objective of this paper is to build a mathematical model to measure the biometrics of human tracking. This tracking method can show humans' and objects' size in a picture so that, if we put this picture in a frame of axes, we can calculate the height and other biometric lengths. To obtain the most accurate results for biometric length surveillance, we need a tracking methodthat is more exact than conventional tracking results. Combining tracking and detection methods using a particle swarm optimization-particle filter shows results with great accuracy in human tracking.

    DOI

  • Bio-soft computational and tabu search methods for solving a multi-task project scheduling problem

    Ikno Kim, Junzo Watada

    Journal of Computers   7 ( 4 ) 1048 - 1055  2012  [Refereed]

     View Summary

    This article presents a novel integrated method in which bio-soft computational and tabu search methods are both used to solve a multi-task project scheduling problem. In this scheduling problem, the main challenge is to determine the most reliable completion time. To solve this problem, we first use our proposed bio-soft computational method. Next, a tabu search method is used to verify the final results of the bio-soft computational method. The biosoft computational method includes molecular techniques, and the tabu search method is an intelligent optimization technique. Based upon this integrated method's success solving a multi-task project scheduling problem, this article proposes that this method can help decision makers in their computational project scheduling. © 2012 ACADEMY PUBLISHER.

    DOI

  • Decision Making of a Portfolio Selection Model Based on Fuzzy Statistic Test

    Pei-Chun Lin, Junzo Watada, Berlin Wu

    6TH INTERNATIONAL CONFERENCE ON SOFT COMPUTING AND INTELLIGENT SYSTEMS, AND THE 13TH INTERNATIONAL SYMPOSIUM ON ADVANCED INTELLIGENT SYSTEMS     1931 - 1937  2012  [Refereed]

     View Summary

    The objective of our research is to build a statistical test that can evaluate the sensitivity of a portfolio selection model with fuzzy data. The central point and radius are used to determine the portfolio selection model and we make a decision for the best return by a fuzzy statistical test. Empirical studies are presented to illustrate the risk of the portfolio selection model with interval values. We conclude that the evaluation by the fuzzy statistical test enables us to obtain a stable expected return and low risk investment with different choices based on the risk level k, which is taken for the risk level.

  • Fuzzy Autocorrelation Model with Confidence Intervals of Fuzzy Random Data

    Yoshiyuki Yabuuchi, Junzo Watada

    6TH INTERNATIONAL CONFERENCE ON SOFT COMPUTING AND INTELLIGENT SYSTEMS, AND THE 13TH INTERNATIONAL SYMPOSIUM ON ADVANCED INTELLIGENT SYSTEMS     1938 - 1943  2012  [Refereed]

     View Summary

    Economic analyses are typical methods based on time-series data or cross-section data. Economic systems are complex because they involve human behaviors and are affected by many factors. When a system includes such uncertainty, as those concerning human behaviors, a fuzzy system approach plays a pivotal role in such analysis.
    In this paper, we propose a fuzzy autocorrelation model with confidence intervals of fuzzy random time-series data. This confidence intervals has an essential role in dealing with fuzzy random data on our fuzzy autocorrelation model which we have presented. We analyze tick-by-tick data of stock dealing and compare two time-series models, a fuzzy autocorrelation model proposed by us, and a new fuzzy time-series model which we propose in this paper.

  • A Fuzzy Support Vector Machine With Qualitative Regression Preset

    Yicheng Wei, Junzo Watada, Witold Pedrycz

    2012 SIXTH INTERNATIONAL CONFERENCE ON GENETIC AND EVOLUTIONARY COMPUTING (ICGEC)     393 - 399  2012  [Refereed]

     View Summary

    In this paper, we formulate a qualitative classification model by means of qualitative fuzzy regression preset-based fuzzy support vector machine (FQR-FSVM). This new model will make it possible to achieve discrimination of output while characterizing membership for each class in terms of multi-dimensional qualitative inputs (attributes). Moreover, the new model will largely shorten the computing time especially for large database by using linear preset of fuzzy qualitative regression classifier to limit the non-linear classification region.

    DOI

  • Developing Marketing Strategies Based on Taste Analysis of Mineral Water

    Le Yu, Junzo Watada, Munenori Shibata

    INTELLIGENT DECISION TECHNOLOGIES (IDT'2012), VOL 1   15   497 - 507  2012  [Refereed]

     View Summary

    This research concerns with the development of marketing strategy of mineral water based on people's taste preference by analyzing taste components of mineral water. A two-dimensional analysis has been used in classifying tastes' data. The characteristics of data are recognized in tastes of mineral water by correlation analysis. A combination of Principal Component Analysis and Self-organizing Map is applied to classify the tastes of mineral water. Some marketing strategies are concluded after the evaluation.

  • A Game-Theoretic Two-echelon Model Approach to Strategy Development of Competitive Ocean Logistics in Thailand

    Thisana Waripan, Junzo Watada

    ADVANCES IN KNOWLEDGE-BASED AND INTELLIGENT INFORMATION AND ENGINEERING SYSTEMS   243   2050 - 2059  2012  [Refereed]

     View Summary

    This paper deals with optimal decision methods under a cooperative situation of the two-echelon model among logistic service providers (LSPs) of Stackelberg structure. Assuming duopolistic shippers and oligopolistic forwarders, the shippers perform as a leader and declare their service to both the forwarders after determining their price and quantity independently under shippers' scheme. The objective of this study is to obtain the optimal strategies of exporters in the three types of rival game behaviours: Stackelberg, Collusion and Cournot, each of which provides the optimal decision for the duopolistic shippers and the oligopolistic forwarders in each scenario. The result of a real situation indicates that: (i) among three scenarios, the oligopolistic treatment of forwarders' actions shows that Stackelberg behaviour can carry out the maximum profit, and (ii) Collusion game can achieve the maximum profit for the shippers.

    DOI

  • Building a Type II Fuzzy Qualitative Regression Model

    Yicheng Wei, Junzo Watada

    INTELLIGENT DECISION TECHNOLOGIES (IDT'2012), VOL 1   15   145 - 154  2012  [Refereed]

     View Summary

    The qualitative regression analysis models quantitatively change in the qualitative object variables by using qualitative values of multivariate data (membership degree or type I fuzzy set), which are given by subjective recognitions and judgments. From fuzzy set-theoretical points of view, uncertainty also exists when associated with the membership function of a type I fuzzy set. It will have much impact on the fuzziness of the qualitative objective external criterion. This paper is trying to model the qualitative change of external criterion's fuzziness by applying type II fuzzy set (we will use type II fuzzy set as well as type II fuzzy data in this paper). Here, qualitative values are assumed to be fuzzy degree of membership in qualitative categories and qualitative change in the objective external criterion is given as the fuzziness of the output.

  • Building Linguistic Random Regression Model and Its Application

    Sha Li, Shinya Imai, Junzo Watada

    INTELLIGENT DECISION TECHNOLOGIES (IDT'2012), VOL 1   15   165 - 174  2012  [Refereed]

     View Summary

    The objective of this paper is to build a model for the linguist random regression model as a vehicle to solve linguistic assessment given by experts. The difficulty in the direct measurement of certain characteristics makes their estimation highly impressive and this situation results in the use of fuzzy sets. In this sense, the linguistic treatment of assessments becomes essential when fully reflecting the subjectivity of the judgment process. When we know the attributes assessment, the linguistic regression model get the total assessment.

  • Rough Set Model based Knowledge Acquisition of Market Movements in Tick-wise Price Data

    Yoshiyuki Matsumoto, Junzo Watada

    6TH INTERNATIONAL CONFERENCE ON SOFT COMPUTING AND INTELLIGENT SYSTEMS, AND THE 13TH INTERNATIONAL SYMPOSIUM ON ADVANCED INTELLIGENT SYSTEMS     1768 - 1771  2012  [Refereed]

     View Summary

    Rough set and its method were proposed by Z. Pawlak in 1982. This method enabled up to mine knowledge granules as decision rules from a database, a web base, a set and so on. The decisions rule can be applicable for data analysis as well. And the decision rules to reason, estimate, evaluate, or forecast an unknown object. The objective of this paper is to apply the rough set theory time series data and to mine. Knowledge granules are minded from the data set of tick-wise price fluctuations.

  • Building Fuzzy Random Autoregression Model and Its Application

    Lu Shao, You-Hsi Tsai, Junzo Watada, Shuming Wang

    INTELLIGENT DECISION TECHNOLOGIES (IDT'2012), VOL 1   15   155 - 164  2012  [Refereed]

     View Summary

    The purpose of economic analysis is to interpret the history, present and future economic situation based on analyzing economical time series data. The autoregression model is widely used in economic analysis to predict an output of an index based on the previous outputs. However, in real-world economic analysis, given the co-existence of stochastic and fuzzy uncertainty, it is better to employ a fuzzy system approach to the analysis. To address regression problems with such hybridly uncertain data, fuzzy random data are introduced to build the autoregression model. In this paper, a fuzzy random autoregression model is introduced and to solve the problem, we resort to some heuristic solution based on sigma-confidence intervals. Finally, a numerical example of Shanghai Composite Index is provided.

  • Two-Stage Multi-Objective Unit Commitment Optimization under Future Load Uncertainty

    Bo Wang, You Li, Junzo Watada

    2012 SIXTH INTERNATIONAL CONFERENCE ON GENETIC AND EVOLUTIONARY COMPUTING (ICGEC)     128 - 131  2012  [Refereed]

     View Summary

    The unit commitment problem is to reduce the total generation cost as much as possible while satisfying future power demands. Therefore, optimization must be performed based on correct predictions of future demands. However, various uncertain factors affect these loads making an exact forecasting unsuccessful. This study mitigates this difficulty by applying fuzzy set theory and the objective is to build a two-stage multi-objective fuzzy programming model. To define the supply reliability effectively, we propose a new concept of maximal blackout time based on the fuzzy credibility theory. In addition, an improved two-layer multi-objective particle swarm optimization algorithm is designed as the solution. Finally, the performance of this study is discussed in comparison with experimental results from several test systems.

    DOI

  • Fuzzy Random Possibilistic Programming Model for Multi-objective Problem

    A. Nureize, J. Watada

    2012 IEEE INTERNATIONAL CONFERENCE ON INDUSTRIAL ENGINEERING AND ENGINEERING MANAGEMENT (IEEM)     2204 - 2208  2012  [Refereed]

     View Summary

    A real-life application faces various kinds of inherent uncertainties which occurs simultaneously. To find solution, formulating real world problem into mathematical programming model is challenging. Uncertain parameters in a problem model can be characterized as vagueness, ambiguous and random of the information. Such uncertainties make the existing multi-objective model incapable of handling such situations. Thus, in this paper we present the multi-objective decision model from the perspective of possibilistic programming approach to scrutinize the uncertainties in the decision making. The proposed concept can be used to build model for multi-objective problem which is exposed with various types of uncertainties. We include an illustrative example to explain the model, and highlight its advantages.

  • Possibilistic regression analysis of influential factors for occupational health and safety management systems

    Azizul Azhar Ramli, Junzo Watada, Witold Pedrycz

    SAFETY SCIENCE   49 ( 8-9 ) 1110 - 1117  2011.10  [Refereed]

     View Summary

    The code of occupational health and safety (OHS) is an influential regulation to improve the on-the-job safety of employees. A number of factors influence the planning and implementation of OHS management systems (OHSMS). The evaluation of OHSMS practice is the most important component when forming a health and safety environmental policy for employees. The objective of this research is to develop an intelligent data analysis (IDA) in which possibilistic regression being endowed with a convex hull approach is used to support the analysis of essential factors that influence OHSMS. Given such subjective terms, the obtained samples can be conveniently regarded as fuzzy input/output data represented by membership functions. The study offers this vehicle of intelligent data analysis as an alternative to evaluate the influential factors in a successful implementation of OHS policies and in this way decrease an overall computational effort. The obtained results show that several related OHSMS influential factors need to be carefully considered to facilitate a successful implementation of the OHSMS procedure. (C) 2011 Elsevier Ltd. All rights reserved.

    DOI

  • A rough set approach to building association rules and its applications

    WATADA J.

    Granular Computing and Intelligent Systems, 2011   13   203-218 - 218  2011.04  [Refereed]

    DOI CiNii

  • A Rough Sets Approach to Human Resource Development in IT Corporations

    Shinya Imai, Junzo Watada

    Intelligent Systems Reference Library   13   249 - 273  2011  [Refereed]

     View Summary

    In IT corporations, it is essential to increase competitive advantages and organizational performance. Employees are critical to a company's success. A new research method is needed to quantify employees' influence on building relationships with customers and to facilitate human resource and customer relationship management. Rough sets theory is a mathematical approach to dealing with vagueness and uncertainty. It can change a qualitative problem into a quantitative one and produce a possible solution by providing useful and valuable information and guidelines for decision making. The objective of this study is to determine through the use of analysis analyzed with rough employee characteristics and behaviors that yield positive or negative relationships with customers. The rough set approach distinguishes between these two groups and leads us to suggest policies to improve human resource and customer relationship management and development. The proper management of employees and customers will ensure project success and good corporate performance. Quality is an attribute that is important for products as well as for management and the company itself. The development and promotion of personnel resources is indispensable for improving the quality of a company's management. Management quality is closely related to corporate culture and a sense of social responsibility. Therefore, personnel resource development and personnel training for employees should be emphasized. In the main discussion of this paper, information was gathered from engineers at a regional IT company through questionnaires and their observable talents were analyzed. The research addressed questions such as what kinds of values should be promoted. An attempt was made to clarify the relation between QWL (Quality of Working Life) and personnel training. This paper suggests that the management quality and CSR (Corporate Social Responsibility) of regional companies is closely related with the quality and improvement of their growth. © Springer-Verlag Berlin Heidelberg 2011.

    DOI

  • Empirical robustness evaluation of DNA-based clustering methods

    Rohani Binti Abu Bakar, Junzo Watada

    International Journal of Intelligent Computing in Medical Sciences and Image Processing   4 ( 1 ) 1 - 12  2011  [Refereed]

     View Summary

    DNA-based computation is one of the latest computation paradigms. Compared to conventional methods that obtain their end results via electronic processes, a DNA-based approach obtains its result from bio-chemical reactions. It is essential in this approach for all experimental processes to be performed without fault. However, some errors may occur while carrying out these bio-chemical experiments. Consequently, it is necessary to overcome their weaknesses. The aim of this study is to examine the robustness of DNA-based techniques in solving a clustering problem. In the broadest sense, robustness can be defined as being able to withstand stresses, pressures, or changes in procedure or circumstance. To examine the robustness of the approach, this research examined the impact of error or added noise on DNA-based procedure results. Comparative studies of different error sets are also provided here. Additionally, two well-known conventional clustering algorithms (Fuzzy C-means and k-means) were applied to the same error sets, to study the reliability and validity of results when comparing DNA-based clustering. © 2011, TSI® Press.

    DOI

  • Design of initial biosensor for measurement of glucose in human blood by using biocomputing technology

    Yuyi Chu, Junzo Watada, Ikno Kim, Juiyu Wu

    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)   6883 ( 3 ) 237 - 245  2011  [Refereed]

     View Summary

    Since biocomputing plays an essential role in the biological and medical fields, the catalytic features of enzymes can be utilized in medical applications as biosensor. In this study, we tried to build an enzymatic system as biosensor equipment application to measure blood sugar with sequential reactions of enzymes, which express logic output signals with latent fluorophore as a reporter. The expected biosensor will analyze three molecules sucrose, maltose and ATP existing in human blood, by adding some blood into the system consisted of various enzymes. Sucrose and Maltose will be decomposed into glucose
    moreover, glucose and ATP have critical influence of glycolysis which shows one crucial reaction of human metabolism. The output signal was visualized by high throughput fluorescence that originated from the long-wavelength latent fluorogenic substrate-Salicylate hydroxylase (SHL) reaction. The experiment results of this research have showed the possibility to determine the combinations of the three important components, by setting the concentration of each input molecules to specific threshold values. Further researches are required to find out the relationship between the specific concentration and measure glucose results, especially in the medical field. © 2011 Springer-Verlag.

    DOI

  • Rough sets based prediction model of tick-wise price fluctuations

    Yoshiyuki Matsumoto, Junzo Watada

    Journal of Advanced Computational Intelligence and Intelligent Informatics   15 ( 4 ) 449 - 453  2011  [Refereed]

     View Summary

    Rough sets theory was proposed by Z. Pawlak in 1982. This theory enables us to mine knowledge granules through a decision rule from a database, a web base, a set and so on. We can apply the decision rule to reason, estimate, evaluate, or forecast unknown objects. In this paper, the rough set model is used to analyze of time series data of tick-wise price fluctuation, whereknowledge granules are mined from the data set of tick-wise price fluctuations.

    DOI

  • Structural Learning Model of the Neural Network and Its Application to LEDs Signal Retrofit

    Junzo Watada, Shamshul Bahar Yaakob

    NEW ADVANCES IN INTELLIGENT SIGNAL PROCESSING   372   55 - +  2011  [Refereed]

     View Summary

    The objective of this research is to realize structural learning within a Boltzmann machine (BM), which enables the effective solution of problems defined in terms of mixed integer quadratic programming. Simulation results show that computation time is up to one fifth faster than conventional BMs. The computational efficiency of the resulting double-layer BM is approximately expressed as the ratio n divided by N, where n denotes the number of selected units (neurons/nodes), and N the total number of units. The double-layer BM is applied to efficiently solve the mean-variance problem using mathematical programming with two objectives: the minimization of risk and the maximization of expected return. Finally, the effectiveness of our method is illustrated by way of a light emitting diodes (LED) signal retrofit example. The double-layer BM enables us to not only obtain a more effective selection of results, but also enhance effective decision making. The results also enable us to reduce the computational overhead, as well as to more easily understand the structure. In other words, decision makers are able to select the best solution given their respective points of view, by means of the alternative solution provided by the proposed method.

  • Service Cost Optimization in Supply Balance of Sustainable Power Generation

    Junzo Watada, Yu-Lien Tai, Yingru Wang, Jaeseok Choi, Mitsushige Shiota

    2011 PROCEEDINGS OF PICMET 11: TECHNOLOGY MANAGEMENT IN THE ENERGY-SMART WORLD (PICMET)    2011  [Refereed]

     View Summary

    With recent trends in utilizing renewable power to develop sustainable energy sources, WTG and PV are increasingly viable economic alternatives for sustainable power generation from conventional fossil fuels. Therefore, multi-state models are being generated to solve the intermittent power production problem of wind turbine generator (WTG) and photo voltaic (PV). However, a disadvantage of these units is the generation of highly variable electricity at several different timescales from hourly, daily, and seasonally. Related to variability is the short-term (hourly or daily) predictability of wind plant output. These problems result in serious damage to sustainable service concerns in both the design and operation of WTG and PV systems. Large scale systems can provide a solution to overcome sustainable service problem but are costly. A sufficient design will cause outages that lead to certain cost losses on the customer side. Therefore, planning a reasonable size is a major dilemma.
    For this objective, the three models of load model, generation model, and service cost model should be built. For the first two models, the loss of load expected (LOEE) and the loss of load expected (LOLE) can be calculated. Then, this reliability characteristic is evaluated with the existing system of generation units to decide the range of required renewable generators. Finally, the cost model is constructed with consideration of the sustainable service cost.
    The fuel and operation costs obviously contribute a rather large portion to the utility cost. From the total service cost chart, the amount that the sustainable service worth is tremendous when forced outage at a high level occurs and the customer is not fulfilled. However, the utility cost becomes the primary of the total service costs when sufficient capacity is constructed in the system, which is invested mainly to the conventional generating. The utility cost of renewable energy changes with an increases in WTG and PV but remains steady. With regard to the excessive variable cost, fixed cost, and capital cost of conventional generating system, the investment cost and maintenance cost are relatively insignificant. If renewable energy replaces conventional generators, particularly thermal sources, this component of utility costs will not occur. Also, a certain service level is ensured to supply the load.

  • Formulation of Fuzzy Random Regression Model

    Junzo Watada, Shuming Wang, Witold Pedrycz

    NEW ADVANCES IN INTELLIGENT SIGNAL PROCESSING   372   1 - +  2011  [Refereed]

     View Summary

    In real-world regression analysis, statistical data may be linguistically imprecise or vague. Given the co-existence of stochastic and fuzzy uncertainty, real data cannot be characterized by using only the formalism of random variables.
    To address regression problems in presence of such hybrid uncertain data, fuzzy random variables are introduced in this study, and serve as an integral component of regression models. A new class of fuzzy regression models based on fuzzy random data is built, and is called the fuzzy random regression model (FRRM). First, a general fuzzy regression model for fuzzy random data is introduced. Then, using expectations and variances of fuzzy random variables, a-confidence intervals are constructed for fuzzy random input-output data. The FRRM is established based on the a-confidence intervals. The proposed regression model gives rise to a non-linear programming problem which consists of fuzzy numbers or interval numbers. Since sign-changes in the fuzzy coefficients modify the entire programming structure of the solution process, the inherent dynamic non-linearity of this optimization makes it hard to exploit the techniques of linear programming or classical non-linear programming. Therefore, we resort to some heuristics. Finally, an illustrative example is provided.

  • Robustness of DNA-Based Clustering

    Rohani Abu Bakar, Chu Yu-Yi, Junzo Watada

    NEW ADVANCES IN INTELLIGENT SIGNAL PROCESSING   372   75 - +  2011  [Refereed]

     View Summary

    The primary objective of clustering is to discover a structure in the data by forming some number of clusters or groups. In order to achieve optimal clustering results in current soft computing approaches, two fundamental questions should be considered; (i) how many clusters should be actually presented in the given data, and (ii) how real or good the clustering itself is. Based on these two fundamental questions, almost clustering method needs to determine the number of clusters. Yet, it is difficult to determine an optimal number of a cluster group should be obtained for each data set. Hence, DNA-based clustering algorithms were proposed to solve clustering problem without considering any preliminary parameters such as a number of clusters, iteration and, etc..
    Because of the nature of processes between DNA-based solutions with a silicon-based solution, the evaluation of obtained results from DNA-based clustering is critical to be conducted. It is to ensure that the obtained results from this proposal can be accepted as well as other soft computing techniques. Thus, this study proposes two different techniques to evaluate the DNA-based clustering algorithm; either it can be accepted as other soft computing techniques or the results that obtained from DNA-based clustering are not reliable for employed.

  • A Convex Hull-Based Fuzzy Regression to Information Granules Problem - An Efficient Solution to Real-Time Data Analysis

    Azizul Azhar Ramli, Junzo Watada, Witold Pedrycz

    SOFTWARE ENGINEERING AND COMPUTER SYSTEMS, PT 2   180   190 - +  2011  [Refereed]

     View Summary

    Regression models are well known and widely used as one of the important categories of models in system modeling. In this paper, we extend the concept of fuzzy regression in order to handle real-time implementation of data analysis of information granules. An ultimate objective of this study is to develop a hybrid of a genetically-guided clustering algorithm called genetic algorithm-based Fuzzy C-Means (GA-FCM) and a convex hull-based regression approach being regarded as a potential solution to the formation of information granules. It is shown that a setting of Granular Computing helps us reduce the computing time, especially in case of real-time data analysis, as well as an overall computational complexity. We propose an efficient real-time information granules regression analysis based on the convex hull approach in which a Beneath-Beyond algorithm is employed to design sub convex hulls as well as a main convex hull structure. In the proposed design setting, we emphasize a pivotal role of the convex hull approach or more specifically the Beneath-Beyond algorithm, which becomes crucial in alleviating limitations of linear programming manifesting in system modeling.

  • Two-stage fuzzy stochastic programming with Value-at-Risk criteria

    Shuming Wang, Junzo Watada

    APPLIED SOFT COMPUTING   11 ( 1 ) 1044 - 1056  2011.01  [Refereed]

     View Summary

    A new class of fuzzy stochastic optimization models-two-stage fuzzy stochastic programming with Value-at-Risk (FSP-VaR) criteria is built in this paper. Some properties of the two-stage FSP-VaR, such as value of perfect information (VPI), value of fuzzy random solution (VFRS), and bounds of the fuzzy random solution, are discussed.
    An Approximation Algorithm is proposed to compute the VaR by combining discretization method of fuzzy variable, random simulation technique and bisection method. The convergence of the approximation algorithm is proved. To solve the two-stage FSP-VaR, a hybrid mutation-neighborhood-based particle swarm optimization (MN-PSO) which comprises the Approximation Algorithm is proposed to search for the approximate optimal solution. Furthermore, a neural network-based acceleration method is discussed. A numerical experiment illustrates the effectiveness of the proposed hybrid MN-PSO algorithm. The comparison shows that the hybrid MN-PSO exhibits better performance than the one when using other approaches such as hybrid PSO and GA. (C) 2010 Elsevier B.V. All rights reserved.

    DOI

  • A PARTICLE FILTER APPROACH FOR MULTI-CAMERA TRACKING SYSTEMS IN A LARGE VIEW SPACE

    Zalili Binti Musa, Junzo Watada

    INTERNATIONAL JOURNAL OF INNOVATIVE COMPUTING INFORMATION AND CONTROL   6 ( 6 ) 2827 - 2836  2010.06  [Refereed]

     View Summary

    A video tracking system. has many potential applications, particularly in security, monitoring and robotics. The most important problem. in tracking systems is object motion tracking. In this paper, we present a new method that combines footstep prediction and particle filter to manage some problems inherent in manipulating a large View image space. We compare various methods and evaluate their capabilities.

  • IDENTIFICATION AND REALIZATION OF CHANGING TECHNICAL EFFICIENCY BASED ON PATH-CONVERGED DESIGN

    Juying Zeng, Bing Zu, Junzo Watada

    INTERNATIONAL JOURNAL OF INNOVATIVE COMPUTING INFORMATION AND CONTROL   6 ( 4 ) 1643 - 1654  2010.04  [Refereed]

     View Summary

    Most of previous researches have been presented to compute and analyze technical efficiency from economics and statistics perspectives. However, none of any application-based research to realize technical efficiency from management engineering perspective was found in literatures. This paper establishes an innovative Path-convoyed design technique not only to identify technical efficiency but also to realize technical progress in different regions. The FDI path identifies technical level increasing trend in Eastern region and declining trend in both Middle and Western regions in China during 1996-2005. TFP growth is mainly attributed to technical progress rather than efficiency improvement in both Eastern and Middle regions. Most importantly, the innovative realizations of technical progress are obtained in Middle and Western provinces with more than 0.8 and 0.65 times of national GDP per capita, respectively.

  • REAL OPTIONS ANALYSIS BASED ON FUZZY RANDOM VARIABLES

    Bo Wang, Shuming Wang, Junzo Watada

    INTERNATIONAL JOURNAL OF INNOVATIVE COMPUTING INFORMATION AND CONTROL   6 ( 4 ) 1689 - 1698  2010.04  [Refereed]

     View Summary

    The objective of this paper is to build a real options model under hybrid uncertain environment of randomness and fuzziness. In order to well describe the real uncertain situation, we utilize fuzzy random variable as a tool to characterize future cash flows, and propose a new real options analysis approach by combing binomial lattice-based model with fuzzy random variable, as named fuzzy random real options analysis (FR-ROA). Then the proposed FR-ROA is applied to an R&D project problem under fuzzy random environment, and the relations of FR-ROA with the classical ROA and the fuzzy ROA are explicitly discussed, respectively.

  • A fuzzy regression approach to a hierarchical evaluation model for oil palm fruit grading

    A. Nureize, J. Watada

    FUZZY OPTIMIZATION AND DECISION MAKING   9 ( 1 ) 105 - 122  2010.03  [Refereed]

     View Summary

    Measurement of quality is an important task in the evaluation of agricultural products and plays a pivotal role in agricultural production. The inspection process normally involves a visual examination according to the ripeness standards of crops, and this grading is subject to expert knowledge and interpretation. Therefore, the quality inspection process of fruits needs to be conducted properly to ensure that high-quality fruit bunches are selected for production. However, human subjective judgments during the evaluation make the fruit grading inexact. The objectives of this paper are to build a fuzzy hierarchical evaluation model that characterises the criteria of oil palm fruits to decide the fuzzy weights of these criteria based on a fuzzy regression model, and to help inspectors conduct a proper total evaluation. A numerical example is included to illustrate the computational process of the proposed model.

    DOI

  • 10A-B-2 Knowledge Acquisition of one-minute chart for Rough Sets

    MATSUMOTO Yoshiyuki, WATADA Junzo

    Proceedings of the Annual Conference of Biomedical Fuzzy Systems Association   23   155 - 158  2010

     View Summary

    Rough set theory was proposed by Z.Pawlak in 1982. This theory can mine knowledge of decision rules from a database, a web base, a set and so on. The decision rules are used for data analysis as well. Then we can reason for an unknown object using the decision rules. In this paper, we apply the rough set theory to analysis of time series data. We acquire knowledge based on one-minute chart fluctuations.

    DOI CiNii

  • Building a Bio-Inspired Reinforcement Medical Network System for Optimal Relationships in Medical Communications(<Special Issue>INNOVATIVE BIOMEDICAL TECHNOLOGIES and INFORMATICS, BMFSA2008)

    KIM Ikno, CHU Yu-Yi, JENG Don Jyh-Fu, WATADA Junzo, WU Jui-Yu

    International Journal of Biomedical Soft Computing and Human Sciences: the official journal of the Biomedical Fuzzy Systems Association   15 ( 2 ) 9 - 16  2010

     View Summary

    Although advanced information technologies provide useful information to medical service workers, the workers in medical workplaces are offered no easy opportunities for enhancing relationships for the exchange of medical information. One reason for this is that it is difficult to find optimal relationships when a number of medical service workers are involved. In this article, we specifically focus on density analysis of medical service teams where medical service workers are connected via their medical work-related values. We apply a DNA computing method as a profound method by which to find optimal relationships for medical communications. The results of the density analysis show how efficient a DNA computing method approach can be in building a reinforced medical network system.

    DOI CiNii

  • Building a Decision Support System for Urban Design Based on the Creative City Concept

    Lee-Chuan Lin, Junzo Watada

    Intelligent Systems Reference Library   4   317 - 346  2010  [Refereed]

     View Summary

    City renaissance has played an increasingly important role in urban regeneration since the mid-1980s. The concept of the Creative City, proposed by Charles Landry is driving the imagination of city redevelopers. Recent developments have focused less on capital projects and more on the ability of activity in the arts to support community-led renewals. It is essential for researchers to pay more attention to the issue of Creative City development. According to UNESCO, the Creative Cities Network connects cities that will share experiences, ideas, and best practices aiming at cultural, social and economic development. It is designed to promote the social, economical and cultural development of cities in both the developed and the developing world. However, Creative City design must be integrated with a wide range of knowledge and a diverse database. The application of urban development is a complex and delicate task. It involves multiple issues including engineering, economics, ecology, sociology, urban development, art, design and other domains. In order to empower efficiency in concurrent city development, appropriate evaluation and decision tools need to be provided. Building a decision support system of Creative City development can help decision-makers to solve semi-structured problems by analyzing data interactively. The decision support system is based on a new approach to treating rough sets. The method will play a pivotal role and will be employed dynamically in the DSS. The approach realizes an efficient sampling method in rough set analysis that distinguishes whether a subset can be classified in the focal set or not. The algorithm of the rough set model will be used to analyze obtained samples. In this paper we will first examine the design rules of Creative City development by urban design experts. Second, we will apply rough set theory to select the decision rules and measure the current status of Japanese cities. Finally, we will initiate a prototype decision support system for Creative City design based on the results obtained from the rough sets analysis. © Springer-Verlag Berlin Heidelberg 2010.

    DOI

  • Decision-Making for the Optimal Strategy of Population Agglomeration in Urban Planning with Path-Converged Design

    Bing Xu, Junzo Watada

    Intelligent Systems Reference Library   4   397 - 425  2010  [Refereed]

     View Summary

    The chapter aims first to identify existing population agglomeration and its efficiency, and second to simulate decision-making for the optimal migration strategy in urban planning to eliminate inefficiency among cities in China. First, identification based on path-converged design reveals inefficiency in existing population agglomeration in China because the population mostly agglomerates to cities with urbanization levels lower than 0.35 and the population gathers into areas with urbanization levels lower than the average level in large, medium and small cities from both regional and urban perspectives. Second, decision-making for regional population migration performs well in eliminating inefficiency. By emigrating about 14, 10, and 14 percents of the regional population from cities at low urbanization levels to cities at higher urbanization levels, inefficiency strengths between benchmark and regional population distributions shrink to 0.058, 0.041, and 0.056 from 0.1464, 0.0985, 0.1397 for small, medium, and large cities, respectively. © Springer-Verlag Berlin Heidelberg 2010.

    DOI

  • Shape Design of Products Based on a Decision Support System

    Yung-chin Hsiao, Junzo Watada

    Intelligent Systems Reference Library   4   55 - 84  2010  [Refereed]

     View Summary

    From a historical perspective, two fundamental issues are observed for industrial designers: (1) what is the shape design process within the context of a modern product design process, and (2) how shape design theories, methods, tools and computer aided software can be effectively utilized for creating product shapes. A framework is proposed to resolve the issues by describing the relationships of the product design problems, product design processes, shape design processes, shape design methods and tools with consideration of the functional, ergonomic, emotional and manufacturing requirements. The framework implemented here is a new type of decision support system (DSS) - an object-oriented decision support system to assist the designers in designing product shapes. A scooter case illustrates the usage of the framework and the implementation of DSS. According to the planned design process and design method, a shape grammar is used as knowledge representation and knowledge reasoning method for creating scooter shapes. The functional and ergonomic requirements can be explicitly expressed in the shape grammar. The designer can interactively apply the shape grammar with consideration of emotional and manufacturing requirements. Accordingly, the industrial designers can use a DSS to plan their own shape design processes and utilize the shape design tools, methods, knowledge for their own design problems without practicing the complicated and interdisciplinary knowledge of the shape design for many years. © Springer-Verlag Berlin Heidelberg 2010.

    DOI

  • Real time model of fuzzy random regression based on a convex hull approach

    Azizul Azhar Ramli, Junzo Watada, Witold Pedrycz

    Proceedings - 2010 2nd International Conference on Advances in Computing, Control and Telecommunication Technologies, ACT 2010     45 - 49  2010  [Refereed]

     View Summary

    In this study, we present a new idea dealing with the analysis of fuzzy random variables (FRVs) being treated as samples of data. The proposed concept can be used to model various real-life situations where uncertainty is not only present in the form of randomness but also comes in the form of imprecision described in terms of fuzzy sets. We propose a hybrid approach, which combines a convex hull approach (called Beneath-Beyond algorithm) with a fuzzy random regression analysis. Falling under the umbrella of intelligent data analysis (IDA) tool, this approach is suitable for real-time implementation of data analysis. For a fuzzy random data set, we include simulation results and highlight two main advantages, namely a decrease of required analysis time and a reduction of computational complexity. This emphasizes that the proposed IDA approach becomes an efficient way for real-time data analysis. © 2010 IEEE.

    DOI

  • Fuzzy random redundancy allocation problems

    Shuming Wang, Junzo Watada

    Studies in Fuzziness and Soft Computing   254   425 - 456  2010  [Refereed]

     View Summary

    Due to subjective judgement, imprecise human knowledge and perception in capturing statistical data, the real data of lifetimes in many systems are both random and fuzzy in nature. Based on the fuzzy random variables that are used to characterize the lifetimes, this paper studies the redundancy allocation problems to a fuzzy random parallel-series system. Two fuzzy random redundancy allocation models (FR-RAM) are developed through reliability maximization and cost minimization, respectively. Some properties of the FR-RAM are obtained, where an analytical formula of reliability with convex lifetimes is derived and the sensitivity of the reliability is discussed. To solve the FR-RAMs, we first address the computation of reliability. A random simulation method based on the derived analytical formula is proposed to compute the reliability with convex lifetimes. As for the reliability with nonconvex lifetimes, the technique of fuzzy random simulation together with the discretization method of fuzzy random variable is employed to compute the reliability, and a convergence theorem of the fuzzy random simulation is proved. Subsequently, we integrate the computation approaches of the reliability and genetic algorithm (GA) to search for the approximately optimal redundancy allocation of the models. Finally, some numerical examples are provided to illustrate the feasibility of the solution algorithm and quantify its effectiveness. © 2010 Springer-Verlag Berlin Heidelberg.

    DOI

  • Constructing Fuzzy Random Goal Constraints for Stochastic Fuzzy Goal Programming

    Nureize Arbaiy, Junzo Watada

    INTEGRATED UNCERTAINTY MANAGEMENT AND APPLICATIONS   68   293 - 304  2010  [Refereed]

     View Summary

    This paper attempts to estimate the coefficient of the goal constraints through a fuzzy random regression model which plays a pivotal role in solving a stochastic fuzzy additive goal programming. We propose the two phase-based solutions; in the first phase, the goal constraints are constructed by fuzzy random-based regression model and, in the second phase, the multi-objective problem is solved with a stochastic fuzzy additive goal programming model. Further, we apply the model to a multi-objective decision-making scheme&apos;s use in palm oil production planning and give a numerical example to illustrate the model.

  • Wise Search Engine Based on LSI

    Yang Jianxiong, Junzo Watada

    AGENTS AND DATA MINING INTERACTION   5980   126 - 136  2010  [Refereed]

     View Summary

    The objective of this work is to provide, as a search engine, latent semantic indexing (LSI), which is a classical method to produce optimal approximations of a term-document matrix and has been used for textual information mining. The use of this technique is examining mine content which based web document, using keyword features of documents. Experimental results show that together with both textual and latent features LSI can extract the underlying semantic structure of web documents, thus improve the search engine performance significantly.

  • Human Tracking: A State-of-Art Survey

    Junzo Watada, Zalili Musa, Lakhmi C. Jain, John Fulcher

    KNOWLEDGE-BASED AND INTELLIGENT INFORMATION AND ENGINEERING SYSTEMS, PT II   6277   454 - +  2010  [Refereed]

     View Summary

    Video tracking can be defined as an action which can estimate the trajectory of an object in the image plane as it moves within a scene. A tracker assigns consistent labels to the tracked objects in different frames of a video. The objective of this paper is to provide information on the present state of the art and to discuss future trends in the use of multi-camera tracking systems. In the literature, three main types of multi-camera tracking system have been outlined. The first type relies on challenges in the camera tracking system. The second concerns the methodology of tracking systems in general. The third type relies on current trends in camera tracking systems. We provide an overview of the current research status by summarizing promising avenues for further research.

  • BIOLOGICALLY INSPIRED FUZZY FORECASTING: A NEW FORECASTING METHODOLOGY

    Don Jyh-Fu Jeng, Junzo Watada, Berlin Wu

    INTERNATIONAL JOURNAL OF INNOVATIVE COMPUTING INFORMATION AND CONTROL   5 ( 12B ) 4835 - 4844  2009.12  [Refereed]

     View Summary

    There are many forecasting techniques including the ARIMA model, GARCH model, exponential smoothing, neural networks, genetic algorithm, etc. Those methods, however, have their drawbacks and advantages. Since financial time series may be influenced by many factors, such as trading volume, business cycle, oil price, and seasonal factor, conventional model based on prediction methodologies and hard computing methods seem inadequate. In recent years, the innovation and improvement of forecasting methodologies have caught more attention, and also provide indispensable information in the decision-making process, especially in the fields of financial economics and engineering management. In this paper, a new forecasting methodology inspired by natural selection is developed The new forecasting methodology may be of use to a nonlinear time series forecasting. The method combines mathematical, computational, and biological sciences, which includes fuzzy logic, DNA encoding, polymerase chain reaction, and DNA quantification. In the empirical study, currency exchange rate forecasting is demonstrated. The Mean Absolute Forecasting Accuracy method is defined for evaluating the performance, and the result comparing with the ARIMA method is illustrated.

  • A ROUGH SET APPROACH TO CLASSIFICATION AND ITS APPLICATION FOR THE CREATIVE CITY DEVELOPMENT

    Lee-Chuan Lin, Zhu Jing, Junzo Watada, Tomoko Kashima, Hiroaki Ishi

    INTERNATIONAL JOURNAL OF INNOVATIVE COMPUTING INFORMATION AND CONTROL   5 ( 12B ) 4859 - 4866  2009.12  [Refereed]

     View Summary

    The objective of this paper is to realize an efficient sampling method in. a rough set approach that distinguishes whether a subset can be classified in. the focal set or not. The algorithm of rough set model will be used to analyze samples obtained and in order to illustrate the method, we use some artificial data in this paper. As its application, we discuss public art and urban development. Also, the concept of the Creative City, proposed by Charles Landry, will be reviewed for urban innovators, policy makers. scientists and artists. City renaissance has played an increasingly important role in urban regeneration since the mid-1980s. However, recent developments have focused less on capital projects, and more on the arts activity to support community-led renewals. It is essential for researchers to pay more attention to the issue of the Creative City development. In this paper, the differentiation is examined whether the cities can be classified into a Creative City or not.

  • PROBABILISTIC PRODUCTION COST CREDIT EVALUATION OF WIND TURBINE GENERATORS

    Jeongje Park, Wu Liang, Jaeseok Choi, A. A. El-Keib, Junzo Watada

    INTERNATIONAL JOURNAL OF INNOVATIVE COMPUTING INFORMATION AND CONTROL   5 ( 11A ) 3635 - +  2009.11  [Refereed]

     View Summary

    This paper proposes an algorithm for probabilistic production cost credit evaluation of wind turbine generators (WTG). Renewable energy resources such as wind, solar, micro hydro, tidal and biomass etc. are becoming increasingly important because of the increased interest in protecting the environment. Wind energy is one of the most successfully used renewable energy sources used to produce electrical energy. The proposed approach was implemented on a power system that includes WTGs. Test results demonstrates the viability of the proposed approach for assessing the wind speed credit from the economics view point.

  • FUZZY AHP APPROACH TO COMPARISON OF GRANT AID FOR ODA IN JAPAN

    Kunio Shibata, Junzo Watada, Yoshiyuki Yabuuchi

    INTERNATIONAL JOURNAL OF INNOVATIVE COMPUTING INFORMATION AND CONTROL   5 ( 6 ) 1539 - 1546  2009.06  [Refereed]

     View Summary

    Today, many Companies contribute to Official Development Assistance (ODA). The details of ODA are publicised for each project, with contributions and companies listed on the company&apos;s website. The objective of this paper is to evaluate these companies in terms of the best practices that characterise ODA donations in four fields: infrastructure, medical service, education and security.
    Analytic hierarchy process (AHP) is proposed by T. L. Saaty to evaluate uncertainty in decision problems. Using comparison matrices, the AHP can evaluate the extent of data fit to practical data. In fuzzy analytic hierarchy process (Fuzzy AHP), interval weights play a pivotal role and can be solved by linear programming. An approach to fuzzy AHPs is to estimate interval priorities on the items discussed. These interval weights are based on the concept of possibility and express the range of the possibility. The method employed by H. Tanaka is to minimise the range including the given data. The range of the possibility can be illustrated as the interval or width of calculated values. Similar to a conventional AHP model, the fuzzy AHP model has a hierarchical structure that is used to decide the priority of each alternative with the minimal evaluation width of each alternative. Saaty proposed that an AHP matrix should have a Consistency Index (C. I.) of less than 0.1, since theory suggests that C.I. should be satisfied. The fuzzy AHP Model can. minimise such vagueness and uncertainty in the hierarchical structure used to evaluate alternatives.

  • Searching Cliques in a Fuzzy Graph Based on an Evolutionary and Biological Method

    Ikno Kim, Junzo Watada

    KNOWLEDGE-BASED AND INTELLIGENT INFORMATION AND ENGINEERING SYSTEMS, PT II, PROCEEDINGS   5712   166 - 173  2009  [Refereed]

     View Summary

    In this paper, a new and systematic approach for the integration of fuzzy-based methods and biological computation, named as an evolutionary and biological method, is proposed for searching cliques in a fuzzy graph. When dealing with a number of nodes in a graph, the most intractable problem is often detecting the maximum clique, which is automatically obtained from finding, a solution to the arranged cliques in descending order. The evolutionary and biological method is proposed to identify all the cliques and to arrange them in a fuzzy graph, and then to structure all the nodes in the graph, based on the searched cliques, in different hierarchical levels. This challenging approach, involving the integration of two techniques, provides a new and better method for solving clique problems.

  • Dynamic Tracking System through PSO and Parzen Particle Filter

    Zalili Binti Musa, Junzo Watada, Sun Yan, Haochen Ding

    KNOWLEDGE-BASED AND INTELLIGENT INFORMATION AND ENGINEERING SYSTEMS, PT II, PROCEEDINGS   5712   220 - 227  2009  [Refereed]

     View Summary

    Transportation plays a pivotal role in our society, especially in a good quality of life and economic prosperity. Intelligent transportation system (ITS) has been developed to manage the transport infrastructure and vehicles since the number of vehicles is rapidly growing and to avoid any accident. Various applications have provided to support ITS. One of them is a driver-assistant system. Considering of heavy vehicles such as bus, truck, trailer and etc., the driver assistant system is of importance in monitoring and recognizing objects in vehicle surrounding. For example, in operating a heavy vehicle, a driver has a limited view of the vehicle surrounding itself. It is difficult for the driver to ensure that the surrounding of vehicle is safe before operating the machine. Thus, in this paper, we employ a video tracking system through PSO and Parzen particle filter to break through several problems such as simultaneous motion and occlusion among objects. This method makes it easy to track a human movement from every frame and indirectly require less a processing time for tracking an object location in a video stream compared to conventional method. The detail outcome and result are discussed using experiments of the method in this paper.

  • A Hybrid Method of Biological Computation and Genetic Algorithms for Resolving Process-Focused Scheduling Problems

    Ikno Kim, Junzo Watada

    KNOWLEDGE-BASED AND INTELLIGENT INFORMATION AND ENGINEERING SYSTEMS, PT II, PROCEEDINGS   5712   159 - 165  2009  [Refereed]

     View Summary

    A huge number of different product types are managed through various processes in facilities with different approaches to scheduling. In this paper, we concentrate mainly on process-focused facilities. Sample groups of such facilities and processes were selected: its orders and times were investigated using both biological computation and genetic algorithms. First, biological computation was used to determine practical schedules. Second, genetic algorithms were used to identify which of the schedules determined by biological computation worked best. Here, we examine how combining these methods can be applied to solving process-focused scheduling problems.

  • A Bio-inspired Evolutionary Approach to Identifying Minimal Length Decision Rules in Emotional Usability Engineering

    Ikno Kim, Junzo Watada

    KNOWLEDGE-BASED AND INTELLIGENT INFORMATION AND ENGINEERING SYSTEMS, PT II, PROCEEDINGS   5712   181 - 187  2009  [Refereed]

     View Summary

    Many of the applied methods and measurement tools of emotional usability engineering have been recommended for use designing products. A rough set method can also be a useful tool to be integrated with the basic concepts of emotional usability engineering. If such a method is applied, the groups of sensory words have to be investigated and their values reduced and classified to provide comprehensive information to product designers. However, a computational problem exists regarding the number of samples, groups of sensory words, and values required when resolving sense-based minimal decision rules. Considering this problem, we discuss the use of DNA computing, and propose a bio-inspired evolutionary method based on the rough set method, which should provide a new tool for emotional usability engineering.

  • Fuzzy Portfolio Selection based on Value-at-Risk

    Bo Wang, Shuming Wang, Junzo Watada

    2009 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN AND CYBERNETICS (SMC 2009), VOLS 1-9     1840 - 1845  2009  [Refereed]

     View Summary

    In this paper, using Value-at-Risk, a new fuzzy portfolio selection model named VaR-FPSM is proposed. The Value-at-Risk is the measure of risk, which describes the greatest loss of an investment with some confidence level. When security returns are same kind of fuzzy variable, we derive two crisp equivalent forms of the VaR-FPSM. Furthermore, in general situations, we designed a fuzzy simulation based particle swarm optimization (PSO) algorithm to find an approximately optimal result. To illustrate the proposed model and hybrid PSO algorithm, a numerical example is provided and some discussions on the results are given.

  • A Fuzzy Density Analysis of Subgroups by Means of DNA Oligonucleotides

    Ikno Kim, Junzo Watada

    INTELLIGENT SYSTEMS AND TECHNOLOGIES: METHODS AND APPLICATIONS   217   31 - 45  2009  [Refereed]

     View Summary

    In complicated industrial and organizational relationships between employees or workers. it is difficult to offer good opportunities for their psychological and skill growth, since our progressive information and industrial societies have created many menial tasks. Redesigning subgroups in a personnel network for work rotation is a method that organizes employees appropriately to address these types of problems. In this article, we focus on a fuzzy density analysis of subgroups where employees are connected via their relationships with fuzzy values. However, it becomes extremely hard to rearrange those employees when there are vast numbers of them, meaning it is an NP-hard problem. In the personnel network, all the possible cohesive subgroups can be detected by making the best use of DNA oligonucleotides. which is also applied as a method by which to rearrange employees via fuzzy values based on the results of a fuzzy density analysis.

  • Fuzzy Synthesis Evaluation on Market Survey with Pop Music Awards

    Pei-Chun Lin, Junzo Watada, Berlin Wu

    PROCEEDING OF THE 10TH INTERNATIONAL CONFERENCE ON INTELLIGENT TECHNOLOGIES     86 - 90  2009  [Refereed]

     View Summary

    In social science research, many decisions, evaluations, or psychological test is performed using surveys or questionnaires to seek people's opinion. But some market decisions are always executed by experts. It may lose some information on a real consumers market because of human subjective recognition. In this paper we propose a fuzzy synthesis evaluation on market survey. We propose some fuzzy questionnaires of the 19th Taiwan Golden Melody Awards (TGMA) and ask people answer them in fuzzy logic. The primary goal is that we can reduce the calculation of the evaluation process and thoroughly understand everyone's thought about pop music. Moreover, we hope that this evaluation process can be used on internet and easily enable us to reach decision based on population in the future.

  • A Fuzzy Random Variable Approach to Restructuring of Rough Sets through Statistical Test

    Junzo Watada, Lee-Chuan Lin, Minji Qiang, Pei-Chun Lin

    ROUGH SETS, FUZZY SETS, DATA MINING AND GRANULAR COMPUTING, PROCEEDINGS   5908   269 - 277  2009  [Refereed]

     View Summary

    Usually it is hard to classify the situation where randomness and fuzziness exist simultaneously. This paper presents a method based on fuzzy random variables and statistical t-test to restructure a rough set. The algorithms of rough set and statistical t-test are used to distinguish whether a subset can be classified in the object set or not. The expected-value-approach is also applied to calculate the fuzzy value with probability into a scalar value.

  • A Fuzzy Regression Approach to Acquisition of Linguistic Rules

    Junzo Watada, Witold Pedrycz

    Handbook of Granular Computing     719 - 732  2008.07  [Refereed]

    DOI

  • Applied Statistics by Means of DNA-Based Clustering for Data Classification

    KIM Ikno, JENG Don Jyh-Fu, WATADA Junzo

    Proceedings of the Annual Conference of Biomedical Fuzzy Systems Association   21   138 - 141  2008

     View Summary

    In a clustering analysis, the main problem is often referred to the uncertainty of the data, which could be possibly clustered, meaning the quality of the designed, improved, or analysed system that could be evaluated by this uncertainty of the clustered data. A reliable optimal solution from clustering data could be found by making the best use ofDNA computing. In this paper, a reliable optimal algorithm is proposed to cluster specific data for supporting a complicated data structure based on DNA computing with applied statistics. Its realization is very challenging while the underlying goal could be easily understood in a dimensional space. Given their nature, clustering problems become NP-complete problems. The use of DNA computing as a vehicle of data clustering with applied statistics is discussed and described in this study.

    DOI CiNii

  • Bio-inspired evolutionary method for cable trench problem

    Don Jyh-Fu Jeng, Ikno Kim, Junzo Watada

    INTERNATIONAL JOURNAL OF INNOVATIVE COMPUTING INFORMATION AND CONTROL   3 ( 1 ) 111 - 118  2007.02  [Refereed]

     View Summary

    A bio-inspired evolutionary method with DNA is presented for solving a cable trench problem in this paper. The cable trench problem is a combination of the shortest path problem and the minimum spanning tree problem, which makes it difficult to be solved by a conventional computing method. DNA computing is applied to overcome the limitation of a silicon-based computer. The numerical values are represented by the fixed-length DNA strands, and the weights are varied by the melting temperatures. Biochemical techniques with DNA thermodynamic properties are used for effective local search of the optimal solution.

  • Trend Analysis of Time Series Data Using Rough Sets

    Matsumoto Yoshiyuki, Watada Junzo

    Proceedings of the Annual Conference of Biomedical Fuzzy Systems Association   20   79 - 82  2007

     View Summary

    Rough set theory was proposed by Z.Pawlak in 1982. This theory can mine knowledge as a decision rule from a database, a web base, a set and so on. The decision rule is used for data analysis as well. And we can reason an unknown object using the decision rule. The objective of this paper is to apply the rough set theory to analysis of time series data. It is possible to acquire knowledge from time series data using regression line and apply a method to predictions..

    DOI CiNii

  • Project Management for Software Development

    Yabuuchi Yoshiyuki, Watada Junzo

    Proceedings of the Annual Conference of Biomedical Fuzzy Systems Association   20   109 - 112  2007

     View Summary

    Project management is one of central issues in management of technology and engineering. Kathy Schwalbe summarized recent statistics that average time overrun is 163% in 2001 and 227% in 1995, and average cost overrun is 145% in 2001 and 189% in 1995. The project management is not so much successful. A software development company is expected to provide high quality and functional software to the world. It is difficult to control software quality because software is invisible and cannot be felt with our finger. In addition, it is hard to make its productivity efficient because software is created as a result of brainwork. Generally speaking, a project team is organized to create software. Human relationships and work environment affect software qualities. Therefore, it is very important to study the project management of software development for the cost of management and the quality control. The objective of this paper is to illustrate the influential features of software development projects by analyzing questionnaires collected from several software development companies. In the analysis, a multivariate model is employed to quantitatively evaluate the influential features of software development projects.

    DOI CiNii

  • Recognition of Human Behaviors Based on Biopsy Information

    Watada Junzo, Hirano Hideyasu, Yubazaki Naoyoshi

    Proceedings of the Annual Conference of Biomedical Fuzzy Systems Association   19   67 - 70  2006

     View Summary

    It is difficult to recognize the state of a human body. In this paper we employ biometrics in order to recognize human behavior. Information obtained form human bodies such as temperature, blood pressure, α brainwave and etc. can be employed to recognize the state of a human body, in other words, the human behaviors. The objective of this paper is to achieve the recognition of the state whether a human is active or relaxed by his biometrics. It is required to recognize the aged person's behavior.

    DOI CiNii

  • Decision of Books Collection Plan Considering User's Lending Situation by Fuzzy Mean-Variance Analysis

    Kawaura Takayuki, Watada Junzo

    Proceedings of the Annual Conference of Biomedical Fuzzy Systems Association   18   55 - 58  2005

     View Summary

    A library provides us with books, journals, news papers and so on according to the demand of its users. The selection of books to buy is also done based on the mission of each library. Usually, they purchase books according to their object as a city, an institute, a school, a university, etc. are founded. It is more important to install books which users are requiring. In this paper we provide a method how to adjust selection of books to the demand of users. The objective of the paper is to propose a method for strategic decision of selecting books. A method is also to deal with latitude in an aspiration level of the decision maker according to their mission. In this method, a librarian and a manager define, for each of expected utility rate and its variance, a necessity level and a sufficient level of users. And then, they can obtain a solution that satisfies an aspiration level of the users. We employ this method to analyze and decide which kind of books and how much rate of the total budget should be spent to buy, where the method of fuzzy mean-variance analysis is employed to solve the problem.

    DOI CiNii

  • Solving elevator scheduling problem using DNA computing approach

    MS Muhammad, S Ueda, O Ono, J Watada, M Khalid

    Soft Computing as Transdisciplinary Science and Technology     359 - 370  2005  [Refereed]

     View Summary

    DNA computing approach has gained wide interest in recent years since Adleman shows that the technique can be used to solve the Hamiltonian Path Problem (HPP). Since then there has been many research results showing how DNA computing is used to solve a variety of similar combinatorial problems which is mainly in the realm of computer science. However, the application of DNA computing in solving engineering related problems has not been well established. In this paper we demonstrate how DNA computing can be used to solve a two-elevator scheduling problem for a six-storey building. The research involves finding a suitable technique to represent the DNA sequences in finding the optimal route for each elevator based on initial conditions such as the present position of the elevator, the destination of passengers in the elevators and hall calls from a floor. The approach shows that the DNA computing approach can be well-suited for solving such real-world application in the near future.

  • Fuzzy multivariant analysis

    J Watada, M Takagi, J Choi

    KNOWLEDGE-BASED INTELLIGENT INFORMATION AND ENGINEERING SYSTEMS, PT 3, PROCEEDINGS   3215   166 - 172  2004  [Refereed]

     View Summary

    In this paper we Will analyze data obtained from a fuzzy set, where samples are defined using a membership grade of a fuzzy set. Generally, multivariant model is to analyze samples characterized using plural variants. In this paper such smaples are also characterized by a fuzzy set. Our aim is to build fuzzy discriminant model and fuzzy pattern classification model.

  • Solving portfolio problems based on meta-controled Boltzmann machine

    J Watada, T Watanabe

    MULTI-OBJECTIVE PROGRAMMING AND GOAL PROGRAMMING     269 - 274  2003  [Refereed]

     View Summary

    It is important that the limited amount of investing funds should be efficiently allocated to many stocks so as to reduce its risk. This problem is formulated as a mixed integer programming problem. However, it is not so easy to solve the mixed integer programming problem because of its combinatorial nature. Therefore, an efficient approximate method is required to solve a large-scale mixed integer programming problem. In this paper we propose a Meta-controlled Boltzmann machine to obtain an approximate solution of the large-scale mixed integer programming problem.

  • 事故のマクロ・エルゴノミックスと経済性

    和多田 淳三

    人間工学   38   186 - 187  2002

    DOI CiNii

  • Efficient Computation of Evidential Reasoning

    Maruo Kousuke, Watada Junzo

    Proceedings of the Annual Conference of Biomedical Fuzzy Systems Association   15   83 - 88  2002

     View Summary

    The objective of this paper is to employ Demster-Shafer theory into analyzing the hierarchical and logical structure with logical operations AND & OR such as a fault tree. One of main differences of the Dempster-Shafer theory from Baysian one is that it enables us to take the lack of knowledge or information into account in its analyses. The considered set of hypothesis must be mutual exclusive and exhaustive in the Dempster-Shafer theory. In this paper, we define state patterns in order to satisfy the mutual exclusiveness and exhaustiveness. The method using state patterns requires us much computational time to solve the evidential reasoning. In order to overcome this difficulty, we also propose the improved method in terms of logical relations of the events in the fault tree.

    DOI CiNii

  • Fuzzy mean-variance approach to strategic decision in agricultural management

    T Kawaura, J Watada

    JOURNAL OF THE OPERATIONS RESEARCH SOCIETY OF JAPAN   44 ( 2 ) 157 - 168  2001.06  [Refereed]

     View Summary

    When we decide the production of each agricultural product in the uncertain circumstance where its sell price. climate, consumers' preference and so on are not known previously, we should take its risk as well ass the best expected selling volume or its interest into consideration. In this paper we employ fuzzy mean-variance analysis to decide the optimal solution in agricultural management which minimizes the risk and maximizes the expected selling volume.
    The mean-variance analysis is proposed by H. Markowitz and widely employed in stock investment. Nevertheless. it. is also hard to treat an aspiration beheld by a decision-maker, because the formulation given by H. Markowitz is written using constant rigid values.
    In this paper a fuzzy number is employed to deal with a vague aspiration level and a fuzzy goal given by a decision-maker, that is, the fuzzy mean-variance analysis enables us to obtain the best solution which iu realized within a vague aspiration level and a fuzzy goal. In the method, the decision maker defines a necessity level and a sufficient level for each of expected interest rate and risk. The decision-maker can obtain a solution that satisfies an aspiration level and a fuzzy goal required.
    The effectiveness of our method is shown using a numerical example.

  • ファジィ平均分散分析による農作物の作付決定問題への応用

    川浦孝之, 和多田淳三

    Journal of Operations Research Society of Japan   44 ( 2 ) 157-168  2001.06  [Refereed]

  • Formulation of Fuzzy Realtime Regression Analysis based on Convex Hull

    WATADA Junzo

    Proceedings of the Annual Conference of Biomedical Fuzzy Systems Association   14   14 - 17  2001

     View Summary

    In order to reduce the computation time in building fuzzy regression model, we propose the effective convex hull method in 1999 to decrease drastically the number of samples which are not influencial in terms of We employ a fuzzy regression analysis in building a gift-rapping method. Recently multivariant methods play a central role in data mining. In the data mining, real time data analysis is a general approach in nature. In the area, the convex hull method can provide us with a powerful tool to reduce its computational time drastically. In this paper it is explained that (1) only vertex points obtained by the convex hull are constraints in a linear programming of building a fuzzy regression model, that(2) the convex hull approach works efficiently in real-time data gathering, that (3) the number of vertexes obtained by the convex hull will not increase so much even in the real time procedure, that (4) on real time arrived data can be effectively and efficinetly treated by the convex hull method in fuzzy regression analysis, that (5) the method does not consume the duplicated computation of the past analysis, and that (6) the analysis can be built on the basis of the past one using newly arrival data.

    DOI CiNii

  • Formulation of regression model based on natural words

    J Watada

    KNOWLEDGE-BASED INTELLIGENT INFORMATION ENGINEERING SYSTEMS & ALLIED TECHNOLOGIES, PTS 1 AND 2   69   710 - 716  2001  [Refereed]

     View Summary

    Generally when human experts express their ideas and thoughts. human words are basically employed in these expressions. It should be useful to employ fuzzy regression analysis in handling human words and finding the latent structures under these human words. a linguistic regression model is formulated in terms of fuzzy regression analysis and vocabulary matching on the basis of fuzzy numbers.

  • 台湾の情報産業人文社会篇

    和多田淳三, 濱田壯志, 川浦孝之

    大阪工業大学紀要   45   17-37  2000.07  [Refereed]

  • Formulation of a Project Portfolio Management

    Kawaura Takayuki, Watada Junzo

    Proceedings of the Annual Conference of Biomedical Fuzzy Systems Association   13   69 - 72  2000

     View Summary

    A project is widely employed in the modern organization of management. The project organization is appropriate to a speed and agile management in uncertain and most changeable economic circumstance today. We propose the method to select members of a new project and distribute budget to each of members based on their past achievement and competencies. The method is named project portfolio management because it takes its risk into consideration.

    DOI CiNii

  • Formulation of Fuzzy Auto-Regressive Time-Series Analysis

    Watada J., Toyoura Y., Yabuuchi Y.

    Proceedings of the Annual Conference of Biomedical Fuzzy Systems Association   13   142 - 145  2000

     View Summary

    In the analysis of medical and economic data, it is important to precisely grasp the present state of their systems. These human systems such as economical system or human bodies are influenced by many uncertain factors. Therefore, the systems are modeled as a complex one. It is not sufficient to understand time-series data by means of statistical methods and most important to analyze and understand the data in terms of the concept of fuzziness. In this paper, we employ the fuzzy concept into modeling time-series data and formulate fuzzy AR regression analysis.

    DOI CiNii

  • Convex Hull Approach to Fuzzy Regression Analysis

    Watada Junzo, Kushiki Yusuke

    Proceedings of the Annual Conference of Biomedical Fuzzy Systems Association   12   27 - 28  1999

    DOI CiNii

  • Short-term Prediction by Chaos Method of Embedding Related Data at the Same Time

    MATSUMOTO Yoshiyuki, WATADA Junzo

    Journal of Japan Industrial Management Association   49 ( 4 ) 209 - 217  1998

     View Summary

    Recently, the chaotic method is employed to forecast a short-term future using uncertain data. This method is feasible by restructuring the attractor of given time-series data in the multi-dimensional space through Takens' embedding theory. Nevertheless, it is hard to obtain data which comes only from a chaotic source. Ordinarily, many uncertain time-series data do not come only from a chaotic source, but also from another source. In this paper, we employ related information in order to remove the influence of the non-chaotic source from the given data. This method makes forecasting precision higher because the chaotic portion of the given data can be easily abstracted. In the end, the effectiveness and usefulness of our method are shown by application to a short-term forecasting simulation of Nikkei mean data of the Tokyo stock market.

    DOI CiNii

  • Genetic Model of Fuzzy Switching Regression Model

    Toyoura Yoshihiro, Yabuuchi Yoshiyuki, Watada Junzo

    Proceedings of the Annual Conference of Biomedical Fuzzy Systems Association   11   91 - 94  1998

     View Summary

    In the case where data which come out of several different systems are analyzed by a regression model, we should separate some partition of data which come from one system and analyze each of partitions by a regression model in the proper way. Bezdek has proposed a switching regression model based on cluster analysis and regression analysis. In this study, we propose the genetic model of fuzzy switching regression model based on a fuzzy regression model.

    DOI CiNii

  • Possibilistic Principal Component Analysis

    YABUUCHI Yoshiyuki, WATADA Junzo, NAKAMORI Yoshiteru

    Proceedings of the Annual Conference of Biomedical Fuzzy Systems Association   11   41 - 42  1998

     View Summary

    In this paper, a fuzzy concept is employed to construct a principal component model which can deal with fuzziness, vagueness or possibility of a system, which is named a possibilistic principal component analysis. The principal component analysis is to analyze a possibility of fuzzy numbers. The possibilistic principal component analysis has three formulations according the portions which the possibilities included in fuzzy numbers are embodied : 1) an eigenvalue, 2) an eigenvector and 3) both eigenvalue and eigenvector. In this paper, we discuss about the first formulation 1) that an eigenvalue is employed to deal with fuzziness of data. The possibilistic principal component analysis is employed in this paper to analyze the features of information technology industry. In this analysis, the financial ratio is employed as indices. And we evaluate the possibility of a company activity in information technology industry.

    DOI CiNii

  • Genetic tuning of a fuzzy controller

    J Watada, Y Matsumoto, K Kuroda, R Nagarajan

    ARTIFICIAL INTELLIGENCE IN REAL-TIME CONTROL 1997     487 - 492  1998  [Refereed]

     View Summary

    In a fuzzy controller, the rules derived from experts and the parameters of a membership function which correspond to a verbal word play an important role. The rule acquisition and the auto-tuning of membership parameters are one of prevalent research fields. In this paper, we propose a tuning method of the membership parameters for the fuzzy controller. The evaluation function of the fuzzy controller is well known as a very complex multi-peaked one against these membership parameters. The auto tuning method proposed here employs genetic algorithm in order to search for optimum solution which avoids falling in local optimum. The effectiveness and efficiency of the proposed method are ensured using a simulated example. Copyright (C) 1998 IFAC.

  • Factor Space Model for Fuzzy Data

    NAKAMORI Yoshiteru, SATO Kazuaki, WATADA Junzo

    Journal of Japan Society for Fuzzy Theory and Systems   9 ( 1 ) 99 - 107  1997

     View Summary

    A set of qualitative data obtained by rating a product usually has a large variance reflecting tastes and preferences of individuals. It is sensible to express such fluctuations by fuzzy numbers to treat vagueness and unvertainty of the feeling of individuals. This paper proposes a factor analysis technique for fuzzy data which are rating scores measured by words that are mainly adjective such as innovative, bright, elegant or cheerful. Fuzzy correlation coefficients are introduced and factor loadings are determined as fuzzy numbers through linear programming. Thus, words are identified as fuzzy objects in the factor space. After fuzzy distances between words in the factor space are defined, a covering problem is formulated as an integer programming problem to determine a set of representative words and an overlapped partition of words simultaneously. This provides a useful information to study the relation between words and design elements.

    DOI CiNii

  • Multidimensional Scaling

    Journal of Japan Society for Fuzzy Theory and Systems   5 ( 2 ) 292 - 292  1993

    DOI CiNii

  • Data Analysis and Its Fuzzy Approach

    Watada Junzo

    JES Ergonomics   28   44 - 47  1992

    DOI CiNii

  • Research on Recognition of Signposts based on Fuzzy Hierarchical Clastering

    Watada Junzo

    JES Ergonomics   27   320 - 321  1991

    DOI CiNii

  • 多変量解析

    和多田 淳三

    日本ファジィ学会誌   3 ( 1 ) 82 - 82  1991

    DOI CiNii

  • Birth and Evolution of Fuzzy Logic : Expectations of Japan's Role

    ZADEH LotfiA., KATAI Osamu, WATADA Junzo

    Journal of Japan Society for Fuzzy Theory and Systems   2 ( 2 ) 182 - 195  1990

    DOI CiNii

  • Fuzziness science. (8). Construction of fuzzy data base on fashion information.

    JOURNAL of the JAPAN RESEARCH ASSOCIATION for TEXTILE END-USES   30 ( 3 ) 113 - 117  1989

    DOI CiNii

  • ファジイ回帰モデルによる習熟特性の解析 (2)

    下村 武, 田中 英夫, 和多田 淳三

    人間工学   22   148 - 149  1986

    DOI CiNii

  • ファジイ回帰モデルによる習熟特性の解析

    田中 英夫, 下村 武, 和多田 淳三, 浅居 喜代治

    人間工学   21   230 - 231  1985

    DOI CiNii

▼display all

Books and Other Publications

  • Haydee Melo, Junzo Watada in , Trends in Practical Applications of Hetero… (2014), A Gaussian Particle Swarm Optimization for Training a Feed Forward Neural Network, DOI: 10.1007/978-319-08254-7_18, pp.61-68

    Springer International Publishing Switzerland  2014

  • Yoshiyuki Matsumoto, Junzo Watada , , Rough Set Model Based Knowledge Acquisition of Market Movements from Economic Data

    Springer International Publishing Switzerland  2014 ISBN: 9783319082530

  • Nureize Arbaiy, Junzo Watada in , , Multi-granular Evaluation Model Through Fuzzy Random Regression to Improve Information Granularity, DOI: 10.1007/978-319-08254-7_11

    Springer International Publishing Switzerland  2014 ISBN: 9783319082530

  • Azizul Azhar Ramli, Junzo Watada, and Witold Pedrycz in ,, Information Granules Problem: An Efficient Solution of Real-Time Fuzzy Regression Analysis, DOI: 10.1007/978-319-08254-7_3

    Springer International Publishing Switzerland  2014 ISBN: 9783319082530

  • Jianxiong Yang, Junzo Watada in , , Improved Latent Semantic Indexing-Based Data Mining Methods and an Application to Big Data Analysis of CRM, DOI: 10.1007/978-319-08254-7_7

    Springer International Publishing Switzerland  2014 ISBN: 9783319082530

  • Junzo Watada, Chee Peng Lim, Yung-chin Hsiao in , , A Bio-Signal-Based Control Approach to Building a Comfortable Space, DOI: 10.1007/978-3-319-04798-0_1, pp.3-18

    Springer International Publishing Switzerland  2014 ISBN: 9783319082530

  • Yoshiyuki Yabuuchi, Junzo Watada , , Building Fuzzy Robust Regression Model Based on Granularity and Possibility Distribution, DOI: 10.1007/978-319-08254-7_12,

    Springer International Publishing Switzerland  2014 ISBN: 9783319082530

  • Industrial Applications of Affective Engineering,

    Springer-Verlag Berlin Heidelberg,  2014 ISBN: 9783319047973

  • Innovative Management in Information and Production,

    Springer New York, ,  2014 ISBN: 9781461448563

  • Editors: Junzo Watada, Bing Xu, Berlin Wu,

    Springer, New York  2014 ISBN: 9781461448563

  • Editors: Junzo Watada, Hisao Shiizuka, Kun-Pyo Lee, Tsuyoshi Otani, Chee-Peng Lim,

    Springer, New York  2014 ISBN: 9783319047973

  • Changes in Production Efficiency in China,, Identification and Measuring,

    Springer Science+Business Media New York  2014 ISBN: 9781461477198

  • Haydee Melo, Junzo Watada in , Trends in Practical Applications of Hetero… (2014), A Gaussian Particle Swarm Optimization for Training a Feed Forward Neural Network, DOI: 10.1007/978-319-08254-7_18, pp.61-68

    Springer International Publishing Switzerland  2014

  • Yoshiyuki Matsumoto, Junzo Watada , , Rough Set Model Based Knowledge Acquisition of Market Movements from Economic Data

    Springer International Publishing Switzerland  2014 ISBN: 9783319082530

  • Nureize Arbaiy, Junzo Watada in , , Multi-granular Evaluation Model Through Fuzzy Random Regression to Improve Information Granularity, DOI: 10.1007/978-319-08254-7_11

    Springer International Publishing Switzerland  2014 ISBN: 9783319082530

  • Azizul Azhar Ramli, Junzo Watada, and Witold Pedrycz in ,, Information Granules Problem: An Efficient Solution of Real-Time Fuzzy Regression Analysis, DOI: 10.1007/978-319-08254-7_3

    Springer International Publishing Switzerland  2014 ISBN: 9783319082530

  • Jianxiong Yang, Junzo Watada in , , Improved Latent Semantic Indexing-Based Data Mining Methods and an Application to Big Data Analysis of CRM, DOI: 10.1007/978-319-08254-7_7

    Springer International Publishing Switzerland  2014 ISBN: 9783319082530

  • Junzo Watada, Chee Peng Lim, Yung-chin Hsiao in , , A Bio-Signal-Based Control Approach to Building a Comfortable Space, DOI: 10.1007/978-3-319-04798-0_1, pp.3-18

    Springer International Publishing Switzerland  2014 ISBN: 9783319082530

  • Yoshiyuki Yabuuchi, Junzo Watada , , Building Fuzzy Robust Regression Model Based on Granularity and Possibility Distribution, DOI: 10.1007/978-319-08254-7_12,

    Springer International Publishing Switzerland  2014 ISBN: 9783319082530

  • Industrial Applications of Affective Engineering,

    Springer-Verlag Berlin Heidelberg,  2014 ISBN: 9783319047973

  • Innovative Management in Information and Production,

    Springer New York, ,  2014 ISBN: 9781461448563

  • Editors: Junzo Watada, Bing Xu, Berlin Wu,

    Springer, New York  2014 ISBN: 9781461448563

  • Editors: Junzo Watada, Hisao Shiizuka, Kun-Pyo Lee, Tsuyoshi Otani, Chee-Peng Lim,

    Springer, New York  2014 ISBN: 9783319047973

  • Changes in Production Efficiency in China,, Identification and Measuring,

    Springer Science+Business Media New York  2014 ISBN: 9781461477198

  • Junzo Watada in On Fuzziness (2013), , The Path of Linguistic Random Regression to Knowledge Acquisition,, pp 749-753,

    2013

  • Ikno Kim, Junzo Watada, Witold Pedrycz in , , DNA Rough-Set Computing in the Development of Decision Rule Reducts, pp 409-438,

    2013

  • Yoshiyuki Yabuuchi, Junzo Watada in , , Building Fuzzy Autocorrelation Model and Its Application to the Analysis of Stock Price Time-Series Data,, pp 347-367

    Springer  2013 ISBN: 9783642334382

  • Yoshiyuki Matsumoto, Junzo Watada in , , Building a Rough Sets-Based Prediction Model of Tick-Wise Stock Price Fluctuations, pp 301-329

    Springer  2013 ISBN: 9783642334382

  • Yoshiyuki Matsumoto, Junzo Watada in , , A Wavelet Transform Approach to Chaotic Short-Term Forecasting, pp 177-197

    Springer  2013 ISBN: 9783642334382

  • Koki Yoshimura, Junzo Watada in , Game-Based Strategy Development for Hotel Yield Management

    Springer New York  2013

  • Junzo Watada in On Fuzziness (2013), , The Path of Linguistic Random Regression to Knowledge Acquisition,, pp 749-753,

    2013

  • Ikno Kim, Junzo Watada, Witold Pedrycz in , , DNA Rough-Set Computing in the Development of Decision Rule Reducts, pp 409-438,

    2013

  • Yoshiyuki Yabuuchi, Junzo Watada in , , Building Fuzzy Autocorrelation Model and Its Application to the Analysis of Stock Price Time-Series Data,, pp 347-367

    Springer  2013 ISBN: 9783642334382

  • Yoshiyuki Matsumoto, Junzo Watada in , , Building a Rough Sets-Based Prediction Model of Tick-Wise Stock Price Fluctuations, pp 301-329

    Springer  2013 ISBN: 9783642334382

  • Yoshiyuki Matsumoto, Junzo Watada in , , A Wavelet Transform Approach to Chaotic Short-Term Forecasting, pp 177-197

    Springer  2013 ISBN: 9783642334382

  • Koki Yoshimura, Junzo Watada in , Game-Based Strategy Development for Hotel Yield Management

    Springer New York  2013

  • Hanachiyo Nagata, Junzo Watada, Ito Yushi…, Hot Fomentation of the Lower-Back for Stress Relief in Students Preparing for a National Examination of Clinical Medical Technologist, pp. 203-218,

    Intelligent Decision Technologies  2012

  • Le Yu, Junzo Watada, Munenori Shibata, , Developing Marketing Strategies Based on Taste Analysis of Mineral Water,

    Springer Verlag Berlin Heidelberg ,  2012

  • Sha Li, Shinya Imai, Junzo Watada,, Building Linguistic Random Regression Model and Its Application,

    Springer Verlag Berlin Heidelberg ,  2012

  • Lu Shao, You-Hsi Tsai, Junzo Watada, Shuming Wang i, Building Fuzzy Random Autoregression Model and Its Application

    2012

  • Yicheng Wei, Junzo Watada , Building a Type II Fuzzy Qualitative Regression Model

    2012

  • Intelligent Decision Technologies: Smart Innovation, Systems and Technologies 15, Vol. 1, and 2,

    Springer-Verlag Berlin Heidelberg,,  2012 ISBN: 9783642221934

  • Intelligent Interactive Multimedia: Systems and Services,

    Springer-Verlag Berlin Heidelberg,  2012 ISBN: 9783642221934

  • Fuzzy Stochastic Optimization: Theory, Models and Applciations,

    Springer, New York  2012

  • Hanachiyo Nagata, Junzo Watada, Ito Yushi…, Hot Fomentation of the Lower-Back for Stress Relief in Students Preparing for a National Examination of Clinical Medical Technologist, pp. 203-218,

    Intelligent Decision Technologies  2012

  • Le Yu, Junzo Watada, Munenori Shibata, , Developing Marketing Strategies Based on Taste Analysis of Mineral Water,

    Springer Verlag Berlin Heidelberg ,  2012

  • Sha Li, Shinya Imai, Junzo Watada,, Building Linguistic Random Regression Model and Its Application,

    Springer Verlag Berlin Heidelberg ,  2012

  • Lu Shao, You-Hsi Tsai, Junzo Watada, Shuming Wang i, Building Fuzzy Random Autoregression Model and Its Application

    2012

  • Yicheng Wei, Junzo Watada , Building a Type II Fuzzy Qualitative Regression Model

    2012

  • Intelligent Decision Technologies: Smart Innovation, Systems and Technologies 15, Vol. 1, and 2,

    Springer-Verlag Berlin Heidelberg,,  2012 ISBN: 9783642221934

  • Intelligent Interactive Multimedia: Systems and Services,

    Springer-Verlag Berlin Heidelberg,  2012 ISBN: 9783642221934

  • Fuzzy Stochastic Optimization: Theory, Models and Applciations,

    Springer, New York  2012

  • Granular Computing and Intelligent Systems, Intelligent Systems Reference Library, 1, vol. 13, : Rough Sets Approach to Human Resource Development of IT Corporations, Granular Computing and Intelligent Systems, written by Shinya IMAI, Junzo WATADA, , p・・・

    Springer,  2011

     View Summary

    Granular Computing and Intelligent Systems, Intelligent Systems Reference Library, 1, vol. 13, : Rough Sets Approach to Human Resource Development of IT Corporations, Granular Computing and Intelligent Systems, written by Shinya IMAI, Junzo WATADA, , pp. 249-273, 2011

  • Granular Computing and Intelligent Systems, Intelligent Systems Reference Library, 1, vol. 13, : A Rough Set Approach to Building Association Rules and Its Applications,written by Junzo WATADA, Takayuki KAWAURA, Li Hao, , pp. 203-218, 2011

    Springer,  2011

  • 和多田淳三,, 第10章3節 ニューロコンピューティング,pp. 250-254,

    日本評論社,  2011

  • Junzo WATADA and Shamshul BAHAR YAAKOB ,, Structural Learning Model of the Neural Network and Its Application to LEDs Signal Retro?t, pp.55-75,

    Springer-Verlag Berlin Heidelberg,  2011 ISBN: 9783642117381

  • Shinya IMAI, Junzo WATADA,, Rough Sets Approach to Human Resource Development of IT Corporations, Granular Computing and Intelligent Systems, , pp. 249-273, 2011

    Springer  2011

  • Rohani Abu Bakar, Chu Yu-Yi, Junzo Watada,, Robustness of DNA-Based Clustering, pp.75-93,

    In: (Eds) by Antonio E.B. Ruano and Annamaria R. Varkonyi-Koczy (editors), in Studies in Computational Intelligence, Springer-Verlag Berlin Heidelberg, 2011,  2011 ISBN: 9783642117381

  • Junzo WATADA, Shuming WANG, and Witold PEDRYCZ, , Formulation of Fuzzy Random Regression Model, pp.1-21.

    In: New Advances of Signal Processing, (Eds) by Antonio E.B. Ruano and Annamaria R. Varkonyi-Koczy (editors), in Studies in Computational Intelligence, Springer-Verlag Berlin Heidelberg, 2011, I  2011 ISBN: 9783642117381

  • Junzo WATADA, Takayuki KAWAURA, Li Hao,, A Rough Set Approach to Building Association Rules and Its Applications,

    Springer, ,  2011

  • 経営システム学入門, : 第10章2節 ニューロコンピューティング, written by 和多田淳三, , pp. 250-254, , 2011.11

    日本評論社  2011

  • Integrated Computing. Technology. First International Conference, INTECH 2011. Sao Carlos, Brazil,

    Springer Heidelberg Dordrecht London New York  2011

  • Intelligent Decision Technologies: Smart Innovation, Systems and Technologies 10, the 3rd International Conference on Intelligent Decision Technologies, (IDT' 2011), Pireus, Greece, Proceedings ,

    Springer Heidelberg Dordrecht London New York  2011 ISBN: 9783642221934

  • Granular Computing and Intelligent Systems, Intelligent Systems Reference Library, 1, vol. 13, : Rough Sets Approach to Human Resource Development of IT Corporations, Granular Computing and Intelligent Systems, written by Shinya IMAI, Junzo WATADA, , p・・・

    Springer,  2011

     View Summary

    Granular Computing and Intelligent Systems, Intelligent Systems Reference Library, 1, vol. 13, : Rough Sets Approach to Human Resource Development of IT Corporations, Granular Computing and Intelligent Systems, written by Shinya IMAI, Junzo WATADA, , pp. 249-273, 2011

  • Granular Computing and Intelligent Systems, Intelligent Systems Reference Library, 1, vol. 13, : A Rough Set Approach to Building Association Rules and Its Applications,written by Junzo WATADA, Takayuki KAWAURA, Li Hao, , pp. 203-218, 2011

    Springer,  2011

  • Junzo WATADA and Shamshul BAHAR YAAKOB ,, Structural Learning Model of the Neural Network and Its Application to LEDs Signal Retro?t, pp.55-75,

    Springer-Verlag Berlin Heidelberg,  2011 ISBN: 9783642117381

  • Shinya IMAI, Junzo WATADA,, Rough Sets Approach to Human Resource Development of IT Corporations, Granular Computing and Intelligent Systems, , pp. 249-273, 2011

    Springer  2011

  • Rohani Abu Bakar, Chu Yu-Yi, Junzo Watada,, Robustness of DNA-Based Clustering, pp.75-93,

    In: (Eds) by Antonio E.B. Ruano and Annamaria R. Varkonyi-Koczy (editors), in Studies in Computational Intelligence, Springer-Verlag Berlin Heidelberg, 2011,  2011 ISBN: 9783642117381

  • Junzo WATADA, Shuming WANG, and Witold PEDRYCZ, , Formulation of Fuzzy Random Regression Model, pp.1-21.

    In: New Advances of Signal Processing, (Eds) by Antonio E.B. Ruano and Annamaria R. Varkonyi-Koczy (editors), in Studies in Computational Intelligence, Springer-Verlag Berlin Heidelberg, 2011, I  2011 ISBN: 9783642117381

  • Junzo WATADA, Takayuki KAWAURA, Li Hao,, A Rough Set Approach to Building Association Rules and Its Applications,

    Springer, ,  2011

  • Integrated Computing. Technology. First International Conference, INTECH 2011. Sao Carlos, Brazil,

    Springer Heidelberg Dordrecht London New York  2011

  • Intelligent Decision Technologies: Smart Innovation, Systems and Technologies 10, the 3rd International Conference on Intelligent Decision Technologies, (IDT' 2011), Pireus, Greece, Proceedings ,

    Springer Heidelberg Dordrecht London New York  2011 ISBN: 9783642221934

  • New Directions in Decision Support Systems: Methodologies and Applications, : Shape Design of Products Based on a Decision Support System, written by Yung-chin Hsiao and Junzo WATADA, , pp. 55-84, 2010..

    Springer-Verlag, Germany, .  2010

  • New Advances in Intelligent Signal Processing on occasion of the ten years existence of the IEEE WISP (IEEE International Symposium on Intelligent Signal Processing) series, in Studies in Computational Intelligence, : Robustness of DNA-based Clustering・・・

    Springer  2010

     View Summary

    New Advances in Intelligent Signal Processing on occasion of the ten years existence of the IEEE WISP (IEEE International Symposium on Intelligent Signal Processing) series, in Studies in Computational Intelligence, : Robustness of DNA-based Clustering,written by Rohani ABU BAKAR, Chu Yu-Yi and Junzo WATADA, ,

  • New Directions in Decision Support Systems: Methodologies and Applications,: Building a Decision Support System for Urban Design Based on the Creative City Conceptwritten by Lee-Chuan Lin and Junzo WATADA, , pp. 317-346, 2010..

    Springer-Verlag, Germany,  2010

  • New Advances in Intelligent Signal Processing on occasion of the ten years existence of the IEEE WISP (IEEE International Symposium on Intelligent Signal Processing) series, in Studies in Computational : Formulation of Fuzzy Random Regression Model, In・・・

    in Studies in Computational Intelligence, Springer  2010

     View Summary

    New Advances in Intelligent Signal Processing on occasion of the ten years existence of the IEEE WISP (IEEE International Symposium on Intelligent Signal Processing) series, in Studies in Computational : Formulation of Fuzzy Random Regression Model, Intelligence, Springerwritten by Junzo WATADA, Shuming WANG, and Witold PEDRYCZ, ,

  • Smart Innovation, Systems and Technologies, 1, Volume 4, Advances in Intelligent Decision Technologies, XIII.,: A Rough-Set-Based Two-Class Classifier for Large Imbalanced Dataset, written by Junzo WATADA, Lee-Chuan Lin, Lei Ding, Mohd. Ibrahim Shapiai・・・

    Springer-Verlag Berlin Heidelberg,  2010

     View Summary

    Smart Innovation, Systems and Technologies, 1, Volume 4, Advances in Intelligent Decision Technologies, XIII.,: A Rough-Set-Based Two-Class Classifier for Large Imbalanced Dataset, written by Junzo WATADA, Lee-Chuan Lin, Lei Ding, Mohd. Ibrahim Shapiai and Lim Chun Chew, Zuwairie Ibrahim, Lee Wen Jau, and Marzuki Khalid, , pp. 641-651, 2010

  • New Advances in Intelligent Signal Processing on occasion of the ten years existence of the IEEE WISP (IEEE International Symposium on Intelligent Signal Processing) series, : Structural Learning Model of the Neural Network and its Application to LEDs ・・・

    in Studies in Computational Intelligence, Springer  2010

     View Summary

    New Advances in Intelligent Signal Processing on occasion of the ten years existence of the IEEE WISP (IEEE International Symposium on Intelligent Signal Processing) series, : Structural Learning Model of the Neural Network and its Application to LEDs Signal Retrofit,written by Junzo WATADA and Shamshul BAHAR YAAKOB , ,

  • New Directions in Decision Support Systems: Methodologies and Applications, : Decision-Making for the Optimal Strategy of Population Agglomeration in Urban Planning with Path-Converged Design,written by Bing XU and Junzo WATADA, pp. 397-425, 2010..

    Springer-Verlag, Germany,  2010

  • New Directions in Decision Support Systems: Methodologies and Applications, : Shape Design of Products Based on a Decision Support System, written by Yung-chin Hsiao and Junzo WATADA, , pp. 55-84, 2010..

    Springer-Verlag, Germany, .  2010

  • New Advances in Intelligent Signal Processing on occasion of the ten years existence of the IEEE WISP (IEEE International Symposium on Intelligent Signal Processing) series, in Studies in Computational Intelligence, : Robustness of DNA-based Clustering・・・

    Springer  2010

     View Summary

    New Advances in Intelligent Signal Processing on occasion of the ten years existence of the IEEE WISP (IEEE International Symposium on Intelligent Signal Processing) series, in Studies in Computational Intelligence, : Robustness of DNA-based Clustering,written by Rohani ABU BAKAR, Chu Yu-Yi and Junzo WATADA, ,

  • New Directions in Decision Support Systems: Methodologies and Applications,: Building a Decision Support System for Urban Design Based on the Creative City Conceptwritten by Lee-Chuan Lin and Junzo WATADA, , pp. 317-346, 2010..

    Springer-Verlag, Germany,  2010

  • New Advances in Intelligent Signal Processing on occasion of the ten years existence of the IEEE WISP (IEEE International Symposium on Intelligent Signal Processing) series, in Studies in Computational : Formulation of Fuzzy Random Regression Model, In・・・

    in Studies in Computational Intelligence, Springer  2010

     View Summary

    New Advances in Intelligent Signal Processing on occasion of the ten years existence of the IEEE WISP (IEEE International Symposium on Intelligent Signal Processing) series, in Studies in Computational : Formulation of Fuzzy Random Regression Model, Intelligence, Springerwritten by Junzo WATADA, Shuming WANG, and Witold PEDRYCZ, ,

  • Smart Innovation, Systems and Technologies, 1, Volume 4, Advances in Intelligent Decision Technologies, XIII.,: A Rough-Set-Based Two-Class Classifier for Large Imbalanced Dataset, written by Junzo WATADA, Lee-Chuan Lin, Lei Ding, Mohd. Ibrahim Shapiai・・・

    Springer-Verlag Berlin Heidelberg,  2010

     View Summary

    Smart Innovation, Systems and Technologies, 1, Volume 4, Advances in Intelligent Decision Technologies, XIII.,: A Rough-Set-Based Two-Class Classifier for Large Imbalanced Dataset, written by Junzo WATADA, Lee-Chuan Lin, Lei Ding, Mohd. Ibrahim Shapiai and Lim Chun Chew, Zuwairie Ibrahim, Lee Wen Jau, and Marzuki Khalid, , pp. 641-651, 2010

  • New Advances in Intelligent Signal Processing on occasion of the ten years existence of the IEEE WISP (IEEE International Symposium on Intelligent Signal Processing) series, : Structural Learning Model of the Neural Network and its Application to LEDs ・・・

    in Studies in Computational Intelligence, Springer  2010

     View Summary

    New Advances in Intelligent Signal Processing on occasion of the ten years existence of the IEEE WISP (IEEE International Symposium on Intelligent Signal Processing) series, : Structural Learning Model of the Neural Network and its Application to LEDs Signal Retrofit,written by Junzo WATADA and Shamshul BAHAR YAAKOB , ,

  • New Directions in Decision Support Systems: Methodologies and Applications, : Decision-Making for the Optimal Strategy of Population Agglomeration in Urban Planning with Path-Converged Design,written by Bing XU and Junzo WATADA, pp. 397-425, 2010..

    Springer-Verlag, Germany,  2010

  • Lecture Notes in Computer Science, Vol.5908, pp.269-277, 2009. (Book Chapter), by J. Watada, L.-C. Lin, M. Qiang, and P.-C. Lin,

    Spring-Verlag, Berlin,  2009

  • Views on Fuzzy Sets and Systems from Different Perspectives - Philosophy and Logic, Criticisms and Applications -, Studies in Fuzziness and Soft Computing, Volume 243, : Optimal Workers' Placement in an Industrial Environment, Chapter 27, written by Sh・・・

    Spring-Verlag, Berlin,  2009 ISBN: 9783642018848

     View Summary

    Views on Fuzzy Sets and Systems from Different Perspectives - Philosophy and Logic, Criticisms and Applications -, Studies in Fuzziness and Soft Computing, Volume 243, : Optimal Workers' Placement in an Industrial Environment, Chapter 27, written by Shamshul BAHAR YAAKOB, Junzo WATADA, , pp.547-566, 2009.

  • Views on Fuzzy Sets and Systems from Different Perspectives - Philosophy and Logic, Criticisms and Applications -, Studies in Fuzziness and Soft Computing, Volume 243, : Regression model based on fuzzy random variables, Chapter 26, written by Junzo WAT・・・

    Spring-Verlag, Berlin,  2009 ISBN: 9783642018848

     View Summary

    Views on Fuzzy Sets and Systems from Different Perspectives - Philosophy and Logic, Criticisms and Applications -, Studies in Fuzziness and Soft Computing, Volume 243, : Regression model based on fuzzy random variables, Chapter 26, written by Junzo WATADA, Shuming WANG, , pp. 533-546, 2009.

  • Intelligent Systems and Technologies, Methods and Applications, Studies in Computational Intelligence 217, : A Fuzzy Density Analysis of Subgroups by Means of DNA Oligonucleotides, written by Ikno KIM and Junzo WATADA, , pp. 31-45, 2009

    Springer,  2009

  • Intelligent Systems and Technologies, Methods and Applications, Studies in Computational Intelligence 217,

    Springer Heidelberg Dordrecht London New York  2009 ISBN: 9783642018848

  • Lecture Notes in Computer Science, Vol.5908, pp.269-277, 2009. (Book Chapter), by J. Watada, L.-C. Lin, M. Qiang, and P.-C. Lin,

    Spring-Verlag, Berlin,  2009

  • Views on Fuzzy Sets and Systems from Different Perspectives - Philosophy and Logic, Criticisms and Applications -, Studies in Fuzziness and Soft Computing, Volume 243, : Optimal Workers' Placement in an Industrial Environment, Chapter 27, written by Sh・・・

    Spring-Verlag, Berlin,  2009 ISBN: 9783642018848

     View Summary

    Views on Fuzzy Sets and Systems from Different Perspectives - Philosophy and Logic, Criticisms and Applications -, Studies in Fuzziness and Soft Computing, Volume 243, : Optimal Workers' Placement in an Industrial Environment, Chapter 27, written by Shamshul BAHAR YAAKOB, Junzo WATADA, , pp.547-566, 2009.

  • Views on Fuzzy Sets and Systems from Different Perspectives - Philosophy and Logic, Criticisms and Applications -, Studies in Fuzziness and Soft Computing, Volume 243, : Regression model based on fuzzy random variables, Chapter 26, written by Junzo WAT・・・

    Spring-Verlag, Berlin,  2009 ISBN: 9783642018848

     View Summary

    Views on Fuzzy Sets and Systems from Different Perspectives - Philosophy and Logic, Criticisms and Applications -, Studies in Fuzziness and Soft Computing, Volume 243, : Regression model based on fuzzy random variables, Chapter 26, written by Junzo WATADA, Shuming WANG, , pp. 533-546, 2009.

  • Intelligent Systems and Technologies, Methods and Applications, Studies in Computational Intelligence 217, : A Fuzzy Density Analysis of Subgroups by Means of DNA Oligonucleotides, written by Ikno KIM and Junzo WATADA, , pp. 31-45, 2009

    Springer,  2009

  • Intelligent Systems and Technologies, Methods and Applications, Studies in Computational Intelligence 217,

    Springer Heidelberg Dordrecht London New York  2009 ISBN: 9783642018848

  • Handbook of Granular Computing, : A Fuzzy Regression Approach to Acquisition of Linguistic Rules, Chapter 32, written by Junzo WATADA and Witold PEDRYCZ, , pp. 719-740, July 2008.

    John Wiley & Sons, Chichester,  2008

  • Computational Intelligence: A Compendium, Studies in Computational Intelligence 115, : DNA Computing and its Application, In: Part X, DNA and Immunity-Based Computing, written by Junzo WATADA, , pp. 1065-1086.

    Springer-Verlag Berlin Heidelberg  2008

  • Handbook of Granular Computing, : A Fuzzy Regression Approach to Acquisition of Linguistic Rules, Chapter 32, written by Junzo WATADA and Witold PEDRYCZ, , pp. 719-740, July 2008.

    John Wiley & Sons, Chichester,  2008

  • Computational Intelligence: A Compendium, Studies in Computational Intelligence 115, : DNA Computing and its Application, In: Part X, DNA and Immunity-Based Computing, written by Junzo WATADA, , pp. 1065-1086.

    Springer-Verlag Berlin Heidelberg  2008

  • 環境問題の理論と政策、: 3章 ファジィ回帰モデルとファジィ相関ARモデルの構築,written by 和多田淳三、, pp. 29--48, 2005.3

    晃洋書房.,  2005

  • 社会科学リテラシーの確立に向けて, : 9章 社会科学のためのソフトコンピューティング技法, written by 和多田淳三, pp. 167--189, 2003.3

    日本評論社.,  2003

  • 中国のコンピュータ産業, : 8章 台湾における情報産業の現状, written by 和多田淳三, , pp. 219--244, 2001.4

    晃洋書房,  2001

  • ファジィとソフトコンピューティング ハンドブック, : 6.ファジィデータ解析, written by 和多田淳三(ハンドブック編集委員), , 2000.4

    共立出版,  2000

  • 「情報技術と企業経営の革新」, : 第3章 共同活動とグループウェア, written by 和多田淳三, , pp. 53--70, 1998.5

    税務経理協会,  1998

  • Fuzzy Structures, Vol. 13,: Fuzzy Portfolio Selection and Its Applications to Decision Making, written by Junzo WATADA, , pp. 219--248, 1997.4

    Tatra Mountains Mathematical Publications,  1997

  • Fuzzy Structures, Vol. 13,: Fuzzy Portfolio Selection and Its Applications to Decision Making, written by Junzo WATADA, , pp. 219--248, 1997.4

    Tatra Mountains Mathematical Publications,  1997

  • Fuzzy Information Engineering, : Possibilistic Time-series Analysis and its analysis of Consumption, written by Junzo WATADA,, pp. 187--200, 1996.4

    John Wiley & Sons, Inc.,  1996

  • Fuzzy Information Engineering, : Possibilistic Time-series Analysis and its analysis of Consumption, written by Junzo WATADA,, pp. 187--200, 1996.4

    John Wiley & Sons, Inc.,  1996

  • Applied Fuzzy System, : Chapter 5 Applications in Business, 5. 5 Multiattribute Decision-Making,written by Junzo WATADA, , pp. 244--252, 1994.5

    AP Professional,  1994

  • Applied Fuzzy System, : Chapter 5 Applications in Business, 5. 5 Multiattribute Decision-Making,written by Junzo WATADA, , pp. 244--252, 1994.5

    AP Professional,  1994

  • 情報化時代の経営戦略, : 第7章商品選択行動の分析、分担執筆, written by 和多田淳三, , pp. 143--170, 1993.5

    同文舘,  1993

  • 講座ファジィ6巻,ファジィOR, : 第6章ファジィ分類手法、分担執筆, written by 和多田淳三, , pp. 187--206, 1993.5

    日刊工業新聞社,  1993

  • ファジィシステム演習問題集解答と解説, : 第7章証拠理論,分担執筆, written by 和多田淳三, , pp. 155--170, 1993.9

    工業調査会,  1993

  • ザデー・ファジィ理論(Fuzzy Sets and Applications, Selected Papers: 第17章PRUF:自然言語のための意味表現, written by 和多田淳三, , pp. 567--641, 1992.11

    日刊工業新聞社,  1992

  • Fuzzy Systems Theory and Its Applications, : Chapter 6. Fuzzy Quantification Theory, written by Junzo WATADA, , pp. 101--124, 1992.4

    Academic Press,  1992

  • Fuzzy Regression Analysis, : Fuzzy Time-series Aanalysis and Forecasting of Sales Vol.ume,written by Junzo WATADA, , pp. 211--217, 1992.5

    Omnitech Press, Warsaw, Poland,  1992

  • Fuzzy Systems Theory and Its Applications, : Chapter 6. Fuzzy Quantification Theory, written by Junzo WATADA, , pp. 101--124, 1992.4

    Academic Press,  1992

  • Fuzzy Regression Analysis, : Fuzzy Time-series Aanalysis and Forecasting of Sales Vol.ume,written by Junzo WATADA, , pp. 211--217, 1992.5

    Omnitech Press, Warsaw, Poland,  1992

  • Computer Integrated Manufacturing from fundamentals to implementation , CIM-基礎と応用-: 第11章翻訳, written by 和多田淳三, , pp. 332--343, 1991.7

    オーム社,  1991

  • 情報のニューフロンティア:経済学・経営学からのアプローチ, : 第10章消費者情報のファジィ分析、分担執筆, written by 和多田淳三, , pp. 169--183, 1989.3

    中央経済社,  1989

  • 応用ファジィシステム入門, : 第5章ビジネスへの応用、5. 5多属性意思決定、分担執筆, written by 和多田淳三,, pp. 244--252, 1989.5

    オーム社,  1989

  • The Japanese Industrial System : 日本の産業システム第5章1-3節、翻訳, written by 和多田淳三, , pp. 119--132, 1987.8

    千倉書房,  1987

  • ファジィシステム入門, : 第6章ファジィ数量化理論、分担執筆, written by 和多田淳三, , pp. 99--118, 1987.3

    オーム社出版,  1987

  • CAD/CAMの知識支援とエキスパートシステム, : 第4章ファジィエキスパートシステム、分担執筆, written by 和多田淳三,, pp. 105--130, 1987.9

    トリッケプス出版,  1987

  • Linear Regression Analysis by Possibilistic Models, : Soft Optimization Models Using Fuzzy Sets and Possibility Theory, written by Hideo TANAKA, Junzo WATADA & Kiyoji ASAI,, pp. 186--199, 1987.5

    Verlag TUV,  1987

  • 模湖多元分析的理論及其応用, 和多田淳三著, pp. 1--150, 1987.9

    中国・科学技術文献出版社,  1987

  • Linear Regression Analysis by Possibilistic Models, : Soft Optimization Models Using Fuzzy Sets and Possibility Theory, written by Hideo TANAKA, Junzo WATADA & Kiyoji ASAI,, pp. 186--199, 1987.5

    Verlag TUV,  1987

  • 模湖多元分析的理論及其応用, 和多田淳三著, pp. 1--150, 1987.9

    中国・科学技術文献出版社,  1987

  • Handbook of Industrial Engineering Salvendy ed., : 第13部6章回帰と相関、翻訳, written by 和多田淳三,, pp. 13. 9. 1--13. 9. 13, 1986.

    日科技連出版社,  1986

  • Applications of Fuzzy Set Theory in Human Factors,: Identification of Learning Curve Possibilistic Concepts, written by Junzo WATADA, Hideo TANAKA and T. Shimomura, , pp. 143--168, 1986.12

    Elsevier Science Publisher,  1986

  • Applications of Fuzzy Set Theory in Human Factors,: Identification of Learning Curve Possibilistic Concepts, written by Junzo WATADA, Hideo TANAKA and T. Shimomura, , pp. 143--168, 1986.12

    Elsevier Science Publisher,  1986

  • 現代企業における会計・情報理論; : 第2部第3章; 経営意思決定支援システムの知識表現;; written by 和多田淳三; , pp. 181--197; 1985.11

    中央経済社  1985

  • Theory of Fuzzy Multivariate analysis and its Applications,: written by Junzo WATADA; , pp. 1--203; 1983.2

    Ph.D. Dissertation; Osaka Prefecture University;  1983

  • Theory of Fuzzy Multivariate analysis and its Applications,: written by Junzo WATADA; , pp. 1--203; 1983.2

    Ph.D. Dissertation; Osaka Prefecture University;  1983

  • A Heuristic Method of Hierarchical Clustering for Fuzzy Intransitive Relations,: Fuzzy Sets and Possibility Theory: Recent Developments, written by Junzo WATADA, Hideo TANAKA & Kiyoji ASAI; pp. 215--220; 1982.4

    Pergamon Press;  1982

  • A Heuristic Method of Hierarchical Clustering for Fuzzy Intransitive Relations,: Fuzzy Sets and Possibility Theory: Recent Developments, written by Junzo WATADA, Hideo TANAKA & Kiyoji ASAI; pp. 215--220; 1982.4

    Pergamon Press;  1982

▼display all

Research Projects

  • Building Optimum Reliable Systems through Value-at-Risk Criterion-based Two Stage Fuzzy Random Optimization Method

    Japan Society for the Promotion of Science  Grants-in-Aid for Scientific Research

    Project Year :

    2011
    -
    2013
     

    WATADA Junzo

     View Summary

    In this research, to deal with complicated decision making problems from a risk framework under hybrid uncertainty consisting randomness and fuzziness, Value-at-Risk (VaR) measure-based fuzzy random optimization models are defined. In building the models, synthesized heuristic algorithms are proposed and particle swarm optimization algorithm is used in VaR simulation to solve the models. The proposed models enables us to treat risk perspective of 2 stage optimization problems and solve the problems under hybrid uncertain environment, which are hard to solve till now. Furthermore, such proposed algorithms are capable of approximate computation of VaR criterion fuzzy random coefficients. Such proposed models are uniquely built with wide applications as two stage VaR models which design and solve real world problems.

  • DNAコンピューティングによるロボットベース生産スケジューリングの最適化法の研究

    国際共同研究

    Project Year :

    2005
    -
    2006
     

  • Reseach on Optimization of Robot-based Production Scheduling by DNA Computing

    International Joint Research Projects

    Project Year :

    2005
    -
    2006
     

  • カオス短期予測モデルによる株価の予測手法の開発

    受託研究

    Project Year :

    2003
    -
    2006
     

  • Development of Method to Construct Confortable Space Based on Human Five Senses

    Funded Research

    Project Year :

    2003
    -
    2006
     

  • マルチカメラトラッキングシステム

    Project Year :

    2005
    -
     
     

  • Building Multi-Camera Tracking System

    Project Year :

    2005
    -
     
     

  • 複雑系システムの経済問題への応用

    共同研究

    Project Year :

    2003
    -
    2005
     

  • Hungary 共同研究プロジェクト核研究地域

    国際共同研究

    Project Year :

    2002
    -
    2005
     

  • Development of Chaotic Short-term Forecasting Model of Stock Prices

    International Joint Research Projects

    Project Year :

    2002
    -
    2005
     

  • DNAコンピューティングの応用研究

    共同研究

    Project Year :

    2003
    -
    2004
     

  • 五感情報による快適空間の構築手法の開発

    受託研究

    Project Year :

    2003
    -
    2004
     

  • Research on Applications of Complex Systems to Economic Problems

    Cooperative Research

    Project Year :

    2003
    -
    2004
     

  • Developing the method of Realization of Comfortable Space Biopsy Information

    Funded Research

    Project Year :

    2003
    -
    2004
     

  • 五感情報に基づく快適空間の制御

    中小企業創造基盤技術研究制度

    Project Year :

    2003
    -
     
     

  • ブランド価値評価

    Project Year :

    2003
    -
     
     

  • DNAコンピューティング

    Project Year :

    2003
    -
     
     

  • Controlling of Confortable Space by 5 senses

    Small and Medium Enterprise Fundamental and Creative Technology Development Program

    Project Year :

    2003
    -
     
     

  • Evaluation of Brand Value

    Project Year :

    2003
    -
     
     

  • DNA Computing

    Project Year :

    2003
    -
     
     

  • カオス短期予測モデルによる株価の予測手法の開発

    受託研究

    Project Year :

    2002
    -
    2003
     

  • Development of Method to Construct Confortable Space Based on Human Five Senses

    Funded Research

    Project Year :

    2002
    -
    2003
     

  • Research on Fuzzy Portfolio Selection method based on Hierarchical Boltzmann Machine

    Japan Society for the Promotion of Science  Grants-in-Aid for Scientific Research

    Project Year :

    1999
    -
    2002
     

    WATADA Junzo

     View Summary

    The research on methods of investment of stocks starts from portfolio selection model proposed by H. Markowits. But it is difficult to realize sufficiently efficient selection of a certain number of stocks out of all stocks in the market. The objective of this research is to build the improved model using the neural network which we have been studied for years.
    The research results obtained in this granted research can be summarized as follows:
    1) We successed to organically drive a neural network by connecting between a Boltzmann machine and a Hopfield machine. This result was presented at international Journals and international conferences.
    2) In order to solve a portfolio selection problem under the consideration of the previous investing pattern, we developed the method to employ the hierarchical neural network in solving. The meta-controlled layer consisting of a Hopfield network is built in to select the appropriate number of stocks and the lower layer of a Boltzmann machine is employed to effectively minimize the difference between the present investment pattern and the previous investment pattern. This model successfully obtained the solution. This result was presented at international conferences.
    3) It enables us to forecast the price of a stock at the next term using Fuzzy Chaotic Short-Term Forecasting Model which the head investigator have studied. The portfolio of stocks can be solved using the forecasted price under the consideration of dealing unit at the market. This result was presented at international conferences.
    4) It enables us to solve the portfolio selection problem considering a dealing unit by combining between the chaotic method and the meta-controlled Boltzmann machine. This result was presented at international conferences.
    5) The above-mentioned methods are applied to solve agricultural to management, production management and project management as well as to portfolio selection problems. This result was presented at international Journals and international conferences.

  • 情報産業の分析

    共同研究

    Project Year :

    2001
     
     
     

  • Survey and Analysis of IT Industry

    Cooperative Research

    Project Year :

    2001
     
     
     

  • 中国のIT産業の分析

    共同研究

    Project Year :

    1997
    -
    2000
     

  • Servey and Analysis of IT Industry in China

    Cooperative Research

    Project Year :

    1997
    -
    2000
     

  • ソフトウエアバグ数の分析と予測

    受託研究

    Project Year :

    1996
    -
    2000
     

  • Analysis and Forecasting of Softwaree buggs

    Funded Research

    Project Year :

    1996
    -
    2000
     

  • 文書データからのデータマイニング手法

    Project Year :

    1998
    -
     
     

  • カオスモデルによる短期予測

    Project Year :

    1998
    -
     
     

  • メタ制御型ボルツマンマシーン

    Project Year :

    1998
    -
     
     

  • Data Mining based on Descriptive Documents

    Project Year :

    1998
    -
     
     

  • Chaotic Short-term Forecasting

    Project Year :

    1998
    -
     
     

  • Meta Controlled Boltzmann Machine

    Project Year :

    1998
    -
     
     

  • Research on Methods of Fuzzy Portfolio Selection with Limited Number of Securities

    Japan Society for the Promotion of Science  Grants-in-Aid for Scientific Research

    Project Year :

    1996
    -
    1998
     

    WATADA Junzo

     View Summary

    This research has been pursued and accomplished for three years. We have worked on developing the solutions of problems cleared and remained in the two previous researches and have obtained the following results.
    1)The interviews with experts on investment in the first and second research years cleared that it is important to reflect the judgment obtained from experts on investment on the decision-making.
    2)Especially, we successfully imprinented expectation level of invest experts in the model of fuzzy portfolio selection.
    3)As the main objective of this research, we developed the fuzzy model that can enable us to select the limited number of stocks out of huge stock market as the result.
    4)As dealing of stocks easily disturbs the stock market and we should take the dealing fee into consideration. it is preferred to select portfolio pattern that has minimum changes from the previous portfolio pattern. We proposed the model that enables us to select portfolio with minimum changes.
    5)In the Boltzmann Machine Model of portfolio selection, the adjustment of the weight between expected return and risk can be changeable according preference of strategy with high risk and high expected return to strategy with low risk and low expected return.
    6)Furthermore, we modeled the hierarchical Boltzmann Machine that enables us to select the limited number of stocks. This model will be presented at the international conference of fuzzy system in August 1999.
    7)We also obtained the following results by developing the above-mentioned research. We have also bui It the approach of the Boltzmann Machine's to solving effectively and efficiently a portfolio select ion problem.
    8)As these applications, we have modeled a hierarchical investment strategy by a Boltzmann Machine to invest.money in the international stock market.

  • ファジィ意思決定支援システムの構築

    共同研究

    Project Year :

    1992
    -
    1998
     

  • Development of Fuzzy Decision Support Systems

    Cooperative Research

    Project Year :

    1992
    -
    1998
     

  • チェコ日本不確定環境における意思決定プロジェクト

    国際共同研究

    Project Year :

    1996
     
     
     

  • 東南アジアの情報産業の分析

    Project Year :

    1996
    -
     
     

  • 公害に関する環境変数の分析

    Project Year :

    1996
    -
     
     

  • Czech-Japan Research Project on Decision Making under Uncertainty

    International Joint Research Projects

    Project Year :

    1996
     
     
     

  • Analysis of IT Industry in Asia

    Project Year :

    1996
    -
     
     

  • Analysis of Environment Variables for Environment Polusion

    Project Year :

    1996
    -
     
     

  • 人工知能の手法に基づくビルディングの開発工程の計画支援システムの開発

    受託研究

    Project Year :

    1995
    -
    1996
     

  • Development of Intelligent PERT Support System in Building Construction

    Funded Research

    Project Year :

    1995
    -
    1996
     

  • 組織の最適人員配置の分析

    受託研究

    Project Year :

    1994
    -
    1995
     

  • Decision-Making of Optimal Personel Allocation in Organization

    Funded Research

    Project Year :

    1994
    -
    1995
     

  • ファジィ多変量解析のパッケージの開発

    受託研究

    Project Year :

    1992
    -
    1994
     

  • Development of Software Package of Fuzzy Multivariant Models

    Funded Research

    Project Year :

    1992
    -
    1994
     

  • 工場の安全性分析

    受託研究

    Project Year :

    1993
     
     
     

  • Analysis of Accidents and Safety of Factories

    Funded Research

    Project Year :

    1993
     
     
     

  • リスクを考慮した経営意思決定手法

    共同研究

    Project Year :

    1990
    -
     
     

  • ポートフォリオ分析

    共同研究

    Project Year :

    1990
    -
     
     

  • ニューラルネットワークによる経営分析

    Project Year :

    1990
    -
     
     

  • ニューラルネットワークの構造化学習

    Project Year :

    1990
    -
     
     

  • Management Decision Making based on Risk

    Cooperative Research

    Project Year :

    1990
    -
     
     

  • Portfolio Analysis

    Cooperative Research

    Project Year :

    1990
    -
     
     

  • Corpolation Evaluation by a Neural Network

    Project Year :

    1990
    -
     
     

  • Structural Learning for Neural Network

    Project Year :

    1990
    -
     
     

  • マーケティング分析

    受託研究

    Project Year :

    1989
    -
    1990
     

  • FTAの情報に基づく階層的推論システムの開発

    受託研究

    Project Year :

    1989
    -
    1990
     

  • Marketing Analysis

    Funded Research

    Project Year :

    1989
    -
    1990
     

  • Development of a Hierarchical Reasoning System based on FTA

    Funded Research

    Project Year :

    1989
    -
    1990
     

  • JIS規格のコンサルテーションシステムの開発

    受託研究

    Project Year :

    1986
    -
    1988
     

  • Development of a Consultation System on JIS Statement

    Funded Research

    Project Year :

    1986
    -
    1988
     

  • 日本における電卓業界の企業行動に関する多変量解析による研究

    日本学術振興会  科学研究費助成事業

    Project Year :

    1986
     
     
     

    大西 謙, 和多田 淳三, 三品 広美, 本岡 昭良

     View Summary

    1.研究計画に示したように、日本の電卓業界の企業行動を技術開発を中心に多変量解析で分析するのが、我々の課題であった。60年度末に電卓メーカー17社(過去生産経験のある会社も含めて)に"電卓アンケート"を送付したが、ほとんど解答を得られなかった。そこで、"技術開発アンケート"を作成し、61年4月、ICメーカー90社に送った。これも解答が少なく、7月拡大して、電気機器メーカー274社にアンケートを送った。得られたアンケート結果をもとに多変量解析を実施した。知見の一つとして、数量化理論【II】類において、外的基基準(説明変数)"研究テーマの最重要点"に大きい影響を与えている説明変数を選び、外的基準のカテゴリーである研究,開発と説明変数の各カテゴリー間の関連をみた。その結果、研究テーマで研究に重点をおいている企業と開発に重点をおいている企業の行動特性の違いを抽出できた。又、数量化理論【III】類を用いて、技術開発を分析する視点として、開発【←!→】応用研究,マーケティング【←!→】製品化という2つの軸を確認できた。
    2.『電卓業界における寡占3社の企業行動ー技術開発を中心としてー』を書いた。カシオ(株),シュープ(株),キャノン(株)3社とも電卓事業でエレクトロニクス技術を蓄積し、さらに拡電卓化という方向で成長しており、各社の技術的発展動向を歴史的に追い、最近の海外生産の意味づけをも試みた。
    3.電卓に関する購買要因の分析を大学生を対象としたアンケート調査で多変量解析的に分析した。文系学生が価格,メーカーを重視し、理系学生が機能を重視して購買していること、又、キャノン(株)の製品は機能を重視して選択されていることが判明した。
    4.研究分担者の一人本岡が、日本商品学会で2度、電卓事業の国際展開について報告した。本岡は、62年度、さらに商品学会の国際学会でも報告の予定である。

  • 知的DSS

    Project Year :

    1985
    -
     
     

  • Intelligent DSS

    Project Year :

    1985
    -
     
     

  • 地震に対する建造物の安全性評価プロジェクト 米国、Purdue University

    国際共同研究

    Project Year :

    1984
    -
    1985
     

  • Puroject of Safety Assessment of Buildings After Earthcuaque 米国、Purdue University

    International Joint Research Projects

    Project Year :

    1984
    -
    1985
     

  • ボランティア活動の構造の分析

    共同研究

    Project Year :

    1980
    -
    1983
     

  • Analysis of Structure of Bolantier Activity

    Cooperative Research

    Project Year :

    1980
    -
    1983
     

  • ファジィ多変量解析

    Project Year :

    1980
    -
     
     

  • Fuzzy Multivariant Analysis

    Project Year :

    1980
    -
     
     

  • 9)東南アジアの情報産業の分析

  • 8)人間工学手法による安全性の分析

  • ファジィ手法やAHP、人工知能の技法、OR技法を用いた意思決定法の開発

  • 7)意思決定手法の研究

  • 上記のポートフォリオ理論やカオス理論など

  • 6)金融工学手法の研究

  • 5)遺伝的アルゴリズムに基づく問題の解法

  • 4-1.株価や債権の投資技法の実用化普及

  • 4)メタ制御型ボルツマンマシーンの開発

  • 3-3.ファジィポートフォリオ農業管理の実用化と普及

  • 3-2.ファジィポートフォリオプロダクションマネージメントの実用化普及

  • 3-1.ファジィポートフォリオプロジェクトマネージメントの実用化普及

  • 応用として

  • 3)ファジィポートフォリオ理論、

  • 2-1.株価の予測技法の実用化と普及

  • 2)カオス理論による短期予測

  • など各種現実問題に適用している

  • 1-1-2.事故の分析

  • 1-1-1.バグ予測モデル

  • 1-1.各種問題の分析

  • 1)ファジィ多変量解析モデルの構築、

  • 以下順不同

▼display all

Misc

  • Metaheuristic Techniques in Enhancing the Efficiency and Performance of Thermo-Electric Cooling Devices

    Pandian Vasant, Utku Kose, Junzo Watada

    ENERGIES   10 ( 11 )  2017.11

    Book review, literature introduction, etc.  

     View Summary

    The objective of this paper is to focus on the technical issues of single-stage thermo-electric coolers (TECs) and two-stage TECs and then apply new methods in optimizing the dimensions of TECs. In detail, some metaheuristics-simulated annealing (SA) and differential evolution (DE)-are applied to search the optimal design parameters of both types of TEC, which yielded cooling rates and coefficients of performance (COPs) individually and simultaneously. The optimization findings obtained by using SA and DE are validated by applying them in some defined test cases taking into consideration non-linear inequality and non-linear equality constraint conditions. The performance of SA and DE are verified after comparing the findings with the ones obtained applying the genetic algorithm (GA) and hybridization technique (HSAGA and HSADE). Mathematical modelling and parameter setting of TEC is combined with SA and DE to find better optimal findings. The work revealed that SA and DE can be applied successfully to solve single-objective and multi-objective TEC optimization problems. In terms of stability, reliability, robustness and computational efficiency, they provide better performance than GA. Multi-objective optimizations considering both objective functions are useful for the designer to find the suitable design parameters of TECs which balance the important roles of cooling rate and COP.

    DOI

  • Using Brainwaves and Eye Tracking to Determine Attention Levels for Auto-Lighting Systems

    Journal of advanced computational intelligence and intelligent informatics   19 ( 5 ) 611 - 618  2015.09

    CiNii

  • Minimax Portfolio Optimization Under Interval Uncertainty

    Journal of advanced computational intelligence and intelligent informatics   19 ( 5 ) 575 - 580  2015.09

    CiNii

  • Preface to special issue on "Fuzzy modeling for optimisation anddecision support"

    Masahiro Inuiguchi, Junzo Watada, Didier Dubois

    FUZZY SETS AND SYSTEMS   274   1 - 3  2015.09

    Other  

    DOI

  • Evolutionary Fuzzy ARTMAP Neural Networks for Classification of Semiconductor Defects

    Shing Chiang Tan, Junzo Watada, Zuwairie Ibrahim, Marzuki Khalid

    IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS   26 ( 5 ) 933 - 950  2015.05

     View Summary

    Wafer defect detection using an intelligent system is an approach of quality improvement in semiconductor manufacturing that aims to enhance its process stability, increase production capacity, and improve yields. Occasionally, only few records that indicate defective units are available and they are classified as a minority group in a large database. Such a situation leads to an imbalanced data set problem, wherein it engenders a great challenge to deal with by applying machine-learning techniques for obtaining effective solution. In addition, the database may comprise overlapping samples of different classes. This paper introduces two models of evolutionary fuzzy ARTMAP (FAM) neural networks to deal with the imbalanced data set problems in a semiconductor manufacturing operations. In particular, both the FAM models and hybrid genetic algorithms are integrated in the proposed evolutionary artificial neural networks (EANNs) to classify an imbalanced data set. In addition, one of the proposed EANNs incorporates a facility to learn overlapping samples of different classes from the imbalanced data environment. The classification results of the proposed evolutionary FAM neural networks are presented, compared, and analyzed using several classification metrics. The outcomes positively indicate the effectiveness of the proposed networks in handling classification problems with imbalanced data sets.

    DOI

  • Evolutionary Fuzzy ARTMAP Neural Networks for Classification of Semiconductor Defects

    Shing Chiang Tan, Junzo Watada, Zuwairie Ibrahim, Marzuki Khalid

    IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS   26 ( 5 ) 933 - 950  2015.05

     View Summary

    Wafer defect detection using an intelligent system is an approach of quality improvement in semiconductor manufacturing that aims to enhance its process stability, increase production capacity, and improve yields. Occasionally, only few records that indicate defective units are available and they are classified as a minority group in a large database. Such a situation leads to an imbalanced data set problem, wherein it engenders a great challenge to deal with by applying machine-learning techniques for obtaining effective solution. In addition, the database may comprise overlapping samples of different classes. This paper introduces two models of evolutionary fuzzy ARTMAP (FAM) neural networks to deal with the imbalanced data set problems in a semiconductor manufacturing operations. In particular, both the FAM models and hybrid genetic algorithms are integrated in the proposed evolutionary artificial neural networks (EANNs) to classify an imbalanced data set. In addition, one of the proposed EANNs incorporates a facility to learn overlapping samples of different classes from the imbalanced data environment. The classification results of the proposed evolutionary FAM neural networks are presented, compared, and analyzed using several classification metrics. The outcomes positively indicate the effectiveness of the proposed networks in handling classification problems with imbalanced data sets.

    DOI

  • B3-1 Similarity Analysis of Time Series Data including the Large Variations Using the Rough Sets

    MATSUMOTO Yoshiyuki, WATADA Junzo

      ( 27 ) 83 - 84  2014.11

     View Summary

    Rough set theory was proposed by Z.Pawlak in 1982. This theory can mine knowledge granules through a decision rule from a database, a web base, a set and so on. The decision rule is used for data analysis as well. And we can apply the decision rule to reason, estimate, evaluate, or forecast an unknown object. In this paper, the rough set theory is used to analysis of time series data. Knowledge granules are minded from the data set of tick-wise price fluctuations. We search for the law of similarity from time-series data using the rough sets.

    CiNii

  • FINNIM: Iterative Imputation of Missing Values in Dissolved Gas Analysis Dataset

    Zahriah Sahri, Rubiyah Yusof, Junzo Watada

    IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS   10 ( 4 ) 2093 - 2102  2014.11

     View Summary

    Missing values are a common occurrence in a number of real world databases, and statistical methods have been developed to deal with this problem, referred to as missing data imputation. In the detection and prediction of incipient faults in power transformers using dissolved gas analysis (DGA), the problem of missing values is significant and has resulted in inconclusive decision-making. This study proposes an efficient nonparametric iterative imputation method named FINNIM, which comprises of three components: 1) the imputation ordering; 2) the imputation estimator; and 3) the iterative imputation. The relationship between gases and faults, and the percentage of missing values in an instance are used as a basis for the imputation ordering; whereas the plausible values for the missing values are estimated from k-nearest neighbor instances in the imputation estimator, and the iterative imputation allows complete and incomplete instances in a DGA dataset to be utilized iteratively for imputing all the missing values. Experimental results on both artificially inserted and actual missing values found in a few DGA datasets demonstrate that the proposed method outperforms the existing methods in imputation accuracy, classification performance, and convergence criteria at different missing percentages.

    DOI

  • FINNIM: Iterative Imputation of Missing Values in Dissolved Gas Analysis Dataset

    Zahriah Sahri, Rubiyah Yusof, Junzo Watada

    IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS   10 ( 4 ) 2093 - 2102  2014.11

     View Summary

    Missing values are a common occurrence in a number of real world databases, and statistical methods have been developed to deal with this problem, referred to as missing data imputation. In the detection and prediction of incipient faults in power transformers using dissolved gas analysis (DGA), the problem of missing values is significant and has resulted in inconclusive decision-making. This study proposes an efficient nonparametric iterative imputation method named FINNIM, which comprises of three components: 1) the imputation ordering; 2) the imputation estimator; and 3) the iterative imputation. The relationship between gases and faults, and the percentage of missing values in an instance are used as a basis for the imputation ordering; whereas the plausible values for the missing values are estimated from k-nearest neighbor instances in the imputation estimator, and the iterative imputation allows complete and incomplete instances in a DGA dataset to be utilized iteratively for imputing all the missing values. Experimental results on both artificially inserted and actual missing values found in a few DGA datasets demonstrate that the proposed method outperforms the existing methods in imputation accuracy, classification performance, and convergence criteria at different missing percentages.

    DOI

  • Changes in production efficiency in China: Identification and measuring

    Bing Xu, Junzo Watada, Juying Zeng

    Changes in Production Efficiency in China: Identification and Measuring   9781461477204   1 - 140  2014.10

    Other  

     View Summary

    Evaluating Production Efficiency in China examines production from engineering and statistics perspectives rather than from economics and mathematics perspectives. The authors present an observable benchmark as the criterion of the production efficiency to replace the unobservable production frontier surface. This book discusses several different computing technologies, controllable variable as a path of identification, changes in production efficiency by decision making on specific operating conditions, and optimal resource allocation. The book provides a channel to tap inside the success stories of China, exploiting the way of changes in production efficiency during China's development in the past 30 years. This book examines the concepts and realization of production efficiencies across all areas of the economy. Also the book provides the perspective of foreign direct investment (FDI) absorption to identify how Chinese economy changes in production efficiency.

    DOI

  • Granular Robust Mean-CVaR Feedstock Flow Planning for Waste-to-Energy Systems Under Integrated Uncertainty

    Shuming Wang, Junzo Watada, Witold Pedrycz

    IEEE TRANSACTIONS ON CYBERNETICS   44 ( 10 ) 1846 - 1857  2014.10

     View Summary

    In the context of robust optimization with information granules for distributional parameters, this paper investigates a two-stage waste-to-energy feedstock flow planning problem with uncertain capacity expansion costs. The objective is to minimize the worst-case overall loss in a mean-risk criterion where the risk is measured by a conditional value-at-risk operator. As a salient feature, an integrated uncertainty is considered which consists of not only the uncertainty in distribution shapes of the uncertain variables, but also the manifold uncertainties of the mean parameters. To tackle the robust optimization under such integrated uncertainty, we first discuss a distributional robust two-stage feedstock flow planning model with precise mean parameters that handles the uncertainty in distribution shape, and the model can be equivalently transformed into a linear program (LP). Furthermore, the precise-mean-based robust model is extended into the case of multifaceted uncertainty for mean-parameters that are allowed to assume intervals, historical-data-based probabilistic estimates, and/or human-knowledge-centric fuzzy set estimates, under different circumstances. These multifaceted uncertain mean-parameters are uniformly represented by using information granules, and a granular robust optimization model is then developed which maximizes the robustness of the solution within a shortfall tolerance, and realizes a tradeoff between the solution conservativeness and robustness. It is showed that the granular robust model is equivalent to solving a series of LPs and can be efficiently handled by a nested binary search algorithm. Finally, the computational study illustrates the model performance, solution analysis, and underlines a much higher scalability of the developed robust model compared to the stochastic programming approach.

    DOI

  • Granular Robust Mean-CVaR Feedstock Flow Planning for Waste-to-Energy Systems Under Integrated Uncertainty

    Shuming Wang, Junzo Watada, Witold Pedrycz

    IEEE TRANSACTIONS ON CYBERNETICS   44 ( 10 ) 1846 - 1857  2014.10

     View Summary

    In the context of robust optimization with information granules for distributional parameters, this paper investigates a two-stage waste-to-energy feedstock flow planning problem with uncertain capacity expansion costs. The objective is to minimize the worst-case overall loss in a mean-risk criterion where the risk is measured by a conditional value-at-risk operator. As a salient feature, an integrated uncertainty is considered which consists of not only the uncertainty in distribution shapes of the uncertain variables, but also the manifold uncertainties of the mean parameters. To tackle the robust optimization under such integrated uncertainty, we first discuss a distributional robust two-stage feedstock flow planning model with precise mean parameters that handles the uncertainty in distribution shape, and the model can be equivalently transformed into a linear program (LP). Furthermore, the precise-mean-based robust model is extended into the case of multifaceted uncertainty for mean-parameters that are allowed to assume intervals, historical-data-based probabilistic estimates, and/or human-knowledge-centric fuzzy set estimates, under different circumstances. These multifaceted uncertain mean-parameters are uniformly represented by using information granules, and a granular robust optimization model is then developed which maximizes the robustness of the solution within a shortfall tolerance, and realizes a tradeoff between the solution conservativeness and robustness. It is showed that the granular robust model is equivalent to solving a series of LPs and can be efficiently handled by a nested binary search algorithm. Finally, the computational study illustrates the model performance, solution analysis, and underlines a much higher scalability of the developed robust model compared to the stochastic programming approach.

    DOI

  • Building Linguistic Random Regression Model from the Perspective of Type-2 Fuzzy Set

    Fei Song, Shinya Imai, Junzo Watada

    2014 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS (FUZZ-IEEE)     2376 - 2383  2014

     View Summary

    Information given in linguistic terms around real life sometimes is vague in meaning, as type-1 fuzzy set was introduced to modulate this uncertainty. Meanwhile, same word may result in various meaning to people, indicating the uncertainty also exist when associated with the membership function of a type-1 fuzzy set. Type-2 fuzzy set attempt to express the hybrid uncertainty of both primary and secondary fuzziness, in order to address regression problems, we built a type-2 Linguistic Random Regression Model based on credibility theory. Confidence intervals are constructed for fuzzy input and output, and the proposed regression model give a rise to a nonlinear programming problem focus on a well-trained model, which would be helpful and useful in linguistic assessment cases. Finally, a numerical example is provided.

    DOI

  • Building a Type-2 Fuzzy Regression Model Based on Credibility Theory and Its Application on Arbitrage Pricing Theory

    Yicheng Wei, Junzo Watada

    2014 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS (FUZZ-IEEE)     2368 - 2375  2014

     View Summary

    Real life circumstances used to provide us with linguistically vague expression of data in nature. Thus, type-1 fuzzy set (T1F set) was introduced to model this uncertainty. Additionally, same words will mean variously to different people, which means ambiguous uncertainty also exists when associated with the membership function of a T1F set. Type-2 fuzzy set(T2F set) is then invented to express the hybrid uncertainty of both primary fuzziness and secondary one of membership functions. On the one hand, T2F variable models the vagueness of information better. On the other hand, those variables are hard to deal with its three-dimensional feature given. To address problems in presence of such variables with hybrid fuzziness, a new class of T2F regression model is built based on credibility theory,called the T2F expected value regression model. The new model will be developed in this paper. This paper is a further work based on our former research of T2F qualitative regression model.

    DOI

  • A Minimax Model of Portfolio Optimization Using Data Mining to Predict Interval Return Rate

    Meng Yuan, Junzo Watada

    2014 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS (FUZZ-IEEE)     2047 - 2054  2014

     View Summary

    In 1950s, Markowitzs first proposed portfolio theory based on a mean-variance (MV) model to balance the risk and profit of decentralized investment. The two main inputs of MV are expected return rate and the variance of expected return rate. The expected return rate is an esti-mated value which is often decided by experts. Various uncertainty of stock price brings difficulties to predict return rate even for experts. MV model has its tendency to maximize the influence of errors in the input assumptions. Some scholars used fuzzy intervals to describe the return rate. However, there were still some variables decided by experts. This paper proposes a classification method to find the latent relationship between the interval return rate and the trading data of a stock and predict the interval of return rate without consulting any expert. Then this paper constructs the portfolio model based on minimax rule with interval numbers. The evaluation results show that the proposed method is reliable.

    DOI

  • A Method for Hybrid Personalized Recommender based on Clustering of Fuzzy User Profiles

    Shan Xu, Junzo Watada

    2014 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS (FUZZ-IEEE)     2171 - 2177  2014

     View Summary

    Personalized Recommenders can help to find potential items and then recommend them for particular users. Conventional recommender methods always work on a rating schema that items are rated from 1 to 5. However, there are several rating schemas (ways that items are rated) in reality, which are overlooked by conventional methods. By transforming rating schemas into fuzzy user profiles to record users' preferences, our proposed method can deal with different system rating schemas, and improve the scalability of recommender systems. Additionally, we incorporate user-based method with item-based collaborative methods by clustering users, which can help us to gain insight into the relationship between users. The aim of this research is to provide a new method for personalized recommendation. Our proposed method is the first to normalize the user vectors using fuzzy set theory before the k-medians clustering method is adjusted, and then to apply itembased collaborative algorithm with item vectors. To evaluate the effectiveness of our approach, the proposed algorithm is compared with two conventional collaborative filtering methods, based on MovieLens data set. As expected, our proposed method outperforms the conventional collaborative filtering methods as it can improve system scalability while maintaining accuracy.

    DOI

  • A Mesh-divide-based Region of Interest Clustering and Forecasting in Video Frames based on the Background/Foreground Construction

    Wei Quan, Zhenyuan Xu, Junzo Watada

    2014 WORLD AUTOMATION CONGRESS (WAC): EMERGING TECHNOLOGIES FOR A NEW PARADIGM IN SYSTEM OF SYSTEMS ENGINEERING     191 - 296  2014

     View Summary

    Image processing and security surveillance system has more and more widely used in recent society such as bank surveillance and pedestrian tracking. The detection of Region of Interest (RoI) is always been regarded as the most significant in tracking system. One of the algorithm which can be used in RoI detecting is "Density-Based Spatial Clustering of Application with Noise" (DBSCAN). But because of its structure, the runtime consuming costs too much when handling large spatial dataset. Considering the features of image processing, a mesh-divide and Kalman Filter forecasting method is proposed combing DBSCAN for RoI detection and forecasting of image processing. The DBSCAN can be used in the RoI detection and position forecast at the next frame in surveillance system to decrease the runtime cost and improve the accuracy at the same time.

    DOI

  • A Genetic Algorithm Based Double Layer Neural Network for Solving Quadratic Bilevel Programming Problem

    Jingru Li, Junzo Watada, Shamshul Bahar Yaakob

    PROCEEDINGS OF THE 2014 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN)     368 - 375  2014

     View Summary

    In this paper, an intelligent genetic algorithm (IGA) and a double layer neural network (NN) are integrated into a hybrid intelligent algorithm for solving the quadratic bilevel programming problem. The intelligent genetic algorithm is used to select a set of potential solution combinations from the entire generated combinations of the upper level. Then a meta-controlled Boltzmann machine, which is formulated by comprising the Hopfield model (HM) and the Boltzmann machine (BM), is used to effectively and efficiently determine the optimal solution of the lower level. Numerical experiments on examples show that the genetic algorithm based double layer neural network enables us to efficiently and effectively solve quadratic bilevel programming problems.

    DOI

  • Fuzzy Robust Regression Model Building through Possibility Maximization,

    Yoshiyuki YABUUCHI, Junzo WATADA

    ICICIC2014    2014

  • Building Linguistic Random Regression Model from the Perspective of Type-2 Fuzzy Set

    Fei Song, Shinya Imai, Junzo Watada

    2014 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS (FUZZ-IEEE)     2376 - 2383  2014

     View Summary

    Information given in linguistic terms around real life sometimes is vague in meaning, as type-1 fuzzy set was introduced to modulate this uncertainty. Meanwhile, same word may result in various meaning to people, indicating the uncertainty also exist when associated with the membership function of a type-1 fuzzy set. Type-2 fuzzy set attempt to express the hybrid uncertainty of both primary and secondary fuzziness, in order to address regression problems, we built a type-2 Linguistic Random Regression Model based on credibility theory. Confidence intervals are constructed for fuzzy input and output, and the proposed regression model give a rise to a nonlinear programming problem focus on a well-trained model, which would be helpful and useful in linguistic assessment cases. Finally, a numerical example is provided.

    DOI

  • Building a Type-2 Fuzzy Regression Model Based on Credibility Theory and Its Application on Arbitrage Pricing Theory

    Yicheng Wei, Junzo Watada

    2014 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS (FUZZ-IEEE)     2368 - 2375  2014

     View Summary

    Real life circumstances used to provide us with linguistically vague expression of data in nature. Thus, type-1 fuzzy set (T1F set) was introduced to model this uncertainty. Additionally, same words will mean variously to different people, which means ambiguous uncertainty also exists when associated with the membership function of a T1F set. Type-2 fuzzy set(T2F set) is then invented to express the hybrid uncertainty of both primary fuzziness and secondary one of membership functions. On the one hand, T2F variable models the vagueness of information better. On the other hand, those variables are hard to deal with its three-dimensional feature given. To address problems in presence of such variables with hybrid fuzziness, a new class of T2F regression model is built based on credibility theory,called the T2F expected value regression model. The new model will be developed in this paper. This paper is a further work based on our former research of T2F qualitative regression model.

    DOI

  • A Minimax Model of Portfolio Optimization Using Data Mining to Predict Interval Return Rate

    Meng Yuan, Junzo Watada

    2014 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS (FUZZ-IEEE)     2047 - 2054  2014

     View Summary

    In 1950s, Markowitzs first proposed portfolio theory based on a mean-variance (MV) model to balance the risk and profit of decentralized investment. The two main inputs of MV are expected return rate and the variance of expected return rate. The expected return rate is an esti-mated value which is often decided by experts. Various uncertainty of stock price brings difficulties to predict return rate even for experts. MV model has its tendency to maximize the influence of errors in the input assumptions. Some scholars used fuzzy intervals to describe the return rate. However, there were still some variables decided by experts. This paper proposes a classification method to find the latent relationship between the interval return rate and the trading data of a stock and predict the interval of return rate without consulting any expert. Then this paper constructs the portfolio model based on minimax rule with interval numbers. The evaluation results show that the proposed method is reliable.

    DOI

  • A Method for Hybrid Personalized Recommender based on Clustering of Fuzzy User Profiles

    Shan Xu, Junzo Watada

    2014 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS (FUZZ-IEEE)     2171 - 2177  2014

     View Summary

    Personalized Recommenders can help to find potential items and then recommend them for particular users. Conventional recommender methods always work on a rating schema that items are rated from 1 to 5. However, there are several rating schemas (ways that items are rated) in reality, which are overlooked by conventional methods. By transforming rating schemas into fuzzy user profiles to record users' preferences, our proposed method can deal with different system rating schemas, and improve the scalability of recommender systems. Additionally, we incorporate user-based method with item-based collaborative methods by clustering users, which can help us to gain insight into the relationship between users. The aim of this research is to provide a new method for personalized recommendation. Our proposed method is the first to normalize the user vectors using fuzzy set theory before the k-medians clustering method is adjusted, and then to apply itembased collaborative algorithm with item vectors. To evaluate the effectiveness of our approach, the proposed algorithm is compared with two conventional collaborative filtering methods, based on MovieLens data set. As expected, our proposed method outperforms the conventional collaborative filtering methods as it can improve system scalability while maintaining accuracy.

    DOI

  • A Mesh-divide-based Region of Interest Clustering and Forecasting in Video Frames based on the Background/Foreground Construction

    Wei Quan, Zhenyuan Xu, Junzo Watada

    2014 WORLD AUTOMATION CONGRESS (WAC): EMERGING TECHNOLOGIES FOR A NEW PARADIGM IN SYSTEM OF SYSTEMS ENGINEERING     191 - 296  2014

     View Summary

    Image processing and security surveillance system has more and more widely used in recent society such as bank surveillance and pedestrian tracking. The detection of Region of Interest (RoI) is always been regarded as the most significant in tracking system. One of the algorithm which can be used in RoI detecting is "Density-Based Spatial Clustering of Application with Noise" (DBSCAN). But because of its structure, the runtime consuming costs too much when handling large spatial dataset. Considering the features of image processing, a mesh-divide and Kalman Filter forecasting method is proposed combing DBSCAN for RoI detection and forecasting of image processing. The DBSCAN can be used in the RoI detection and position forecast at the next frame in surveillance system to decrease the runtime cost and improve the accuracy at the same time.

    DOI

  • A Genetic Algorithm Based Double Layer Neural Network for Solving Quadratic Bilevel Programming Problem

    Jingru Li, Junzo Watada, Shamshul Bahar Yaakob

    PROCEEDINGS OF THE 2014 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN)     368 - 375  2014

     View Summary

    In this paper, an intelligent genetic algorithm (IGA) and a double layer neural network (NN) are integrated into a hybrid intelligent algorithm for solving the quadratic bilevel programming problem. The intelligent genetic algorithm is used to select a set of potential solution combinations from the entire generated combinations of the upper level. Then a meta-controlled Boltzmann machine, which is formulated by comprising the Hopfield model (HM) and the Boltzmann machine (BM), is used to effectively and efficiently determine the optimal solution of the lower level. Numerical experiments on examples show that the genetic algorithm based double layer neural network enables us to efficiently and effectively solve quadratic bilevel programming problems.

    DOI

  • Fuzzy Robust Regression Model Building through Possibility Maximization,

    Yoshiyuki YABUUCHI, Junzo WATADA

    ICICIC2014    2014

  • A-2-5 Identification of time-series data with different behavior using the rough sets

    MATSUMOTO Yoshiyuki, Watada Junzo

      ( 26 ) 73 - 76  2013.10

     View Summary

    Rough set theory was proposed by Z.Pawlak in 1982. This theory can mine knowledge granules through a decision rule from a database, a web base, a set and so on. The decision rule is used for data analysis as well. And we can apply the decision rule to reason, estimate, evaluate, or forecast an unknown object. In this paper, the rough set theory is used to analysis of time series data. Knowledge granules are minded from the data set of tick-wise price fluctuations. We Knowledge Acquisition If the data has changed greatly.

    CiNii

  • Multiobjective particle swarm optimization for a novel fuzzy portfolio selection problem

    Bo Wang, Junzo Watada

    IEEJ TRANSACTIONS ON ELECTRICAL AND ELECTRONIC ENGINEERING   8 ( 2 ) 146 - 154  2013.03

     View Summary

    On the basis of the portfolio selection theory, this paper proposes a novel fuzzy multiobjective model that can evaluate investment risk properly and increase the probability of obtaining an expected return. In building this model, fuzzy value-at-risk (VaR) is used to evaluate the exact future risk in terms of loss. The VaR can directly reflect the greatest loss of a selection case under a given confidence level. Conversely, variance, the measure of the spread of a distribution around its expected value, is utilized to make the selection more stable. This model can provide investors with more significant information for decision making. To solve this model, an improved Pareto-optimal-set-based multiobjective particle swarm optimization (IMOPSO) algorithm is designed to obtain better solutions in the Pareto front. The proposed model and algorithm are exemplified by specific numerical examples. Furthermore, comparisons are made between IMOPSO and other existing approaches. Experiments show that the model and algorithm are effective in solving the multiobjective portfolio selection problem. (c) 2012 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

    DOI

  • Multiobjective particle swarm optimization for a novel fuzzy portfolio selection problem

    Bo Wang, Junzo Watada

    IEEJ TRANSACTIONS ON ELECTRICAL AND ELECTRONIC ENGINEERING   8 ( 2 ) 146 - 154  2013.03

     View Summary

    On the basis of the portfolio selection theory, this paper proposes a novel fuzzy multiobjective model that can evaluate investment risk properly and increase the probability of obtaining an expected return. In building this model, fuzzy value-at-risk (VaR) is used to evaluate the exact future risk in terms of loss. The VaR can directly reflect the greatest loss of a selection case under a given confidence level. Conversely, variance, the measure of the spread of a distribution around its expected value, is utilized to make the selection more stable. This model can provide investors with more significant information for decision making. To solve this model, an improved Pareto-optimal-set-based multiobjective particle swarm optimization (IMOPSO) algorithm is designed to obtain better solutions in the Pareto front. The proposed model and algorithm are exemplified by specific numerical examples. Furthermore, comparisons are made between IMOPSO and other existing approaches. Experiments show that the model and algorithm are effective in solving the multiobjective portfolio selection problem. (c) 2012 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

    DOI

  • Facility Location Problems with Fuzzy Demands Based on Parametric Assessment

    Chun Lin, Junzo Watada, Berlin Wu

    PROCEEDINGS OF THE 2013 JOINT IFSA WORLD CONGRESS AND NAFIPS ANNUAL MEETING (IFSA/NAFIPS)     795 - 800  2013

     View Summary

    We discuss uncertainty included in demands in facility location problem in this paper. The uncertain demand is named as fuzzy demand in the paper. In the facility location model, the parameters of fuzzy demand are determined by calculating the estimated expected value of the fuzzy demand, which is obtained by using estimated parameters of underlying probability distribution function of fuzzy data. Moreover, we propose a defuzzification formula of the fuzzy demand named a realization of fuzzy demand. The defuzzification formula of fuzzy demand composes the upper bound of fuzzy demand and the lower bound of fuzzy demand. Moreover, an error of fuzzy demand is assessed as mean absolute percentage error of fuzzy demand. Empirical studies show that we can solve the real-life location problem by using the defuzzification formula of fuzzy demand and get higher profit in our facility location model than conventional methods.

    DOI

  • Building Multi-objective Fuzzy Random Programming Model

    Nureize Arbaiy, Junzo Watada

    2013 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS (FUZZ - IEEE 2013)     1 - 5  2013

     View Summary

    A real-life application faces various kinds of inherent uncertainties which occurs simultaneously. To find solution, formulating real world problem into mathematical programming model is challenging. Uncertain parameters in a problem model can be characterized as vagueness, ambiguous and random of the information. Such uncertainties make the existing multi-objective model incapable of handling such situations. Thus, in this paper we present the multi-objective decision model from the perspective of possibilistic programming approach to scrutinize the uncertainties in the decision making. The proposed concept can be used to build model for multi-objective problem which is exposed with various types of uncertainties. We include an illustrative example to explain the model, and highlight its advantages.

    DOI

  • Building a Recognition System of Speech Emotion and Emotional States

    Xiaoyan Feng, Junzo Watada

    2013 SECOND INTERNATIONAL CONFERENCE ON ROBOT, VISION AND SIGNAL PROCESSING (RVSP)     253 - 258  2013

     View Summary

    To make a decision in companies or public organizations, the priority ordering plays an essential. For example, their discussion is essential for stakeholder to achieve mutual consensus,. In the discussion, the difference among consensus building processes can affect the last conclusion. Therefore, it is necessary for analysis to find critical remarks reaching the consensus ("focus remark"). However, it is a basis to confirm the gfocus remark" that the consensus building process can understand exactly from the disagreement state consent and detailed exposition parties. The consensus discussion is very helpful to promote interaction by the speech. The paper addresses the design of recognition system and results are achieved by means of MFCC (Mel Frequency Campestral Coefficients) and HMM (Hidden Markov Model). Results in recognition of six emotion patterns obtained 86.8% recognition rate. According to the relation of emotional states and emotions we analyzed the support more objectively

    DOI

  • A Kernel Density Estimation-Maximurn Likelihood Approach to Risk Analysis of Portfolio

    Junzo Watada

    2013 IEEE 8TH INTERNATIONAL SYMPOSIUM ON INTELLIGENT SIGNAL PROCESSING (WISP)     37 - 42  2013

     View Summary

    Nowadays one of the most studied issues in economic or finance field is to get the best possible return with the minimum risk. Therefore, the objective of the paper is to select the optimal investment portfolio from SP500 stock market and OBOE Interest Rate 10-Year Bond to obtain the minimum risk in the financial market.
    For this purpose, the paper consists of: 1) the marginal density distribution of the two financial assets is described with kernel density estimation to get the "high-picky and fat-tail" shape; 2) the relation structure of assets is studied with copula function to describe the correlation of financial assets in a nonlinear condition; 3) value at risk (VaR) is computed through the combination of Copula method and Monte Carlo simulation to measure the possible maximum loss better.
    Therefore, through the above three steps methodology, the risk of the portifolio is described more accuratly than the conventional method, which always underestimates the risk in the finicial market.
    So it is necessary to pay attention to the happening of extreme cases like "Black Friday 2008" and appropriate investment allocation is a wise strategy to make diversification and spread risks in financial market.

    DOI

  • Capacitated two-stage facility location problem with fuzzy costs and demands

    Shuming Wang, Junzo Watada

    International Journal of Machine Learning and Cybernetics   4 ( 1 ) 65 - 74  2013

     View Summary

    In this study, we develop a two-stage capacitated facility location model with fuzzy costs and demands. The proposed model is a task of 0-1 integer two-stage fuzzy programming problem. In order to solve the problem, we first apply an approximation approach to estimate the objective function (with fuzzy random parameters) and prove the convergence of the approach. Then, we design a hybrid algorithm which integrates the approximation approach, neural network and particle swarm optimization, to solve the proposed facility location problem. Finally, a numerical example is provided to test the hybrid algorithm. © 2012 Springer-Verlag.

    DOI

  • Supply reliability and generation cost analysis due to load forecast uncertainty in unit commitment problems

    Bo Wang, You Li, Junzo Watada

    IEEE Transactions on Power Systems   28 ( 3 ) 2242 - 2252  2013

     View Summary

    The goal of a unit commitment optimization problem is to reduce the total generation cost as much as possible while satisfying future power demands. Thus, analysis must be performed based on correct predictions of future demands. However, various uncertain factors affect these loads making an exact forecasting unsuccessful. This study mitigates this difficulty by applying fuzzy set theory to evaluate the future uncertain loads. The objective of this research is to build a two-stage multi-objective fuzzy programming model based on 24-hour uncertain load forecasting. The first stage is a decision-making process on the interval data of the imprecise power loads, whereas the second stage pursues the optimization of the unit commitment scheduling, which can help find both optima simultaneously by maximizing power supply reliability and minimizing total generation cost. To define the supply reliability under uncertain forecasting, we propose a new concept of maximal blackout time during successful operation, which is based on the fuzzy credibility theory. Furthermore, as a solution approach to this model, an improved two-stage multi-objective particle swarm optimization algorithm is designed based on our previous studies. Finally, the performance of this algorithm is discussed in comparison with experimental results from several test systems. © 1969-2012 IEEE.

    DOI

  • Consumer and Service Characteristic Segmentations in Services Marketing Using a Biologically Systematic Computational Method

    Kim, I, Watada,J

    Systems Journal, IEEE (ISSN :1932-8184)   PP ( 99 ) 1 - 9  2013

  • Facility Location Problems with Fuzzy Demands Based on Parametric Assessment

    Chun Lin, Junzo Watada, Berlin Wu

    PROCEEDINGS OF THE 2013 JOINT IFSA WORLD CONGRESS AND NAFIPS ANNUAL MEETING (IFSA/NAFIPS)     795 - 800  2013

     View Summary

    We discuss uncertainty included in demands in facility location problem in this paper. The uncertain demand is named as fuzzy demand in the paper. In the facility location model, the parameters of fuzzy demand are determined by calculating the estimated expected value of the fuzzy demand, which is obtained by using estimated parameters of underlying probability distribution function of fuzzy data. Moreover, we propose a defuzzification formula of the fuzzy demand named a realization of fuzzy demand. The defuzzification formula of fuzzy demand composes the upper bound of fuzzy demand and the lower bound of fuzzy demand. Moreover, an error of fuzzy demand is assessed as mean absolute percentage error of fuzzy demand. Empirical studies show that we can solve the real-life location problem by using the defuzzification formula of fuzzy demand and get higher profit in our facility location model than conventional methods.

    DOI

  • Building multi-objective fuzzy random programming model

    Nureize Arbaiy, Junzo Watada

    IEEE International Conference on Fuzzy Systems     1 - 5  2013

     View Summary

    A real-life application faces various kinds of inherent uncertainties which occurs simultaneously. To find solution, formulating real world problem into mathematical programming model is challenging. Uncertain parameters in a problem model can be characterized as vagueness, ambiguous and random of the information. Such uncertainties make the existing multi-objective model incapable of handling such situations. Thus, in this paper we present the multi-objective decision model from the perspective of possibilistic programming approach to scrutinize the uncertainties in the decision making. The proposed concept can be used to build model for multi-objective problem which is exposed with various types of uncertainties. We include an illustrative example to explain the model, and highlight its advantages. © 2013 IEEE.

    DOI

  • Building a Recognition System of Speech Emotion and Emotional States

    Xiaoyan Feng, Junzo Watada

    2013 SECOND INTERNATIONAL CONFERENCE ON ROBOT, VISION AND SIGNAL PROCESSING (RVSP)     253 - 258  2013

     View Summary

    To make a decision in companies or public organizations, the priority ordering plays an essential. For example, their discussion is essential for stakeholder to achieve mutual consensus,. In the discussion, the difference among consensus building processes can affect the last conclusion. Therefore, it is necessary for analysis to find critical remarks reaching the consensus ("focus remark"). However, it is a basis to confirm the gfocus remark" that the consensus building process can understand exactly from the disagreement state consent and detailed exposition parties. The consensus discussion is very helpful to promote interaction by the speech. The paper addresses the design of recognition system and results are achieved by means of MFCC (Mel Frequency Campestral Coefficients) and HMM (Hidden Markov Model). Results in recognition of six emotion patterns obtained 86.8% recognition rate. According to the relation of emotional states and emotions we analyzed the support more objectively

    DOI

  • A kernel density estimation-maximum likelihood approach to risk analysis of portfolio

    Junzo Watada

    2013 IEEE 8th International Symposium on Intelligent Signal Processing, WISP 2013 - Proceedings     37 - 42  2013

     View Summary

    Nowadays one of the most studied issues in economic or finance field is to get the best possible return with the minimum risk. Therefore, the objective of the paper is to select the optimal investment portfolio from SP500 stock market and CBOE Interest Rate 10-Year Bond to obtain the minimum risk in the financial market. © 2013 IEEE.

    DOI

  • Capacitated two-stage facility location problem with fuzzy costs and demands

    Shuming Wang, Junzo Watada

    International Journal of Machine Learning and Cybernetics   4 ( 1 ) 65 - 74  2013

     View Summary

    In this study, we develop a two-stage capacitated facility location model with fuzzy costs and demands. The proposed model is a task of 0-1 integer two-stage fuzzy programming problem. In order to solve the problem, we first apply an approximation approach to estimate the objective function (with fuzzy random parameters) and prove the convergence of the approach. Then, we design a hybrid algorithm which integrates the approximation approach, neural network and particle swarm optimization, to solve the proposed facility location problem. Finally, a numerical example is provided to test the hybrid algorithm. © 2012 Springer-Verlag.

    DOI

  • Supply reliability and generation cost analysis due to load forecast uncertainty in unit commitment problems

    Bo Wang, You Li, Junzo Watada

    IEEE Transactions on Power Systems   28 ( 3 ) 2242 - 2252  2013

     View Summary

    The goal of a unit commitment optimization problem is to reduce the total generation cost as much as possible while satisfying future power demands. Thus, analysis must be performed based on correct predictions of future demands. However, various uncertain factors affect these loads making an exact forecasting unsuccessful. This study mitigates this difficulty by applying fuzzy set theory to evaluate the future uncertain loads. The objective of this research is to build a two-stage multi-objective fuzzy programming model based on 24-hour uncertain load forecasting. The first stage is a decision-making process on the interval data of the imprecise power loads, whereas the second stage pursues the optimization of the unit commitment scheduling, which can help find both optima simultaneously by maximizing power supply reliability and minimizing total generation cost. To define the supply reliability under uncertain forecasting, we propose a new concept of maximal blackout time during successful operation, which is based on the fuzzy credibility theory. Furthermore, as a solution approach to this model, an improved two-stage multi-objective particle swarm optimization algorithm is designed based on our previous studies. Finally, the performance of this algorithm is discussed in comparison with experimental results from several test systems. © 1969-2012 IEEE.

    DOI

  • Consumer and Service Characteristic Segmentations in Services Marketing Using a Biologically Systematic Computational Method

    Kim, I, Watada,J

    Systems Journal, IEEE (ISSN :1932-8184)   PP ( 99 ) 1 - 9  2013

  • Fuzzy stochastic optimization: Theory, models and applications

    Shuming Wang, Junzo Watada

    Fuzzy Stochastic Optimization: Theory, Models and Applications   9781441995605   1 - 248  2012.11

    Other  

     View Summary

    Covering in detail both theoretical and practical perspectives, this book is a self-contained and systematic depiction of current fuzzy stochastic optimization that deploys the fuzzy random variable as a core mathematical tool to model the integrated fuzzy random uncertainty. It proceeds in an orderly fashion from the requisite theoretical aspects of the fuzzy random variable to fuzzy stochastic optimization models and their real-life case studies. The volume reflects the fact that randomness and fuzziness (or vagueness) are two major sources of uncertainty in the real world, with significant implications in a number of settings. In industrial engineering, management and economics, the chances are high that decision makers will be confronted with information that is simultaneously probabilistically uncertain and fuzzily imprecise, and optimization in the form of a decision must be made in an environment that is doubly uncertain, characterized by a co-occurrence of randomness and fuzziness. This book begins by outlining the history and development of the fuzzy random variable before detailing numerous optimization models and applications that include the design of system controls for a dam.

    DOI

  • Professor Kiyoji Asai (1923-2012) Obituary

    Hideo Tanaka, Junzo Watada, Hidetomo Ichihashi

    FUZZY SETS AND SYSTEMS   204   III - V  2012.10

    Other  

    DOI

  • RECIPE GENERATION FROM SMALL SAMPLES: INCORPORATING WEIGHTED KERNEL REGRESSION WITH ARTIFICIAL SAMPLES

    Mohd Ibrahim Shapiai, Zuwairie Ibrahim, Marzuki Khalid, Lee Wen Jau, Soon-Chuan Ong, Junzo Watada

    INTERNATIONAL JOURNAL OF INNOVATIVE COMPUTING INFORMATION AND CONTROL   8 ( 10B ) 7321 - 7328  2012.10

     View Summary

    The cost of the experimental setup during the assembly process development of a chipset, particularly the under-fill process, can often result in insufficient data samples. In INTEL Malaysia, for example, the historical chipset data from an under-fill process consist of only a few samples. As a result, existing machine learning algorithms for predictive modeling cannot be applied in this setting. Despite this challenge, the use of data-driven decisions remains critical for further optimization of this engineering process. In this study, a weighted kernel regression with artificial samples (WKRAS) is introduced to improve the predictive modeling in a setting with limited data samples. In the proposed framework, the original weighted kernel regression (WKR) is strengthened by incorporating artificial samples to fill the information gaps between available training samples. The artificial samples generation is based on the dependency measurement between every independent variable and dependent variable with subject to the calculated correlation coefficients. Even though only four samples are used during the training stage of the setup experiment, the proposed technique is able to provide an accurate prediction within the engineer's requirements as compared with other existing predictive modeling systems, including the WKR and the artificial neural networks with back-propagation algorithm (ANNBP).

  • RECIPE GENERATION FROM SMALL SAMPLES: INCORPORATING WEIGHTED KERNEL REGRESSION WITH ARTIFICIAL SAMPLES

    Mohd Ibrahim Shapiai, Zuwairie Ibrahim, Marzuki Khalid, Lee Wen Jau, Soon-Chuan Ong, Junzo Watada

    INTERNATIONAL JOURNAL OF INNOVATIVE COMPUTING INFORMATION AND CONTROL   8 ( 10B ) 7321 - 7328  2012.10

     View Summary

    The cost of the experimental setup during the assembly process development of a chipset, particularly the under-fill process, can often result in insufficient data samples. In INTEL Malaysia, for example, the historical chipset data from an under-fill process consist of only a few samples. As a result, existing machine learning algorithms for predictive modeling cannot be applied in this setting. Despite this challenge, the use of data-driven decisions remains critical for further optimization of this engineering process. In this study, a weighted kernel regression with artificial samples (WKRAS) is introduced to improve the predictive modeling in a setting with limited data samples. In the proposed framework, the original weighted kernel regression (WKR) is strengthened by incorporating artificial samples to fill the information gaps between available training samples. The artificial samples generation is based on the dependency measurement between every independent variable and dependent variable with subject to the calculated correlation coefficients. Even though only four samples are used during the training stage of the setup experiment, the proposed technique is able to provide an accurate prediction within the engineer's requirements as compared with other existing predictive modeling systems, including the WKR and the artificial neural networks with back-propagation algorithm (ANNBP).

  • SPECIAL ISSUE ON MANAGEMENT ENGINEERING

    Berlin Wu, Hisao Shiizuka, Junzo Watada

    INTERNATIONAL JOURNAL OF INNOVATIVE COMPUTING INFORMATION AND CONTROL   8 ( 8 ) 5691 - 5692  2012.08

    Other  

  • PORTFOLIO SELECTION MODEL WITH INTERVAL VALUES BASED ON FUZZY PROBABILITY DISTRIBUTION FUNCTIONS

    Pei-Chun Lin, Junzo Watada, Berlin Wu

    INTERNATIONAL JOURNAL OF INNOVATIVE COMPUTING INFORMATION AND CONTROL   8 ( 8 ) 5935 - 5944  2012.08

     View Summary

    In order to analyze uncertain phenomena in real world, the concept of fuzzy random variables is widely employed in model building. In dealing with fuzzy data, defuzzification plays a central role. In this paper, portfolio selection problems are dealt as interval values. We calculate the expected values, variance and covariance by using the estimated parameters of underlying probability distribution function. The estimated values enable us to build up a portfolio selection model with estimated parameters on the basic of Markowitz's mean-variance model. The result exemplified that we have different choices of k which can decide the best expected return and less risk level in our model, also that we can provide not only one choice of portfolio selection but also two or more for decision makers.

  • RELIABILITY EVALUATION OF INTERCONNECTED POWER SYSTEMS INCLUDING WIND TURBINE GENERATORS

    Jeongje Park, Taegon Oh, Kyeonghee Cho, Jaeseok Choi, Sang-Seung Lee, Junzo Watada, A. A. El-Keib

    INTERNATIONAL JOURNAL OF INNOVATIVE COMPUTING INFORMATION AND CONTROL   8 ( 8 ) 5797 - 5808  2012.08

     View Summary

    Wind energy has become the most successful renewable energy source. This is also evident in the Northeast Asia area including Northeast China, South Korea and Japan, etc. This paper proposes a tie-line constrained equivalent assisting generator model (WTEAG) considering wind turbine generator (WTG) newly. An interconnection power system reliability evaluation program "NEAREL-II" using the proposed model is developed. Additionally, this paper presents results of case studies of reliability evaluation for the actual power systems of six countries in the Northeast Asia area including WTG.

  • SPECIAL ISSUE ON MANAGEMENT ENGINEERING

    Berlin Wu, Hisao Shiizuka, Junzo Watada

    INTERNATIONAL JOURNAL OF INNOVATIVE COMPUTING INFORMATION AND CONTROL   8 ( 8 ) 5691 - 5692  2012.08

    Other  

  • PORTFOLIO SELECTION MODEL WITH INTERVAL VALUES BASED ON FUZZY PROBABILITY DISTRIBUTION FUNCTIONS

    Pei-Chun Lin, Junzo Watada, Berlin Wu

    INTERNATIONAL JOURNAL OF INNOVATIVE COMPUTING INFORMATION AND CONTROL   8 ( 8 ) 5935 - 5944  2012.08

     View Summary

    In order to analyze uncertain phenomena in real world, the concept of fuzzy random variables is widely employed in model building. In dealing with fuzzy data, defuzzification plays a central role. In this paper, portfolio selection problems are dealt as interval values. We calculate the expected values, variance and covariance by using the estimated parameters of underlying probability distribution function. The estimated values enable us to build up a portfolio selection model with estimated parameters on the basic of Markowitz's mean-variance model. The result exemplified that we have different choices of k which can decide the best expected return and less risk level in our model, also that we can provide not only one choice of portfolio selection but also two or more for decision makers.

  • RELIABILITY EVALUATION OF INTERCONNECTED POWER SYSTEMS INCLUDING WIND TURBINE GENERATORS

    Jeongje Park, Taegon Oh, Kyeonghee Cho, Jaeseok Choi, Sang-Seung Lee, Junzo Watada, A. A. El-Keib

    INTERNATIONAL JOURNAL OF INNOVATIVE COMPUTING INFORMATION AND CONTROL   8 ( 8 ) 5797 - 5808  2012.08

     View Summary

    Wind energy has become the most successful renewable energy source. This is also evident in the Northeast Asia area including Northeast China, South Korea and Japan, etc. This paper proposes a tie-line constrained equivalent assisting generator model (WTEAG) considering wind turbine generator (WTG) newly. An interconnection power system reliability evaluation program "NEAREL-II" using the proposed model is developed. Additionally, this paper presents results of case studies of reliability evaluation for the actual power systems of six countries in the Northeast Asia area including WTG.

  • Pattern Clustering With Statistical Methods Using a DNA-Based Algorithm

    Ikno Kim, Junzo Watada, Witold Pedrycz, Jui-Yu Wu

    IEEE TRANSACTIONS ON NANOBIOSCIENCE   11 ( 2 ) 100 - 110  2012.06

     View Summary

    Clustering is commonly exploited in engineering, management, and science fields with the objective of revealing structure in pattern data sets. In this article, through clustering we construct meaningful collections of information granules (clusters). Although the underlying goal is obvious, its realization is fully challenging. Given their nature, clustering is a well-known NP-complete problem. The existing algorithms commonly produce some suboptimal solutions. As a vehicle of pattern clustering, we discuss in this article how to use a DNA-based algorithm. We also discuss the details of encoding being used here with statistical methods combined with the DNA-based algorithm for pattern clustering.

    DOI

  • A hybrid modified PSO approach to VaR-based facility location problems with variable capacity in fuzzy random uncertainty

    Shuming Wang, Junzo Watada

    INFORMATION SCIENCES   192   3 - 18  2012.06

     View Summary

    This paper studies a facility location model with fuzzy random parameters and its swarm intelligence approach. A Value-at-Risk (VaR) based fuzzy random facility location model (VaR-FRFLM) is built in which both the costs and demands are assumed to be fuzzy random variables, and the capacity of each facility is unfixed but a decision variable assuming continuous values. Under this setting, the VaR-FRFLM is inherently a two-stage mixed 0-1 integer fuzzy random programming problem, to which analytical nonlinear programming methods are not applicable.
    A hybrid modified particle swarm optimization (MPSO) approach is proposed to solve the VaR-FRFLM. In this hybrid mechanism, an approximation algorithm is utilized to compute the fuzzy random VaR objective, a continuous Nbest-Gbest-based PSO and a genotype-phenotype-based binary PSO vehicles are designed to deal with the continuous capacity decisions and the binary location decisions, respectively, and two mutation operators are incorporated into the PSO to further decrease the possibility of becoming trapped in the local optima. A numerical experiment illustrates the application of the proposed hybrid MPSO algorithm and lays out its robustness to the system parameter settings. The comparison shows that the hybrid MPSO exhibits better performance than that when hybrid regular continuous-binary PSO and genetic algorithm (GA) are used to solve the VaR-FRFLM. (C) 2010 Elsevier Inc. All rights reserved.

    DOI

  • Pattern Clustering With Statistical Methods Using a DNA-Based Algorithm

    Ikno Kim, Junzo Watada, Witold Pedrycz, Jui-Yu Wu

    IEEE TRANSACTIONS ON NANOBIOSCIENCE   11 ( 2 ) 100 - 110  2012.06

     View Summary

    Clustering is commonly exploited in engineering, management, and science fields with the objective of revealing structure in pattern data sets. In this article, through clustering we construct meaningful collections of information granules (clusters). Although the underlying goal is obvious, its realization is fully challenging. Given their nature, clustering is a well-known NP-complete problem. The existing algorithms commonly produce some suboptimal solutions. As a vehicle of pattern clustering, we discuss in this article how to use a DNA-based algorithm. We also discuss the details of encoding being used here with statistical methods combined with the DNA-based algorithm for pattern clustering.

    DOI

  • A hybrid modified PSO approach to VaR-based facility location problems with variable capacity in fuzzy random uncertainty

    Shuming Wang, Junzo Watada

    INFORMATION SCIENCES   192   3 - 18  2012.06

     View Summary

    This paper studies a facility location model with fuzzy random parameters and its swarm intelligence approach. A Value-at-Risk (VaR) based fuzzy random facility location model (VaR-FRFLM) is built in which both the costs and demands are assumed to be fuzzy random variables, and the capacity of each facility is unfixed but a decision variable assuming continuous values. Under this setting, the VaR-FRFLM is inherently a two-stage mixed 0-1 integer fuzzy random programming problem, to which analytical nonlinear programming methods are not applicable.
    A hybrid modified particle swarm optimization (MPSO) approach is proposed to solve the VaR-FRFLM. In this hybrid mechanism, an approximation algorithm is utilized to compute the fuzzy random VaR objective, a continuous Nbest-Gbest-based PSO and a genotype-phenotype-based binary PSO vehicles are designed to deal with the continuous capacity decisions and the binary location decisions, respectively, and two mutation operators are incorporated into the PSO to further decrease the possibility of becoming trapped in the local optima. A numerical experiment illustrates the application of the proposed hybrid MPSO algorithm and lays out its robustness to the system parameter settings. The comparison shows that the hybrid MPSO exhibits better performance than that when hybrid regular continuous-binary PSO and genetic algorithm (GA) are used to solve the VaR-FRFLM. (C) 2010 Elsevier Inc. All rights reserved.

    DOI

  • A TWO-STEP SUPERVISED LEARNING ARTIFICIAL NEURAL NETWORK FOR IMBALANCED DATASET PROBLEMS

    Asrul Adam, Zuwairie Ibrahim, Mohd Ibrahim Shapiai, Lim Chun Chew, Lee Wen Jau, Marzuki Khalid, Junzo Watada

    INTERNATIONAL JOURNAL OF INNOVATIVE COMPUTING INFORMATION AND CONTROL   8 ( 5A ) 3163 - 3172  2012.05

     View Summary

    In this paper, a two-step supervised learning algorithm of a single layer feedforward Artificial Neural Network (ANN) is proposed for solving Unbalanced dataset problems. Levenberg Marquart backpropagation learning algorithm is utilized in the first step learning, while the second step learning mechanism is introduced by optimizing the decision threshold of the step function at the output layer of ANN using particle swarm optimization (PSO). After all the steps learning are accomplished, the best weights and decision threshold value are obtained to be used for testing process. Several unbalanced datasets, which are available in UCI Machine Learning Repository, are chosen as case study. The prediction performance is assessed by Geometric Mean (G-mean), which is a standard measure to indicate the efficiency of classifier for Unbalanced datasets. Based on the experimental results, the proposed method is able to provide good G-mean value compared with the conventional ANN approaches.

  • A TWO-STEP SUPERVISED LEARNING ARTIFICIAL NEURAL NETWORK FOR IMBALANCED DATASET PROBLEMS

    Asrul Adam, Zuwairie Ibrahim, Mohd Ibrahim Shapiai, Lim Chun Chew, Lee Wen Jau, Marzuki Khalid, Junzo Watada

    INTERNATIONAL JOURNAL OF INNOVATIVE COMPUTING INFORMATION AND CONTROL   8 ( 5A ) 3163 - 3172  2012.05

     View Summary

    In this paper, a two-step supervised learning algorithm of a single layer feedforward Artificial Neural Network (ANN) is proposed for solving Unbalanced dataset problems. Levenberg Marquart backpropagation learning algorithm is utilized in the first step learning, while the second step learning mechanism is introduced by optimizing the decision threshold of the step function at the output layer of ANN using particle swarm optimization (PSO). After all the steps learning are accomplished, the best weights and decision threshold value are obtained to be used for testing process. Several unbalanced datasets, which are available in UCI Machine Learning Repository, are chosen as case study. The prediction performance is assessed by Geometric Mean (G-mean), which is a standard measure to indicate the efficiency of classifier for Unbalanced datasets. Based on the experimental results, the proposed method is able to provide good G-mean value compared with the conventional ANN approaches.

  • AUTOMATION OF DNA COMPUTING READOUT METHOD BASED ON REAL-TIME PCR IMPLEMENTED ON DNA ENGINE OPTICON 2 SYSTEM

    Muhammad Faiz Mohamed Saaid, Ismail Ibrahim, Shahdan Sudin, Mohd Saberi Mohamad, Zulkifli Md Yusof, Jameel Abdulla Ahmed Mukred, Kamal Khalil, Zuwairie Ibrahim, Junzo Watada

    INTERNATIONAL JOURNAL OF INNOVATIVE COMPUTING INFORMATION AND CONTROL   8 ( 3A ) 1907 - 1916  2012.03

     View Summary

    Previously, an automation of a DNA computing readout method for the Hamiltonian Path Problem (HPP) has been implemented based on LightCycler System. In this study, a similar readout approach is implemented based on DNA Engine Opticon 2 System. The readout approach consists of two steps: real-time amplification in vitro using Tag Man-based real-time PCR, followed by an in silico phase. The in silico phase consists of a data clustering algorithm and an information processing to extract the Hamiltonian path after the Tag Man "YES" and "NO" reactions have been identified. The result indicates that the automation of DNA computing readout method can be efficiently implemented on DNA Engine Opticon 2 System.

  • AUTOMATION OF DNA COMPUTING READOUT METHOD BASED ON REAL-TIME PCR IMPLEMENTED ON DNA ENGINE OPTICON 2 SYSTEM

    Muhammad Faiz Mohamed Saaid, Ismail Ibrahim, Shahdan Sudin, Mohd Saberi Mohamad, Zulkifli Md Yusof, Jameel Abdulla Ahmed Mukred, Kamal Khalil, Zuwairie Ibrahim, Junzo Watada

    INTERNATIONAL JOURNAL OF INNOVATIVE COMPUTING INFORMATION AND CONTROL   8 ( 3A ) 1907 - 1916  2012.03

     View Summary

    Previously, an automation of a DNA computing readout method for the Hamiltonian Path Problem (HPP) has been implemented based on LightCycler System. In this study, a similar readout approach is implemented based on DNA Engine Opticon 2 System. The readout approach consists of two steps: real-time amplification in vitro using Tag Man-based real-time PCR, followed by an in silico phase. The in silico phase consists of a data clustering algorithm and an information processing to extract the Hamiltonian path after the Tag Man "YES" and "NO" reactions have been identified. The result indicates that the automation of DNA computing readout method can be efficiently implemented on DNA Engine Opticon 2 System.

  • Serviceability Based Investment to Power System

    Junzo Watada

    PICMET '12: PROCEEDINGS - TECHNOLOGY MANAGEMENT FOR EMERGING TECHNOLOGIES     1319 - 1329  2012

     View Summary

    Recently, power-supply failures have caused major social losses. Therefore, power-supply systems need to be discussed from various points of view. The objective of this study is to present a concept of serviceability in investment to a power system. In this study, the serviceability is interpreted from the reliability and risks of units, which are evaluated with a variance-covariance matrix, and the effects and expenses of replacement are analyzed. The mean-variance analysis is formulated as a mathematical program with the following two objectives: (1) to maximize the serviceability, that is, minimize the risk and (2) to maximize the expected return. Finally, a structural learning model of a mutual connection neural network is proposed to solve these problems defined by mixed-integer quadratic programming, and employed in the mean-variance analysis after proving its convegence. Our method is applied to a power system network in a cetain urban area. This method enables us to select results more effectively and enhance decision making. In other words, decision-makers can select the investment rate and werviceability of each ward within a given total budget.

  • Redundancy Allocation problem for a Series-Parallel system using Estimation of Distribution Algorithm

    Haydee Melo, Watada Junzo

    2012 WORLD AUTOMATION CONGRESS (WAC)     1 - 6  2012

     View Summary

    Reliability is an engineering field that recently has captivated the attention of researches. Its goal is to develop new techniques to improve the security and performance of the systems. The increasing complexity in the systems as a result of growing technology makes them more susceptible for failures. In the redundancy allocation problem (RAP) its principal objective is to maximize the availability while reducing the cost, volume or weight of the system. In this research an Estimation-of-Distribution Algorithm (EDA) approach is proposed for solving the redundancy allocation problem for a series-parallel system.

  • Meta-Controlled Boltzmann Machine: Its Convergence and Application to Power System

    Junzo Watada, Haydee Rocio Melo Cisneros

    2012 WORLD AUTOMATION CONGRESS (WAC)     1 - 6  2012

     View Summary

    In this paper, meta controlled Boltzmann machine; the double-layered Boltzmann machine consisting of upper (Hopfield network) and lower (Boltzmann network) layers, is built and the double-layered Boltzmann machine is proved to converge the optimal solution, after then, is efficiently applied to solve the investment problem to power system where the problem is understood as mean-variance problem using mathematical programming with two objectives: the minimization of risk and the maximization of expected return. The proposed method is applied both diffusion processes and simulated annealing. The convergence proof of the proposed method is showed in this paper. Meta-controlled Boltzmann machine shows an ability to solve combinatorial optimization problems better than either Hopfield networks or Boltzmann machines.

  • Linear fractional programming for fuzzy random based possibilistic programming problem

    Nureize Binti Arbaiy, Junzo Watada

    Proceedings of International Conference on Computational Intelligence, Modelling and Simulation     99 - 104  2012

     View Summary

    The uncertainty in real-world decision making originates from several sources, i.e., fuzziness, randomness, ambiguous. These uncertainties should be included while translating real-world problem into mathematical programming model though handling such uncertainties in the decision making model increases the complexities of the problem and make the solution of the problem hard. In this paper, a linear fractional programming is used to solve multi-objective fuzzy random based possibilistic programming problems to address the vague decision maker's preference (aspiration) and ambiguous data (coefficient), in a fuzzy random environment. The developed model plays a vital role in the construction of fuzzy multiobjective linear programming model, which is exposed to various types of uncertainties that should be treated properly. An illustrative example explains the developed model and highlights it's effectiveness. © 2012 IEEE.

    DOI

  • A Comprehensive Evaluation of Determinants in Collaborative R&D Partner Selection of Small Businesses in Taiwan

    Yu-Lien Tai, Junzo Watada, Hsiu Hsien Su

    PICMET '12: PROCEEDINGS - TECHNOLOGY MANAGEMENT FOR EMERGING TECHNOLOGIES     482 - 494  2012

     View Summary

    Despite the increasing number of firms committed to R&D strategic alliances over the past few decades, a large number of alliances have failed because of the incompatibility of the partners. The purpose of this study is to evaluate comprehensively the determinants, particularly for small-and medium-sized enterprises (SMEs) that affect collaborative R&D partner selection. In this study, a two-stage Fuzzy multi-criteria decision making approach (FMCDM), combined with the Fuzzy Delphi Method (FDM) and Fuzzy Analytical Hierarchical Processing (FAHP) was adopted. Two sets of experts were chosen from the SME Technology-Intensive Clustering Assistance (TICA) project to participate in the research study. In this study, we discovered that two contributions to the selection of collaborative R&D partners among SMEs lead to the successful formation of R&D alliances. First, we provide a framework for solving the problem of multiple-criteria decision making in the process of selecting collaborative R&D partners among small firms. Second, we comprehensively evaluate the determinants that influence the success of the collaborative R&D partner selection process (particularly for SMEs) in the future.

  • Ranking Method of Web Mining Based on Latent Semantic Indexing,

    Yang Jianxiong, Junzo WATADA

    SICE, JCMSI (ISSN, 18824889)   5 ( 5 ) 290 - 295  2012

    DOI

  • A gasoline consumption model based on the harmony search algorithm: Study case of Indonesia

    Riesta Anggarani, Junzo Watada

    Intelligent Decision Technologies   6 ( 3 ) 233 - 241  2012

     View Summary

    Indonesia has a discrepancy between the realized amount and the planned amount of gasoline consumption. This discrepancy has burdened the national budget in Indonesia because this unplanned increase in the imported amount of gasoline has made the government pay more to the importers. The objectives of this research are to develop a robust and accurate model to forecast future gasoline consumption and to provide an attractive alternative model for gasoline consumption forecasting by applying a metaheuristic approach. We apply the harmony search (HS) algorithm for developing a model of gasoline consumption in Indonesia using general socioeconomic variables that can be easily retrieved from public data. The variables used are the gross domestic product (GDP), population, and the total numbers of passenger cars and motorcycles. The HS algolithm selects the optimal weight factors within the proposed exponential model. The results show that the proposed exponential HS algorithm-based model outperforms the conventional nonlinear regression method and particle swarm optimization (PSO)-based model in terms of the mean absolute percentage error (MAPE). ©2012-IOS Press and the authors. All rights reserved.

    DOI

  • Multi-Objective Top-Down Decision Making through Additive Fuzzy Goal Programming

    ARBAIY Nureize, WATADA Junzo

      5 ( 2 ) 63 - 69  2012

    DOI CiNii

  • Exploring the Influence Degree of Project Team Management Processes Combing Danpwith Mcdm Model

    Meng-Jong Kuan, Yao-Feng Chang, Bing-Qing Yang, Junzo Watada

    International Journal of Innovative Management, Information & Production (IMIP) ()   3 ( 1 ) 36 - 49  2012

  • An Optimization Approach to Bi-Level Quadratic Programming Problems

    Junzo Watada, Jingjing Liang

    International Journal of Innovative Management, Information & Production (IMIP) ()   3 ( 2 ) 1 - 10  2012

  • A Clustering Method for Web Mining Based on Probabilistic Latent Semantic Indexing

    Jianxiong YANG, Junzo WATADA

    SICE Journal of Control, Measurement, and System Integration, Vol. 5, No. 5, pp. 290?295, September 2012 ()   5 ( 5 ) 290 - 295  2012

    DOI

  • Fuzzy Optimization and Decision Making (2012), Obituary

    Junzo Watada, I. Burhan, T?rk?en, Laszlo T. Koczy in

    Fuzzy Optimization and Decision Making, ()   11 ( 4 ) 353 - 361  2012

    DOI

  • Serviceability based investment to power system

    Watada, J

    Technology Management for Emerging Technologies (PICMET), 2012 Proceedings of PICMET '12:     1319 - 1329  2012

  • Redundancy Allocation problem for a Series-Parallel system using Estimation of Distribution Algorithm

    Haydee Melo, Watada Junzo

    2012 WORLD AUTOMATION CONGRESS (WAC)     1 - 6  2012

     View Summary

    Reliability is an engineering field that recently has captivated the attention of researches. Its goal is to develop new techniques to improve the security and performance of the systems. The increasing complexity in the systems as a result of growing technology makes them more susceptible for failures. In the redundancy allocation problem (RAP) its principal objective is to maximize the availability while reducing the cost, volume or weight of the system. In this research an Estimation-of-Distribution Algorithm (EDA) approach is proposed for solving the redundancy allocation problem for a series-parallel system.

  • Meta-Controlled Boltzmann Machine: Its Convergence and Application to Power System

    Junzo Watada, Haydee Rocio Melo Cisneros

    2012 WORLD AUTOMATION CONGRESS (WAC)     1 - 6  2012

     View Summary

    In this paper, meta controlled Boltzmann machine; the double-layered Boltzmann machine consisting of upper (Hopfield network) and lower (Boltzmann network) layers, is built and the double-layered Boltzmann machine is proved to converge the optimal solution, after then, is efficiently applied to solve the investment problem to power system where the problem is understood as mean-variance problem using mathematical programming with two objectives: the minimization of risk and the maximization of expected return. The proposed method is applied both diffusion processes and simulated annealing. The convergence proof of the proposed method is showed in this paper. Meta-controlled Boltzmann machine shows an ability to solve combinatorial optimization problems better than either Hopfield networks or Boltzmann machines.

  • Linear fractional programming for fuzzy random based possibilistic programming problem

    Nureize Binti Arbaiy, Junzo Watada

    Proceedings of International Conference on Computational Intelligence, Modelling and Simulation     99 - 104  2012

     View Summary

    The uncertainty in real-world decision making originates from several sources, i.e., fuzziness, randomness, ambiguous. These uncertainties should be included while translating real-world problem into mathematical programming model though handling such uncertainties in the decision making model increases the complexities of the problem and make the solution of the problem hard. In this paper, a linear fractional programming is used to solve multi-objective fuzzy random based possibilistic programming problems to address the vague decision maker's preference (aspiration) and ambiguous data (coefficient), in a fuzzy random environment. The developed model plays a vital role in the construction of fuzzy multiobjective linear programming model, which is exposed to various types of uncertainties that should be treated properly. An illustrative example explains the developed model and highlights it's effectiveness. © 2012 IEEE.

    DOI

  • A Comprehensive Evaluation of Determinants in Collaborative R&D Partner Selection of Small Businesses in Taiwan

    Yu-Lien Tai, Junzo Watada, Hsiu Hsien Su

    PICMET '12: PROCEEDINGS - TECHNOLOGY MANAGEMENT FOR EMERGING TECHNOLOGIES     482 - 494  2012

     View Summary

    Despite the increasing number of firms committed to R&D strategic alliances over the past few decades, a large number of alliances have failed because of the incompatibility of the partners. The purpose of this study is to evaluate comprehensively the determinants, particularly for small-and medium-sized enterprises (SMEs) that affect collaborative R&D partner selection. In this study, a two-stage Fuzzy multi-criteria decision making approach (FMCDM), combined with the Fuzzy Delphi Method (FDM) and Fuzzy Analytical Hierarchical Processing (FAHP) was adopted. Two sets of experts were chosen from the SME Technology-Intensive Clustering Assistance (TICA) project to participate in the research study. In this study, we discovered that two contributions to the selection of collaborative R&D partners among SMEs lead to the successful formation of R&D alliances. First, we provide a framework for solving the problem of multiple-criteria decision making in the process of selecting collaborative R&D partners among small firms. Second, we comprehensively evaluate the determinants that influence the success of the collaborative R&D partner selection process (particularly for SMEs) in the future.

  • Ranking Method of Web Mining Based on Latent Semantic Indexing,

    Yang Jianxiong, Junzo WATADA

    SICE, JCMSI (ISSN, 18824889)   5 ( 5 ) 290 - 295  2012

    DOI

  • A gasoline consumption model based on the harmony search algorithm: Study case of Indonesia

    Riesta Anggarani, Junzo Watada

    Intelligent Decision Technologies   6 ( 3 ) 233 - 241  2012

     View Summary

    Indonesia has a discrepancy between the realized amount and the planned amount of gasoline consumption. This discrepancy has burdened the national budget in Indonesia because this unplanned increase in the imported amount of gasoline has made the government pay more to the importers. The objectives of this research are to develop a robust and accurate model to forecast future gasoline consumption and to provide an attractive alternative model for gasoline consumption forecasting by applying a metaheuristic approach. We apply the harmony search (HS) algorithm for developing a model of gasoline consumption in Indonesia using general socioeconomic variables that can be easily retrieved from public data. The variables used are the gross domestic product (GDP), population, and the total numbers of passenger cars and motorcycles. The HS algolithm selects the optimal weight factors within the proposed exponential model. The results show that the proposed exponential HS algorithm-based model outperforms the conventional nonlinear regression method and particle swarm optimization (PSO)-based model in terms of the mean absolute percentage error (MAPE). ©2012-IOS Press and the authors. All rights reserved.

    DOI

  • Top-Down Multi-objective Decision Making through Fuzzy Additive Goal Programming,

    Nureize Arbaiya, JunzoWATADA

    SICE Journal of Control, Measurement, and System Integration, ()   5 ( 2 ) 63 - 69  2012

    DOI

  • Exploring the Influence Degree of Project Team Management Processes Combing Danpwith Mcdm Model

    Meng-Jong Kuan, Yao-Feng Chang, Bing-Qing Yang, Junzo Watada

    International Journal of Innovative Management, Information & Production (IMIP) ()   3 ( 1 ) 36 - 49  2012

  • An Optimization Approach to Bi-Level Quadratic Programming Problems

    Junzo Watada, Jingjing Liang

    International Journal of Innovative Management, Information & Production (IMIP) ()   3 ( 2 ) 1 - 10  2012

  • A Clustering Method for Web Mining Based on Probabilistic Latent Semantic Indexing

    Jianxiong YANG, Junzo WATADA

    SICE Journal of Control, Measurement, and System Integration, Vol. 5, No. 5, pp. 290?295, September 2012 ()   5 ( 5 ) 290 - 295  2012

    DOI

  • Fuzzy Optimization and Decision Making (2012), Obituary

    Junzo Watada, I. Burhan, T?rk?en, Laszlo T. Koczy in

    Fuzzy Optimization and Decision Making, ()   11 ( 4 ) 353 - 361  2012

    DOI

  • FUNCTION AND SURFACE APPROXIMATION BASED ON ENHANCED KERNEL REGRESSION FOR SMALL SAMPLE SETS

    Mohd Ibrahim Shapiai, Zuwairie Ibrahim, Marzuki Khalid, Lee Wen Jau, Vladimir Pavlovic, Junzo Watada

    INTERNATIONAL JOURNAL OF INNOVATIVE COMPUTING INFORMATION AND CONTROL   7 ( 10 ) 5947 - 5960  2011.10

     View Summary

    The function approximation problem is to find the appropriate relationship between a dependent and independent variable(s). Function approximation algorithms generally require sufficient samples to approximate a function. Insufficient samples may cause any function approximation algorithm to result in unsatisfactory predictions. To solve this problem, a function approximation algorithm called Weighted Kernel Regression (WKR), which is based on Nadaraya-Watson kernel regression (NWKR), is proposed. In the proposed framework, the original NW K R algorithm is enhanced by expressing the observed samples in a square kernel matrix. The WKR is trained to estimate the weight for the testing phase. The weight is estimated iteratively and governed by the error function to find a good approximation model. Four experiments are conducted to show the capability of the WKR. The results show that the proposed WKR model is effective in cases where the target function is non-linear and the given training sample is small. The performance of the WKR is also compared with other existing function approximation algorithms, such as artificial neural networks (ANN).

  • FUNCTION AND SURFACE APPROXIMATION BASED ON ENHANCED KERNEL REGRESSION FOR SMALL SAMPLE SETS

    Mohd Ibrahim Shapiai, Zuwairie Ibrahim, Marzuki Khalid, Lee Wen Jau, Vladimir Pavlovic, Junzo Watada

    INTERNATIONAL JOURNAL OF INNOVATIVE COMPUTING INFORMATION AND CONTROL   7 ( 10 ) 5947 - 5960  2011.10

     View Summary

    The function approximation problem is to find the appropriate relationship between a dependent and independent variable(s). Function approximation algorithms generally require sufficient samples to approximate a function. Insufficient samples may cause any function approximation algorithm to result in unsatisfactory predictions. To solve this problem, a function approximation algorithm called Weighted Kernel Regression (WKR), which is based on Nadaraya-Watson kernel regression (NWKR), is proposed. In the proposed framework, the original NW K R algorithm is enhanced by expressing the observed samples in a square kernel matrix. The WKR is trained to estimate the weight for the testing phase. The weight is estimated iteratively and governed by the error function to find a good approximation model. Four experiments are conducted to show the capability of the WKR. The results show that the proposed WKR model is effective in cases where the target function is non-linear and the given training sample is small. The performance of the WKR is also compared with other existing function approximation algorithms, such as artificial neural networks (ANN).

  • A DNA-Based Algorithm for Minimizing Decision Rules: A Rough Sets Approach

    Ikno Kim, Yu-Yi Chu, Junzo Watada, Jui-Yu Wu, Witold Pedrycz

    IEEE TRANSACTIONS ON NANOBIOSCIENCE   10 ( 3 ) 139 - 151  2011.09

     View Summary

    Rough sets are often exploited for data reduction and classification. While they are conceptually appealing, the techniques used with rough sets can be computationally demanding. To address this obstacle, the objective of this study is to investigate the use of DNA molecules and associated techniques as an optimization vehicle to support algorithms of rough sets. In particular, we develop a DNA-based algorithm to derive decision rules of minimal length. This new approach can be of value when dealing with a large number of objects and their attributes, in which case the complexity of rough-sets-based methods is NP-hard. The proposed algorithm shows how the essential components involved in the minimization of decision rules in data processing can be realized.

    DOI

  • A DNA-Based Algorithm for Minimizing Decision Rules: A Rough Sets Approach

    Ikno Kim, Yu-Yi Chu, Junzo Watada, Jui-Yu Wu, Witold Pedrycz

    IEEE TRANSACTIONS ON NANOBIOSCIENCE   10 ( 3 ) 139 - 151  2011.09

     View Summary

    Rough sets are often exploited for data reduction and classification. While they are conceptually appealing, the techniques used with rough sets can be computationally demanding. To address this obstacle, the objective of this study is to investigate the use of DNA molecules and associated techniques as an optimization vehicle to support algorithms of rough sets. In particular, we develop a DNA-based algorithm to derive decision rules of minimal length. This new approach can be of value when dealing with a large number of objects and their attributes, in which case the complexity of rough-sets-based methods is NP-hard. The proposed algorithm shows how the essential components involved in the minimization of decision rules in data processing can be realized.

    DOI

  • A DNA-Based Algorithm for Minimizing Decision Rules: A Rough Sets Approach

    Ikno Kim, Yu-Yi Chu, Junzo Watada, Jui-Yu Wu, Witold Pedrycz

    IEEE TRANSACTIONS ON NANOBIOSCIENCE   10 ( 3 ) 139 - 151  2011.09

     View Summary

    Rough sets are often exploited for data reduction and classification. While they are conceptually appealing, the techniques used with rough sets can be computationally demanding. To address this obstacle, the objective of this study is to investigate the use of DNA molecules and associated techniques as an optimization vehicle to support algorithms of rough sets. In particular, we develop a DNA-based algorithm to derive decision rules of minimal length. This new approach can be of value when dealing with a large number of objects and their attributes, in which case the complexity of rough-sets-based methods is NP-hard. The proposed algorithm shows how the essential components involved in the minimization of decision rules in data processing can be realized.

    DOI PubMed CiNii

  • A DNA-Based Algorithm for Minimizing Decision Rules: A Rough Sets Approach

    Ikno Kim, Yu-Yi Chu, Junzo Watada, Jui-Yu Wu, Witold Pedrycz

    IEEE TRANSACTIONS ON NANOBIOSCIENCE   10 ( 3 ) 139 - 151  2011.09

     View Summary

    Rough sets are often exploited for data reduction and classification. While they are conceptually appealing, the techniques used with rough sets can be computationally demanding. To address this obstacle, the objective of this study is to investigate the use of DNA molecules and associated techniques as an optimization vehicle to support algorithms of rough sets. In particular, we develop a DNA-based algorithm to derive decision rules of minimal length. This new approach can be of value when dealing with a large number of objects and their attributes, in which case the complexity of rough-sets-based methods is NP-hard. The proposed algorithm shows how the essential components involved in the minimization of decision rules in data processing can be realized.

    DOI PubMed CiNii

  • Fuzzy-portfolio-selection models with value-at-risk

    Bo Wang, Shuming Wang, Junzo Watada

    IEEE Transactions on Fuzzy Systems   19 ( 4 ) 758 - 769  2011.08

     View Summary

    Based on fuzzy value-at-risk (VaR), this paper proposes a new portfolio-selection model (PSM) called the VaR-based fuzzy PSM (VaR-FPSM). Compared with the existing FPSMs, the VaR can directly reflect the greatest loss of a selected case under a given confidence level. In this study, when the security returns are taken as trapezoidal, triangular, and Gaussian fuzzy numbers, several crisp equivalent models of the VaR-FPSM are derived, which can be handled by any linear programming solvers. In general situations, an improved particle swarm optimization algorithm on the basis of fuzzy simulation is designed to search for the approximate optimal solutions. To illustrate the proposed model and the behavior of the improved particle swarm optimization algorithm, two numerical examples are provided, and the results are discussed. Furthermore, the proposed algorithm is compared with some existing approaches to fuzzy portfolio selection, such as the genetic algorithm and simulated annealing. © 2006 IEEE.

    DOI

  • Fuzzy-portfolio-selection models with value-at-risk

    Bo Wang, Shuming Wang, Junzo Watada

    IEEE Transactions on Fuzzy Systems   19 ( 4 ) 758 - 769  2011.08

     View Summary

    Based on fuzzy value-at-risk (VaR), this paper proposes a new portfolio-selection model (PSM) called the VaR-based fuzzy PSM (VaR-FPSM). Compared with the existing FPSMs, the VaR can directly reflect the greatest loss of a selected case under a given confidence level. In this study, when the security returns are taken as trapezoidal, triangular, and Gaussian fuzzy numbers, several crisp equivalent models of the VaR-FPSM are derived, which can be handled by any linear programming solvers. In general situations, an improved particle swarm optimization algorithm on the basis of fuzzy simulation is designed to search for the approximate optimal solutions. To illustrate the proposed model and the behavior of the improved particle swarm optimization algorithm, two numerical examples are provided, and the results are discussed. Furthermore, the proposed algorithm is compared with some existing approaches to fuzzy portfolio selection, such as the genetic algorithm and simulated annealing. © 2006 IEEE.

    DOI

  • Fuzzy-Portfolio-Selection Models With Value-at-Risk

    Bo Wang, Shuming Wang, Junzo Watada

    IEEE TRANSACTIONS ON FUZZY SYSTEMS   19 ( 4 ) 758 - 769  2011.08

     View Summary

    Based on fuzzy value-at-risk (VaR), this paper proposes a new portfolio-selection model (PSM) called the VaR-based fuzzy PSM (VaR-FPSM). Compared with the existing FPSMs, the VaR can directly reflect the greatest loss of a selected case under a given confidence level. In this study, when the security returns are taken as trapezoidal, triangular, and Gaussian fuzzy numbers, several crisp equivalent models of the VaR-FPSM are derived, which can be handled by any linear programming solvers. In general situations, an improved particle swarm optimization algorithm on the basis of fuzzy simulation is designed to search for the approximate optimal solutions. To illustrate the proposed model and the behavior of the improved particle swarm optimization algorithm, two numerical examples are provided, and the results are discussed. Furthermore, the proposed algorithm is compared with some existing approaches to fuzzy portfolio selection, such as the genetic algorithm and simulated annealing.

    DOI

  • Fuzzy-portfolio-selection models with value-at-risk

    Bo Wang, Shuming Wang, Junzo Watada

    IEEE Transactions on Fuzzy Systems   19 ( 4 ) 758 - 769  2011.08

     View Summary

    Based on fuzzy value-at-risk (VaR), this paper proposes a new portfolio-selection model (PSM) called the VaR-based fuzzy PSM (VaR-FPSM). Compared with the existing FPSMs, the VaR can directly reflect the greatest loss of a selected case under a given confidence level. In this study, when the security returns are taken as trapezoidal, triangular, and Gaussian fuzzy numbers, several crisp equivalent models of the VaR-FPSM are derived, which can be handled by any linear programming solvers. In general situations, an improved particle swarm optimization algorithm on the basis of fuzzy simulation is designed to search for the approximate optimal solutions. To illustrate the proposed model and the behavior of the improved particle swarm optimization algorithm, two numerical examples are provided, and the results are discussed. Furthermore, the proposed algorithm is compared with some existing approaches to fuzzy portfolio selection, such as the genetic algorithm and simulated annealing. © 2006 IEEE.

    DOI

  • Fuzzy-Portfolio-Selection Models With Value-at-Risk

    Bo Wang, Shuming Wang, Junzo Watada

    IEEE TRANSACTIONS ON FUZZY SYSTEMS   19 ( 4 ) 758 - 769  2011.08

     View Summary

    Based on fuzzy value-at-risk (VaR), this paper proposes a new portfolio-selection model (PSM) called the VaR-based fuzzy PSM (VaR-FPSM). Compared with the existing FPSMs, the VaR can directly reflect the greatest loss of a selected case under a given confidence level. In this study, when the security returns are taken as trapezoidal, triangular, and Gaussian fuzzy numbers, several crisp equivalent models of the VaR-FPSM are derived, which can be handled by any linear programming solvers. In general situations, an improved particle swarm optimization algorithm on the basis of fuzzy simulation is designed to search for the approximate optimal solutions. To illustrate the proposed model and the behavior of the improved particle swarm optimization algorithm, two numerical examples are provided, and the results are discussed. Furthermore, the proposed algorithm is compared with some existing approaches to fuzzy portfolio selection, such as the genetic algorithm and simulated annealing.

    DOI

  • Fuzzy-Portfolio-Selection Models With Value-at-Risk

    Bo Wang, Shuming Wang, Junzo Watada

    IEEE TRANSACTIONS ON FUZZY SYSTEMS   19 ( 4 ) 758 - 769  2011.08

     View Summary

    Based on fuzzy value-at-risk (VaR), this paper proposes a new portfolio-selection model (PSM) called the VaR-based fuzzy PSM (VaR-FPSM). Compared with the existing FPSMs, the VaR can directly reflect the greatest loss of a selected case under a given confidence level. In this study, when the security returns are taken as trapezoidal, triangular, and Gaussian fuzzy numbers, several crisp equivalent models of the VaR-FPSM are derived, which can be handled by any linear programming solvers. In general situations, an improved particle swarm optimization algorithm on the basis of fuzzy simulation is designed to search for the approximate optimal solutions. To illustrate the proposed model and the behavior of the improved particle swarm optimization algorithm, two numerical examples are provided, and the results are discussed. Furthermore, the proposed algorithm is compared with some existing approaches to fuzzy portfolio selection, such as the genetic algorithm and simulated annealing.

    DOI

  • Re-Scheduling of Unit Commitment Based on Customers' Fuzzy Requirements for Power Reliability

    Bo Wang, You Li, Junzo Watada

    IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS   E94D ( 7 ) 1378 - 1385  2011.07

     View Summary

    The development of the electricity market enables us to provide electricity of varied quality and price in order to fulfill power consumers' needs. Such customers choices should influence the process of adjusting power generation and spinning reserve, and, as a result, change the structure of a unit commitment optimization problem (UCP). To build a unit commitment model that considers customer choices, we employ fuzzy variables in this study to better characterize customer requirements and forecasted future power loads. To measure system reliability and determine the schedule of real power generation and spinning reserve, fuzzy Value-at-Risk (VaR) is utilized in building the model, which evaluates the peak values of power demands under given confidence levels. Based on the information obtained using fuzzy VaR, we proposed a heuristic algorithm called local convergence-averse binary particle swarm optimization (LCA-PSO) to solve the UCP. The proposed model and algorithm are used to analyze several test systems. Comparisons between the proposed algorithm and the conventional approaches show that the LCA-PSO performs better in finding the optimal solutions.

    DOI

  • Re-Scheduling of Unit Commitment Based on Customers' Fuzzy Requirements for Power Reliability

    Bo Wang, You Li, Junzo Watada

    IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS   E94D ( 7 ) 1378 - 1385  2011.07

     View Summary

    The development of the electricity market enables us to provide electricity of varied quality and price in order to fulfill power consumers' needs. Such customers choices should influence the process of adjusting power generation and spinning reserve, and, as a result, change the structure of a unit commitment optimization problem (UCP). To build a unit commitment model that considers customer choices, we employ fuzzy variables in this study to better characterize customer requirements and forecasted future power loads. To measure system reliability and determine the schedule of real power generation and spinning reserve, fuzzy Value-at-Risk (VaR) is utilized in building the model, which evaluates the peak values of power demands under given confidence levels. Based on the information obtained using fuzzy VaR, we proposed a heuristic algorithm called local convergence-averse binary particle swarm optimization (LCA-PSO) to solve the UCP. The proposed model and algorithm are used to analyze several test systems. Comparisons between the proposed algorithm and the conventional approaches show that the LCA-PSO performs better in finding the optimal solutions.

    DOI

  • Re-Scheduling of Unit Commitment Based on Customers' Fuzzy Requirements for Power Reliability

    Bo Wang, You Li, Junzo Watada

    IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS   E94D ( 7 ) 1378 - 1385  2011.07

     View Summary

    The development of the electricity market enables us to provide electricity of varied quality and price in order to fulfill power consumers' needs. Such customers choices should influence the process of adjusting power generation and spinning reserve, and, as a result, change the structure of a unit commitment optimization problem (UCP). To build a unit commitment model that considers customer choices, we employ fuzzy variables in this study to better characterize customer requirements and forecasted future power loads. To measure system reliability and determine the schedule of real power generation and spinning reserve, fuzzy Value-at-Risk (VaR) is utilized in building the model, which evaluates the peak values of power demands under given confidence levels. Based on the information obtained using fuzzy VaR, we proposed a heuristic algorithm called local convergence-averse binary particle swarm optimization (LCA-PSO) to solve the UCP. The proposed model and algorithm are used to analyze several test systems. Comparisons between the proposed algorithm and the conventional approaches show that the LCA-PSO performs better in finding the optimal solutions.

    DOI CiNii

  • Structural learning of the Boltzmann machine and its application to life cycle management

    Shamshul Bahar Yaakob, Junzo Watada, John Fulcher

    NEUROCOMPUTING   74 ( 12-13 ) 2193 - 2200  2011.06

     View Summary

    The objective of this research is to realise structural learning within a Boltzmann machine (BM), which enables the effective solution of problems defined as mixed integer quadratic programming. Simulation results show that computation time is reduced by up to one-fifth compared to conventional BMs. The computational efficiency of the resulting double-layer BM is approximately expressed as the ratio n divided by N, where n is the number of selected units (neurons/nodes) and N is the total number of units. The double-layer BM was applied to efficiently solve a mean-variance problem using mathematical programming with two objectives: the minimisation of risk and the maximisation of expected return. Finally, the effectiveness of our method is illustrated by way of a life cycle management example. The double-layer BM was able to more effectively select results with lower computational overhead. The results also enable us to more easily understand the internal structure of the BM. Using our proposed model, decision makers are able to select the best solution based on their risk preference from the alternative solutions provided by the proposed method. (C) 2011 Elsevier B.V. All rights reserved.

    DOI

  • Structural learning of the Boltzmann machine and its application to life cycle management

    Shamshul Bahar Yaakob, Junzo Watada, John Fulcher

    NEUROCOMPUTING   74 ( 12-13 ) 2193 - 2200  2011.06

     View Summary

    The objective of this research is to realise structural learning within a Boltzmann machine (BM), which enables the effective solution of problems defined as mixed integer quadratic programming. Simulation results show that computation time is reduced by up to one-fifth compared to conventional BMs. The computational efficiency of the resulting double-layer BM is approximately expressed as the ratio n divided by N, where n is the number of selected units (neurons/nodes) and N is the total number of units. The double-layer BM was applied to efficiently solve a mean-variance problem using mathematical programming with two objectives: the minimisation of risk and the maximisation of expected return. Finally, the effectiveness of our method is illustrated by way of a life cycle management example. The double-layer BM was able to more effectively select results with lower computational overhead. The results also enable us to more easily understand the internal structure of the BM. Using our proposed model, decision makers are able to select the best solution based on their risk preference from the alternative solutions provided by the proposed method. (C) 2011 Elsevier B.V. All rights reserved.

    DOI

  • Structural learning of the Boltzmann machine and its application to life cycle management

    Shamshul Bahar Yaakob, Junzo Watada, John Fulcher

    Neurocomputing   74 ( 12-13 ) 2193 - 2200  2011.06

     View Summary

    The objective of this research is to realise structural learning within a Boltzmann machine (BM), which enables the effective solution of problems defined as mixed integer quadratic programming. Simulation results show that computation time is reduced by up to one-fifth compared to conventional BMs. The computational efficiency of the resulting double-layer BM is approximately expressed as the ratio n divided by N, where n is the number of selected units (neurons/nodes) and N is the total number of units. The double-layer BM was applied to efficiently solve a mean-variance problem using mathematical programming with two objectives: the minimisation of risk and the maximisation of expected return. Finally, the effectiveness of our method is illustrated by way of a life cycle management example. The double-layer BM was able to more effectively select results with lower computational overhead. The results also enable us to more easily understand the internal structure of the BM. Using our proposed model, decision makers are able to select the best solution based on their risk preference from the alternative solutions provided by the proposed method. © 2011 Elsevier B.V.

    DOI

  • Structural learning of the Boltzmann machine and its application to life cycle management

    Shamshul Bahar Yaakob, Junzo Watada, John Fulcher

    NEUROCOMPUTING   74 ( 12-13 ) 2193 - 2200  2011.06

     View Summary

    The objective of this research is to realise structural learning within a Boltzmann machine (BM), which enables the effective solution of problems defined as mixed integer quadratic programming. Simulation results show that computation time is reduced by up to one-fifth compared to conventional BMs. The computational efficiency of the resulting double-layer BM is approximately expressed as the ratio n divided by N, where n is the number of selected units (neurons/nodes) and N is the total number of units. The double-layer BM was applied to efficiently solve a mean-variance problem using mathematical programming with two objectives: the minimisation of risk and the maximisation of expected return. Finally, the effectiveness of our method is illustrated by way of a life cycle management example. The double-layer BM was able to more effectively select results with lower computational overhead. The results also enable us to more easily understand the internal structure of the BM. Using our proposed model, decision makers are able to select the best solution based on their risk preference from the alternative solutions provided by the proposed method. (C) 2011 Elsevier B.V. All rights reserved.

    DOI

  • Real-time fuzzy regression analysis: A convex hull approach

    Azizul Azhar Ramli, Junzo Watada, Witold Pedrycz

    EUROPEAN JOURNAL OF OPERATIONAL RESEARCH   210 ( 3 ) 606 - 617  2011.05

     View Summary

    In this study, we present an enhancement of fuzzy regression analysis with regard to its aspect of real-time processing. Let us recall that fuzzy regression generalizes the concept of classical (numeric) regression in the sense of bringing additional capabilities that allow the model to deal with fuzzy (granular) data. We show that a convex hull method provides a useful vehicle to reduce computing time, which becomes of particular relevance in case of real-time data analysis. Our objective is to develop an efficient real-time fuzzy regression analysis based on the use of convex hull, specifically a Beneath-Beyond algorithm. In this algorithm, the re-construction of convex hull edges depends on incoming vertices while a re-computing procedure can be realized in real-time. We demonstrate the use of the developed enhancement to application to unit performance assessment and air pollution data. An important role of convex hull is contrasted with the limitations of linear programming used in the "standard" regression. (C) 2010 Elsevier B.V. All rights reserved.

    DOI

  • Real-time fuzzy regression analysis: A convex hull approach

    Azizul Azhar Ramli, Junzo Watada, Witold Pedrycz

    EUROPEAN JOURNAL OF OPERATIONAL RESEARCH   210 ( 3 ) 606 - 617  2011.05

     View Summary

    In this study, we present an enhancement of fuzzy regression analysis with regard to its aspect of real-time processing. Let us recall that fuzzy regression generalizes the concept of classical (numeric) regression in the sense of bringing additional capabilities that allow the model to deal with fuzzy (granular) data. We show that a convex hull method provides a useful vehicle to reduce computing time, which becomes of particular relevance in case of real-time data analysis. Our objective is to develop an efficient real-time fuzzy regression analysis based on the use of convex hull, specifically a Beneath-Beyond algorithm. In this algorithm, the re-construction of convex hull edges depends on incoming vertices while a re-computing procedure can be realized in real-time. We demonstrate the use of the developed enhancement to application to unit performance assessment and air pollution data. An important role of convex hull is contrasted with the limitations of linear programming used in the "standard" regression. (C) 2010 Elsevier B.V. All rights reserved.

    DOI

  • Real-time fuzzy regression analysis: A convex hull approach

    Azizul Azhar Ramli, Junzo Watada, Witold Pedrycz

    EUROPEAN JOURNAL OF OPERATIONAL RESEARCH   210 ( 3 ) 606 - 617  2011.05

     View Summary

    In this study, we present an enhancement of fuzzy regression analysis with regard to its aspect of real-time processing. Let us recall that fuzzy regression generalizes the concept of classical (numeric) regression in the sense of bringing additional capabilities that allow the model to deal with fuzzy (granular) data. We show that a convex hull method provides a useful vehicle to reduce computing time, which becomes of particular relevance in case of real-time data analysis. Our objective is to develop an efficient real-time fuzzy regression analysis based on the use of convex hull, specifically a Beneath-Beyond algorithm. In this algorithm, the re-construction of convex hull edges depends on incoming vertices while a re-computing procedure can be realized in real-time. We demonstrate the use of the developed enhancement to application to unit performance assessment and air pollution data. An important role of convex hull is contrasted with the limitations of linear programming used in the "standard" regression. (C) 2010 Elsevier B.V. All rights reserved.

    DOI

  • Real-time fuzzy regression analysis: A convex hull approach

    Azizul Azhar Ramli, Junzo Watada, Witold Pedrycz

    European Journal of Operational Research   210 ( 3 ) 606 - 617  2011.05

     View Summary

    In this study, we present an enhancement of fuzzy regression analysis with regard to its aspect of real-time processing. Let us recall that fuzzy regression generalizes the concept of classical (numeric) regression in the sense of bringing additional capabilities that allow the model to deal with fuzzy (granular) data. We show that a convex hull method provides a useful vehicle to reduce computing time, which becomes of particular relevance in case of real-time data analysis. Our objective is to develop an efficient real-time fuzzy regression analysis based on the use of convex hull, specifically a Beneath-Beyond algorithm. In this algorithm, the re-construction of convex hull edges depends on incoming vertices while a re-computing procedure can be realized in real-time. We demonstrate the use of the developed enhancement to application to unit performance assessment and air pollution data. An important role of convex hull is contrasted with the limitations of linear programming used in the "standard" regression. © 2010 Elsevier B.V. All rights reserved.

    DOI

  • Some properties of T-independent fuzzy variables

    Shuming Wang, Junzo Watada

    MATHEMATICAL AND COMPUTER MODELLING   53 ( 5-6 ) 970 - 984  2011.03

     View Summary

    T-independence of fuzzy variables is a more general concept than the classical independence. The objective of this study is to deal with some new properties of T-independent fuzzy variables. First of all, for any general t-norm, some criteria of T-independence are discussed for fuzzy variables under possibility, necessity and credibility measures. Subsequently, on the basis of left continuous t-norms, some formulas are derived on the "max" and "min" operations of the T-independent fuzzy variables in possibility distribution and in expectation. Finally, making use of continuous Archimedean t-norms, several convergence properties are discussed for T-independent fuzzy variables in credibility and in expectation, respectively, and some laws of large numbers are proved as well. (C) 2010 Elsevier Ltd. All rights reserved.

    DOI

  • Some properties of T-independent fuzzy variables

    Shuming Wang, Junzo Watada

    MATHEMATICAL AND COMPUTER MODELLING   53 ( 5-6 ) 970 - 984  2011.03

     View Summary

    T-independence of fuzzy variables is a more general concept than the classical independence. The objective of this study is to deal with some new properties of T-independent fuzzy variables. First of all, for any general t-norm, some criteria of T-independence are discussed for fuzzy variables under possibility, necessity and credibility measures. Subsequently, on the basis of left continuous t-norms, some formulas are derived on the "max" and "min" operations of the T-independent fuzzy variables in possibility distribution and in expectation. Finally, making use of continuous Archimedean t-norms, several convergence properties are discussed for T-independent fuzzy variables in credibility and in expectation, respectively, and some laws of large numbers are proved as well. (C) 2010 Elsevier Ltd. All rights reserved.

    DOI

  • Some properties of T-independent fuzzy variables

    Shuming Wang, Junzo Watada

    Mathematical and Computer Modelling   53 ( 5-6 ) 970 - 984  2011.03

     View Summary

    T-independence of fuzzy variables is a more general concept than the classical independence. The objective of this study is to deal with some new properties of T-independent fuzzy variables. First of all, for any general t-norm, some criteria of T-independence are discussed for fuzzy variables under possibility, necessity and credibility measures. Subsequently, on the basis of left continuous t-norms, some formulas are derived on the "max" and "min" operations of the T-independent fuzzy variables in possibility distribution and in expectation. Finally, making use of continuous Archimedean t-norms, several convergence properties are discussed for T-independent fuzzy variables in credibility and in expectation, respectively, and some laws of large numbers are proved as well. © 2010 Elsevier Ltd.

    DOI

  • Solving bilevel quadratic programming problems and its application

    Shamshul Bahar Yaakob, Junzo Watada

    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)   6883 ( 3 ) 187 - 196  2011

     View Summary

    Bilevel programming is a powerful method recently developed to tackle multi variable and double layer programming problems. These problems appear everywhere especially in the industrial, supply-chain management, and marketing programming applications. In this study, a novel approach was proposed to solve the bilevel quadratic programming problems. The proposed approach named ergodic branch-and-bound method, respectively solving small and large variable-space problems. It's perform better accuracy and computing efficiency compared with traditional approaches even when tackling the non-linear (quadratic) bilevel problems which cannot be solved sometimes by the conventional methods. Furthermore, a logistic distribution centres' application was introduced as an example to present our new proposed approach. The application results indicated that the proposed approach is applicable and advantageous. © 2011 Springer-Verlag.

    DOI

  • Statistic Test on Fuzzy Portfolio Selection Model

    Pei-Chun Lin, Junzo Watada, Berlin Wu

    IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS (FUZZ 2011)     1103 - 1110  2011

     View Summary

    Markowitz's mean-variance model is based on probability distribution functions which have known or were assumed as some kinds of probability distribution functions. When our data are vague, we can't know the underlying distribution functions. The objective of our research was to develop a method of decision making to solve portfolio selection model by statistic test. We used central point and radius to determine the fuzzy portfolio selection model and statistic test. Empirical studies were presented to illustrate the risk of fuzzy portfolio selection model with interval values. We can conclude that it is more explicit to know the risk of portfolio selection model. According to statistic test, we can get a stable expected return and low risk investment in different choose K.

    DOI

  • Robust color image segmentation by Karhunen-Loeve transform based Otsu multi-thresholding and K-Means Clustering

    Chenxue Wang, Junzo Watada

    Proceedings - 2011 5th International Conference on Genetic and Evolutionary Computing, ICGEC 2011     377 - 380  2011

     View Summary

    In this paper, a novel fast approach is proposed to achieve image segmentation in color image. This method helps to refine the foreground regions and achieves the goal of robust color image segmentation throw the following four steps. First, modified Karhunen-Loeve transform is performed to reduce the redundant component, thus selecting the most important part of the color images. Second, a multi-threshold Otsu method is carried out to select the best thresholds from image histogram. Thereby, the conventional Otsu method has been extended from gray level to color level. Third, improved Sobel edge detection is added to enhance the weight of edge detail of the foreground image. Finally, a K-Means Clustering is used to merge the over-segmented regions. Experimental results prove that this method has a good performance even when the color image has a complicated structure in the background. © 2011 IEEE.

    DOI

  • Re-Scheduling the Unit Commitment Problem in Fuzzy Environment

    Bo Wang, You Li, Junzo Watada

    IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS (FUZZ 2011)     1090 - 1095  2011

     View Summary

    The conventional prediction of future power demands are always made based on the historical data. However, the real power demands are affected by many other factors as weather, temperature and unexpected emergencies. The use of historical information alone cannot well predict real future demands. In this study, the experts' opinions from related fields are taken into consideration. To deal the uncertainty of historical data and imprecise experts' opinions, we employ fuzzy variables to better characterize the forecasted future power loads. The conventional unit commitment problem (UCP) is updated here by considering the spinning reserve costs in a fuzzy environment. As the solution, we proposed a heuristic algorithm called local convergence averse binary particle swarm optimization (LCA-PSO) to solve the UCP. The proposed model and algorithm are used to analyze several test systems. The comparisons between the proposed algorithm and the conventional approaches show that the LCA-PSO performs better in finding the optimal solutions.

    DOI

  • Possibilistic Programming Decision Making in Modality Perspective

    Arbaiy Nureize, Junzo Watada

    2011 IEEE INTERNATIONAL CONFERENCE ON INDUSTRIAL ENGINEERING AND ENGINEERING MANAGEMENT (IEEM)     709 - 713  2011

     View Summary

    The decision making faces a number of inherent uncertainties. The uncertainty of parameters of a model comes from vagueness and ambiguity included in the model structure and information. In this paper we present the decision model from the perspective of possibilistic programming to treat properly uncertainties in the decision making. The proposed concept plays a pivotal role in building fuzzy linear programming model, which is exposed with various types of uncertainties. The treatment of vagueness and ambiguity is given and a modality approach is used to solve the fuzzy linear program. An illustrative example explains the proposed model.

    DOI

  • Particle filter-based height estimation in human tracking

    Zhenyuan Xu, Junzo Watada, Zalili Binti Musa

    Proceedings - 2011 5th International Conference on Genetic and Evolutionary Computing, ICGEC 2011     385 - 388  2011

     View Summary

    Today, high quality image processing is required in security and surveillance systems. These systems must not only track the motions of humans, but they must also, in some situations, measure features such as height and weight. Few methods have been proposed for height surveying. Some studies show that an infrared ray technique can survey the height of a human, but the equipment required is complicated. The objective of this paper is to build a mathematical model and method for height surveying. This human tracking method can mark humans' size in a picture so that, if we put this picture in a frame of axes, we can calculate the human's/object's height or other features. To obtain more accurate height of an object, we need a method to measure more exact results. Combining tracking/detecting methods with a particle filter provides great accuracy for human tracking. © 2011 IEEE.

    DOI

  • Multi-level Multi-Objective Decision Problem through Fuzzy Random Regression based Objective Function

    Nureize Arbaiy, Junzo Watada

    IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS (FUZZ 2011)     557 - 563  2011

     View Summary

    A multi-level decision making problem confronts several issues especially in coordinating decision in hierarchic processes and in compromising conflicting objectives for each decision level. Therefore, its mathematical model plays a pivotal role in solving such problem, and is influencing to the final result. However, it is sometimes difficult to estimate the coefficients of objective functions of the model in real situations specifically when the statistical data contain random and fuzzy information. Thus, decision making scheme should provide an appropriate method to handle the presence of such uncertainties. Hence, this paper proposes a fuzzy random regression method to estimate the coefficients value for the objective functions of multi-level multi-objective model. The algorithm is constructed to obtain a satisfaction solution, which fulfills at least weak Pareto optimality. A numerical example illustrates the proposed solution procedure.

    DOI

  • Learning with Imbalanced Datasets using Fuzzy ARTMAP-based Neural Network Models

    Shing Chiang Tan, Junzo Watada, Zuwarie Ibrahim, Marzuki Khalid, Lee Wen Jau, Lim Chun Chew

    IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS (FUZZ 2011)     1084 - 1089  2011

     View Summary

    One of the main difficulties in real-world data classification and analysis tasks is that the data distribution can be imbalanced. In this paper, a variant of the supervised learning neural network from the Adaptive Resonance Theory (ART) family, i.e., Fuzzy ARTMAP (FAM) which is equipped with a conflict-resolving facility, is proposed to classify an imbalanced dataset that represents a real problem in the semiconductor industry. The FAM model is combined with the Dynamic Decay Adjustment (DDA) algorithm to form a hybrid FAMDDA network. The classification results of FAM and FAMDDA are presented, compared, and analyzed using several classification metrics. The outcomes positively indicate the effectiveness of the proposed FAMDDA network in undertaking classification problems with imbalanced datasets.

    DOI

  • Fuzzy Game-based Real Option Analysis in competitive investment situation

    Tanatch Tangsajanaphakul, Junzo Watada

    Proceedings - 2011 5th International Conference on Genetic and Evolutionary Computing, ICGEC 2011     381 - 384  2011

     View Summary

    In real option pricing, it is impractical to assume the net present value of expected cash flow payoff as an exact number because it is a forecasted vague one. The price can be defined as a fuzzy number to express its estimated uncertain values and the Binomial Tree is used to price a real option. A modified pricing approach to real options is thus proposed to transform the forecasted uncertain values evaluated by experts into some normal fuzzy numbers. Futthermore, Fuzzy Game is employed to find optimal strategy. The paper's objective is to propose the method that fulfills the lacking competitive view in investment decision making. The approach consistes of the combination of Real Option Analysis and Game Theory. The integration of these two methods helps a decision maker to view uncertainty of the project from competition perspective. A real investment case is given to illustrate the validity of the proposed approach. © 2011 IEEE.

    DOI

  • Decomposition of Term-Document Matrix Representation for Clustering Analysis

    Jianxiong Yang, Junzo Watada

    IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS (FUZZ 2011)     976 - 983  2011

     View Summary

    Latent Semantic Indexing (LSI) is an information retrieval technique using a low-rank singular value decomposition (SVD) of term-document matrix. The aim of this method is to reduce the matrix dimension by finding a pattern in document collection with concurrently referring terms. The methods are implemented to calculate the weight of term-document in vector space model (VSM) for document clustering using fuzzy clustering algorithm. LSI is an attempt to exploit the underlying semantic structure of word usage in documents. During the query-matching phase of LSI, a user&apos;s query is first projected into the term-document space, and then compared to all terms and documents represented in the vector space. Using some similarity measure, the nearest (most relevant) terms and documents are identified and returned to the user. The current LSI query-matching method requires computing the similarity measure about the query of every term and document in the vector space. In this paper, the Maximal Tree Algorithm is used within a recent LSI implementation to mitigate the computational time and computational complexity of query matching. The Maximal Tree data structure stores the term and document vectors in such a way that only those terms and documents are most likely qualified as the nearest neighbor to the query will be examined and retrieved. In a word, this novel algorithm is suitable for improving the accuracy of data miners.

    DOI

  • Building models based on environment with hybrid uncertainty

    Watada, J

    PLENARY TALK Modeling, Simulation and Applied Optimization (ICMSAO), 2011 4th International Conference on     1 - 10  2011

    DOI

  • Adoption of Hierarchical Structure for Web Document Analysis in Knowledge Management System

    R. Mohamed, J. Watada

    2011 IEEE INTERNATIONAL CONFERENCE ON INDUSTRIAL ENGINEERING AND ENGINEERING MANAGEMENT (IEEM)     659 - 663  2011

     View Summary

    The objective of this paper is to analyze a web structure by means of using evidential reasoning to logical hierarchy structure. During the searching on the web, the search engine will return a set of web documents. But some web documents do not fit what we are looking for. The targeted documents are called relevant document, and the rests are irrelevant documents. Our focus is placed on the web document structure and link analysis. The web documents are grouped in an appropriate label and organized in logical hierarchy structure. The theorems proposed by Watada will employed to analyze the value of concepts or events in logical hierarchy structure according to belief and plausibility functions. From these values "influence events" can be determining when an irrelevant document is included in the web document about Tourism Management.

    DOI

  • A Real-Time Analysis of Granular Information: Some Initial Thoughts on a Convex Hull-based Fuzzy Regression Approach

    Azizul Azhar Ramli, Witold Pedrycz, Junzo Watada, Nureize Arbaiy

    IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS (FUZZ 2011)     2851 - 2858  2011

     View Summary

    Regression models are well known and widely used as one of the important categories of models in system modeling. In this paper, we extend the concept of fuzzy regression in order to handle real-time implementation of data analysis of information granules. An ultimate objective of this study is to develop a hybrid of a genetically-guided clustering algorithm called genetic algorithm-Fuzzy C-Means (GA-FCM) and a convex hull-based fuzzy regression approach being regarded as a potential solution to the formation of information granules. It is anticipated that the setting of Granular Computing will help us reduce the computing time, especially in case of real-time data analysis, as well as an overall computational complexity. We propose an efficient real-time granular fuzzy regression analysis based on the convex hull approach in which a Beneath-Beyond algorithm is employed to design a convex hull. In the proposed design setting, we emphasize a pivotal role of the convex hull approach, which becomes crucial in alleviating limitations of linear programming manifesting in system modeling.

    DOI

  • A mobile camera tracking system using GbLN-PSO with an adaptive window

    Zalili Musa, Rohani Abu Bakar, Junzo Watada

    Proceedings - CIMSim 2011: 3rd International Conference on Computational Intelligence, Modelling and Simulation     259 - 264  2011

     View Summary

    The availability of high quality and inexpensive video camera, as well as the increasing need for automated video analysis is leading towards a great deal of interest in numerous applications. However the video tracking systems is still having many open problems. Thus, some of research activities in a video tracking system are still being explored. Generally, most of the researchers are used a static camera in order to track an object motion. However, the use of a static camera system for detecting and tracking the motion of an object is only capable for capturing a limited view. Therefore, to overcome the above mentioned problem in a large view space, researcher may use several cameras to capture images. Thus, the cost will increases with the number of cameras. To overcome the cost increment a mobile camera is employed with the ability to track the wide field of view in an environment. Conversely, mobile camera technologies for tracking applications have faced several problems
    simultaneous motion (when an object and camera are concurrently movable), distinguishing objects in occlusion, and dynamic changes in the background during data capture. In this study we propose a new method of Global best Local Neighborhood Oriented Particle Swarm Optimization (GbLN-PSO) to address these problems. The advantages of tracking using GbLN-PSO are demonstrated in experiments for intelligent human and vehicle tracking systems in comparison to a conventional method. The comparative study of the method is provided to evaluate its capabilities at the end of this paper. © 2011 IEEE.

    DOI

  • Fuzzy Goal Programming for Multi-level Multi-objective Problem: An Additive Model

    Nureize Arbaiy, Junzo Watada

    SOFTWARE ENGINEERING AND COMPUTER SYSTEMS, PT 2   180   81 - 95  2011

     View Summary

    The coordination of decision authority is noteworthy especially in a complex multi-level structured organization, which faces multi-objective problems to achieve overall organization targets. However, the standard formulation of mathematical programming problems assumes that a single decision maker made the decisions. Nevertheless it should be appropriate to establish the formulations of mathematical models based on multi-level programming method embracing multiple objectives. Yet, it is realized that sometimes estimating the coefficients of objective functions in the multi-objective model are difficult when the statistical data contain random and fuzzy information. Hence, this paper proposes a fuzzy goal programming additive model, to solve a multi-level multi-objective problem in a fuzzy environment, which can attain a satisfaction solution. A numerical example of production planning problem illustrates the proposed solution approach and highlights its advantages that consider the inherent uncertainties in developing the multi-level multi-objective model.

    DOI

  • Building a Fuzzy Multi-objective Portfolio Selection model with Distinct Risk Measurements

    You Li, Bo Wang, Junzo Watada

    IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS (FUZZ 2011)     1096 - 1102  2011

     View Summary

    Based on portfolio selection theory, this study proposes an improved fuzzy multi-objective model that can evaluate the invest risk exactly and increase the probability of obtaining the expected return. In building the model, fuzzy Value-at-Risk (VaR) is used to evaluate the exact future risk, in term of loss. The VaR can directly reflect the greatest loss of a selection case under a given confidence level. On the other hand, variance is utilized to make the selection more stable. This model can provide investors with more significant information in decision-making. To better solve this model, an improved particle swarm optimization algorithm is designed to mitigate the conventional local convergence problem. Finally, the proposed model and algorithm are exemplified by some numerical examples. Experiment results show that the model and algorithm are effective in solving the multi-objective portfolio selection problem.

    DOI

  • Operating Enzyme-based OR and AND Logic Gates with molecular signals,

    Yu-yi Chu, Ikno KIM, Junzo WATADA, Jui-yu Wu

    Proceedings, KES-IDT2011 held at Pireus, Greece on    2011

  • Building multi-attribute decision model based on Kansei information in environment with hybrid uncertainty

    Junzo Watada, Nureize Arbaiy

    Smart Innovation, Systems and Technologies   10   103 - 112  2011

     View Summary

    The objective of this paper is to build multi attribute decision model considering Kansei information in hybrid uncertain environment. First, fuzzy random variable is explained to deal with the models in hybrid uncertain environment. Second, using fuzzy random variables, linear regression model (FRRM) is formulated. Third, multi-attribute decision model (MADM) is built based on linear regression model. Finally, multi-attribute decision model is presented in presence of Kansei information given by experts in an environment with hybrid uncertainty involving both randomness and fuzziness. © Springer-Verlag Berlin Heidelberg 2011.

    DOI

  • A Service Cost Optimization Approach to Supply Balance of Sustainable Power Generation,

    Junzo WATADA, Yu-Lien Tai, Yingru Wang, Jaeseok Choi, Mitsushige Shiota

    Proceedings, PICMET2011 (IEEE-TEM), held at Portland on    2011

  • A new MOPSO to solve a multi-objective portfolio selection model with fuzzy value-at-risk

    Bo Wang, You Li, Junzo Watada

    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)   6883 ( 3 ) 217 - 226  2011

     View Summary

    This study proposes an novel fuzzy multi-objective model that can evaluate the invest risk properly and increase the probability of obtaining the expected return. In building the model, fuzzy Value-at-Risk is used to evaluate the exact future risk, in term of loss. And, variance is utilized to make the selection more stable. This model can provide investors with more significant information in decision-making. To solve this model, a new Pareto-optimal set based multi-objective particle swarm optimization algorithm is designed to obtain better solutions among the Pareto-front. At the end of this study, the proposed model and algorithm are exemplified by one numerical example. Experiment results show that the model and algorithm are effective in solving the multi-objective portfolio selection problem. © 2011 Springer-Verlag.

    DOI

  • Building a memetic algorithm based support vector machine for imbalaced classification

    Wu Mingnan, Junzo Watada, Zuwarie Ibrahim, Marzuki Khalid

    Proceedings - 2011 5th International Conference on Genetic and Evolutionary Computing, ICGEC 2011     389 - 392  2011

     View Summary

    Classification analysis is one of core research topics in pattern recognition field. According to the distribution of samples, algorithms like artificial network (ANN) and support vector machine (SVM) have been proposed to perform binary classification. But these traditional classification algorithms hardly work well for imbalanced dataset. In this study, a novel model on the basis of memetic algorithm (MA) and support vector machine (SVM) is proposed to perform the classification for large imbalanced dataset. It is named MSVC (memetic support vector classification) model. Memetic Algorithm is recently proposed and used as a heuristic framework for the large imbalanced classification. Because of the high performance of SVM in balanced binary classification, support vector classification (SVC) is combined with MA to improve the classification accuracy. G-mean is used to check the final result. Compared with some conventional models, the results showed that this model is a proper alternative for imbalanced dataset classification, and it expends the applications of memetic algorithm. © 2011 IEEE.

    DOI

  • Solving bilevel quadratic programming problems and its application

    Shamshul Bahar Yaakob, Junzo Watada

    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)   6883 ( 3 ) 187 - 196  2011

     View Summary

    Bilevel programming is a powerful method recently developed to tackle multi variable and double layer programming problems. These problems appear everywhere especially in the industrial, supply-chain management, and marketing programming applications. In this study, a novel approach was proposed to solve the bilevel quadratic programming problems. The proposed approach named ergodic branch-and-bound method, respectively solving small and large variable-space problems. It's perform better accuracy and computing efficiency compared with traditional approaches even when tackling the non-linear (quadratic) bilevel problems which cannot be solved sometimes by the conventional methods. Furthermore, a logistic distribution centres' application was introduced as an example to present our new proposed approach. The application results indicated that the proposed approach is applicable and advantageous. © 2011 Springer-Verlag.

    DOI

  • Probabilistic Reliability Evaluation of Interconnecting Power Systems Including Wind Turbine Generators,

    Jeongje Park, Taegon Oh, Kyeonghee Cho, Jaeseok Choi, Junzo WATADA

    IJICIC,, IF=2.791 (ISSN 1349-4198)   8 ( 8 ) 5797 - 5808  2011

  • Evaluation Criteria Analysis in Selecting an Online Securities Trading System by Brokerage Firms in Taiwan,

    Shih-Tong Lu, Neng-Chieh Liu, Yao-Feng Chang, Junzo WATADA

    ICIC EL,, (ISSN 1881-803X)   5 ( 11 ) 4033 - 4039  2011

  • Rough Set Based Optimization for Data Mining : An Improved Fuzzy Clustering Approach

    YANG Jianxiong, WATADA Junzo

    SICE JCMSI   5 ( 5 ) 210 - 217  2011

     View Summary

    The objective of this paper is to provide an improved fuzzy clustering approach to data mining. The method consists of fuzzy clustering and rough set model that together deal with the uncertainty of data. To describe the proposed method, the rough set model is used to optimize the mined knowledge, then after embedding the sample data for data mining, fuzzy clustering is applied to cluster the target data by sample data to extract desired data. The process behind algorithm and its applicability are illustrated through an application of the proposed method to the knowledge mining of accident cases, and this shows that improved fuzzy clustering should have wider-ranging applications.

    DOI CiNii

  • Improving Particle Swarm Optimization Convergence with Spread and Momentum Factors,

    Shamshul Bahar Yaakob, Junzo WATADA

    JCSES,,, IF=2.791 ()    2011

  • Fuzzy Random Multi-attribute Evaluation for Oil Palm Fruit,

    Nureize binti Arbaiy, Junzo WATADA

    IJCSES,, ()    2011

  • Multi-attribute decision making in contractor selection under hybrid uncertainty

    Arbaiy Nureize, Junzo Watada

    Journal of Advanced Computational Intelligence and Intelligent Informatics   15 ( 4 ) 465 - 472  2011

     View Summary

    The successful of a construction industry project depends on contractor evaluation and selection. Further, human judgment and unknown evaluation risk make evaluation and selection increasingly complex. Such situations show that a contractor selection is influenced by multiple attributes that often have the hybrid uncertainty of fuzziness and probability. The objective of this study is therefore to propose a fuzzy random variable based multi-attribute decision scheme that enables us to solve such problems within the bounds of hybrid uncertainty by using a fuzzy random regression model. The proposed model is explained in examples and its usefulness is clarified. This decision model is facilitated in its use by evaluating alternatives and enables us to indicate the optimum choice in the presence of hybrid uncertainty.

    DOI

  • Reliability enhancement of power systems through a mean?variance approach

    Shamshul Bahar Yaakob, Junzo Watada, Tsuguhiro Takahashi, Tatsuki Okamoto in

    Neural Computing and Applications, September ()   21 ( 6 ) 1363 - 1373  2011

    DOI

  • Multi-objective Top-Down Decision Making through Additive Fuzzy Goal Programming,

    Nureize ARBAIY, Junzo WATADA

    SICE, JCMSI (ISSN, 18824889)   5 ( 2 ) 63 - 69  2011

    DOI

  • A Database for a New Fuzzy Probability Distribution Function and Its Application

    Peichun Lin, Junzo Watada, Berlin Wu

    International Journal of Innovative Management, Information & Production (IMIP) ()   2 ( 2 ) 1 - 7  2011

  • Fuzzy robust regression model by possibility maximization

    Yoshiyuki Yabuuchi, Junzo Watada

    Journal of Advanced Computational Intelligence and Intelligent Informatics   15 ( 4 ) 479 - 484  2011

     View Summary

    Since management and economic systems are complex, it is hard to handle data obtained in management and economic areas. Hitherto, in the fields, much research has focused on the structure and analysis of such data. H. Tanaka et al. proposed a fuzzy regression model to illustrate the potential possibilities inherent in the target system. J. C. Bezdek proposed a switching regression model based on a fuzzy clustering model to separate mixed samples coming from plural latent systems and apply regression models to the groups of samples coming from each system. It is hard to illustrate a rough and moderate possibility of the target system. In this paper, to deal with the possibility of a social system, we propose a new fuzzy robust regression model.

    DOI

  • Short-term Power Load Forecasting Method by Radial-basis-function Neural Network with Support Vector Machine Model,

    Jiliang Xue, Junzo WATADA

    ICIC Express Letters, (ISSN 1881-803X)   5 ( 5 ) 1523 - 1528  2011

  • Prediction of Tick-wise price fluctuations for Rough Sets,

    Yoshiyuki MATSUMOTO, Junzo WATADA

    JACIII, (ISSN : 1343-0130)   5 ( 4 ) 438 - 448  2011

  • Supply Balance Optimization of Sustainable Power Generation from Service Cost Perspective,

    Junzo WATADA, Yingru Wang, Yu-Lien Tai, Jaeseok Choi, Mitsushige Shiota

    International Journal of Intelligent Technologies and Applied Statistics, ()   4 ( 2 ) 221 - 243  2011

  • A hybrid particle swarm optimization approach and its application to solving portfolio selection problems

    Shamshul Bahar Yaakob, Junzo Watada

    Journal of Advanced Computational Intelligence and Intelligent Informatics   15 ( 4 ) 473 - 478  2011

     View Summary

    In modern portfolio theory, the basic topic is how to construct a diversified portfolio of financial securities to improve trade-offs between risk and return. The objective of this paper is to apply a heuristic algorithm using Particle Swarm Optimization (PSO) to the portfolio selection problem. PSO makes the search algorithm efficient by combining a local search method through self-experience with the global search method through neighboring experience. PSO attempts to balance the exploration-exploitation tradeoff that achieves efficiency and accuracy of optimization. In this paper, a newly obtained approach is proposed by making simple modifications to the standard PSO: the velocity is controlled and the mutation operator of Genetic Algorithms (GA) is added to solve a stagnation problem. Our adaptation and implementation of the PSO search strategy are applied to portfolio selection. Results of typical applications demonstrate that the Velocity Control Hybrid PSO (VC-HPSO) proposed in this study effectively finds optimum solution to portfolio selection problems. Results also show that our proposedmethod is a viable approach to portfolio selection.

    DOI

  • Fuzzy Clustering Analysis of Data Mining: Application to An Accident Mining System,

    Jianxiong Yang, Junzo WATADA

    IJICIC, IF=2.791 (ISSN 1349-4198)   8 ( 8 ) 5715 - 5724  2011

  • A Reliability Enhancement for A Traffic Signal Lights System Through A Mean-variance Approach,

    Shamshul BAHAR YAAKOB, Junzo WATADA

    IJICIC,, IF=2.791 (ISSN 1349-4198)   8 ( 8 ) 5835 - 5845  2011

  • An Ant Ccolony System for Solving DNA Sequence Dedign Problem in DNA Computing,

    Farhaana Yakop, Amar Faiz Zainal Abidin, Zuwairie Ibrahim, Zulkifli Md. Yusof, Marzuki Khalid, N. Mokhtar, Junzo WATADA

    International Journal of Innovative Computing, Information and Control (IJICIC,) ()   8 ( 10(B) ) 7329 - 7339  2011

  • Statistic Test on Fuzzy Portfolio Selection Model

    Pei-Chun Lin, Junzo Watada, Berlin Wu

    IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS (FUZZ 2011)     1103 - 1110  2011

     View Summary

    Markowitz's mean-variance model is based on probability distribution functions which have known or were assumed as some kinds of probability distribution functions. When our data are vague, we can't know the underlying distribution functions. The objective of our research was to develop a method of decision making to solve portfolio selection model by statistic test. We used central point and radius to determine the fuzzy portfolio selection model and statistic test. Empirical studies were presented to illustrate the risk of fuzzy portfolio selection model with interval values. We can conclude that it is more explicit to know the risk of portfolio selection model. According to statistic test, we can get a stable expected return and low risk investment in different choose K.

    DOI

  • Robust color image segmentation by Karhunen-Loeve transform based Otsu multi-thresholding and K-Means Clustering

    Chenxue Wang, Junzo Watada

    Proceedings - 2011 5th International Conference on Genetic and Evolutionary Computing, ICGEC 2011     377 - 380  2011

     View Summary

    In this paper, a novel fast approach is proposed to achieve image segmentation in color image. This method helps to refine the foreground regions and achieves the goal of robust color image segmentation throw the following four steps. First, modified Karhunen-Loeve transform is performed to reduce the redundant component, thus selecting the most important part of the color images. Second, a multi-threshold Otsu method is carried out to select the best thresholds from image histogram. Thereby, the conventional Otsu method has been extended from gray level to color level. Third, improved Sobel edge detection is added to enhance the weight of edge detail of the foreground image. Finally, a K-Means Clustering is used to merge the over-segmented regions. Experimental results prove that this method has a good performance even when the color image has a complicated structure in the background. © 2011 IEEE.

    DOI

  • Re-Scheduling the Unit Commitment Problem in Fuzzy Environment

    Bo Wang, You Li, Junzo Watada

    IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS (FUZZ 2011)     1090 - 1095  2011

     View Summary

    The conventional prediction of future power demands are always made based on the historical data. However, the real power demands are affected by many other factors as weather, temperature and unexpected emergencies. The use of historical information alone cannot well predict real future demands. In this study, the experts' opinions from related fields are taken into consideration. To deal the uncertainty of historical data and imprecise experts' opinions, we employ fuzzy variables to better characterize the forecasted future power loads. The conventional unit commitment problem (UCP) is updated here by considering the spinning reserve costs in a fuzzy environment. As the solution, we proposed a heuristic algorithm called local convergence averse binary particle swarm optimization (LCA-PSO) to solve the UCP. The proposed model and algorithm are used to analyze several test systems. The comparisons between the proposed algorithm and the conventional approaches show that the LCA-PSO performs better in finding the optimal solutions.

    DOI

  • Possibilistic Programming Decision Making in Modality Perspective

    Arbaiy Nureize, Junzo Watada

    2011 IEEE INTERNATIONAL CONFERENCE ON INDUSTRIAL ENGINEERING AND ENGINEERING MANAGEMENT (IEEM)     709 - 713  2011

     View Summary

    The decision making faces a number of inherent uncertainties. The uncertainty of parameters of a model comes from vagueness and ambiguity included in the model structure and information. In this paper we present the decision model from the perspective of possibilistic programming to treat properly uncertainties in the decision making. The proposed concept plays a pivotal role in building fuzzy linear programming model, which is exposed with various types of uncertainties. The treatment of vagueness and ambiguity is given and a modality approach is used to solve the fuzzy linear program. An illustrative example explains the proposed model.

    DOI

  • Particle filter-based height estimation in human tracking

    Zhenyuan Xu, Junzo Watada, Zalili Binti Musa

    Proceedings - 2011 5th International Conference on Genetic and Evolutionary Computing, ICGEC 2011     385 - 388  2011

     View Summary

    Today, high quality image processing is required in security and surveillance systems. These systems must not only track the motions of humans, but they must also, in some situations, measure features such as height and weight. Few methods have been proposed for height surveying. Some studies show that an infrared ray technique can survey the height of a human, but the equipment required is complicated. The objective of this paper is to build a mathematical model and method for height surveying. This human tracking method can mark humans' size in a picture so that, if we put this picture in a frame of axes, we can calculate the human's/object's height or other features. To obtain more accurate height of an object, we need a method to measure more exact results. Combining tracking/detecting methods with a particle filter provides great accuracy for human tracking. © 2011 IEEE.

    DOI

  • Multi-level Multi-Objective Decision Problem through Fuzzy Random Regression based Objective Function

    Nureize Arbaiy, Junzo Watada

    IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS (FUZZ 2011)     557 - 563  2011

     View Summary

    A multi-level decision making problem confronts several issues especially in coordinating decision in hierarchic processes and in compromising conflicting objectives for each decision level. Therefore, its mathematical model plays a pivotal role in solving such problem, and is influencing to the final result. However, it is sometimes difficult to estimate the coefficients of objective functions of the model in real situations specifically when the statistical data contain random and fuzzy information. Thus, decision making scheme should provide an appropriate method to handle the presence of such uncertainties. Hence, this paper proposes a fuzzy random regression method to estimate the coefficients value for the objective functions of multi-level multi-objective model. The algorithm is constructed to obtain a satisfaction solution, which fulfills at least weak Pareto optimality. A numerical example illustrates the proposed solution procedure.

    DOI

  • Learning with Imbalanced Datasets using Fuzzy ARTMAP-based Neural Network Models

    Shing Chiang Tan, Junzo Watada, Zuwarie Ibrahim, Marzuki Khalid, Lee Wen Jau, Lim Chun Chew

    IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS (FUZZ 2011)     1084 - 1089  2011

     View Summary

    One of the main difficulties in real-world data classification and analysis tasks is that the data distribution can be imbalanced. In this paper, a variant of the supervised learning neural network from the Adaptive Resonance Theory (ART) family, i.e., Fuzzy ARTMAP (FAM) which is equipped with a conflict-resolving facility, is proposed to classify an imbalanced dataset that represents a real problem in the semiconductor industry. The FAM model is combined with the Dynamic Decay Adjustment (DDA) algorithm to form a hybrid FAMDDA network. The classification results of FAM and FAMDDA are presented, compared, and analyzed using several classification metrics. The outcomes positively indicate the effectiveness of the proposed FAMDDA network in undertaking classification problems with imbalanced datasets.

    DOI

  • Fuzzy Game-Based Real Option Analysis in Competitive Investment Situation

    Tangsajanaphakul, T, Watada, J

    Genetic and Evolutionary Computing (ICGEC), 2011 Fifth International Conference on     381 - 384  2011

    DOI

  • Decomposition of term-document matrix representation for clustering analysis

    Jianxiong Yang, Watada, J

    Fuzzy Systems (FUZZ), 2011 IEEE International Conference on     976 - 983  2011

    DOI

  • Building models based on environment with hybrid uncertainty

    Junzo Watada

    2011 4th International Conference on Modeling, Simulation and Applied Optimization, ICMSAO 2011     1 - 10  2011

     View Summary

    The objective of this paper is to build several models considering hybrid uncertain environment. First, fuzzy random variable is explained to deal with the models in hybrid uncertain environment. Second, using fuzzy random variables, linear regression model (FRRM) is formulated. Third, multi-attribute decision model (MADM) is built based on linear regression model. Finally, to study facility location problems in presence of a hybrid uncertain environment involving both randomness and fuzziness. A two-stage fuzzy random facility location model with recourse (FLMR) is developed in which both the demands and costs are assumed to be fuzzy random variables. © 2011 IEEE.

    DOI

  • Adoption of Hierarchical Structure for Web Document Analysis in Knowledge Management System

    R. Mohamed, J. Watada

    2011 IEEE INTERNATIONAL CONFERENCE ON INDUSTRIAL ENGINEERING AND ENGINEERING MANAGEMENT (IEEM)     659 - 663  2011

     View Summary

    The objective of this paper is to analyze a web structure by means of using evidential reasoning to logical hierarchy structure. During the searching on the web, the search engine will return a set of web documents. But some web documents do not fit what we are looking for. The targeted documents are called relevant document, and the rests are irrelevant documents. Our focus is placed on the web document structure and link analysis. The web documents are grouped in an appropriate label and organized in logical hierarchy structure. The theorems proposed by Watada will employed to analyze the value of concepts or events in logical hierarchy structure according to belief and plausibility functions. From these values "influence events" can be determining when an irrelevant document is included in the web document about Tourism Management.

    DOI

  • A Real-Time Analysis of Granular Information: Some Initial Thoughts on a Convex Hull-based Fuzzy Regression Approach

    Azizul Azhar Ramli, Witold Pedrycz, Junzo Watada, Nureize Arbaiy

    IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS (FUZZ 2011)     2851 - 2858  2011

     View Summary

    Regression models are well known and widely used as one of the important categories of models in system modeling. In this paper, we extend the concept of fuzzy regression in order to handle real-time implementation of data analysis of information granules. An ultimate objective of this study is to develop a hybrid of a genetically-guided clustering algorithm called genetic algorithm-Fuzzy C-Means (GA-FCM) and a convex hull-based fuzzy regression approach being regarded as a potential solution to the formation of information granules. It is anticipated that the setting of Granular Computing will help us reduce the computing time, especially in case of real-time data analysis, as well as an overall computational complexity. We propose an efficient real-time granular fuzzy regression analysis based on the convex hull approach in which a Beneath-Beyond algorithm is employed to design a convex hull. In the proposed design setting, we emphasize a pivotal role of the convex hull approach, which becomes crucial in alleviating limitations of linear programming manifesting in system modeling.

    DOI

  • A mobile camera tracking system using GbLN-PSO with an adaptive window

    Zalili Musa, Rohani Abu Bakar, Junzo Watada

    Proceedings - CIMSim 2011: 3rd International Conference on Computational Intelligence, Modelling and Simulation     259 - 264  2011

     View Summary

    The availability of high quality and inexpensive video camera, as well as the increasing need for automated video analysis is leading towards a great deal of interest in numerous applications. However the video tracking systems is still having many open problems. Thus, some of research activities in a video tracking system are still being explored. Generally, most of the researchers are used a static camera in order to track an object motion. However, the use of a static camera system for detecting and tracking the motion of an object is only capable for capturing a limited view. Therefore, to overcome the above mentioned problem in a large view space, researcher may use several cameras to capture images. Thus, the cost will increases with the number of cameras. To overcome the cost increment a mobile camera is employed with the ability to track the wide field of view in an environment. Conversely, mobile camera technologies for tracking applications have faced several problems
    simultaneous motion (when an object and camera are concurrently movable), distinguishing objects in occlusion, and dynamic changes in the background during data capture. In this study we propose a new method of Global best Local Neighborhood Oriented Particle Swarm Optimization (GbLN-PSO) to address these problems. The advantages of tracking using GbLN-PSO are demonstrated in experiments for intelligent human and vehicle tracking systems in comparison to a conventional method. The comparative study of the method is provided to evaluate its capabilities at the end of this paper. © 2011 IEEE.

    DOI

  • Fuzzy Goal Programming for Multi-level Multi-objective Problem: An Additive Model

    Nureize Arbaiy, Junzo Watada

    SOFTWARE ENGINEERING AND COMPUTER SYSTEMS, PT 2   180   81 - 95  2011

     View Summary

    The coordination of decision authority is noteworthy especially in a complex multi-level structured organization, which faces multi-objective problems to achieve overall organization targets. However, the standard formulation of mathematical programming problems assumes that a single decision maker made the decisions. Nevertheless it should be appropriate to establish the formulations of mathematical models based on multi-level programming method embracing multiple objectives. Yet, it is realized that sometimes estimating the coefficients of objective functions in the multi-objective model are difficult when the statistical data contain random and fuzzy information. Hence, this paper proposes a fuzzy goal programming additive model, to solve a multi-level multi-objective problem in a fuzzy environment, which can attain a satisfaction solution. A numerical example of production planning problem illustrates the proposed solution approach and highlights its advantages that consider the inherent uncertainties in developing the multi-level multi-objective model.

    DOI

  • Building a Fuzzy Multi-objective Portfolio Selection model with Distinct Risk Measurements

    You Li, Bo Wang, Junzo Watada

    IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS (FUZZ 2011)     1096 - 1102  2011

     View Summary

    Based on portfolio selection theory, this study proposes an improved fuzzy multi-objective model that can evaluate the invest risk exactly and increase the probability of obtaining the expected return. In building the model, fuzzy Value-at-Risk (VaR) is used to evaluate the exact future risk, in term of loss. The VaR can directly reflect the greatest loss of a selection case under a given confidence level. On the other hand, variance is utilized to make the selection more stable. This model can provide investors with more significant information in decision-making. To better solve this model, an improved particle swarm optimization algorithm is designed to mitigate the conventional local convergence problem. Finally, the proposed model and algorithm are exemplified by some numerical examples. Experiment results show that the model and algorithm are effective in solving the multi-objective portfolio selection problem.

    DOI

  • Operating Enzyme-based OR and AND Logic Gates with molecular signals,

    Yu-yi Chu, Ikno KIM, Junzo WATADA, Jui-yu Wu

    Proceedings, KES-IDT2011 held at Pireus, Greece on    2011

  • Building multi-attribute decision model based on Kansei information in environment with hybrid uncertainty

    Junzo Watada, Nureize Arbaiy

    Smart Innovation, Systems and Technologies   10   103 - 112  2011

     View Summary

    The objective of this paper is to build multi attribute decision model considering Kansei information in hybrid uncertain environment. First, fuzzy random variable is explained to deal with the models in hybrid uncertain environment. Second, using fuzzy random variables, linear regression model (FRRM) is formulated. Third, multi-attribute decision model (MADM) is built based on linear regression model. Finally, multi-attribute decision model is presented in presence of Kansei information given by experts in an environment with hybrid uncertainty involving both randomness and fuzziness. © Springer-Verlag Berlin Heidelberg 2011.

    DOI

  • A Service Cost Optimization Approach to Supply Balance of Sustainable Power Generation,

    Junzo WATADA, Yu-Lien Tai, Yingru Wang, Jaeseok Choi, Mitsushige Shiota

    Proceedings, PICMET2011 (IEEE-TEM), held at Portland on    2011

  • A new MOPSO to solve a multi-objective portfolio selection model with fuzzy value-at-risk

    Bo Wang, You Li, Junzo Watada

    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)   6883 ( 3 ) 217 - 226  2011

     View Summary

    This study proposes an novel fuzzy multi-objective model that can evaluate the invest risk properly and increase the probability of obtaining the expected return. In building the model, fuzzy Value-at-Risk is used to evaluate the exact future risk, in term of loss. And, variance is utilized to make the selection more stable. This model can provide investors with more significant information in decision-making. To solve this model, a new Pareto-optimal set based multi-objective particle swarm optimization algorithm is designed to obtain better solutions among the Pareto-front. At the end of this study, the proposed model and algorithm are exemplified by one numerical example. Experiment results show that the model and algorithm are effective in solving the multi-objective portfolio selection problem. © 2011 Springer-Verlag.

    DOI

  • Building a memetic algorithm based support vector machine for imbalaced classification

    Wu Mingnan, Junzo Watada, Zuwarie Ibrahim, Marzuki Khalid

    Proceedings - 2011 5th International Conference on Genetic and Evolutionary Computing, ICGEC 2011     389 - 392  2011

     View Summary

    Classification analysis is one of core research topics in pattern recognition field. According to the distribution of samples, algorithms like artificial network (ANN) and support vector machine (SVM) have been proposed to perform binary classification. But these traditional classification algorithms hardly work well for imbalanced dataset. In this study, a novel model on the basis of memetic algorithm (MA) and support vector machine (SVM) is proposed to perform the classification for large imbalanced dataset. It is named MSVC (memetic support vector classification) model. Memetic Algorithm is recently proposed and used as a heuristic framework for the large imbalanced classification. Because of the high performance of SVM in balanced binary classification, support vector classification (SVC) is combined with MA to improve the classification accuracy. G-mean is used to check the final result. Compared with some conventional models, the results showed that this model is a proper alternative for imbalanced dataset classification, and it expends the applications of memetic algorithm. © 2011 IEEE.

    DOI

  • Solving Bilevel Quadratic Programming Problems and Its Application,

    Shamshul Bahar Yaakob, Junzo Watada

    Proceedings, 15th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems (KES 2011), Kaiserslautern, Germany, Sep. 2011     187 - 196  2011

    DOI

  • Probabilistic Reliability Evaluation of Interconnecting Power Systems Including Wind Turbine Generators,

    Jeongje Park, Taegon Oh, Kyeonghee Cho, Jaeseok Choi, Junzo WATADA

    IJICIC,, IF=2.791 (ISSN 1349-4198)   8 ( 8 ) 5797 - 5808  2011

  • Evaluation Criteria Analysis in Selecting an Online Securities Trading System by Brokerage Firms in Taiwan,

    Shih-Tong Lu, Neng-Chieh Liu, Yao-Feng Chang, Junzo WATADA

    ICIC EL,, (ISSN 1881-803X)   5 ( 11 ) 4033 - 4039  2011

  • Rough Set Based Optimization for Data Mining: An Improved Fuzzy, Clustering Approach,

    Yang Jianxiong, Junzo WATADA

    SICE, JCMSI ( ISSN : 1745-1361)   5 ( 5 ) 210 - 217  2011

    DOI

  • Improving Particle Swarm Optimization Convergence with Spread and Momentum Factors,

    Shamshul Bahar Yaakob, Junzo WATADA

    JCSES,,, IF=2.791 ()    2011

  • Fuzzy Random Multi-attribute Evaluation for Oil Palm Fruit,

    Nureize binti Arbaiy, Junzo WATADA

    IJCSES,, ()    2011

  • Decision Making in Contractor Selection under Hybrid Uncertainty,

    Nureize binti Arbaiy, Junzo WATADA

    International Journal of Computer Sciences and Engineering Systems, published by Serials Publication, India., ()    2011

  • Reliability enhancement of power systems through a mean?variance approach

    Shamshul Bahar Yaakob, Junzo Watada, Tsuguhiro Takahashi, Tatsuki Okamoto in

    Neural Computing and Applications, September ()   21 ( 6 ) 1363 - 1373  2011

    DOI

  • Multi-objective Top-Down Decision Making through Additive Fuzzy Goal Programming,

    Nureize ARBAIY, Junzo WATADA

    SICE, JCMSI (ISSN, 18824889)   5 ( 2 ) 63 - 69  2011

    DOI

  • A Database for a New Fuzzy Probability Distribution Function and Its Application

    Peichun Lin, Junzo Watada, Berlin Wu

    International Journal of Innovative Management, Information & Production (IMIP) ()   2 ( 2 ) 1 - 7  2011

  • Fuzzy Robust Regression Model by Possibility Maximization,

    Yoshiyuki YABUUCHI, Junzo WATADA

    JACIII, ()   15 ( 4 ) 479 - 484  2011

  • Short-term Power Load Forecasting Method by Radial-basis-function Neural Network with Support Vector Machine Model,

    Jiliang Xue, Junzo WATADA

    ICIC Express Letters, (ISSN 1881-803X)   5 ( 5 ) 1523 - 1528  2011

  • Prediction of Tick-wise price fluctuations for Rough Sets,

    Yoshiyuki MATSUMOTO, Junzo WATADA

    JACIII, (ISSN : 1343-0130)   5 ( 4 ) 438 - 448  2011

  • Supply Balance Optimization of Sustainable Power Generation from Service Cost Perspective,

    Junzo WATADA, Yingru Wang, Yu-Lien Tai, Jaeseok Choi, Mitsushige Shiota

    International Journal of Intelligent Technologies and Applied Statistics, ()   4 ( 2 ) 221 - 243  2011

  • A hybrid particle swarm optimization approach and its application to solving portfolio selection problems,

    Shamshul BAHAR YAAKOB, Junzo WATADA

    JACIII,, (ISSN : 1343-0130)   15 ( 4 ) 473 - 478  2011

  • Fuzzy Clustering Analysis of Data Mining: Application to An Accident Mining System,

    Jianxiong Yang, Junzo WATADA

    IJICIC, IF=2.791 (ISSN 1349-4198)   8 ( 8 ) 5715 - 5724  2011

  • A Reliability Enhancement for A Traffic Signal Lights System Through A Mean-variance Approach,

    Shamshul BAHAR YAAKOB, Junzo WATADA

    IJICIC,, IF=2.791 (ISSN 1349-4198)   8 ( 8 ) 5835 - 5845  2011

  • An Ant Ccolony System for Solving DNA Sequence Dedign Problem in DNA Computing,

    Farhaana Yakop, Amar Faiz Zainal Abidin, Zuwairie Ibrahim, Zulkifli Md. Yusof, Marzuki Khalid, N. Mokhtar, Junzo WATADA

    International Journal of Innovative Computing, Information and Control (IJICIC,) ()   8 ( 10(B) ) 7329 - 7339  2011

  • Building a memetic algorithm based support vector machine for imbalaced classification

    Wu Mingnan, Junzo Watada, Zuwarie Ibrahim, Marzuki Khalid

    Proceedings - 2011 5th International Conference on Genetic and Evolutionary Computing, ICGEC 2011     389 - 392  2011

     View Summary

    Classification analysis is one of core research topics in pattern recognition field. According to the distribution of samples, algorithms like artificial network (ANN) and support vector machine (SVM) have been proposed to perform binary classification. But these traditional classification algorithms hardly work well for imbalanced dataset. In this study, a novel model on the basis of memetic algorithm (MA) and support vector machine (SVM) is proposed to perform the classification for large imbalanced dataset. It is named MSVC (memetic support vector classification) model. Memetic Algorithm is recently proposed and used as a heuristic framework for the large imbalanced classification. Because of the high performance of SVM in balanced binary classification, support vector classification (SVC) is combined with MA to improve the classification accuracy. G-mean is used to check the final result. Compared with some conventional models, the results showed that this model is a proper alternative for imbalanced dataset classification, and it expends the applications of memetic algorithm. © 2011 IEEE.

    DOI

  • Particle filter-based height estimation in human tracking

    Zhenyuan Xu, Junzo Watada, Zalili Binti Musa

    Proceedings - 2011 5th International Conference on Genetic and Evolutionary Computing, ICGEC 2011     385 - 388  2011

     View Summary

    Today, high quality image processing is required in security and surveillance systems. These systems must not only track the motions of humans, but they must also, in some situations, measure features such as height and weight. Few methods have been proposed for height surveying. Some studies show that an infrared ray technique can survey the height of a human, but the equipment required is complicated. The objective of this paper is to build a mathematical model and method for height surveying. This human tracking method can mark humans' size in a picture so that, if we put this picture in a frame of axes, we can calculate the human's/object's height or other features. To obtain more accurate height of an object, we need a method to measure more exact results. Combining tracking/detecting methods with a particle filter provides great accuracy for human tracking. © 2011 IEEE.

    DOI

  • Decomposition of term-document matrix representation for clustering analysis

    Jianxiong Yang, Junzo Watada

    IEEE International Conference on Fuzzy Systems     976 - 983  2011

     View Summary

    Latent Semantic Indexing (LSI) is an information retrieval technique using a low-rank singular value decomposition (SVD) of term-document matrix. The aim of this method is to reduce the matrix dimension by finding a pattern in document collection with concurrently referring terms. The methods are implemented to calculate the weight of term-document in vector space model (VSM) for document clustering using fuzzy clustering algorithm. LSI is an attempt to exploit the underlying semantic structure of word usage in documents. During the query-matching phase of LSI, a user's query is first projected into the term-document space, and then compared to all terms and documents represented in the vector space. Using some similarity measure, the nearest (most relevant) terms and documents are identified and returned to the user. The current LSI query-matching method requires computing the similarity measure about the query of every term and document in the vector space. In this paper, the Maximal Tree Algorithm is used within a recent LSI implementation to mitigate the computational time and computational complexity of query matching. The Maximal Tree data structure stores the term and document vectors in such a way that only those terms and documents are most likely qualified as the nearest neighbor to the query will be examined and retrieved. In a word, this novel algorithm is suitable for improving the accuracy of data miners. © 2011 IEEE.

    DOI

  • A real-time analysis of granular information: Some initial thoughts on a convex hull-based fuzzy regression approach

    Azizul Azhar Ramli, Witold Pedrycz, Junzo Watada, Nureize Arbaiy

    IEEE International Conference on Fuzzy Systems     2851 - 2858  2011

     View Summary

    Regression models are well known and widely used as one of the important categories of models in system modeling. In this paper, we extend the concept of fuzzy regression in order to handle real-time implementation of data analysis of information granules. An ultimate objective of this study is to develop a hybrid of a genetically-guided clustering algorithm called genetic algorithm-Fuzzy C-Means (GA-FCM) and a convex hull-based fuzzy regression approach being regarded as a potential solution to the formation of information granules. It is anticipated that the setting of Granular Computing will help us reduce the computing time, especially in case of real-time data analysis, as well as an overall computational complexity. We propose an efficient real-time granular fuzzy regression analysis based on the convex hull approach in which a Beneath-Beyond algorithm is employed to design a convex hull. In the proposed design setting, we emphasize a pivotal role of the convex hull approach, which becomes crucial in alleviating limitations of linear programming manifesting in system modeling. © 2011 IEEE.

    DOI

  • Statistic test on fuzzy portfolio selection model

    Pei-Chun Lin, Junzo Watada, Berlin Wu

    IEEE International Conference on Fuzzy Systems     1103 - 1110  2011

     View Summary

    Markowitz's mean-variance model is based on probability distribution functions which have known or were assumed as some kinds of probability distribution functions. When our data are vague, we can't know the underlying distribution functions. The objective of our research was to develop a method of decision making to solve portfolio selection model by statistic test. We used central point and radius to determine the fuzzy portfolio selection model and statistic test. Empirical studies were presented to illustrate the risk of fuzzy portfolio selection model with interval values. We can conclude that it is more explicit to know the risk of portfolio selection model. According to statistic test, we can get a stable expected return and low risk investment in different choose K. © 2011 IEEE.

    DOI

  • Learning with imbalanced datasets using fuzzy ARTMAP-based neural network models

    Shing Chiang Tan, Junzo Watada, Zuwarie Ibrahim, Marzuki Khalid, Lee Wen Jau, Lim Chun Chew

    IEEE International Conference on Fuzzy Systems     1084 - 1089  2011

     View Summary

    One of the main difficulties in real-world data classification and analysis tasks is that the data distribution can be imbalanced. In this paper, a variant of the supervised learning neural network from the Adaptive Resonance Theory (ART) family, i.e., Fuzzy ARTMAP (FAM) which is equipped with a conflict-resolving facility, is proposed to classify an imbalanced dataset that represents a real problem in the semiconductor industry. The FAM model is combined with the Dynamic Decay Adjustment (DDA) algorithm to form a hybrid FAMDDA network. The classification results of FAM and FAMDDA are presented, compared, and analyzed using several classification metrics. The outcomes positively indicate the effectiveness of the proposed FAMDDA network in undertaking classification problems with imbalanced datasets. © 2011 IEEE.

    DOI

  • Re-scheduling the unit commitment problem in fuzzy environment

    Bo Wang, You Li, Junzo Watada

    IEEE International Conference on Fuzzy Systems     1090 - 1095  2011

     View Summary

    The conventional prediction of future power demands are always made based on the historical data. However, the real power demands are affected by many other factors as weather, temperature and unexpected emergencies. The use of historical information alone cannot well predict real future demands. In this study, the experts' opinions from related fields are taken into consideration. To deal the uncertainty of historical data and imprecise experts' opinions, we employ fuzzy variables to better characterize the forecasted future power loads. The conventional unit commitment problem (UCP) is updated here by considering the spinning reserve costs in a fuzzy environment. As the solution, we proposed a heuristic algorithm called local convergence averse binary particle swarm optimization (LCA-PSO) to solve the UCP. The proposed model and algorithm are used to analyze several test systems. The comparisons between the proposed algorithm and the conventional approaches show that the LCA-PSO performs better in finding the optimal solutions. © 2011 IEEE.

    DOI

  • A convex hull-based fuzzy regression to information granules problem - An efficient solution to real-time data analysis

    Azizul Azhar Ramli, Junzo Watada, Witold Pedrycz

    Communications in Computer and Information Science   180 ( 2 ) 190 - 204  2011

     View Summary

    Regression models are well known and widely used as one of the important categories of models in system modeling. In this paper, we extend the concept of fuzzy regression in order to handle real-time implementation of data analysis of information granules. An ultimate objective of this study is to develop a hybrid of a genetically-guided clustering algorithm called genetic algorithm-based Fuzzy C-Means (GA-FCM) and a convex hull-based regression approach being regarded as a potential solution to the formation of information granules. It is shown that a setting of Granular Computing helps us reduce the computing time, especially in case of real-time data analysis, as well as an overall computational complexity. We propose an efficient real-time information granules regression analysis based on the convex hull approach in which a Beneath-Beyond algorithm is employed to design sub convex hulls as well as a main convex hull structure. In the proposed design setting, we emphasize a pivotal role of the convex hull approach or more specifically the Beneath-Beyond algorithm, which becomes crucial in alleviating limitations of linear programming manifesting in system modeling. © 2011 Springer-Verlag.

    DOI

  • Re-Scheduling of Unit Commitment Based on Customers' Fuzzy Requirements for Power Reliability,

    Bo Wang, You Li, Junzo Watada

    IEICE Transactions D, .   E94-D ( 7 ) 1378 - 1385  2011

    DOI CiNii

  • An Ant Ccolony System for Solving DNA Sequence Dedign Problem in DNA Computing,

    Farhaana Yakop, Amar Faiz Zainal Abidin, Zuwairie Ibrahim, Zulkifli Md. Yusof, Marzuki Khalid, N. Mokhtar, Junzo WATADA

    IJICIC,    2011

  • Rough Set Based Optimization for Data Mining : An Improved Fuzzy Clustering Approach

    YANG Jianxiong, WATADA Junzo

    SICE JCMSI   5 ( 4 ) 210 - 217  2011

     View Summary

    The objective of this paper is to provide an improved fuzzy clustering approach to data mining. The method consists of fuzzy clustering and rough set model that together deal with the uncertainty of data. To describe the proposed method, the rough set model is used to optimize the mined knowledge, then after embedding the sample data for data mining, fuzzy clustering is applied to cluster the target data by sample data to extract desired data. The process behind algorithm and its applicability are illustrated through an application of the proposed method to the knowledge mining of accident cases, and this shows that improved fuzzy clustering should have wider-ranging applications.

    DOI CiNii

  • Ranking Method of Web Mining Based on Latent Semantic Indexing,

    Yang Jianxiong, Junzo WATADA

    SICE,    2011

  • Capacitated Two-Stage Facility Location Problem with Fuzzy Costs and Demands,

    Shuming WANG, Junzo WATADA

    International Journal of Machine Learning and Cybernetics (IJMLC) published by Springer,    2011

  • Multi-objective Top-Down Decision Making through Additive Fuzzy Goal Programming,

    Nureize ARBAIY, Junzo WATADA

    SICE,    2011

    DOI

  • A Reliability Enhancement for A Traffic Signal Lights System Through A Mean-variance Approach,

    Shamshul BAHAR YAAKOB, Junzo WATADA

    IJICIC,, IF=2.791    2011

  • Fuzzy Portfolio Selection Model with Interval Values Based on Probability Distribution Functions,

    Pei-Chun Lin, Junzo WATADA, Berlin Wu

    IJICIC,, IF=2.791    2011

  • Top-Down Multi-objective Decision Making through Fuzzy Additive Goal Programming,

    Nureize Arbaiya, JunzoWATADA

    JACIII,    2011

  • Supply Balance Optimization of Sustainable Power Generation from Service Cost Perspective,

    Junzo WATADA, Yingru Wang, Yu-Lien Tai, Jaeseok Choi, Mitsushige Shiota

    International Journal of Intelligent Technologies and Applied Statistics,    2011

  • Fuzzy Clustering Analysis of Data Mining: Application to An Accident Mining System,

    Jianxiong Yang, Junzo WATADA

    IJICIC, 2011,, IF=2.791    2011

  • Automation of DNA Computing Readout Method Based on Real-Time PCR Implemented on DNA Engine Opticon 2 System,

    Muhammad Faiz, Mohamed Saaid, Shahdan Sudin, Ismail Ibrahim, Zulkifli Md. Yusof, Kamal Khalil, Jameel Abdulla Ahmed Mukred, Moh, Saberi Mohamad, N. Mokhtar, Zuwairie Ibrahim, Junzo WATADA

    International Journal of Innovative Computing, Information, and Control,, IF=2.791    2011

  • Prediction of Tick-wise price fluctuations for Rough Sets,

    Yoshiyuki MATSUMOTO, Junzo WATADA

    JACIII,    2011

  • Fuzzy robust regression model by possibility maximization

    Yoshiyuki Yabuuchi, Junzo Watada

    Journal of Advanced Computational Intelligence and Intelligent Informatics   15 ( 4 ) 479 - 484  2011

     View Summary

    Since management and economic systems are complex, it is hard to handle data obtained in management and economic areas. Hitherto, in the fields, much research has focused on the structure and analysis of such data. H. Tanaka et al. proposed a fuzzy regression model to illustrate the potential possibilities inherent in the target system. J. C. Bezdek proposed a switching regression model based on a fuzzy clustering model to separate mixed samples coming from plural latent systems and apply regression models to the groups of samples coming from each system. It is hard to illustrate a rough and moderate possibility of the target system. In this paper, to deal with the possibility of a social system, we propose a new fuzzy robust regression model.

    DOI

  • Probabilistic Reliability Evaluation of Interconnecting Power Systems Including Wind Turbine Generators,

    Jeongje Park, Taegon Oh, Kyeonghee Cho, Jaeseok Choi, Junzo WATADA

    IJICIC,, IF=2.791    2011

  • A Hybrid Modified PSO Approach to VaR-Based Facility Location Problems with Variable Capacity in Fuzzy Random Uncertainty,

    Shuming WANG, Junzo WATADA

    Information Science, ,, IF=3.095    2011

    DOI

  • Evaluation Criteria Analysis in Selecting an Online Securities Trading System by Brokerage Firms in Taiwan,

    Shih-Tong Lu, Neng-Chieh Liu, Yao-Feng Chang, Junzo WATADA

    ICIC EL,,    2011

  • A hybrid particle swarm optimization approach and its application to solving portfolio selection problems

    Shamshul Bahar Yaakob, Junzo Watada

    Journal of Advanced Computational Intelligence and Intelligent Informatics   15 ( 4 ) 473 - 478  2011

     View Summary

    In modern portfolio theory, the basic topic is how to construct a diversified portfolio of financial securities to improve trade-offs between risk and return. The objective of this paper is to apply a heuristic algorithm using Particle Swarm Optimization (PSO) to the portfolio selection problem. PSO makes the search algorithm efficient by combining a local search method through self-experience with the global search method through neighboring experience. PSO attempts to balance the exploration-exploitation tradeoff that achieves efficiency and accuracy of optimization. In this paper, a newly obtained approach is proposed by making simple modifications to the standard PSO: the velocity is controlled and the mutation operator of Genetic Algorithms (GA) is added to solve a stagnation problem. Our adaptation and implementation of the PSO search strategy are applied to portfolio selection. Results of typical applications demonstrate that the Velocity Control Hybrid PSO (VC-HPSO) proposed in this study effectively finds optimum solution to portfolio selection problems. Results also show that our proposedmethod is a viable approach to portfolio selection.

    DOI

  • Improving Particle Swarm Optimization Convergence with Spread and Momentum Factors,

    Shamshul Bahar Yaakob, Junzo WATADA

    JCSES,,, IF=2.791    2011

  • Fuzzy Random Multi-attribute Evaluation for Oil Palm Fruit,

    Nureize binti Arbaiy, Junzo WATADA

    IJCSES,,    2011

  • Multi-attribute decision making in contractor selection under hybrid uncertainty

    Arbaiy Nureize, Junzo Watada

    Journal of Advanced Computational Intelligence and Intelligent Informatics   15 ( 4 ) 465 - 472  2011

     View Summary

    The successful of a construction industry project depends on contractor evaluation and selection. Further, human judgment and unknown evaluation risk make evaluation and selection increasingly complex. Such situations show that a contractor selection is influenced by multiple attributes that often have the hybrid uncertainty of fuzziness and probability. The objective of this study is therefore to propose a fuzzy random variable based multi-attribute decision scheme that enables us to solve such problems within the bounds of hybrid uncertainty by using a fuzzy random regression model. The proposed model is explained in examples and its usefulness is clarified. This decision model is facilitated in its use by evaluating alternatives and enables us to indicate the optimum choice in the presence of hybrid uncertainty.

    DOI

  • A SVM-RBF Method for Solving Imbalanced Data Problem,

    Lei Ding, Junzo WATADA, Lim Chun Chew, Zuwairie Ibrahim, Lee Wen Jau, Marzuki Khalid

    ICIC EL,   4 ( 6(B) )  2011

  • Building a memetic algorithm based support vector machine for imbalaced classification

    Wu Mingnan, Junzo Watada, Zuwarie Ibrahim, Marzuki Khalid

    Proceedings - 2011 5th International Conference on Genetic and Evolutionary Computing, ICGEC 2011     389 - 392  2011

     View Summary

    Classification analysis is one of core research topics in pattern recognition field. According to the distribution of samples, algorithms like artificial network (ANN) and support vector machine (SVM) have been proposed to perform binary classification. But these traditional classification algorithms hardly work well for imbalanced dataset. In this study, a novel model on the basis of memetic algorithm (MA) and support vector machine (SVM) is proposed to perform the classification for large imbalanced dataset. It is named MSVC (memetic support vector classification) model. Memetic Algorithm is recently proposed and used as a heuristic framework for the large imbalanced classification. Because of the high performance of SVM in balanced binary classification, support vector classification (SVC) is combined with MA to improve the classification accuracy. G-mean is used to check the final result. Compared with some conventional models, the results showed that this model is a proper alternative for imbalanced dataset classification, and it expends the applications of memetic algorithm. © 2011 IEEE.

    DOI

  • Fuzzy Game-based Real Option Analysis in competitive investment situation

    Tanatch Tangsajanaphakul, Junzo Watada

    Proceedings - 2011 5th International Conference on Genetic and Evolutionary Computing, ICGEC 2011     381 - 384  2011

     View Summary

    In real option pricing, it is impractical to assume the net present value of expected cash flow payoff as an exact number because it is a forecasted vague one. The price can be defined as a fuzzy number to express its estimated uncertain values and the Binomial Tree is used to price a real option. A modified pricing approach to real options is thus proposed to transform the forecasted uncertain values evaluated by experts into some normal fuzzy numbers. Futthermore, Fuzzy Game is employed to find optimal strategy. The paper's objective is to propose the method that fulfills the lacking competitive view in investment decision making. The approach consistes of the combination of Real Option Analysis and Game Theory. The integration of these two methods helps a decision maker to view uncertainty of the project from competition perspective. A real investment case is given to illustrate the validity of the proposed approach. © 2011 IEEE.

    DOI

  • A new MOPSO to solve a multi-objective portfolio selection model with fuzzy value-at-risk

    Bo Wang, You Li, Junzo Watada

    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)   6883 ( 3 ) 217 - 226  2011

     View Summary

    This study proposes an novel fuzzy multi-objective model that can evaluate the invest risk properly and increase the probability of obtaining the expected return. In building the model, fuzzy Value-at-Risk is used to evaluate the exact future risk, in term of loss. And, variance is utilized to make the selection more stable. This model can provide investors with more significant information in decision-making. To solve this model, a new Pareto-optimal set based multi-objective particle swarm optimization algorithm is designed to obtain better solutions among the Pareto-front. At the end of this study, the proposed model and algorithm are exemplified by one numerical example. Experiment results show that the model and algorithm are effective in solving the multi-objective portfolio selection problem. © 2011 Springer-Verlag.

    DOI

  • Decomposition of term-document matrix representation for clustering analysis

    Jianxiong Yang, Junzo Watada

    IEEE International Conference on Fuzzy Systems     976 - 983  2011

     View Summary

    Latent Semantic Indexing (LSI) is an information retrieval technique using a low-rank singular value decomposition (SVD) of term-document matrix. The aim of this method is to reduce the matrix dimension by finding a pattern in document collection with concurrently referring terms. The methods are implemented to calculate the weight of term-document in vector space model (VSM) for document clustering using fuzzy clustering algorithm. LSI is an attempt to exploit the underlying semantic structure of word usage in documents. During the query-matching phase of LSI, a user's query is first projected into the term-document space, and then compared to all terms and documents represented in the vector space. Using some similarity measure, the nearest (most relevant) terms and documents are identified and returned to the user. The current LSI query-matching method requires computing the similarity measure about the query of every term and document in the vector space. In this paper, the Maximal Tree Algorithm is used within a recent LSI implementation to mitigate the computational time and computational complexity of query matching. The Maximal Tree data structure stores the term and document vectors in such a way that only those terms and documents are most likely qualified as the nearest neighbor to the query will be examined and retrieved. In a word, this novel algorithm is suitable for improving the accuracy of data miners. © 2011 IEEE.

    DOI

  • A real-time analysis of granular information: Some initial thoughts on a convex hull-based fuzzy regression approach

    Azizul Azhar Ramli, Witold Pedrycz, Junzo Watada, Nureize Arbaiy

    IEEE International Conference on Fuzzy Systems     2851 - 2858  2011

     View Summary

    Regression models are well known and widely used as one of the important categories of models in system modeling. In this paper, we extend the concept of fuzzy regression in order to handle real-time implementation of data analysis of information granules. An ultimate objective of this study is to develop a hybrid of a genetically-guided clustering algorithm called genetic algorithm-Fuzzy C-Means (GA-FCM) and a convex hull-based fuzzy regression approach being regarded as a potential solution to the formation of information granules. It is anticipated that the setting of Granular Computing will help us reduce the computing time, especially in case of real-time data analysis, as well as an overall computational complexity. We propose an efficient real-time granular fuzzy regression analysis based on the convex hull approach in which a Beneath-Beyond algorithm is employed to design a convex hull. In the proposed design setting, we emphasize a pivotal role of the convex hull approach, which becomes crucial in alleviating limitations of linear programming manifesting in system modeling. © 2011 IEEE.

    DOI

  • Learning with imbalanced datasets using fuzzy ARTMAP-based neural network models

    Shing Chiang Tan, Junzo Watada, Zuwarie Ibrahim, Marzuki Khalid, Lee Wen Jau, Lim Chun Chew

    IEEE International Conference on Fuzzy Systems     1084 - 1089  2011

     View Summary

    One of the main difficulties in real-world data classification and analysis tasks is that the data distribution can be imbalanced. In this paper, a variant of the supervised learning neural network from the Adaptive Resonance Theory (ART) family, i.e., Fuzzy ARTMAP (FAM) which is equipped with a conflict-resolving facility, is proposed to classify an imbalanced dataset that represents a real problem in the semiconductor industry. The FAM model is combined with the Dynamic Decay Adjustment (DDA) algorithm to form a hybrid FAMDDA network. The classification results of FAM and FAMDDA are presented, compared, and analyzed using several classification metrics. The outcomes positively indicate the effectiveness of the proposed FAMDDA network in undertaking classification problems with imbalanced datasets. © 2011 IEEE.

    DOI

  • Building a fuzzy multi-objective portfolio selection model with distinct risk measurements

    You Li, Bo Wang, Junzo Watada

    IEEE International Conference on Fuzzy Systems     1096 - 1102  2011

     View Summary

    Based on portfolio selection theory, this study proposes an improved fuzzy multi-objective model that can evaluate the invest risk exactly and increase the probability of obtaining the expected return. In building the model, fuzzy Value-at-Risk (VaR) is used to evaluate the exact future risk, in term of loss. The VaR can directly reflect the greatest loss of a selection case under a given confidence level. On the other hand, variance is utilized to make the selection more stable. This model can provide investors with more significant information in decision-making. To better solve this model, an improved particle swarm optimization algorithm is designed to mitigate the conventional local convergence problem. Finally, the proposed model and algorithm are exemplified by some numerical examples. Experiment results show that the model and algorithm are effective in solving the multi-objective portfolio selection problem. © 2011 IEEE.

    DOI

  • A convex hull-based fuzzy regression to information granules problem - An efficient solution to real-time data analysis

    Azizul Azhar Ramli, Junzo Watada, Witold Pedrycz

    Communications in Computer and Information Science   180 ( 2 ) 190 - 204  2011

     View Summary

    Regression models are well known and widely used as one of the important categories of models in system modeling. In this paper, we extend the concept of fuzzy regression in order to handle real-time implementation of data analysis of information granules. An ultimate objective of this study is to develop a hybrid of a genetically-guided clustering algorithm called genetic algorithm-based Fuzzy C-Means (GA-FCM) and a convex hull-based regression approach being regarded as a potential solution to the formation of information granules. It is shown that a setting of Granular Computing helps us reduce the computing time, especially in case of real-time data analysis, as well as an overall computational complexity. We propose an efficient real-time information granules regression analysis based on the convex hull approach in which a Beneath-Beyond algorithm is employed to design sub convex hulls as well as a main convex hull structure. In the proposed design setting, we emphasize a pivotal role of the convex hull approach or more specifically the Beneath-Beyond algorithm, which becomes crucial in alleviating limitations of linear programming manifesting in system modeling. © 2011 Springer-Verlag.

    DOI

  • An Ant Ccolony System for Solving DNA Sequence Dedign Problem in DNA Computing,

    Farhaana Yakop, Amar Faiz Zainal Abidin, Zuwairie Ibrahim, Zulkifli Md. Yusof, Marzuki Khalid, N. Mokhtar, Junzo WATADA

    IJICIC,    2011

  • Rough Set Based Optimization for Data Mining: An Improved Fuzzy, Clustering Approach,

    Yang Jianxiong, Junzo WATADA

    IEICE transaction on Information and System,    2011

    DOI

  • Ranking Method of Web Mining Based on Latent Semantic Indexing,

    Yang Jianxiong, Junzo WATADA

    SICE,    2011

  • Capacitated Two-Stage Facility Location Problem with Fuzzy Costs and Demands,

    Shuming WANG, Junzo WATADA

    International Journal of Machine Learning and Cybernetics (IJMLC) published by Springer,    2011

  • Multi-objective Top-Down Decision Making through Additive Fuzzy Goal Programming,

    Nureize ARBAIY, Junzo WATADA

    SICE,    2011

    DOI

  • A Reliability Enhancement for A Traffic Signal Lights System Through A Mean-variance Approach,

    Shamshul BAHAR YAAKOB, Junzo WATADA

    IJICIC,, IF=2.791    2011

  • Fuzzy Portfolio Selection Model with Interval Values Based on Probability Distribution Functions,

    Pei-Chun Lin, Junzo WATADA, Berlin Wu

    IJICIC,, IF=2.791    2011

  • Top-Down Multi-objective Decision Making through Fuzzy Additive Goal Programming,

    Nureize Arbaiya, JunzoWATADA

    JACIII,    2011

  • Supply Balance Optimization of Sustainable Power Generation from Service Cost Perspective,

    Junzo WATADA, Yingru Wang, Yu-Lien Tai, Jaeseok Choi, Mitsushige Shiota

    International Journal of Intelligent Technologies and Applied Statistics,    2011

  • Fuzzy Clustering Analysis of Data Mining: Application to An Accident Mining System,

    Jianxiong Yang, Junzo WATADA

    IJICIC, 2011,, IF=2.791    2011

  • Automation of DNA Computing Readout Method Based on Real-Time PCR Implemented on DNA Engine Opticon 2 System,

    Muhammad Faiz, Mohamed Saaid, Shahdan Sudin, Ismail Ibrahim, Zulkifli Md. Yusof, Kamal Khalil, Jameel Abdulla Ahmed Mukred, Moh, Saberi Mohamad, N. Mokhtar, Zuwairie Ibrahim, Junzo WATADA

    International Journal of Innovative Computing, Information, and Control,, IF=2.791    2011

  • Prediction of Tick-wise price fluctuations for Rough Sets,

    Yoshiyuki MATSUMOTO, Junzo WATADA

    JACIII,    2011

  • Fuzzy Robust Regression Model by Possibility Maximization,

    Yoshiyuki YABUUCHI, Junzo WATADA

    JACIII,    2011

  • Probabilistic Reliability Evaluation of Interconnecting Power Systems Including Wind Turbine Generators,

    Jeongje Park, Taegon Oh, Kyeonghee Cho, Jaeseok Choi, Junzo WATADA

    IJICIC,, IF=2.791    2011

  • A Hybrid Modified PSO Approach to VaR-Based Facility Location Problems with Variable Capacity in Fuzzy Random Uncertainty,

    Shuming WANG, Junzo WATADA

    Information Science, ,, IF=3.095    2011

    DOI

  • Evaluation Criteria Analysis in Selecting an Online Securities Trading System by Brokerage Firms in Taiwan,

    Shih-Tong Lu, Neng-Chieh Liu, Yao-Feng Chang, Junzo WATADA

    ICIC EL,,    2011

  • A hybrid particle swarm optimization approach and its application to solving portfolio selection problems,

    Shamshul BAHAR YAAKOB, Junzo WATADA

    JACIII,,    2011

  • Improving Particle Swarm Optimization Convergence with Spread and Momentum Factors,

    Shamshul Bahar Yaakob, Junzo WATADA

    JCSES,,, IF=2.791    2011

  • Fuzzy Random Multi-attribute Evaluation for Oil Palm Fruit,

    Nureize binti Arbaiy, Junzo WATADA

    IJCSES,,    2011

  • Decision Making in Contractor Selection under Hybrid Uncertainty,

    Nureize binti Arbaiy, Junzo WATADA

    International Journal of Computer Sciences and Engineering Systems, published by Serials Publication, India.,    2011

  • A SVM-RBF Method for Solving Imbalanced Data Problem,

    Lei Ding, Junzo WATADA, Lim Chun Chew, Zuwairie Ibrahim, Lee Wen Jau, Marzuki Khalid

    ICIC EL,   4 ( 6(B) )  2011

  • AUTOMATION OF A DNA COMPUTING READOUT METHOD BASED ON REAL-TIME PCR IMPLEMENTED ON A LIGHTCYCLER SYSTEM

    Muhammad Faiz Mohamed Saaid, Zuwairie Ibrahim, Zulkifli Md Yusof, Junzo Watada

    INTERNATIONAL JOURNAL OF INNOVATIVE COMPUTING INFORMATION AND CONTROL   6 ( 10 ) 4263 - 4272  2010.10

     View Summary

    A DNA computer readout approach based on real-time polymerase chain reaction (PCR) for the computation of Hamiltonian Path Problem (HPP) is the main interest in this research. Based on this methodology, real-time amplification of DNA template with TaqMan, probes is performed with some modifications to realize the readout. The readout approach consists of two phases: real-time amplification in vitro followed by information processing in silico to assess the results of real-time amplification. The in silico information processing enables the extraction of the Hamiltonian path but the TaqMan "YES" and "NO" reactions produced by real-time PCR need to be identified manually beforehand. Manual identification or classification limits the capability of automated readout. Hence, in this study, the readout approach is further improved by incorporating Fuzzy C-means clustering algorithm in the in silico information processing phase. As a result, automatic classification of the TagMan "YES" and "NO" reactions is possible as demonstrated by the experimental results. Finally, an automated readout method can be realized as supported by the advantages of real-time PCR and Fuzzy C-means clustering algorithm.

  • AUTOMATION OF A DNA COMPUTING READOUT METHOD BASED ON REAL-TIME PCR IMPLEMENTED ON A LIGHTCYCLER SYSTEM

    Muhammad Faiz Mohamed Saaid, Zuwairie Ibrahim, Zulkifli Md Yusof, Junzo Watada

    INTERNATIONAL JOURNAL OF INNOVATIVE COMPUTING INFORMATION AND CONTROL   6 ( 10 ) 4263 - 4272  2010.10

     View Summary

    A DNA computer readout approach based on real-time polymerase chain reaction (PCR) for the computation of Hamiltonian Path Problem (HPP) is the main interest in this research. Based on this methodology, real-time amplification of DNA template with TaqMan, probes is performed with some modifications to realize the readout. The readout approach consists of two phases: real-time amplification in vitro followed by information processing in silico to assess the results of real-time amplification. The in silico information processing enables the extraction of the Hamiltonian path but the TaqMan "YES" and "NO" reactions produced by real-time PCR need to be identified manually beforehand. Manual identification or classification limits the capability of automated readout. Hence, in this study, the readout approach is further improved by incorporating Fuzzy C-means clustering algorithm in the in silico information processing phase. As a result, automatic classification of the TagMan "YES" and "NO" reactions is possible as demonstrated by the experimental results. Finally, an automated readout method can be realized as supported by the advantages of real-time PCR and Fuzzy C-means clustering algorithm.

  • Recourse-Based Facility-Location Problems in Hybrid Uncertain Environment

    Shuming Wang, Junzo Watada, Witold Pedrycz

    IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS   40 ( 4 ) 1176 - 1187  2010.08

     View Summary

    The objective of this paper is to study facility-location problems in the presence of a hybrid uncertain environment involving both randomness and fuzziness. A two-stage fuzzy-random facility-location model with recourse (FR-FLMR) is developed in which both the demands and costs are assumed to be fuzzy-random variables. The bounds of the optimal objective value of the two-stage FR-FLMR are derived. As, in general, the fuzzy-random parameters of the FR-FLMR can be regarded as continuous fuzzy-random variables with an infinite number of realizations, the computation of the recourse requires solving infinite second-stage programming problems. Owing to this requirement, the recourse function cannot be determined analytically, and, hence, the model cannot benefit from the use of techniques of classical mathematical programming. In order to solve the location problems of this nature, we first develop a technique of fuzzy-random simulation to compute the recourse function. The convergence of such simulation scenarios is discussed. In the sequel, we propose a hybrid mutation-based binary ant-colony optimization (MBACO) approach to the two-stage FR-FLMR, which comprises the fuzzy-random simulation and the simplex algorithm. A numerical experiment illustrates the application of the hybrid MBACO algorithm. The comparison shows that the hybrid MBACO finds better solutions than the one using other discrete metaheuristic algorithms, such as binary particle-swarm optimization, genetic algorithm, and tabu search.

    DOI

  • Recourse-Based Facility-Location Problems in Hybrid Uncertain Environment

    Shuming Wang, Junzo Watada, Witold Pedrycz

    IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS   40 ( 4 ) 1176 - 1187  2010.08

     View Summary

    The objective of this paper is to study facility-location problems in the presence of a hybrid uncertain environment involving both randomness and fuzziness. A two-stage fuzzy-random facility-location model with recourse (FR-FLMR) is developed in which both the demands and costs are assumed to be fuzzy-random variables. The bounds of the optimal objective value of the two-stage FR-FLMR are derived. As, in general, the fuzzy-random parameters of the FR-FLMR can be regarded as continuous fuzzy-random variables with an infinite number of realizations, the computation of the recourse requires solving infinite second-stage programming problems. Owing to this requirement, the recourse function cannot be determined analytically, and, hence, the model cannot benefit from the use of techniques of classical mathematical programming. In order to solve the location problems of this nature, we first develop a technique of fuzzy-random simulation to compute the recourse function. The convergence of such simulation scenarios is discussed. In the sequel, we propose a hybrid mutation-based binary ant-colony optimization (MBACO) approach to the two-stage FR-FLMR, which comprises the fuzzy-random simulation and the simplex algorithm. A numerical experiment illustrates the application of the hybrid MBACO algorithm. The comparison shows that the hybrid MBACO finds better solutions than the one using other discrete metaheuristic algorithms, such as binary particle-swarm optimization, genetic algorithm, and tabu search.

    DOI

  • Recourse-based facility-location problems in hybrid uncertain environment

    Shuming Wang, Junzo Watada, Witold Pedrycz

    IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics   40 ( 4 ) 1176 - 1187  2010.08

     View Summary

    The objective of this paper is to study facility-location problems in the presence of a hybrid uncertain environment involving both randomness and fuzziness. A two-stage fuzzy-random facility-location model with recourse (FR-FLMR) is developed in which both the demands and costs are assumed to be fuzzy-random variables. The bounds of the optimal objective value of the two-stage FR-FLMR are derived. As, in general, the fuzzy-random parameters of the FR-FLMR can be regarded as continuous fuzzy-random variables with an infinite number of realizations, the computation of the recourse requires solving infinite second-stage programming problems. Owing to this requirement, the recourse function cannot be determined analytically, and, hence, the model cannot benefit from the use of techniques of classical mathematical programming. In order to solve the location problems of this nature, we first develop a technique of fuzzy-random simulation to compute the recourse function. The convergence of such simulation scenarios is discussed. In the sequel, we propose a hybrid mutation-based binary ant-colony optimization (MBACO) approach to the two-stage FR-FLMR, which comprises the fuzzy-random simulation and the simplex algorithm. A numerical experiment illustrates the application of the hybrid MBACO algorithm. The comparison shows that the hybrid MBACO finds better solutions than the one using other discrete metaheuristic algorithms, such as binary particle-swarm optimization, genetic algorithm, and tabu search. © 2006 IEEE.

    DOI PubMed

  • Recourse-Based Facility-Location Problems in Hybrid Uncertain Environment

    Shuming Wang, Junzo Watada, Witold Pedrycz

    IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS   40 ( 4 ) 1176 - 1187  2010.08

     View Summary

    The objective of this paper is to study facility-location problems in the presence of a hybrid uncertain environment involving both randomness and fuzziness. A two-stage fuzzy-random facility-location model with recourse (FR-FLMR) is developed in which both the demands and costs are assumed to be fuzzy-random variables. The bounds of the optimal objective value of the two-stage FR-FLMR are derived. As, in general, the fuzzy-random parameters of the FR-FLMR can be regarded as continuous fuzzy-random variables with an infinite number of realizations, the computation of the recourse requires solving infinite second-stage programming problems. Owing to this requirement, the recourse function cannot be determined analytically, and, hence, the model cannot benefit from the use of techniques of classical mathematical programming. In order to solve the location problems of this nature, we first develop a technique of fuzzy-random simulation to compute the recourse function. The convergence of such simulation scenarios is discussed. In the sequel, we propose a hybrid mutation-based binary ant-colony optimization (MBACO) approach to the two-stage FR-FLMR, which comprises the fuzzy-random simulation and the simplex algorithm. A numerical experiment illustrates the application of the hybrid MBACO algorithm. The comparison shows that the hybrid MBACO finds better solutions than the one using other discrete metaheuristic algorithms, such as binary particle-swarm optimization, genetic algorithm, and tabu search.

    DOI PubMed

  • Construct logic operation gates of OR and and with multiple enzymes

    Chu Yuyi, Juiyu Wu, Junzo Watada, Ikno Kim

    Proceedings - 4th International Conference on Genetic and Evolutionary Computing, ICGEC 2010     35 - 38  2010

     View Summary

    Biomoleculars act as a tool to build a multiple enzyme system based on human metabolic actions to perform basic logic operation connecting OR and AND gates. Three enzymes (invertase, amyloglucosidase, hexokinase) concert as a logic operation part, processing three molecular input signals (sucrose, maltose, and ATP) to produce G6P (glucose-6-phosphate). The other two enzymes glucose-6-phosphate dehydrogenase (G6PD) and salicylate hydroxylase (SHL) compose to function as a signal displayer. Furthermore, we used a latent fluorescent molecule composed of sacylate and fluorescence which can be catalyzed and release fluorescent molecular to produce output signal. According to our experiments, firstly, our design is proved. The typical characteristics of enzyme reactions have been discovered through comparing the expected theoretical cures with the result cures. Secondly, the possibility of applying multiple logic gates into complicate networks has been shown. © 2010 IEEE.

    DOI

  • A hybrid BPSO approach for fuzzy facility location problems with VaR

    Shuming Wang, Junzo Watada

    Proceedings - 4th International Conference on Genetic and Evolutionary Computing, ICGEC 2010     43 - 46  2010

     View Summary

    In this paper, a fuzzy facility location model with Value at Risk (VaR) is proposed, which is a two-stage fuzzy zero-one integer programming. Since the fuzzy parameters of the location problem are continuous fuzzy variables with an infinite support, the computation of VaR is inherently an infinite-dimensional optimization problem, which can not be solved analytically. In order to solve the model, first of all, the objective function VaR is approximated through discretization method of fuzzy variables. Therefore, the original problem is converted to the task of a finite-dimensional optimization. Then, a hybrid heuristic algorithm integrating binary particle swarm optimization (BPSO), simplex algorithm and the approximation approach is designed to solve the location model. Finally, a numerical example is provided. © 2010 IEEE.

    DOI

  • Value of Information and Solution under VaR Criterion for Fuzzy Random Optimization Problems

    Shuming Wang, Junzo Watada

    2010 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS (FUZZ-IEEE 2010)     Publication Year: 2010 , Page(s): 1-6  2010

     View Summary

    Under the Value-at-Risk (VaR) criterion, this paper studies on the value of information and solution for two-stage fuzzy random optimization problems. First, the value of perfect information (VPI) in VaR criterion is discussed by studying the difference of the wait-and-see (WS) solution and the here-and-now (HN) solution to the two-stage fuzzy random programming with VaR criterion. Then, the value of fuzzy random solution (VFRS) in VaR is examined by investigating the difference of the HN solution and the random solution (RS), as well as the difference of HN solution and the expected value (EV) solution. Finally, a lower bound and an upper bound for the HN solution are derived.

    DOI

  • The Diagnosis of Power Transformer Failures by Fuzzy Random Based Rough Sets Analysis,

    Junzo WATADA, Shamshul BAHAR YAAKOB, Waseda University, Tsuguhiro Takahashi, Tatsuki Okamoto (Central Research Institute of Electric Power Industry

    International Conference on Condition Monitoring and Diagnosis 2010 ( CMD2010) Shibaura, JAPAN,    2010

  • Structurizing Complex Contextual Relations using a Biological Encoding Method,

    Ikno KIM, Yu-Yi Chu, Junzo WATADA

    IFMIP 2010, WAC2010,     1 - 6  2010

  • Short-term Power Load Forecasting Method by Radial-basis-function Neural Network with Support Vector Machine Model,

    Jiliang Xue, Junzo WATADA

    ICICIC2010, Xi'an, China,    2010

  • Restructuring of Rough Sets for Fuzzy Random Data of Creative City Evaluation

    Lee-Chuan Lin, Junzo Watada

    INTEGRATED UNCERTAINTY MANAGEMENT AND APPLICATIONS   68   523 - 534  2010

     View Summary

    In this paper we provide the restructuring method of rough sets for analyzing fuzzy random data that many experts evaluate creative cities. Usually it is hard to clarify the situation where randomness and fuzziness exist simultaneously. This paper presents a method based on fuzzy random variables to restructure a rough set. The algorithms of rough set is used to distinguish whether a subset can he classified in the object set or not based on confidence interval. The expected-value-approach is also applied to calculate the fuzzy value with probability into a scalar value.

  • Prestent State of Art in Human Tracking: Survey,

    Junzo WATADA, Zalili Musa, Lukmi C Jain, John Fulcher

    KES2010, Cardiff, UK,     45 - 49  2010

    DOI

  • Possibilistic Regression Analysis of Influential Factors in the Planning and Implementation of Occupational Health and Safety Management Systems

    Azizul Azhar Ramli, Junzo Watada, Witold Pedrycz

    2010 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS (FUZZ-IEEE 2010)     Publication Year: 2010 , Page(s): 1-8  2010

     View Summary

    The code of Occupational Health and Safety (OHS) is an important regulation to improve the on-the-job safety of employees. Several factors affect the planning and implementation of OHS management systems (OHSMS). The evaluation of OHS practice is the most important component when building a safety environment policy for employees and administration. Begin aware of subjective nature of factors affecting OHS and the use of statistical method, it becomes controversial as to a way of handling this type of survey data. This research presents a combination of possibilistic regression analysis with a convex hull approach to analyze the fitting factors that impact good practices of OHS. In addition, selected samples of data could be represented as fuzzy sets. This study offers an alternative platform to evaluate influential factors being used towards a successful implementation of the OHS policy.

    DOI

  • Optimal Supply Balance of Power System Based on Sustainable Generators by Reliability Cost/Worth Method,

    Yingru WANG, Junzo WATADA, Shamshul Bahar Bin YAAKOB, Jaeseok CHOI

    Czeck-Japan Seminar 2010 (CJS2010), Otaru, JAPAN,    2010

  • Multi-Camera Tracking Method Based on Particle Filtering,

    Zalili Binti Musa, Junzo WATADA, Junzo WATADA

    IFMIP 2010, WAC2010,     1 - 6  2010

  • Kolmogorov-Smirnov Two Sample Test with Continuous Fuzzy Data

    Pei-Chun Lin, Berlin Wu, Junzo Watada

    INTEGRATED UNCERTAINTY MANAGEMENT AND APPLICATIONS   68   175 - +  2010

     View Summary

    The Kolmogorov-Smirnov two-sample test (K-S two sample test) is a goodness-of-fit test which is used to determine whether two underlying one-dimensional probability distributions differ. In order to find the statistic pivot of a K-S two-sample test, we calculate the cumulative function by means of empirical distribution function. When we deal with fuzzy data, it is essential to know how to find the empirical distribution function for continuous fuzzy data. In our paper, we define a new function, the weight function that can he used to deal with continuous fuzzy data. Moreover we can divide samples into different classes. The cumulative function can be calculated with those divided data. The paper explains that the K-S two sample test for continuous fuzzy data can make it possible to judge whether two independent samples of continuous fuzzy data conic from the same population. The results show that it is realistic and reasonable in social science research to use the K-S two-sample test for continuous fuzzy data.

  • Global optimization using meta-controlled Boltzmann machine

    Shamshul Bahar Yaakob, Junzo Watada

    Proceedings - 4th International Conference on Genetic and Evolutionary Computing, ICGEC 2010     39 - 42  2010

     View Summary

    In this study, a new artificial neuron network model called the meta-controlled Boltzmann machine is introduced. The meta-controlled Boltzmann machine model includes the McCulloch-Pitts model, the Hopfield network, and also the Boltzmann machine. The proposed method are applied both diffusion processes and simulated annealing. The convergence proof of the proposed method is shows in this paper. Meta-controlled Boltzmann machine show an ability to solve combinatorial optimization problems better than either Hopfield networks or Boltzmann machines. © 2010 IEEE.

    DOI

  • Fuzzy Random Based Rough Sets Analysis and Its Application,

    Junzo WATADA, Lee-Chuan Lin, Yoshiyuki MATSUMOTO

    IFMIP 2010, WAC2010,     1 - 9  2010

  • Diagnosis system based on rough sets analysis

    Junzo Watada, Shamshul Bahar Yaakob

    Proceedings - 4th International Conference on Genetic and Evolutionary Computing, ICGEC 2010     47 - 50  2010

     View Summary

    Nowadays, power systems play an important role in the whole electric industry. Failures of such systems should result in serious social and economical damages. Therefore, the power systems should be highly reliable. This paper presents the method to build a new type of failure diagnosis system based on rough set theory. The testing data of power systems for their failure conditions are based on experts' evaluations with uncertainty, especially little knowledge and human experiences are available on power system failure diagnosis. The rough set theory plays a vital role in handling them. © 2010 IEEE.

    DOI

  • Creating SMES’ Innovation Capabilities Through Formation of Collaborative Innovation Network in Taiwan,

    Yu-Lien Tai, Junzo WATADA

    IFMIP 2010, WAC2010,     1 - 8  2010

  • Building fuzzy random objective function for interval fuzzy goal programming

    A. Nureize, J. Watada

    IEEM2010 - IEEE International Conference on Industrial Engineering and Engineering Management     980 - 984  2010

     View Summary

    Estimating the coefficients of objective functions in multi-objective model is sometimes difficult in real situations. Mathematical analysis of statistical data is used to determine the coefficients. In various cases, the statistical data may not contain only randomness, but also fuzziness, which should be treated properly. Thus, this paper employs fuzzy random regression model to approximate the coefficients values for objective functions of multi-objective model. The presented model consists of two stages
    first, developing the objective functions by fuzzy random regression model and second, introducing an interval fuzzy goal programming model to solve the multi-objective problem. An experimental example is provided to illustrate the model. ©2010 IEEE.

    DOI

  • Approximation of Goal Constraint Coefficients in Fuzzy Goal Programming

    Nureize Arbaiy, Junzow Watada

    2010 SECOND INTERNATIONAL CONFERENCE ON COMPUTER ENGINEERING AND APPLICATIONS: ICCEA 2010, PROCEEDINGS, VOL 1     161 - 165  2010

     View Summary

    It is sometimes difficult in real situations to estimate the coefficients of decision variables in multi-objective model. Even though mathematical analysis may contribute to determine these coefficients, historical data used may contain fuzzy and random properties and should be treated properly. Thus, this paper introduces a fuzzy random regression to approximate the coefficients; specifically the goal constraints of goal programming model. We propose a two phase-based approach for the solution model; first, we construct the goal constraints using fuzzy random regression model and, second, we solve the multi-objective problem with a fuzzy additive goal programming. A numerical example is presented to illustrate the model.

    DOI

  • Analysis of the Familiarity and Mutual Dependency of Firms from the Perspective of SME CINs' Effectiveness

    Yu-Lien Tai, Junzo Watada, Hsiu Hsien Su

    PICMET 2010: TECHNOLOGY MANAGEMENT FOR GLOBAL ECONOMIC GROWTH     Publication Year: 2010 , Page(s): 1-13  2010

     View Summary

    The main objective of this study is to define the core attributes that influence the member firms of small and medium enterprise collaborative innovation networks (SME CINs) to join collaborative research and development (R&D) projects provided by the inter-firm networking of SMEs in technology-intensive clustering assistance (TICA) projects. We used social network analysis, resource dependence theory, and transaction cost analysis to select the attributes of firms and a rough sets approach to mine rules that explain whether firms join collaborative projects. Especially, this study utilized a rough sets model and identified the core attributes. The familiarity and connections that members share with one another are found to be the core attributes of members in SME CINs that push them to join collaborative innovation activities. Further, the existence of relationships among SMEs is more important than the strength of the relationships themselves.

  • An evidential reasoning based LSA approach to document classification for knowledge acquisition

    R. Mohamed, J. Watada

    IEEM2010 - IEEE International Conference on Industrial Engineering and Engineering Management     1092 - 1096  2010

     View Summary

    Web is one of major information sources. Failure in proper management of knowledge leads to incorrect results returned by search engines. Therefore, the web should have an effective information retrieval system to improve the correctness of retrieval results. This study provides a method to assign a new document to the fittest category out of predefined categories, where latent semantic analysis (LSA) is used to evaluate each term in documents, the similarity between terms and documents as well as the one between terms and categories. The objective of our method is to fuse evidential reasoning method with LSA which can assign a new document to a predefined category. The method provides better results in performance of classification comparing to the fusion of an evidential reasoning approach with term frequency inverse document frequency (TFIDF). ©2010 IEEE.

    DOI

  • A Simulated Annealing Based Possibilistic Fuzzy C-means Algorithm for Clustering Problems,

    Wen Song, Shuming WANG, Junzo WATADA

    IFMIP 2010, WAC2010, September 19 ? September 23, 2010, Kobe International Conference Center, Kobe, JAPAN,    2010

  • A Novel Idea of Real-Time Fuzzy Switching Regression Analysis: A Nuclear Power Plants Case Study

    Azizul Azhar Ramli, Junzo Watada

    INTEGRATED UNCERTAINTY MANAGEMENT AND APPLICATIONS   68   535 - 546  2010

     View Summary

    In this paper, the concept of regression models is extended to handle hybrid data from various sources that quite often exhibit diverse levels of data quality specifically in nuclear power plants. The major objective of this study is to develop a convex hull method as a potential vehicle which reduces the computing time, especially in the case of real-time data analysis as well as minimizes the computational complexity. We propose an efficient real-time fuzzy switching regression analysis based on a convex hull approach, in which a beneath-beyond algorithm is used in building a convex hull when alleviating limitations of a linear programming in system modeling. Additionally, the method addresses situations when we have to deal with heterogeneous data.

  • A Modified Artificial Neural Network Learning Algorithm for Imbalanced Data Set Problem

    Asrul Adam, Ibrahim Shapiai, Zuwairie Ibrahim, Marzuki Khalid, Lim Chun Chew, Lee Wen Jau, Junzo Watada

    2010 SECOND INTERNATIONAL CONFERENCE ON COMPUTATIONAL INTELLIGENCE, COMMUNICATION SYSTEMS AND NETWORKS (CICSYN)     44 - 48  2010

     View Summary

    A modified learning algorithm of Artificial Neural Networks (ANN) is introduced in this paper to solve imbalanced data set problems. In solving imbalanced data set, it is critical to predict the minority class due to their imbalanced nature. In order to improve the standard ANN classifier prediction performance, this paper focuses on optimizing the decision boundary of the step function at the output layer of ANN using particle swarm optimization (PSO). A feedforward ANN is chosen in this study. Firstly, a conventional back propagation algorithm is employed to train the ANN. PSO is then applied to train the real predicted output of training data from this trained network. As the result, the optimum value of decision boundary is found and applied to the classifier. Prediction performance is assessed by G-mean, which is a measure to indicate the efficiency of classifiers for imbalanced data sets. Based on experimental results, the proposed model is able to solve imbalanced data sets problem with better performance compared to the standard ANN.

    DOI

  • Power System Equipments Investment Decision-Making under Uncertainty: A Real Options Approach

    Shamshul Bahar Yaakob, Junzo Watada

    ADVANCES IN INTELLIGENT DECISION TECHNOLOGIES   4   699 - 708  2010

     View Summary

    Power supply failures have caused major social losses in the information society in the present age. Such a loss is estimated up to approximately two trillion yen when a large power failure happens in a big city such as Metropolitan Tokyo. Therefore, it is necessary to provide some remedies such as a diagnosis of the power system equipments not only for preventing the system accident of the equipment beforehand from its failures but also for guarding the social cost from increasing. The objective of the paper is to provide the preliminary research on life cycle management. In this paper, net present value (NPV) analysis and real options approach (ROA) are employed in life cycle management in investment and maintenance of power supply systems in order to keep the continuous normal operation under uncertainty.

    DOI

  • A Rough-Set-Based Two-Class Classifier for Large Imbalanced Dataset

    Junzo Watada, Lee-Chuan Lin, Lei Ding, Mohd Ibrahim Shapiai, Lim Chun Chew, Zuwairie Ibrahim, Lee Wen Jau, Marzuki Khalid

    ADVANCES IN INTELLIGENT DECISION TECHNOLOGIES   4   641 - +  2010

     View Summary

    The objective of this paper is to provide a rouch-set-based two-class classifier approach to classifying samples in large and imbalanced dataset. A database has plenty of hidden knowledge, which can be used in decision making to support commerce, research and other activities. Prediction is another form of expanding data analysis. It enables us to establish a data model using existing data and to predict the trend of data in future. In this paper, a method consists of data scaling, rough sets analysis and support vector machine with radial basis function (SVM-RBF), which is used to classify a large and imbalanced data set obtained in semiconductor industry.

    DOI

  • Solving Portfolio Selection Problems using Hybrid Particle Swarm Optimization Approach,

    Shamshul BAHAR YAAKOB, Junzo WATADA

    ISME2010, Kokura, Japan,    2010

  • Redundancy Optimization for a Dam Control System in Integrated Uncertainty,

    Haydee Melo, Shuming WANG, Junzo WATADA

    ISME2010, Kokura, Japan,    2010

  • Prediction of Tick-wise Price Fluctuations for Rough Sets,

    Yoshiyuki MATSUMOTO, Junzo WATADA

    ISME2010, Kokura, Japan,    2010

  • Possibilistic Forecasting Model used in Management and Economic Areas,

    Yoshiyuki YABUUCHI, Junzo WATADA

    ISME2010, Kokura, Japan,    2010

  • Efficient data mining method based on fuzzy clustering

    YANG J. X.

    Proceedings of the 7th International Symposium on Management Engineering, 2010     242 - 249  2010

    CiNii

  • Decision Factors Analysis for Vendor Selection of Online Securities Trading System in Taiwan,

    Shih-Tong Lu, Neng-Chieh Liu, Yao-Feng Chang, Junzo WATADA

    ISME2010, Kokura, Japan,    2010

  • Building Fuzzy Portfolio Selection Model with Interval Data under Probability Distribution Function,

    Pei-Chun Lin, Junzo WATADA

    ISME2010, Kokura, Japan,    2010

  • A SVM-RBF Method for Solving Imbalanced Data Problem,

    Lei Ding, Junzo WATADA, Lim Chun Chew, Zuwairie Ibrahim, Lee Wen Jau, Marzuki Khalid

    ISII2010-255, ISII2010, Dalian, China,    2010

  • A Fuzzy Regression Based Support Vector Machine (SVM) Approach to Fuzzy Classification

    Yu Chen, Witold Pedrycz, Junzo Watada

    ISII2010-175, ISII2010, Dalian, China,    2010

  • Performance Measurement in Manufacturing Enterprises: New Paradigm on Intelligent Data Analysis (IDA) Implementation,

    Azizul Azhar Ramli, Junzo WATADA, Witold PEDRYCZ

    JCSES,, Software Engineering and Computer Systems ()    2010

  • Combining Biological Computation and Fuzzy-Based Methods for Organisationally Cohesive Subgroups

    Ikno Kim, Junzo Watada

    INTERNATIONAL JOURNAL OF UNCONVENTIONAL COMPUTING   6 ( 3-4 ) 285 - 300  2010

     View Summary

    Cohesive subgroups in complicated employee relationships are commonly discovered and organised when personnel managers need to efficiently execute a job rotation. This provides employees with a better work life quality and encourages them to work more efficiently. Rearranging a small number of employees using electronic computation can be easily accomplished, but rearranging a larger number of employees is NP-hard. This paper proposes an unconventional approach to determine organisationally cohesive subgroups for better job rotation by combining biological computation and fuzzy-based methods to firstly detect all possible employees in cliques and components, secondly find employees in fuzzy cliques, and finally arrange the employees into similar groups. Moreover, the efficiency of performing a fuzzy analysis with biological computation is measured.

  • A Molecular Computational Approach to Solving a Work Centre Sequence-Oriented Manufacturing Problem of Classical Job Shop Scheduling

    Ikno Kim, Junzo Watada

    INTERNATIONAL JOURNAL OF UNCONVENTIONAL COMPUTING   6 ( 6 ) 473 - 487  2010

     View Summary

    Classical job shop scheduling includes an intractable manufacturing scheduling problem. The problem is how to minimise the maximum manufacturing completion time in high variety and low volume requirements. Further, we have to consider work centre sequences for how all the given jobs are sequenced by work centre orders, meaning that it is an NP-hard problem. A number of different types of methods and algorithms have been proposed for solving this issue. In this article, we focused on molecular computational methods and biological techniques to propose a new algorithm. Our algorithm can be used to search all the feasible manufacturing schedules, and isolate the schedule with the truly minimised maximum manufacturing completion time.

  • A Particle Swarm Optimization Approach for Routing in PCB Holes Drilling,

    Asrul Adam, Amar Faiz Zainal Abidin, Zuwairie Ibrahim, Abdul Rashid Husain, ZulkiliMd Yusof, Ismail Ibrahim, Junzo WATADA

    submitted to IJICIC submitted and first review result in 20100923,, IF=2.791 ()    2010

  • T-norm-based limit theorems for fuzzy random variables

    S. Wang, J. Watada

    JOURNAL OF INTELLIGENT & FUZZY SYSTEMS   21 ( 4 ) 233 - 242  2010

     View Summary

    The objective of this paper is to derive some limit theorems of fuzzy random variables under the extension principle associated with continuous Archimedean triangular norms (t-norms). First of all, some convergence theorems for the sum of fuzzy random variables in chance measure and expected value are proved respectively based on the arithmetics of continuous Archimedean triangular norms. Then, a law of large numbers for fuzzy random variables is established by using the obtained convergence theorems. The results of the derived law of large numbers can degenerate to the strong laws of large numbers for random variables and fuzzy variables, respectively.

    DOI

  • A Fuzzy Regression Based Support Vector Machine (SVM) Approach to Fuzzy Classification

    Yu Chen, Witold Pedrycz, Junzo Watada

    ICIC EL, (ISSN 1881-803X)   4 ( 6(B) ) 2355 - 2362  2010

  • Strategy Building for Foreign Exchange Exposure

    Junzo Watada, Song Wen

    International Journal of Innovative Management, Information & Production (IMIP) ()   1 ( 1 ) 1 - 17  2010

  • A SVM-RBF Method for Solving Imbalanced Data Problem,

    Lei Ding, Junzo WATADA, Lim Chun Chew, Zuwairie Ibrahim, Lee Wen Jau, Marzuki Khalid

    ICIC EL, (ISSN 1881-803X)   4 ( 6(B) ) 2419 - 2424  2010

  • Decision making of facility locations based on fuzzy probability distribution function

    P. C. Lin, S. Wang, J. Watada

    IEEM2010 - IEEE International Conference on Industrial Engineering and Engineering Management     1911 - 1915  2010

     View Summary

    Facility locations can appear to be a challenge for both novice and experienced analysts. But, it is far more efficient if its decision making follows a logical, systematic procedure. Such an approach markedly increases the chances of finding a location and improves the firm's objectives. This paper aims to provide Fuzzy Probability Distribution Functions (FPDF) so that the decision making can be pursued under hybrid uncertainly. FPDF is defined using three parameters of central point, right radius and left radius. Moreover, a new fuzzy probability distribution function is defined on the basis of these three parameters. When FPDF are properly generated, the functions can easily be used in the decision making of facility locations by means of optimal model proposed in Shuming Wang, et al.. ©2010 IEEE.

    DOI

  • Construct logic operation gates of OR and and with multiple enzymes

    Chu Yuyi, Juiyu Wu, Junzo Watada, Ikno Kim

    Proceedings - 4th International Conference on Genetic and Evolutionary Computing, ICGEC 2010     35 - 38  2010

     View Summary

    Biomoleculars act as a tool to build a multiple enzyme system based on human metabolic actions to perform basic logic operation connecting OR and AND gates. Three enzymes (invertase, amyloglucosidase, hexokinase) concert as a logic operation part, processing three molecular input signals (sucrose, maltose, and ATP) to produce G6P (glucose-6-phosphate). The other two enzymes glucose-6-phosphate dehydrogenase (G6PD) and salicylate hydroxylase (SHL) compose to function as a signal displayer. Furthermore, we used a latent fluorescent molecule composed of sacylate and fluorescence which can be catalyzed and release fluorescent molecular to produce output signal. According to our experiments, firstly, our design is proved. The typical characteristics of enzyme reactions have been discovered through comparing the expected theoretical cures with the result cures. Secondly, the possibility of applying multiple logic gates into complicate networks has been shown. © 2010 IEEE.

    DOI

  • A hybrid BPSO approach for fuzzy facility location problems with VaR

    Shuming Wang, Junzo Watada

    Proceedings - 4th International Conference on Genetic and Evolutionary Computing, ICGEC 2010     43 - 46  2010

     View Summary

    In this paper, a fuzzy facility location model with Value at Risk (VaR) is proposed, which is a two-stage fuzzy zero-one integer programming. Since the fuzzy parameters of the location problem are continuous fuzzy variables with an infinite support, the computation of VaR is inherently an infinite-dimensional optimization problem, which can not be solved analytically. In order to solve the model, first of all, the objective function VaR is approximated through discretization method of fuzzy variables. Therefore, the original problem is converted to the task of a finite-dimensional optimization. Then, a hybrid heuristic algorithm integrating binary particle swarm optimization (BPSO), simplex algorithm and the approximation approach is designed to solve the location model. Finally, a numerical example is provided. © 2010 IEEE.

    DOI

  • Value of Information and Solution under VaR Criterion for Fuzzy Random Optimization Problems

    Shuming Wang, Junzo Watada

    2010 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS (FUZZ-IEEE 2010)     Publication Year: 2010 , Page(s): 1-6  2010

     View Summary

    Under the Value-at-Risk (VaR) criterion, this paper studies on the value of information and solution for two-stage fuzzy random optimization problems. First, the value of perfect information (VPI) in VaR criterion is discussed by studying the difference of the wait-and-see (WS) solution and the here-and-now (HN) solution to the two-stage fuzzy random programming with VaR criterion. Then, the value of fuzzy random solution (VFRS) in VaR is examined by investigating the difference of the HN solution and the random solution (RS), as well as the difference of HN solution and the expected value (EV) solution. Finally, a lower bound and an upper bound for the HN solution are derived.

    DOI

  • The Diagnosis of Power Transformer Failures by Fuzzy Random Based Rough Sets Analysis,

    Junzo WATADA, Shamshul BAHAR YAAKOB, Waseda University, Tsuguhiro Takahashi, Tatsuki Okamoto (Central Research Institute of Electric Power Industry

    International Conference on Condition Monitoring and Diagnosis 2010 ( CMD2010) Shibaura, JAPAN,    2010

  • Structurizing Complex Contextual Relations using a Biological Encoding Method,

    Ikno KIM, Yu-Yi Chu, Junzo WATADA

    IFMIP 2010, WAC2010,     1 - 6  2010

  • Short-term Power Load Forecasting Method by Radial-basis-function Neural Network with Support Vector Machine Model,

    Jiliang Xue, Junzo WATADA

    ICICIC2010, Xi'an, China,    2010

  • Restructuring of Rough Sets for Fuzzy Random Data of Creative City Evaluation

    Lee-Chuan Lin, Junzo Watada

    INTEGRATED UNCERTAINTY MANAGEMENT AND APPLICATIONS   68   523 - 534  2010

     View Summary

    In this paper we provide the restructuring method of rough sets for analyzing fuzzy random data that many experts evaluate creative cities. Usually it is hard to clarify the situation where randomness and fuzziness exist simultaneously. This paper presents a method based on fuzzy random variables to restructure a rough set. The algorithms of rough set is used to distinguish whether a subset can he classified in the object set or not based on confidence interval. The expected-value-approach is also applied to calculate the fuzzy value with probability into a scalar value.

  • Prestent State of Art in Human Tracking: Survey,

    Junzo WATADA, Zalili Musa, Lukmi C Jain, John Fulcher

    KES2010, Cardiff, UK,     45 - 49  2010

    DOI

  • Possibilistic Regression Analysis of Influential Factors in the Planning and Implementation of Occupational Health and Safety Management Systems

    Azizul Azhar Ramli, Junzo Watada, Witold Pedrycz

    2010 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS (FUZZ-IEEE 2010)     Publication Year: 2010 , Page(s): 1-8  2010

     View Summary

    The code of Occupational Health and Safety (OHS) is an important regulation to improve the on-the-job safety of employees. Several factors affect the planning and implementation of OHS management systems (OHSMS). The evaluation of OHS practice is the most important component when building a safety environment policy for employees and administration. Begin aware of subjective nature of factors affecting OHS and the use of statistical method, it becomes controversial as to a way of handling this type of survey data. This research presents a combination of possibilistic regression analysis with a convex hull approach to analyze the fitting factors that impact good practices of OHS. In addition, selected samples of data could be represented as fuzzy sets. This study offers an alternative platform to evaluate influential factors being used towards a successful implementation of the OHS policy.

    DOI

  • Optimal Supply Balance of Power System Based on Sustainable Generators by Reliability Cost/Worth Method,

    Yingru WANG, Junzo WATADA, Shamshul Bahar Bin YAAKOB, Jaeseok CHOI

    Czeck-Japan Seminar 2010 (CJS2010), Otaru, JAPAN,    2010

  • Multi-Camera Tracking Method Based on Particle Filtering,

    Zalili Binti Musa, Junzo WATADA, Junzo WATADA

    IFMIP 2010, WAC2010,     1 - 6  2010

  • Kolmogorov-Smirnov Two Sample Test with Continuous Fuzzy Data

    Pei-Chun Lin, Berlin Wu, Junzo Watada

    INTEGRATED UNCERTAINTY MANAGEMENT AND APPLICATIONS   68   175 - +  2010

     View Summary

    The Kolmogorov-Smirnov two-sample test (K-S two sample test) is a goodness-of-fit test which is used to determine whether two underlying one-dimensional probability distributions differ. In order to find the statistic pivot of a K-S two-sample test, we calculate the cumulative function by means of empirical distribution function. When we deal with fuzzy data, it is essential to know how to find the empirical distribution function for continuous fuzzy data. In our paper, we define a new function, the weight function that can he used to deal with continuous fuzzy data. Moreover we can divide samples into different classes. The cumulative function can be calculated with those divided data. The paper explains that the K-S two sample test for continuous fuzzy data can make it possible to judge whether two independent samples of continuous fuzzy data conic from the same population. The results show that it is realistic and reasonable in social science research to use the K-S two-sample test for continuous fuzzy data.

  • Global optimization using meta-controlled Boltzmann machine

    Shamshul Bahar Yaakob, Junzo Watada

    Proceedings - 4th International Conference on Genetic and Evolutionary Computing, ICGEC 2010     39 - 42  2010

     View Summary

    In this study, a new artificial neuron network model called the meta-controlled Boltzmann machine is introduced. The meta-controlled Boltzmann machine model includes the McCulloch-Pitts model, the Hopfield network, and also the Boltzmann machine. The proposed method are applied both diffusion processes and simulated annealing. The convergence proof of the proposed method is shows in this paper. Meta-controlled Boltzmann machine show an ability to solve combinatorial optimization problems better than either Hopfield networks or Boltzmann machines. © 2010 IEEE.

    DOI

  • Fuzzy Random Based Rough Sets Analysis and Its Application,

    Junzo WATADA, Lee-Chuan Lin, Yoshiyuki MATSUMOTO

    IFMIP 2010, WAC2010,     1 - 9  2010

  • Diagnosis System Based on Rough Sets Analysis, ICGEC-2010-IS24-04, Fourth

    Junzo WATADA, Shamshul BAHAR YAAKOB

    International Conference on Genetic and Evolutionary Computing (ICGEC-2010), Shenzhen, China,    2010

  • Creating SMES’ Innovation Capabilities Through Formation of Collaborative Innovation Network in Taiwan,

    Yu-Lien Tai, Junzo WATADA

    IFMIP 2010, WAC2010,     1 - 8  2010

  • Building fuzzy random objective function for interval fuzzy goal programming

    A. Nureize, J. Watada

    IEEM2010 - IEEE International Conference on Industrial Engineering and Engineering Management     980 - 984  2010

     View Summary

    Estimating the coefficients of objective functions in multi-objective model is sometimes difficult in real situations. Mathematical analysis of statistical data is used to determine the coefficients. In various cases, the statistical data may not contain only randomness, but also fuzziness, which should be treated properly. Thus, this paper employs fuzzy random regression model to approximate the coefficients values for objective functions of multi-objective model. The presented model consists of two stages
    first, developing the objective functions by fuzzy random regression model and second, introducing an interval fuzzy goal programming model to solve the multi-objective problem. An experimental example is provided to illustrate the model. ©2010 IEEE.

    DOI

  • Approximation of Goal Constraint Coefficients in Fuzzy Goal Programming

    Nureize Arbaiy, Junzow Watada

    2010 SECOND INTERNATIONAL CONFERENCE ON COMPUTER ENGINEERING AND APPLICATIONS: ICCEA 2010, PROCEEDINGS, VOL 1     161 - 165  2010

     View Summary

    It is sometimes difficult in real situations to estimate the coefficients of decision variables in multi-objective model. Even though mathematical analysis may contribute to determine these coefficients, historical data used may contain fuzzy and random properties and should be treated properly. Thus, this paper introduces a fuzzy random regression to approximate the coefficients; specifically the goal constraints of goal programming model. We propose a two phase-based approach for the solution model; first, we construct the goal constraints using fuzzy random regression model and, second, we solve the multi-objective problem with a fuzzy additive goal programming. A numerical example is presented to illustrate the model.

    DOI

  • Analysis of the Familiarity and Mutual Dependency of Firms from the Perspective of SME CINs' Effectiveness

    Yu-Lien Tai, Junzo Watada, Hsiu Hsien Su

    PICMET 2010: TECHNOLOGY MANAGEMENT FOR GLOBAL ECONOMIC GROWTH     Publication Year: 2010 , Page(s): 1-13  2010

     View Summary

    The main objective of this study is to define the core attributes that influence the member firms of small and medium enterprise collaborative innovation networks (SME CINs) to join collaborative research and development (R&D) projects provided by the inter-firm networking of SMEs in technology-intensive clustering assistance (TICA) projects. We used social network analysis, resource dependence theory, and transaction cost analysis to select the attributes of firms and a rough sets approach to mine rules that explain whether firms join collaborative projects. Especially, this study utilized a rough sets model and identified the core attributes. The familiarity and connections that members share with one another are found to be the core attributes of members in SME CINs that push them to join collaborative innovation activities. Further, the existence of relationships among SMEs is more important than the strength of the relationships themselves.

  • An evidential reasoning based LSA approach to document classification for knowledge acquisition

    R. Mohamed, J. Watada

    IEEM2010 - IEEE International Conference on Industrial Engineering and Engineering Management     1092 - 1096  2010

     View Summary

    Web is one of major information sources. Failure in proper management of knowledge leads to incorrect results returned by search engines. Therefore, the web should have an effective information retrieval system to improve the correctness of retrieval results. This study provides a method to assign a new document to the fittest category out of predefined categories, where latent semantic analysis (LSA) is used to evaluate each term in documents, the similarity between terms and documents as well as the one between terms and categories. The objective of our method is to fuse evidential reasoning method with LSA which can assign a new document to a predefined category. The method provides better results in performance of classification comparing to the fusion of an evidential reasoning approach with term frequency inverse document frequency (TFIDF). ©2010 IEEE.

    DOI

  • A Simulated Annealing Based Possibilistic Fuzzy C-means Algorithm for Clustering Problems,

    Wen Song, Shuming WANG, Junzo WATADA

    IFMIP 2010, WAC2010, September 19 ? September 23, 2010, Kobe International Conference Center, Kobe, JAPAN,    2010

  • A Novel Idea of Real-Time Fuzzy Switching Regression Analysis: A Nuclear Power Plants Case Study

    Azizul Azhar Ramli, Junzo Watada

    INTEGRATED UNCERTAINTY MANAGEMENT AND APPLICATIONS   68   535 - 546  2010

     View Summary

    In this paper, the concept of regression models is extended to handle hybrid data from various sources that quite often exhibit diverse levels of data quality specifically in nuclear power plants. The major objective of this study is to develop a convex hull method as a potential vehicle which reduces the computing time, especially in the case of real-time data analysis as well as minimizes the computational complexity. We propose an efficient real-time fuzzy switching regression analysis based on a convex hull approach, in which a beneath-beyond algorithm is used in building a convex hull when alleviating limitations of a linear programming in system modeling. Additionally, the method addresses situations when we have to deal with heterogeneous data.

  • A Modified Artificial Neural Network Learning Algorithm for Imbalanced Data Set Problem

    Asrul Adam, Ibrahim Shapiai, Zuwairie Ibrahim, Marzuki Khalid, Lim Chun Chew, Lee Wen Jau, Junzo Watada

    2010 SECOND INTERNATIONAL CONFERENCE ON COMPUTATIONAL INTELLIGENCE, COMMUNICATION SYSTEMS AND NETWORKS (CICSYN)     44 - 48  2010

     View Summary

    A modified learning algorithm of Artificial Neural Networks (ANN) is introduced in this paper to solve imbalanced data set problems. In solving imbalanced data set, it is critical to predict the minority class due to their imbalanced nature. In order to improve the standard ANN classifier prediction performance, this paper focuses on optimizing the decision boundary of the step function at the output layer of ANN using particle swarm optimization (PSO). A feedforward ANN is chosen in this study. Firstly, a conventional back propagation algorithm is employed to train the ANN. PSO is then applied to train the real predicted output of training data from this trained network. As the result, the optimum value of decision boundary is found and applied to the classifier. Prediction performance is assessed by G-mean, which is a measure to indicate the efficiency of classifiers for imbalanced data sets. Based on experimental results, the proposed model is able to solve imbalanced data sets problem with better performance compared to the standard ANN.

    DOI

  • Power System Equipments Investment Decision-Making under Uncertainty: A Real Options Approach

    Shamshul Bahar Yaakob, Junzo Watada

    ADVANCES IN INTELLIGENT DECISION TECHNOLOGIES   4   699 - 708  2010

     View Summary

    Power supply failures have caused major social losses in the information society in the present age. Such a loss is estimated up to approximately two trillion yen when a large power failure happens in a big city such as Metropolitan Tokyo. Therefore, it is necessary to provide some remedies such as a diagnosis of the power system equipments not only for preventing the system accident of the equipment beforehand from its failures but also for guarding the social cost from increasing. The objective of the paper is to provide the preliminary research on life cycle management. In this paper, net present value (NPV) analysis and real options approach (ROA) are employed in life cycle management in investment and maintenance of power supply systems in order to keep the continuous normal operation under uncertainty.

    DOI

  • A Rough-Set-Based Two-Class Classifier for Large Imbalanced Dataset

    Junzo Watada, Lee-Chuan Lin, Lei Ding, Mohd Ibrahim Shapiai, Lim Chun Chew, Zuwairie Ibrahim, Lee Wen Jau, Marzuki Khalid

    ADVANCES IN INTELLIGENT DECISION TECHNOLOGIES   4   641 - +  2010

     View Summary

    The objective of this paper is to provide a rouch-set-based two-class classifier approach to classifying samples in large and imbalanced dataset. A database has plenty of hidden knowledge, which can be used in decision making to support commerce, research and other activities. Prediction is another form of expanding data analysis. It enables us to establish a data model using existing data and to predict the trend of data in future. In this paper, a method consists of data scaling, rough sets analysis and support vector machine with radial basis function (SVM-RBF), which is used to classify a large and imbalanced data set obtained in semiconductor industry.

    DOI

  • Solving Portfolio Selection Problems using Hybrid Particle Swarm Optimization Approach,

    Shamshul BAHAR YAAKOB, Junzo WATADA

    ISME2010, Kokura, Japan,    2010

  • Redundancy Optimization for a Dam Control System in Integrated Uncertainty,

    Haydee Melo, Shuming WANG, Junzo WATADA

    ISME2010, Kokura, Japan,    2010

  • Prediction of Tick-wise Price Fluctuations for Rough Sets,

    Yoshiyuki MATSUMOTO, Junzo WATADA

    ISME2010, Kokura, Japan,    2010

  • Possibilistic Forecasting Model used in Management and Economic Areas,

    Yoshiyuki YABUUCHI, Junzo WATADA

    ISME2010, Kokura, Japan,    2010

  • Efficient Data Mining Method based on Fuzzy Clustering,

    Jianxiong Yang, Junzo WATADA

    ISME2010, Kokura, Japan,    2010

  • Decision Factors Analysis for Vendor Selection of Online Securities Trading System in Taiwan,

    Shih-Tong Lu, Neng-Chieh Liu, Yao-Feng Chang, Junzo WATADA

    ISME2010, Kokura, Japan,    2010

  • Building Fuzzy Portfolio Selection Model with Interval Data under Probability Distribution Function,

    Pei-Chun Lin, Junzo WATADA

    ISME2010, Kokura, Japan,    2010

  • A Hybrid Neural Network Approach for Solving Bilevel Programming Problems and its Application in Power System,

    Shamshul BAHAR YAAKOB, Junzo WATADA, Liu Szu Wen

    ISME2010, Kokura, Japan,    2010

    DOI

  • A SVM-RBF Method for Solving Imbalanced Data Problem,

    Lei Ding, Junzo WATADA, Lim Chun Chew, Zuwairie Ibrahim, Lee Wen Jau, Marzuki Khalid

    ISII2010-255, ISII2010, Dalian, China,    2010

  • A Fuzzy Regression Based Support Vector Machine (SVM) Approach to Fuzzy Classification

    Yu Chen, Witold Pedrycz, Junzo Watada

    ISII2010-175, ISII2010, Dalian, China,    2010

  • Performance Measurement in Manufacturing Enterprises: New Paradigm on Intelligent Data Analysis (IDA) Implementation,

    Azizul Azhar Ramli, Junzo WATADA, Witold PEDRYCZ

    JCSES,, Software Engineering and Computer Systems ()    2010

  • Combining Biological Computation and Fuzzy-Based Methods for Organisationally Cohesive Subgroups

    Ikno Kim, Junzo Watada

    INTERNATIONAL JOURNAL OF UNCONVENTIONAL COMPUTING   6 ( 3-4 ) 285 - 300  2010

     View Summary

    Cohesive subgroups in complicated employee relationships are commonly discovered and organised when personnel managers need to efficiently execute a job rotation. This provides employees with a better work life quality and encourages them to work more efficiently. Rearranging a small number of employees using electronic computation can be easily accomplished, but rearranging a larger number of employees is NP-hard. This paper proposes an unconventional approach to determine organisationally cohesive subgroups for better job rotation by combining biological computation and fuzzy-based methods to firstly detect all possible employees in cliques and components, secondly find employees in fuzzy cliques, and finally arrange the employees into similar groups. Moreover, the efficiency of performing a fuzzy analysis with biological computation is measured.

  • A Molecular Computational Approach to Solving a Work Centre Sequence-Oriented Manufacturing Problem of Classical Job Shop Scheduling

    Ikno Kim, Junzo Watada

    INTERNATIONAL JOURNAL OF UNCONVENTIONAL COMPUTING   6 ( 6 ) 473 - 487  2010

     View Summary

    Classical job shop scheduling includes an intractable manufacturing scheduling problem. The problem is how to minimise the maximum manufacturing completion time in high variety and low volume requirements. Further, we have to consider work centre sequences for how all the given jobs are sequenced by work centre orders, meaning that it is an NP-hard problem. A number of different types of methods and algorithms have been proposed for solving this issue. In this article, we focused on molecular computational methods and biological techniques to propose a new algorithm. Our algorithm can be used to search all the feasible manufacturing schedules, and isolate the schedule with the truly minimised maximum manufacturing completion time.

  • A Particle Swarm Optimization Approach for Routing in PCB Holes Drilling,

    Asrul Adam, Amar Faiz Zainal Abidin, Zuwairie Ibrahim, Abdul Rashid Husain, ZulkiliMd Yusof, Ismail Ibrahim, Junzo WATADA

    submitted to IJICIC submitted and first review result in 20100923,, IF=2.791 ()    2010

  • T-norm-based limit theorems for fuzzy random variables

    S. Wang, J. Watada

    JOURNAL OF INTELLIGENT & FUZZY SYSTEMS   21 ( 4 ) 233 - 242  2010

     View Summary

    The objective of this paper is to derive some limit theorems of fuzzy random variables under the extension principle associated with continuous Archimedean triangular norms (t-norms). First of all, some convergence theorems for the sum of fuzzy random variables in chance measure and expected value are proved respectively based on the arithmetics of continuous Archimedean triangular norms. Then, a law of large numbers for fuzzy random variables is established by using the obtained convergence theorems. The results of the derived law of large numbers can degenerate to the strong laws of large numbers for random variables and fuzzy variables, respectively.

    DOI

  • A Fuzzy Regression Based Support Vector Machine (SVM) Approach to Fuzzy Classification

    Yu Chen, Witold Pedrycz, Junzo Watada

    ICIC EL, (ISSN 1881-803X)   4 ( 6(B) ) 2355 - 2362  2010

  • Strategy Building for Foreign Exchange Exposure

    Junzo Watada, Song Wen

    International Journal of Innovative Management, Information & Production (IMIP) ()   1 ( 1 ) 1 - 17  2010

  • A SVM-RBF Method for Solving Imbalanced Data Problem,

    Lei Ding, Junzo WATADA, Lim Chun Chew, Zuwairie Ibrahim, Lee Wen Jau, Marzuki Khalid

    ICIC EL, (ISSN 1881-803X)   4 ( 6(B) ) 2419 - 2424  2010

  • Fuzzy Goal Programming for Multi-level Multi-objective Problem: An Additive Model,

    Nureize Arbaiy, Junzo Watada

    ICSECS 2011, pp.81-95, Kuantan, Malaysia, June 2011    2010

    DOI

  • Building Multi-Attribute Decision Model Based on Kansei Information in Environment with Hybrid Uncertainty,

    Nureize Arbaiy, Junzo WATADA

    Proceedings, 3rd International Conference on Intelligent Decision Technologies (KES-IDT2011), Pireus, Greece, July 2011    2010

  • Solving Bilevel Quadratic Programming Problems and Its Application,

    Shamshul Bahar Yaakob, Junzo Watada

    Proceedings, 15th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems (KES 2011), Kaiserslautern, Germany, Sep. 2011     187 - 196  2010

    DOI

  • Robust Color Image Segmentation by Karhunen-Loeve Transform-Based Otsu Multi-Thresholding and K-neabs Clustering,

    Chenxue Wang, Junzo WATADA

    ICGEC2011, IEEE, Kinmen Taiwan,    2010

    DOI

  • Fuzzy Game-Based Real Option Analysis in Competitive Investment Situation,

    Tanatch Tangsajanaphakul, Huiming Zhang, Junzo WATADA

    ICGEC2011, IEEE, Kinmen Taiwan,    2010

    DOI

  • A new MOPSO to solve a multi-objective portfolio selection model with fuzzy Value-at-Risk,

    Bo Wang, Yu Li, Junzo WATADA

    Proceedings, KES-IDT2011 held at Pireus, Greece on    2010

    DOI

  • Operating Enzyme-based OR and AND Logic Gates with molecular signals,

    Yu-yi Chu, Ikno KIM, Junzo WATADA, Jui-yu Wu

    Proceedings, KES-IDT2011 held at Pireus, Greece on    2010

  • A Service Cost Optimization Approach to Supply Balance of Sustainable Power Generation,

    Junzo WATADA, Yu-Lien Tai, Yingru Wang, Jaeseok Choi, Mitsushige Shiota

    Proceedings, PICMET2011 (IEEE-TEM), held at Portland on    2010

  • Multi-level Multi-Objective Decision Problem through Fuzzy Random Regression based Objective Function,

    Nureize Arbaiy, Junzo WATADA

    FUZZ-IEEE2011, Taipei,    2010

    DOI

  • Building a Fuzzy Multi-objective Portfolio Selection model with Distinct Risk Measurements

    You Li, Bo Wang, Junzo WATADA

    FUZZ-IEEE2011, Taipei,    2010

    DOI

  • Building models based on environment with fuzzy random uncertainty,

    Junzo WATADA

    Plenary Talk, CD Proceedings, ICMSAO2011, Kuala Lumpur,     .1-15.  2010

  • Construct logic operation gates of OR and and with multiple enzymes

    Chu Yuyi, Juiyu Wu, Junzo Watada, Ikno Kim

    Proceedings - 4th International Conference on Genetic and Evolutionary Computing, ICGEC 2010     35 - 38  2010

     View Summary

    Biomoleculars act as a tool to build a multiple enzyme system based on human metabolic actions to perform basic logic operation connecting OR and AND gates. Three enzymes (invertase, amyloglucosidase, hexokinase) concert as a logic operation part, processing three molecular input signals (sucrose, maltose, and ATP) to produce G6P (glucose-6-phosphate). The other two enzymes glucose-6-phosphate dehydrogenase (G6PD) and salicylate hydroxylase (SHL) compose to function as a signal displayer. Furthermore, we used a latent fluorescent molecule composed of sacylate and fluorescence which can be catalyzed and release fluorescent molecular to produce output signal. According to our experiments, firstly, our design is proved. The typical characteristics of enzyme reactions have been discovered through comparing the expected theoretical cures with the result cures. Secondly, the possibility of applying multiple logic gates into complicate networks has been shown. © 2010 IEEE.

    DOI

  • Decision making of facility locations based on fuzzy probability distribution function

    P. C. Lin, S. Wang, J. Watada

    IEEM2010 - IEEE International Conference on Industrial Engineering and Engineering Management     1911 - 1915  2010

     View Summary

    Facility locations can appear to be a challenge for both novice and experienced analysts. But, it is far more efficient if its decision making follows a logical, systematic procedure. Such an approach markedly increases the chances of finding a location and improves the firm's objectives. This paper aims to provide Fuzzy Probability Distribution Functions (FPDF) so that the decision making can be pursued under hybrid uncertainly. FPDF is defined using three parameters of central point, right radius and left radius. Moreover, a new fuzzy probability distribution function is defined on the basis of these three parameters. When FPDF are properly generated, the functions can easily be used in the decision making of facility locations by means of optimal model proposed in Shuming Wang, et al.. ©2010 IEEE.

    DOI

  • A Hybrid Intelligent Algorithm for Solving the Bilevel Programming Models

    Shamshul Bahar Yaakob, Junzo Watada

    KNOWLEDGE-BASED AND INTELLIGENT INFORMATION AND ENGINEERING SYSTEMS, PT II   6277   485 - 494  2010

     View Summary

    In this paper, genetic algorithm (GA) and neural network (NN) are integrated to produce a hybrid intelligent algorithm for solving the bilevel programming models. GA is used to select a set of potential combination from the entire generated combination. Then a meta-controlled Boltzmann machine which is formulated by comprising a Hopfield network and a Boltzmann machine (BM) is used to effectively and efficiently determine the optimal solution. The proposed method is used to solve the examples of bilevel programming for two- level investment in a new facility. The upper layer will decide the optimal company investment. The lower layer is used to decide the optimal department investment. Typical application examples are provided to illustrate the effectiveness and practicability of the hybrid intelligent algorithm.

    DOI

  • Fuzzy power system reliability model based on value-at-risk

    Bo Wang, You Li, Junzo Watada

    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)   6277 ( 2 ) 445 - 453  2010

     View Summary

    Conventional power system optimization problems deal with the power demand and spinning reserve through real values. In this research, we employ fuzzy variables to better characterize these values in uncertain environment. In building the fuzzy power system reliable model, fuzzy Value-at-Risk (VaR) can evaluate the greatest value under given confidence level and is a new technique to measure the constraints and system reliability. The proposed model is a complex nonlinear optimization problem which cannot be solved by simplex algorithm. In this paper, particle swarm optimization (PSO) is used to find optimal solution. The original PSO algorithm is improved to straighten out local convergence problem. Finally, the proposed model and algorithm are exemplified by one numerical example. © 2010 Springer-Verlag.

    DOI

  • A rough-set-based two-class classifier for large imbalanced dataset

    Junzo Watada, Lee-Chuan Lin, Lei Ding, Mohd. Ibrahim Shapiai, Lim Chun Chew, Zuwairie Ibrahim, Lee Wen Jau, Marzuki Khalid

    Smart Innovation, Systems and Technologies   4   641 - 651  2010

     View Summary

    The objective of this paper is to provide a rouch-set-based two-class classifier approach to classifying samples in large and imbalanced dataset. A database has plenty of hidden knowledge, which can be used in decision making to support commerce, research and other activities. Prediction is another form of expanding data analysis. It enables us to establish a data model using existing data and to predict the trend of data in future. In this paper, a method consists of data scaling, rough sets analysis and support vector machine with radial basis function (SVM-RBF), which is used to classify a large and imbalanced data set obtained in semiconductor industry. © Springer-Verlag Berlin Heidelberg 2010.

    DOI

  • An evidential reasoning based LSA approach to document classification for knowledge acquisition,

    Rozlini Mohamed, Junzo WATADA

    IEEM2010, Industrial Engineering and Engineering Management (IEEM), 2010 IEEE International Conference on,IEEM.2010.5674188, Publication Year:     1092 - 1096  2010

    DOI

  • A Simulated Annealing Based Possibilistic Fuzzy C-means Algorithm for Clustering Problems,

    Wen Song, Shuming WANG, Junzo WATADA

    IFMIP 2010, WAC2010, September 19 ? September 23, 2010, Kobe International Conference Center, Kobe, JAPAN,    2010

  • Building Fuzzy Random Objective Function for Interval Fuzzy Goal Programming,

    Nureize binti Arbaiy, Junzo WATADA

    IEEE International Conference on Industrial Engineering and Engineering Management,    2010

    DOI

  • Function and Surface Approximation based on Enhanced Kernel Regression for Small Sample Sets,

    Mohd Ibrahim Shapiai, Zuwairie Ibrahim, Marzuki Khalid, Lee Wen Jau, Vladimir Pavlovic, Junzo WATADA

    International Journal of Innovative Computing, Information, and Control (IJICIC),, IF=2.791   7 ( 10 )  2010

  • A two-step supervised learning artificial neural network for imbalanced data set problems,

    Asrul Adam, Mohd, Ibrahim Shapiai (UTM, Lim Chun Chew(Intell, Zuwairie Ibrahim(UTM, Lee Wen Jau(INTEL, Marzuki Khalid(UTM, Junzo WATADA(Waseda

    IJICIC,, IF=2.791    2010

  • Short-term Power Load Forecasting Method by Radial-basis-function Neural Network with Support Vector Machine Model,

    Jiliang Xue, Junzo WATADA

    ICIC Express Letters,   5 ( 6(B) )  2010

  • Fuzzy Portfolio Selection Models with Value-at-Risk,

    Bo Wang, Shuming WANG, Junzo WATADA

    IEEE transactions on Fuzzy Systems, accepted,, IF=3.624    2010

  • Performance Measurement in Manufacturing Enterprises: New Paradigm on Intelligent Data Analysis (IDA) Implementation,

    Azizul Azhar Ramli, Junzo WATADA, Witold PEDRYCZ

    JCSES,,    2010

  • A Particle Swarm Optimization Approach for Routing in PCB Holes Drilling,

    Asrul Adam, Amar Faiz Zainal Abidin, Zuwairie Ibrahim, Abdul Rashid Husain, ZulkiliMd Yusof, Ismail Ibrahim, Junzo WATADA

    submitted to IJICIC submitted and first review result in 20100923,, IF=2.791    2010

  • Fuzzy Goal Programming for Multi-level Multi-objective Problem: An Additive Model,

    Nureize Arbaiy, Junzo Watada

    ICSECS 2011, pp.81-95, Kuantan, Malaysia, June 2011    2010

    DOI

  • Building Multi-Attribute Decision Model Based on Kansei Information in Environment with Hybrid Uncertainty,

    Nureize Arbaiy, Junzo WATADA

    Proceedings, 3rd International Conference on Intelligent Decision Technologies (KES-IDT2011), Pireus, Greece, July 2011    2010

  • Solving Bilevel Quadratic Programming Problems and Its Application,

    Shamshul Bahar Yaakob, Junzo Watada

    Proceedings, 15th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems (KES 2011), Kaiserslautern, Germany, Sep. 2011     187 - 196  2010

    DOI

  • Robust Color Image Segmentation by Karhunen-Loeve Transform-Based Otsu Multi-Thresholding and K-neabs Clustering,

    Chenxue Wang, Junzo WATADA

    ICGEC2011, IEEE, Kinmen Taiwan,    2010

    DOI

  • Particle Filter-Based Height estimation in Human Tracking

    Zhenyuan Xu, Junzo WATADA, Zalili Biniti Musa

    ICGEC2011, IEEE, at Kinmen Taiwan,    2010

    DOI

  • Operating Enzyme-based OR and AND Logic Gates with molecular signals,

    Yu-yi Chu, Ikno KIM, Junzo WATADA, Jui-yu Wu

    Proceedings, KES-IDT2011 held at Pireus, Greece on    2010

  • A Service Cost Optimization Approach to Supply Balance of Sustainable Power Generation,

    Junzo WATADA, Yu-Lien Tai, Yingru Wang, Jaeseok Choi, Mitsushige Shiota

    Proceedings, PICMET2011 (IEEE-TEM), held at Portland on    2010

  • Multi-level Multi-Objective Decision Problem through Fuzzy Random Regression based Objective Function,

    Nureize Arbaiy, Junzo WATADA

    FUZZ-IEEE2011, Taipei,    2010

    DOI

  • Statistic Test on Fuzzy Portfolio Selection Model,

    Pei-Chun Lin, Junzo WATADA, Berlin Wu

    FUZZ-IEEE2011, Taipei,    2010

    DOI

  • Re-Scheduling the Unit Commitment Problem in Fuzzy Environment,

    Bo Wang, You Li, Junzo WATADA

    FUZZ-IEEE2011, Taipei,    2010

    DOI

  • Building models based on environment with fuzzy random uncertainty,

    Junzo WATADA

    Plenary Talk, CD Proceedings, ICMSAO2011, Kuala Lumpur,     .1-15.  2010

  • Construct logic operation gates of OR and and with multiple enzymes

    Chu Yuyi, Juiyu Wu, Junzo Watada, Ikno Kim

    Proceedings - 4th International Conference on Genetic and Evolutionary Computing, ICGEC 2010     35 - 38  2010

     View Summary

    Biomoleculars act as a tool to build a multiple enzyme system based on human metabolic actions to perform basic logic operation connecting OR and AND gates. Three enzymes (invertase, amyloglucosidase, hexokinase) concert as a logic operation part, processing three molecular input signals (sucrose, maltose, and ATP) to produce G6P (glucose-6-phosphate). The other two enzymes glucose-6-phosphate dehydrogenase (G6PD) and salicylate hydroxylase (SHL) compose to function as a signal displayer. Furthermore, we used a latent fluorescent molecule composed of sacylate and fluorescence which can be catalyzed and release fluorescent molecular to produce output signal. According to our experiments, firstly, our design is proved. The typical characteristics of enzyme reactions have been discovered through comparing the expected theoretical cures with the result cures. Secondly, the possibility of applying multiple logic gates into complicate networks has been shown. © 2010 IEEE.

    DOI

  • Decision making of facility locations based on fuzzy probability distribution function

    P. C. Lin, S. Wang, J. Watada

    IEEM2010 - IEEE International Conference on Industrial Engineering and Engineering Management     1911 - 1915  2010

     View Summary

    Facility locations can appear to be a challenge for both novice and experienced analysts. But, it is far more efficient if its decision making follows a logical, systematic procedure. Such an approach markedly increases the chances of finding a location and improves the firm's objectives. This paper aims to provide Fuzzy Probability Distribution Functions (FPDF) so that the decision making can be pursued under hybrid uncertainly. FPDF is defined using three parameters of central point, right radius and left radius. Moreover, a new fuzzy probability distribution function is defined on the basis of these three parameters. When FPDF are properly generated, the functions can easily be used in the decision making of facility locations by means of optimal model proposed in Shuming Wang, et al.. ©2010 IEEE.

    DOI

  • A hybrid intelligent algorithm for solving the bilevel programming models

    Shamshul Bahar Yaakob, Junzo Watada

    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)   6277 ( 2 ) 485 - 494  2010

     View Summary

    In this paper, genetic algorithm (GA) and neural network (NN) are integrated to produce a hybrid intelligent algorithm for solving the bilevel programming models. GA is used to select a set of potential combination from the entire generated combination. Then a meta-controlled Boltzmann machine which is formulated by comprising a Hopfield network and a Boltzmann machine (BM) is used to effectively and efficiently determine the optimal solution. The proposed method is used to solve the examples of bilevel programming for two- level investment in a new facility. The upper layer will decide the optimal company investment. The lower layer is used to decide the optimal department investment. Typical application examples are provided to illustrate the effectiveness and practicability of the hybrid intelligent algorithm. © 2010 Springer-Verlag.

    DOI

  • Fuzzy power system reliability model based on value-at-risk

    Bo Wang, You Li, Junzo Watada

    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)   6277 ( 2 ) 445 - 453  2010

     View Summary

    Conventional power system optimization problems deal with the power demand and spinning reserve through real values. In this research, we employ fuzzy variables to better characterize these values in uncertain environment. In building the fuzzy power system reliable model, fuzzy Value-at-Risk (VaR) can evaluate the greatest value under given confidence level and is a new technique to measure the constraints and system reliability. The proposed model is a complex nonlinear optimization problem which cannot be solved by simplex algorithm. In this paper, particle swarm optimization (PSO) is used to find optimal solution. The original PSO algorithm is improved to straighten out local convergence problem. Finally, the proposed model and algorithm are exemplified by one numerical example. © 2010 Springer-Verlag.

    DOI

  • A rough-set-based two-class classifier for large imbalanced dataset

    Junzo Watada, Lee-Chuan Lin, Lei Ding, Mohd. Ibrahim Shapiai, Lim Chun Chew, Zuwairie Ibrahim, Lee Wen Jau, Marzuki Khalid

    Smart Innovation, Systems and Technologies   4   641 - 651  2010

     View Summary

    The objective of this paper is to provide a rouch-set-based two-class classifier approach to classifying samples in large and imbalanced dataset. A database has plenty of hidden knowledge, which can be used in decision making to support commerce, research and other activities. Prediction is another form of expanding data analysis. It enables us to establish a data model using existing data and to predict the trend of data in future. In this paper, a method consists of data scaling, rough sets analysis and support vector machine with radial basis function (SVM-RBF), which is used to classify a large and imbalanced data set obtained in semiconductor industry. © Springer-Verlag Berlin Heidelberg 2010.

    DOI

  • An evidential reasoning based LSA approach to document classification for knowledge acquisition,

    Rozlini Mohamed, Junzo WATADA

    IEEM2010, Industrial Engineering and Engineering Management (IEEM), 2010 IEEE International Conference on,IEEM.2010.5674188, Publication Year:     1092 - 1096  2010

    DOI

  • A Simulated Annealing Based Possibilistic Fuzzy C-means Algorithm for Clustering Problems,

    Wen Song, Shuming WANG, Junzo WATADA

    IFMIP 2010, WAC2010, September 19 ? September 23, 2010, Kobe International Conference Center, Kobe, JAPAN,    2010

  • Building Fuzzy Random Objective Function for Interval Fuzzy Goal Programming,

    Nureize binti Arbaiy, Junzo WATADA

    IEEE International Conference on Industrial Engineering and Engineering Management,    2010

    DOI

  • Some properties of T-independent fuzzy variables,

    Shuming Wang, Junzo Watada

    Mathematical and Computer Modelling,   53 ( 5-6 ) 970 - 984  2010

    DOI

  • Function and Surface Approximation based on Enhanced Kernel Regression for Small Sample Sets,

    Mohd Ibrahim Shapiai, Zuwairie Ibrahim, Marzuki Khalid, Lee Wen Jau, Vladimir Pavlovic, Junzo WATADA

    International Journal of Innovative Computing, Information, and Control (IJICIC),, IF=2.791   7 ( 10 )  2010

  • A two-step supervised learning artificial neural network for imbalanced data set problems,

    Asrul Adam, Mohd, Ibrahim Shapiai (UTM, Lim Chun Chew(Intell, Zuwairie Ibrahim(UTM, Lee Wen Jau(INTEL, Marzuki Khalid(UTM, Junzo WATADA(Waseda

    IJICIC,, IF=2.791    2010

  • Short-term Power Load Forecasting Method by Radial-basis-function Neural Network with Support Vector Machine Model,

    Jiliang Xue, Junzo WATADA

    ICIC Express Letters,   5 ( 6(B) )  2010

  • Fuzzy Portfolio Selection Models with Value-at-Risk,

    Bo Wang, Shuming WANG, Junzo WATADA

    IEEE transactions on Fuzzy Systems, accepted,, IF=3.624    2010

  • Performance Measurement in Manufacturing Enterprises: New Paradigm on Intelligent Data Analysis (IDA) Implementation,

    Azizul Azhar Ramli, Junzo WATADA, Witold PEDRYCZ

    JCSES,,    2010

  • A Particle Swarm Optimization Approach for Routing in PCB Holes Drilling,

    Asrul Adam, Amar Faiz Zainal Abidin, Zuwairie Ibrahim, Abdul Rashid Husain, ZulkiliMd Yusof, Ismail Ibrahim, Junzo WATADA

    submitted to IJICIC submitted and first review result in 20100923,, IF=2.791    2010

  • KNOWLEDGE ACQUISITION FROM TIME SERIES DATA THROUGH ROUGH SETS ANALYSIS

    Yoshiyuki Matsumoto, Junzo Watada

    INTERNATIONAL JOURNAL OF INNOVATIVE COMPUTING INFORMATION AND CONTROL   5 ( 12B ) 4885 - 4897  2009.12

     View Summary

    Z. Pawlak proposed rough set theory in 1982. This theory provides a tool to mine knowledge as decision rules from a database, web-based information and so on. Decision rules are also used for data analysis. These decision rules can reason the conclusion of an unknown object using various premises. The objective of this paper is to apply the rough set theory to the analysis of time-series data. Using an example, this paper shows how knowledge is acquired and illustrates the difference among decision rules obtained using different time periods.

  • Building Confidence-Interval-Based Fuzzy Random Regression Models

    Junzo Watada, Shuming Wang, Witold Pedrycz

    IEEE TRANSACTIONS ON FUZZY SYSTEMS   17 ( 6 ) 1273 - 1283  2009.12

     View Summary

    In real-world regression analysis, statistical data may be linguistically imprecise or vague. Given the co-existence of stochastic and fuzzy uncertainty, real data cannot be characterized by using only the formalism of random variables. In order to address regression problems in the presence of such hybrid uncertain data, fuzzy random variables are introduced in this study to serve as an integral component of regression models. A new class of fuzzy regression models that is based on fuzzy random data is built, and is called the confidence-interval-based fuzzy random regression model (CI-FRRM). First, a general fuzzy regression model for fuzzy random data is introduced. Then, using expectations and variances of fuzzy random variables, sigma-confidence intervals are constructed for fuzzy random input-output data. The CI-FRRM is established based on the sigma-confidence intervals. The proposed regression model gives rise to a nonlinear programming problem that consists of fuzzy numbers or interval numbers. Since sign changes in the fuzzy coefficients modify the entire programming structure of the solution process, the inherent dynamic nonlinearity of this optimization makes it difficult to exploit the techniques of linear programming or classical nonlinear programming. Therefore, we resort to some heuristics. Finally, an illustrative example is provided.

    DOI

  • KNOWLEDGE ACQUISITION FROM TIME SERIES DATA THROUGH ROUGH SETS ANALYSIS

    Yoshiyuki Matsumoto, Junzo Watada

    INTERNATIONAL JOURNAL OF INNOVATIVE COMPUTING INFORMATION AND CONTROL   5 ( 12B ) 4885 - 4897  2009.12

     View Summary

    Z. Pawlak proposed rough set theory in 1982. This theory provides a tool to mine knowledge as decision rules from a database, web-based information and so on. Decision rules are also used for data analysis. These decision rules can reason the conclusion of an unknown object using various premises. The objective of this paper is to apply the rough set theory to the analysis of time-series data. Using an example, this paper shows how knowledge is acquired and illustrates the difference among decision rules obtained using different time periods.

  • Building Confidence-Interval-Based Fuzzy Random Regression Models

    Junzo Watada, Shuming Wang, Witold Pedrycz

    IEEE TRANSACTIONS ON FUZZY SYSTEMS   17 ( 6 ) 1273 - 1283  2009.12

     View Summary

    In real-world regression analysis, statistical data may be linguistically imprecise or vague. Given the co-existence of stochastic and fuzzy uncertainty, real data cannot be characterized by using only the formalism of random variables. In order to address regression problems in the presence of such hybrid uncertain data, fuzzy random variables are introduced in this study to serve as an integral component of regression models. A new class of fuzzy regression models that is based on fuzzy random data is built, and is called the confidence-interval-based fuzzy random regression model (CI-FRRM). First, a general fuzzy regression model for fuzzy random data is introduced. Then, using expectations and variances of fuzzy random variables, sigma-confidence intervals are constructed for fuzzy random input-output data. The CI-FRRM is established based on the sigma-confidence intervals. The proposed regression model gives rise to a nonlinear programming problem that consists of fuzzy numbers or interval numbers. Since sign changes in the fuzzy coefficients modify the entire programming structure of the solution process, the inherent dynamic nonlinearity of this optimization makes it difficult to exploit the techniques of linear programming or classical nonlinear programming. Therefore, we resort to some heuristics. Finally, an illustrative example is provided.

    DOI

  • Building Confidence-Interval-Based Fuzzy Random Regression Models

    Junzo Watada, Shuming Wang, Witold Pedrycz

    IEEE TRANSACTIONS ON FUZZY SYSTEMS   17 ( 6 ) 1273 - 1283  2009.12

     View Summary

    In real-world regression analysis, statistical data may be linguistically imprecise or vague. Given the co-existence of stochastic and fuzzy uncertainty, real data cannot be characterized by using only the formalism of random variables. In order to address regression problems in the presence of such hybrid uncertain data, fuzzy random variables are introduced in this study to serve as an integral component of regression models. A new class of fuzzy regression models that is based on fuzzy random data is built, and is called the confidence-interval-based fuzzy random regression model (CI-FRRM). First, a general fuzzy regression model for fuzzy random data is introduced. Then, using expectations and variances of fuzzy random variables, sigma-confidence intervals are constructed for fuzzy random input-output data. The CI-FRRM is established based on the sigma-confidence intervals. The proposed regression model gives rise to a nonlinear programming problem that consists of fuzzy numbers or interval numbers. Since sign changes in the fuzzy coefficients modify the entire programming structure of the solution process, the inherent dynamic nonlinearity of this optimization makes it difficult to exploit the techniques of linear programming or classical nonlinear programming. Therefore, we resort to some heuristics. Finally, an illustrative example is provided.

    DOI

  • Fuzzy random renewal reward process and its applications

    Shuming Wang, Junzo Watada

    INFORMATION SCIENCES   179 ( 23 ) 4057 - 4069  2009.11

     View Summary

    This paper studies a renewal reward process with fuzzy random interarrival times and rewards under the T-independence associated with any continuous Archimedean t-norm T. The interarrival times and rewards of the renewal reward process are assumed to be positive fuzzy random variables whose fuzzy realizations are T-independent fuzzy variables. Under these conditions, some limit theorems in mean chance measure are derived for fuzzy random renewal rewards: In the sequel, a fuzzy random renewal reward theorem is proved for the long-run expected reward per unit time of the renewal reward process. The renewal reward theorem obtained in this paper can degenerate to that of stochastic renewal theory. Finally, some application examples are provided to illustrate the utility of the result. (C) 2009 Elsevier Inc. All rights reserved.

    DOI

  • Value-at-Risk-Based Two-Stage Fuzzy Facility Location Problems

    Shuming Wang, Junzo Watada, Witold Pedrycz

    IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS   5 ( 4 ) 465 - 482  2009.11

     View Summary

    Reducing risks in location decisions when coping with imprecise information is critical in supply chain management so as to increase competitiveness and profitability. In this paper, a two-stage fuzzy facility location problem with Value-at-Risk (VaR), called VaR-FFLP, is proposed, which results in a two-stage fuzzy zero-one integer programming problem. Some properties of the VaR-FFLP, including the value of perfect information (VPI), the value of fuzzy solution (VFS), and the bounds of the fuzzy solution, are discussed. Since the fuzzy parameters of the location problem are represented in the form of continuous fuzzy variables, the determination of VaR is inherently an infinite-dimensional optimization problem that cannot be solved analytically. Therefore, a method based on the discretization of the fuzzy variables is proposed to approximate the VaR. The Approximation Approach converts the original problem into a finite-dimensional optimization problem. A pertinent convergence theorem for the Approximation Approach is proved. Subsequently, by combining the Simplex Algorithm, the Approximation Approach, and a mechanism of genotype-phenotype-mutation-based binary particle swarm optimization (GPM-BPSO), a hybrid GPM-BPSO algorithm is being exploited to solve the VaR-FFLP. A numerical example illustrates the effectiveness of the hybrid GPM-BPSO algorithm and shows its enhanced performance in comparison with the results obtained by other approaches using genetic algorithm (GA), tabu search (TS), and Boolean BPSO (B-BPSO).

    DOI

  • Value-at-Risk-Based Two-Stage Fuzzy Facility Location Problems

    Shuming Wang, Junzo Watada, Witold Pedrycz

    IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS   5 ( 4 ) 465 - 482  2009.11

     View Summary

    Reducing risks in location decisions when coping with imprecise information is critical in supply chain management so as to increase competitiveness and profitability. In this paper, a two-stage fuzzy facility location problem with Value-at-Risk (VaR), called VaR-FFLP, is proposed, which results in a two-stage fuzzy zero-one integer programming problem. Some properties of the VaR-FFLP, including the value of perfect information (VPI), the value of fuzzy solution (VFS), and the bounds of the fuzzy solution, are discussed. Since the fuzzy parameters of the location problem are represented in the form of continuous fuzzy variables, the determination of VaR is inherently an infinite-dimensional optimization problem that cannot be solved analytically. Therefore, a method based on the discretization of the fuzzy variables is proposed to approximate the VaR. The Approximation Approach converts the original problem into a finite-dimensional optimization problem. A pertinent convergence theorem for the Approximation Approach is proved. Subsequently, by combining the Simplex Algorithm, the Approximation Approach, and a mechanism of genotype-phenotype-mutation-based binary particle swarm optimization (GPM-BPSO), a hybrid GPM-BPSO algorithm is being exploited to solve the VaR-FFLP. A numerical example illustrates the effectiveness of the hybrid GPM-BPSO algorithm and shows its enhanced performance in comparison with the results obtained by other approaches using genetic algorithm (GA), tabu search (TS), and Boolean BPSO (B-BPSO).

    DOI

  • Fuzzy random renewal reward process and its applications

    Shuming Wang, Junzo Watada

    INFORMATION SCIENCES   179 ( 23 ) 4057 - 4069  2009.11

     View Summary

    This paper studies a renewal reward process with fuzzy random interarrival times and rewards under the T-independence associated with any continuous Archimedean t-norm T. The interarrival times and rewards of the renewal reward process are assumed to be positive fuzzy random variables whose fuzzy realizations are T-independent fuzzy variables. Under these conditions, some limit theorems in mean chance measure are derived for fuzzy random renewal rewards: In the sequel, a fuzzy random renewal reward theorem is proved for the long-run expected reward per unit time of the renewal reward process. The renewal reward theorem obtained in this paper can degenerate to that of stochastic renewal theory. Finally, some application examples are provided to illustrate the utility of the result. (C) 2009 Elsevier Inc. All rights reserved.

    DOI

  • Value-at-Risk-Based Two-Stage Fuzzy Facility Location Problems

    Shuming Wang, Junzo Watada, Witold Pedrycz

    IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS   5 ( 4 ) 465 - 482  2009.11

     View Summary

    Reducing risks in location decisions when coping with imprecise information is critical in supply chain management so as to increase competitiveness and profitability. In this paper, a two-stage fuzzy facility location problem with Value-at-Risk (VaR), called VaR-FFLP, is proposed, which results in a two-stage fuzzy zero-one integer programming problem. Some properties of the VaR-FFLP, including the value of perfect information (VPI), the value of fuzzy solution (VFS), and the bounds of the fuzzy solution, are discussed. Since the fuzzy parameters of the location problem are represented in the form of continuous fuzzy variables, the determination of VaR is inherently an infinite-dimensional optimization problem that cannot be solved analytically. Therefore, a method based on the discretization of the fuzzy variables is proposed to approximate the VaR. The Approximation Approach converts the original problem into a finite-dimensional optimization problem. A pertinent convergence theorem for the Approximation Approach is proved. Subsequently, by combining the Simplex Algorithm, the Approximation Approach, and a mechanism of genotype-phenotype-mutation-based binary particle swarm optimization (GPM-BPSO), a hybrid GPM-BPSO algorithm is being exploited to solve the VaR-FFLP. A numerical example illustrates the effectiveness of the hybrid GPM-BPSO algorithm and shows its enhanced performance in comparison with the results obtained by other approaches using genetic algorithm (GA), tabu search (TS), and Boolean BPSO (B-BPSO).

    DOI

  • Modelling redundancy allocation for a fuzzy random parallel-series system

    Shuming Wang, Junzo Watada

    Journal of Computational and Applied Mathematics   232 ( 2 ) 539 - 557  2009.10

     View Summary

    Due to subjective judgment, imprecise human knowledge and perception in capturing statistical data, the real data of lifetimes in many systems are both random and fuzzy in nature. Based on the fuzzy random variables that are used to characterize the lifetimes, this paper studies the redundancy allocation problems to a fuzzy random parallel-series system. Two fuzzy random redundancy allocation models (FR-RAM) are developed through reliability maximization and cost minimization, respectively. Some properties of the FR-RAM are obtained, in which an analytical formula of reliability with convex lifetimes is derived and the sensitivity of the reliability is discussed. To solve the FR-RAMs, we first address the computation of reliability. A random simulation method based on the derived analytical formula is proposed to compute the reliability with convex lifetimes. As for the reliability with nonconvex lifetimes, the technique of fuzzy random simulation together with the discretization method of fuzzy random variable is employed to compute the reliability, and a convergence theorem of the fuzzy random simulation is proved. Subsequently, we integrate the computation approaches of the reliability and genetic algorithm (GA) to search for the approximately optimal redundancy allocation of the models. Finally, some numerical examples are provided to illustrate the feasibility of the solution algorithm and quantify its effectiveness. © 2009 Elsevier B.V. All rights reserved.

    DOI

  • Modelling redundancy allocation for a fuzzy random parallel-series system

    Shuming Wang, Junzo Watada

    JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS   232 ( 2 ) 539 - 557  2009.10

     View Summary

    Due to subjective judgment, imprecise human knowledge and perception in capturing statistical data, the real data of lifetimes in many systems are both random and fuzzy in nature. Based on the fuzzy random variables that are used to characterize the lifetimes, this paper studies the redundancy allocation problems to a fuzzy random parallel-series system.
    Two fuzzy random redundancy allocation models (FR-RAM) are developed through reliability maximization and cost minimization, respectively. Some properties of the FR-RAM are obtained, in which an analytical formula of reliability with convex lifetimes is derived and the sensitivity of the reliability is discussed. To solve the FR-RAMs, we first address the computation of reliability. A random simulation method based on the derived analytical formula is proposed to compute the reliability with convex lifetimes. As for the reliability with nonconvex lifetimes; the technique of fuzzy random simulation together with the discretization method of fuzzy random variable is employed to compute the reliability, and a convergence theorem of the fuzzy random simulation is proved. Subsequently, we integrate the computation approaches of the reliability and genetic algorithm (GA) to search for the approximately optimal redundancy allocation of the models. Finally, some numerical examples are provided to illustrate the feasibility of the solution algorithm and quantify its effectiveness. (C) 2009 Elsevier B.V. All rights reserved.

    DOI

  • Modelling redundancy allocation for a fuzzy random parallel-series system

    Shuming Wang, Junzo Watada

    JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS   232 ( 2 ) 539 - 557  2009.10

     View Summary

    Due to subjective judgment, imprecise human knowledge and perception in capturing statistical data, the real data of lifetimes in many systems are both random and fuzzy in nature. Based on the fuzzy random variables that are used to characterize the lifetimes, this paper studies the redundancy allocation problems to a fuzzy random parallel-series system.
    Two fuzzy random redundancy allocation models (FR-RAM) are developed through reliability maximization and cost minimization, respectively. Some properties of the FR-RAM are obtained, in which an analytical formula of reliability with convex lifetimes is derived and the sensitivity of the reliability is discussed. To solve the FR-RAMs, we first address the computation of reliability. A random simulation method based on the derived analytical formula is proposed to compute the reliability with convex lifetimes. As for the reliability with nonconvex lifetimes; the technique of fuzzy random simulation together with the discretization method of fuzzy random variable is employed to compute the reliability, and a convergence theorem of the fuzzy random simulation is proved. Subsequently, we integrate the computation approaches of the reliability and genetic algorithm (GA) to search for the approximately optimal redundancy allocation of the models. Finally, some numerical examples are provided to illustrate the feasibility of the solution algorithm and quantify its effectiveness. (C) 2009 Elsevier B.V. All rights reserved.

    DOI

  • RELIABILITY OPTIMIZATION OF A SERIES-PARALLEL SYSTEM WITH FUZZY RANDOM LIFETIMES

    Shuming Wang, Junzo Watada

    INTERNATIONAL JOURNAL OF INNOVATIVE COMPUTING INFORMATION AND CONTROL   5 ( 6 ) 1547 - 1558  2009.06

     View Summary

    This paper studies reliability optimization of a series-parallel system with fuzzy random lifetimes. A fuzzy random reliability model is developed to maximize the system reliability. Since the reliability function consists of fuzzy random parameters, classical mathematical programming methods is not applicable to the reliability model. Therefore, in order to solve the model, a fuzzy random simulation method is first proposed to compute the system reliability, and a theorem is proved which ensures the convergence of the fuzzy random simulation. Furthermore, a hybrid binary particle swarm optimization (BPSO) algorithm incorporating the fuzzy random simulation is proposed. Finally, a numerical example is provided to illustrate the proposed hybrid algorithm.

  • Decision Making With an Interpretive Structural Modeling Method Using a DNA-Based Algorithm

    Ikno Kim, Junzo Watada

    IEEE TRANSACTIONS ON NANOBIOSCIENCE   8 ( 2 ) 181 - 191  2009.06

     View Summary

    A novel method of interpretive structural modeling (ISM) using a DNA-based algorithm is proposed in this paper. ISM is commonly used when the current technology and its application to business administration, industrial and systems engineering, organizational behavior, etc., concern complicated or problematic issues, or situations among an element set of the given problem context for making decisions. When structuring a problem with a large number of elements in an ISM process, the crossings among elements should be minimized. This computationally complex minimization is NP-complete. The proposed algorithm describes how to calculate complex relations among elements to create a hierarchically restructured digraph. This paper also presents a new approach for applying a biological method to ISM to measure the efficiency of the algorithm in calculating a large number of elements for decision making.

    DOI

  • RELIABILITY OPTIMIZATION OF A SERIES-PARALLEL SYSTEM WITH FUZZY RANDOM LIFETIMES

    Shuming Wang, Junzo Watada

    INTERNATIONAL JOURNAL OF INNOVATIVE COMPUTING INFORMATION AND CONTROL   5 ( 6 ) 1547 - 1558  2009.06

     View Summary

    This paper studies reliability optimization of a series-parallel system with fuzzy random lifetimes. A fuzzy random reliability model is developed to maximize the system reliability. Since the reliability function consists of fuzzy random parameters, classical mathematical programming methods is not applicable to the reliability model. Therefore, in order to solve the model, a fuzzy random simulation method is first proposed to compute the system reliability, and a theorem is proved which ensures the convergence of the fuzzy random simulation. Furthermore, a hybrid binary particle swarm optimization (BPSO) algorithm incorporating the fuzzy random simulation is proposed. Finally, a numerical example is provided to illustrate the proposed hybrid algorithm.

  • Decision Making With an Interpretive Structural Modeling Method Using a DNA-Based Algorithm

    Ikno Kim, Junzo Watada

    IEEE TRANSACTIONS ON NANOBIOSCIENCE   8 ( 2 ) 181 - 191  2009.06

     View Summary

    A novel method of interpretive structural modeling (ISM) using a DNA-based algorithm is proposed in this paper. ISM is commonly used when the current technology and its application to business administration, industrial and systems engineering, organizational behavior, etc., concern complicated or problematic issues, or situations among an element set of the given problem context for making decisions. When structuring a problem with a large number of elements in an ISM process, the crossings among elements should be minimized. This computationally complex minimization is NP-complete. The proposed algorithm describes how to calculate complex relations among elements to create a hierarchically restructured digraph. This paper also presents a new approach for applying a biological method to ISM to measure the efficiency of the algorithm in calculating a large number of elements for decision making.

    DOI

  • Decision making with an interpretive structural modeling method using a DNA-based algorithm

    Ikno Kim, Junzo Watada

    IEEE Transactions on Nanobioscience   8 ( 2 ) 181 - 191  2009.06

     View Summary

    A novel method of interpretive structural modeling (ISM) using a DNA-based algorithm is proposed in this paper. ISM is commonly used when the current technology and its application to business administration, industrial and systems engineering, organizational behavior, etc., concern complicated or problematic issues, or situations among an element set of the given problem context for making decisions. When structuring a problem with a large number of elements in an ISM process, the crossings among elements should be minimized. This computationally complex minimization is NP-complete. The proposed algorithm describes how to calculate complex relations among elements to create a hierarchically restructured digraph. This paper also presents a new approach for applying a biological method to ISM to measure the efficiency of the algorithm in calculating a large number of elements for decision making. © 2009 IEEE.

    DOI PubMed CiNii

  • Keynote Speech

    Ronen Sen

    IEEE MICROWAVE MAGAZINE   10 ( 2 ) 115 - 116  2009.04

    Other  

    DOI

  • Fuzzy random renewal process with queueing applications

    Shuming Wang, Yan-Kui Liu, Junzo Watada

    COMPUTERS & MATHEMATICS WITH APPLICATIONS   57 ( 7 ) 1232 - 1248  2009.04

     View Summary

    Using extension principle associated with a class of continuous Archimedean triangular norms. this paper studies a fuzzy random renewal process in which the interarrival times are assumed to be independent and identically distributed fuzzy random variables. Some limit theorems in chance measure and in expected value for the sum of fuzzy random variables are proved on the basis of the continuous Archimedean triangular norm based arithmetics. Furthermore, we discuss the fuzzy random renewal process based on the obtained limit theorems, and derive a fuzzy random elementary renewal theorem for the long-run expected renewal rate. The renewal theorem obtained in this paper can degenerate to the corresponding classical result in stochastic renewal process. Finally, two case studies of queueing systems are provided to illustrate the application of the fuzzy random elementary renewal theorem. (C) 2009 Elsevier Ltd. All rights reserved.

    DOI

  • Fuzzy random renewal process with queueing applications

    Shuming Wang, Yan-Kui Liu, Junzo Watada

    COMPUTERS & MATHEMATICS WITH APPLICATIONS   57 ( 7 ) 1232 - 1248  2009.04

     View Summary

    Using extension principle associated with a class of continuous Archimedean triangular norms. this paper studies a fuzzy random renewal process in which the interarrival times are assumed to be independent and identically distributed fuzzy random variables. Some limit theorems in chance measure and in expected value for the sum of fuzzy random variables are proved on the basis of the continuous Archimedean triangular norm based arithmetics. Furthermore, we discuss the fuzzy random renewal process based on the obtained limit theorems, and derive a fuzzy random elementary renewal theorem for the long-run expected renewal rate. The renewal theorem obtained in this paper can degenerate to the corresponding classical result in stochastic renewal process. Finally, two case studies of queueing systems are provided to illustrate the application of the fuzzy random elementary renewal theorem. (C) 2009 Elsevier Ltd. All rights reserved.

    DOI

  • Fuzzy random renewal process with queueing applications

    Shuming Wang, Yan-Kui Liu, Junzo Watada

    COMPUTERS & MATHEMATICS WITH APPLICATIONS   57 ( 7 ) 1232 - 1248  2009.04

     View Summary

    Using extension principle associated with a class of continuous Archimedean triangular norms. this paper studies a fuzzy random renewal process in which the interarrival times are assumed to be independent and identically distributed fuzzy random variables. Some limit theorems in chance measure and in expected value for the sum of fuzzy random variables are proved on the basis of the continuous Archimedean triangular norm based arithmetics. Furthermore, we discuss the fuzzy random renewal process based on the obtained limit theorems, and derive a fuzzy random elementary renewal theorem for the long-run expected renewal rate. The renewal theorem obtained in this paper can degenerate to the corresponding classical result in stochastic renewal process. Finally, two case studies of queueing systems are provided to illustrate the application of the fuzzy random elementary renewal theorem. (C) 2009 Elsevier Ltd. All rights reserved.

    DOI

  • STUDYING DISTRIBUTION FUNCTIONS OF FUZZY RANDOM VARIABLES AND ITS APPLICATIONS TO CRITICAL VALUE FUNCTIONS

    Shuming Wang, Junzo Watada

    INTERNATIONAL JOURNAL OF INNOVATIVE COMPUTING INFORMATION AND CONTROL   5 ( 2 ) 279 - 292  2009.02

     View Summary

    In many fuzzy random optimization models, the objectives and constraints may consist of some distribution functions and critical value functions of prescribed fuzzy random variables. Therefore, we need to analyze the properties of those distribution functions and critical value functions so as to design more precise algorithms to solve such optimization problems. In this paper, we deal with the analytical properties of distributions functions of fuzzy random variables and discuss its applications to critical value functions. We first establish some continuity theorems for distribution functions of fuzzy random variables, which characterize the properties of right continuity, left continuity and continuity, respectively. Then, applying those continuity theorems, we study the properties of critical value functions of fuzzy random variables. The results obtained in this paper are useful in fuzzy random programming models.

  • STUDYING DISTRIBUTION FUNCTIONS OF FUZZY RANDOM VARIABLES AND ITS APPLICATIONS TO CRITICAL VALUE FUNCTIONS

    Shuming Wang, Junzo Watada

    INTERNATIONAL JOURNAL OF INNOVATIVE COMPUTING INFORMATION AND CONTROL   5 ( 2 ) 279 - 292  2009.02

     View Summary

    In many fuzzy random optimization models, the objectives and constraints may consist of some distribution functions and critical value functions of prescribed fuzzy random variables. Therefore, we need to analyze the properties of those distribution functions and critical value functions so as to design more precise algorithms to solve such optimization problems. In this paper, we deal with the analytical properties of distributions functions of fuzzy random variables and discuss its applications to critical value functions. We first establish some continuity theorems for distribution functions of fuzzy random variables, which characterize the properties of right continuity, left continuity and continuity, respectively. Then, applying those continuity theorems, we study the properties of critical value functions of fuzzy random variables. The results obtained in this paper are useful in fuzzy random programming models.

  • Wise Mining Method through Ant Colony Optimization

    Yang Jianxiong, Junzo Watada

    2009 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN AND CYBERNETICS (SMC 2009), VOLS 1-9     1833 - 1839  2009

     View Summary

    This paper proposes an algorithm for data mining named Pheromone-Miner (ant-colony-based data miner). The algorithm is inspired by both researches on the behavior of real ant colonies and data mining concepts as well as principles. The goal of Pheromone-Miner is to extract more exact knowledge from a database. Pheromone-based mining breaks through limitations of other mining approaches. We compare the performance of pheromone-miner with a general semantic miner. The accident causes discovered by ant-miner are considerably more accurate than those discovered by a general semantic miner. In a word, this evolutionary algorithm is suitable for improving the accuracy of data miners.

    DOI

  • The effect of the operational performance on competitive advantage ? an empiraical study of Taiwan,

    You-Hsin Tsai, Junzo WATADA

    Proceedings, International Conference on Control, Instrumentaion and Mechatoronics Engineering (CIM 2009),    2009

  • Towards a New Medical Decision Support System with Bio-inspired Interpretive Structural Modelling

    Ikno Kim, Junzo Watada

    NEW ADVANCES IN INTELLIGENT DECISION TECHNOLOGIES   199   459 - 466  2009

     View Summary

    Interpretive structural modelling (ISM) is a useful method employed in decision making in industrial and systems engineering fields. Moreover, ISM often plays an important role in structuralising particular issues or problems related to medical issues. A small number of elements can be straightforwardly calculated using ISM, but it is difficult to structuralise the problem with a large number of elements using electronic computers in polynomial time. In the real world, medical decision support systems (MDSS) are basically composed of electronic computer-based systems. Therefore, in this paper, we show results on the basis of using a bio-inspired ISM that measures the efficiency of combining a computer-based decision support system towards the creation of a new MDSS, using an example of a rehabilitation centre selection problem.

    DOI

  • Mining method through ant colony optimization,

    Jianxiong, Yang, WATADA, Junzo, Wise

    Systems, Man and Cybernetics, 2009. SMC 2009. IEEE International Conference on 11-14 Oct. 2009     1833 - 1839  2009

    DOI

  • Fuzzy Random Multi-attribute Evaluation for Oil Palm Fruit,

    Nureize binti Arbaiy, Junzo WATADA, Xin Liu

    Proceedings, International Conference on Control, Instrumentaion and Mechatoronics Engineering (CIM 2009),    2009

  • Fuzzy Random Facility Location Problems with Recourse

    Shuming Wang, Junzo Watada, Witold Pedrycz

    2009 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN AND CYBERNETICS (SMC 2009), VOLS 1-9     1846 - +  2009

     View Summary

    The objective of this paper is to study facility location problems under a hybrid uncertain environment involving randomness and fuzziness. A two-stage fuzzy random facility location model with recourse is developed in which the demands and the costs are assumed to be fuzzy random variables. As in general the fuzzy random parameters in the model can be regarded as continuous fuzzy random variables with infinite realizations, the computation of the recourse requires solving infinite second-stage programming problems. Owing to this fact, the recourse function cannot be calculated analytically, which implies that the model cannot benefit from the use of methods of classical mathematical programming. In order to solve the location problems of this nature, we first develop techniques of fuzzy random simulation. In the sequel, by combining the fuzzy random simulation, simplex algorithm and binary particle swarm optimization (BPSO), a hybrid algorithm is proposed to solve the two-stage fuzzy random facility location model. Finally, an illustrative numerical example is provided.

    DOI

  • Foreign Exchange Exposure-Based Strategy Building for Non-financial Corporations

    Wen Song, Min-Hsiu Tsai, Junzo Watada

    2009 IEEE INTERNATIONAL CONFERENCE ON INDUSTRIAL ENGINEERING AND ENGINEERING MANAGEMENT, VOLS 1-4     1180 - 1184  2009

     View Summary

    In the presence of deviations from parity conditions, the influence of foreign exchange rate variability beyond the range of financial companies only, and now represents to be an important source of risk for non-financial corporations. This research takes Japan as an example and examines how foreign currency movements affect Japanese manufacturing companies. From firm and industry perspectives, we find 16% of 65 sample companies and three out of six sub sectors of manufacturing industry experienced an economically significant effect from exposure to the U.S. Dollar, the Chinese Yuan, or the European Euro from January 2002 to December 2007. Based on the findings, we propose a hedging method using real options analysis and a binomial decision tree model as a strategy to mitigate the impact of exchange rate exposure, which illustrates that options theory can provide useful financial hedges by introducing adjustment costs or providing faster adjustment procedures.

    DOI

  • Factors on Design of Product Forms from A Historical Persective

    Yung-chin HSIAO, Junzo WATADA, Waseda University, Hsing Hsuan W, Cubic Creativity Corporation

    KEER 2009 Conference, Osaka,    2009

  • Design of the Creative City from a Kansei Engineering Approach, KEER 2009 Conference, Osaka, pp. Febrary 26-29, 2009

    Lee-Chuan Lin, Junzo WATADA, Waseda University

    KEER 2009 Conference, Osaka,    2009

  • A DNA encoding method to determine and sequence all cliques in a weighted graph

    Ikno Kim, Junzo Watada, Jui-Yu Wu

    2009 4th International Conference on Innovative Computing, Information and Control, ICICIC 2009     1532 - 1537  2009

     View Summary

    In many aspects of advanced applied information technology, science, and bioinformatics, having theoretical concepts based on graph theory provides an important way to create or develop new hybrids, combined information, and intelligent techniques or methods. Finding the maximum weighted clique problem can be a significant issue and concept in graph theory. Meanwhile, encoding biological codes, represented as biological sequence information, is an important process in executing biological computations. In this paper, we focus on a way of encoding biological sequences to create a new encoding method particularly designed to solve clique problems in a weighted graph. © 2009 IEEE.

    DOI

  • A DNA-Based Clustering Method Based on Statistics Adapted to Heterogeneous Coordinate Data

    Ikno Kim, Junzo Watada

    CISIS: 2009 INTERNATIONAL CONFERENCE ON COMPLEX, INTELLIGENT AND SOFTWARE INTENSIVE SYSTEMS, VOLS 1 AND 2     892 - 897  2009

     View Summary

    A cluster analysis is often used in social sciences, management, general science and engineering, etc. with the objective of characterising structures in heterogeneous data sets. In this case, collections of information granules are obviously constructed through clustering techniques. However, clustering problems are intractable and NP-complete problems with a number of patterns, In this article, we discuss the use of DNA computing as a vehicle of heterogeneous coordinated data clustering, and elaborate on the fundamentals of DNA computing in the context of clustering tasks. A novel DNA-based clustering method is proposed, using statistics-based encoding of DNA strands, for clustering coordinated data from simulated DNA studies and experiments. The results also show the capabilities of this method when adapted to heterogeneous coordinate data.

    DOI

  • A Novel Biological Computation Method for Deriving and Resolving Discernibility Relations

    Ikno Kim, Junzo Watada, Jui-Yu Wu, Yu-Yi Chu

    2009 9TH IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOENGINEERING     9 - +  2009

     View Summary

    Corporate and advanced information and database technologies make it possible to solve potential and hidden problems, such as uncertainty data interactions, disputable resolutions, unclear processes, etc. In this case, a rough set method can be used to grasp characteristics of the classified objects included in those problems. The rough set method is often used for classifying data while figuring out the distinctive features of the given objects in problem solutions. These given object problems that emerge, especially in database handling and resolving discernibility relations with the rough set method, are often computed by electronic computations. On the other hand, in this paper, we basically focus on taking advantage of biological molecular functions to create a novel biological computation method with which we proposed to derive and resolve all the discernibility relations.

    DOI

  • IMPROVED REAL OPTION ANALYSIS BASED ON FUZZY RANDOM VARIABLES

    Bo Wang, Shu-Ming Wang, Junzo Watada

    PROCEEDINGS OF 2009 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS, VOLS 1-6     694 - 699  2009

     View Summary

    The objective of this paper is to build a real options model under hybrid uncertain environment of randomness and fuzziness. In order to well describe the real uncertain situation, we utilize fuzzy random variable as a tool to characterize future cash flows, and propose a new real options analysis approach by combing binomial lattice-based model with fuzzy random variable, as named fuzzy random real options analysis (FR-ROA). Then the proposed FR-ROA is applied to an R&D project problem under fuzzy random environment, and the relations of FR-ROA with the classical ROA and the fuzzy ROA are explicitly discussed, respectively.

    DOI

  • Value-at-Risk-Based Fuzzy Stochastic Optimization Problems

    Shuming Wang, Junzo Watada

    2009 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS, VOLS 1-3     1402 - 1407  2009

     View Summary

    A new class of fuzzy stochastic optimization models - two-stage fuzzy stochastic programming with Value-at-Risk (VaR) criteria is established in this paper. An approximation algorithm is proposed to compute the VaR by combining discretization method of fuzzy variable, random simulation technique and bisection method. The convergence theorem of the approximation algorithm is also proved. To solve the two-stage fuzzy stochastic programming problems with VaR criteria, we integrate the approximation algorithm, neural network (NN) and particle swarm optimization (PSO) algorithm, and hence produce a hybrid PSO algorithm to search for the optimal solution. A numerical example is provided to illustrate the designed hybrid PSO algorithm.

    DOI

  • Simulation of Change of Production Efficiency Based on FDI Path Identification,

    Bing XU, Juying Zeng, Junzo WATADA

    ISME2009,     3  2009

  • New Perspectives and Applications of Real-Time Fuzzy Regression

    Azizul Azhar Ramli, Junzo Watada, Witold Pedrycz

    2009 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS, VOLS 1-3     1451 - +  2009

     View Summary

    Fuzzy, regression is one of important methods for data analysis. Fuzzy regression extends the concept of classical regression which has been constructed in the statistical framework. We show that a convex hull method can provide a powerful tool to reduce the computing time, especially for real-time data analysis. The main objective of this study is to propose an efficient real-time fuzzy regression analysis based on the use of convex hull, specifically a Beneath-Beyond algorithm. The reconstruction of convex hull edges depends on incoming vertices while a recomputing procedure can be implemented in real-time. An air pollution data is analyzed by applying the proposed approach. An important role of convex hull is emphasized in particular when dealing with the limitations of linear programming.

    DOI

  • Multi-Camera Tracking System in a Large Area Case

    Zalili Binti Musa, Junzo Watada

    2009 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS, VOLS 1-3     1959 - 1964  2009

     View Summary

    A video tracking system raises a wide range of possibilities in today&apos;s society, particularly in security, monitoring, and robotics. The most important research in tracking systems is to discover and develop an available method and algorithm for tracking an object&apos;s motion. The objective of this paper is to propose a new method that combines a prediction method and particle filter to manage problems in a wide area of observation. The comparative study of the method is provided and its capabilities are evaluated.

    DOI

  • Fuzzy Theory-Based Best Generation Mix Considering Renewable Energy Generators

    Jeongje Park, Liang Wu, Jaeseok Choi, Junmin Cha, A. A. El-Keib, Junzo Watada

    2009 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS, VOLS 1-3     1462 - +  2009

     View Summary

    This paper proposes a fuzzy linear programming (LP)-based solution approach for the long-term multi-stages best generation mix (BGM) problem considering wind turbine generators (WTG) and solar cell generators (SCG), and CO2 emissions constraints. The proposed method uses fuzzy set theory to consider the uncertain circumstances ambiguities associated with budgets and reliability criterion level. The proposed approach provides a more flexible solution compared to a crisp robust plan. The effectiveness of the proposed approach is demonstrated by applying it to solve the multi-years best generation mix problem on the Korean power system, which contains nuclear, coal, LNG, oil, pumped-storage hydro, and WTGs and SCGs.

    DOI

  • Fuzzy Regression Model building through Possibility Maximization and Its Application,

    Yoshiyuki YABUUCHI, Junzo WATADA

    ISME2009,     3  2009

  • Fuzzy Approach for Assignment Problem

    Shamshul Bahar Yaakob, Junzo Watada

    2009 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS, VOLS 1-3     1408 - 1413  2009

     View Summary

    In workers' evaluation and placement, numerous workers with different skills and expertise may share the same role in an organization, making it hard to select appropriate workers based merely on the assignment relation between role and a job. To bridge the gap between abstract roles and real workers, this paper proposed a workers' placement model capable of evaluating workers' suitability for a specified task according their performance, social and mental factor. For this type of problems, an analysis using a fuzzy number approach promises to be potentially effective. In order to make a more convincing and accurate decision, the relationship among workers is included in the workers' assignment in an industrial environment. Finally candidates are ranked based on their suitability grades to support decision makers in selecting appropriate workers to perform the job. Numerical examples are also presented to demonstrate that the workers' relationship is an important factor and our method is effective for the decision making process.

    DOI

  • Dilemma of Behavioral Uncertainty of R&D Alliance in Taiwan Machinery Industry

    You-Hsin Tsai, Wen-Hsiang Lai, Pao-Long Chang, Junzo Watada

    2009 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS, VOLS 1-3     1439 - +  2009

     View Summary

    In view of global business competition, industries in Taiwan can no longer made use of its manufacturing advantage to create future businesses without constant research, development (R&D) and innovation as it could survive on the market in the past. As industries confronted with fast changing and ever increasingly competitive environment, the industries can make use of R&D alliance of project to swiftly obtain complementary resources and assets so that they can create knowledge assets for the organization internally and effective transfer of technology. However, the impact of hidden social resources is most significant in the formation and operation of R&D alliance. Therefore, the objective of this paper is to examine the three aspects of industrial environment, trust relationship among working partners and formation motif of R&D alliance and to investigate the current situation and dilemma of the R&D alliance in the Taiwan machinery industry (TMI). This study has employed expert interviews, questionnaires, and their rough sets analysis to clarify influential factors the TMI should confronts.

    DOI

  • Determining Feasible Operating Schedules for a Job Shop Scheduling Problem Based on Bio-Soft Computing

    Ikno Kim, Junzo Watada, Don Jyh-Fu Jeng

    2009 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS, VOLS 1-3     1426 - +  2009

     View Summary

    Meta-heuristic methods, which are often used in order to deal with job shop scheduling problems, are also often applied to combinatorial optimization problems. On another front, bio-soft computing is a new way of computing associated with DNA molecules. It is a new massively parallel computation, compared to micro-soft computing. In this paper, a novel way of determining feasible operating schedules is proposed for a job shop scheduling problem, paying particular attention to machine sequences. This new study shows how the goal of deriving reliable solutions using bio-soft computing can be achieved, for more reliable solutions based on this new bio-soft computing approach. A sample of various types of job shop scheduling problems is selected and its feasible operating schedules are determined using the strengths of our proposed method.

    DOI

  • DC Load Flow Method considering Movement of Electric Trains,

    A. El-keib, Junzo WATADA

    ISME2009,     6  2009

  • Computational Cluster Validation in DNA-Based Computation

    Rohani Binti Abu Bakar, Junzo Watada

    WISP 2009: 6TH IEEE INTERNATIONAL SYMPOSIUM ON INTELLIGENT SIGNAL PROCESSING, PROCEEDINGS     187 - 192  2009

     View Summary

    DNA-based computation is one of new computation paradigms. In this approach, bio-chemical wet experiment plays a central role in obtaining results. However, some errors happen during the wet-experiment procedure is executed. The aim of this study is to examine validity of DNA-based techniques in solving a clustering problem. In the broadest sense, we can define validity as a measure of performance of algorithm in various environments and inputs. We will study how some errors or noises from an input influence on results of the procedures. The comparative study for the different set of errors is discussed at the end of this paper.

    DOI

  • Particle Swarm Optimization for Multi-function Worker Assignment Problem

    Shamshul Bahar Yaakob, Junzo Watada

    KNOWLEDGE-BASED AND INTELLIGENT INFORMATION AND ENGINEERING SYSTEMS, PT II, PROCEEDINGS   5712   203 - 211  2009

     View Summary

    A problem of worker assignment in cellular manufacturing (CM) environment is studied in this paper. The worker assignment problem is an NP-complete problem. In this paper, worker assignment method is modeled based on the principles of particle swarm optimization (PSO). PSO applies a collaborative population-based search, which models over the social behavior of fish schooling and bird flocking. PSO system combines local search method through self-experience with global search methods through neighboring experience, attempting to balance the exploration-exploitation trade-off which determines the efficiency and accuracy of an optimization. An effect of velocity controlled for the PSO's is newly included in this paper. We applied the adaptation and implementation of the PSO search strategy to the worker assignment problem. Typical application examples are also presented: the results demonstrate that the velocity information is an important factor for searching best Solution and our method is a viable approach for the worker assignment problem.

    DOI

  • Evidential Reasoning Based on DNA Computation

    Rohani Binti Abu Bakar, Junzo Watada

    KNOWLEDGE-BASED AND INTELLIGENT INFORMATION AND ENGINEERING SYSTEMS, PT II, PROCEEDINGS   5712   212 - 219  2009

     View Summary

    The objective of this study is to present an alternative approach to solve reasoning problems. DNA computing technique shows to solve evidential reasoning problems in this study. The reasoning is here executed on the basis of the concepts of plausibility and belief function. The evidential reasoning is a process dealing with problems that having both quantitative and qualitative criteria under various uncertainties including ignorance and randomness of information. The procedure to solve reasoning problem by means of DNA computing has been illustrated. An experiment shows the steadfastness of DNA computing to reach the solution of a reasoning problem.

    DOI

  • Evidence Based Similarity Measure for Text Categorization,

    Rozlini Mohamed, Junzo WATADA

    IFMIP 2010, WAC2010,     1 - 6  2009

  • Dynamic Tracking System Using Prediction Formula and Parzen Particle Filter

    Zalili Musa, Junzo WATADA

    KES2009, Sachago, Chili,    2009

  • Determining Workstation Groups in a Fixed Factory Facility Based on Biological Computation

    Ikno Kim, Junzo Watada

    KNOWLEDGE-BASED AND INTELLIGENT INFORMATION AND ENGINEERING SYSTEMS, PT II, PROCEEDINGS   5712   188 - 194  2009

     View Summary

    A strategy for making layout decisions is an important element in developing operating systems in manufacturing factories or other industrial plants. In this paper, we look at fixed factory facilities and propose a method for designing different sorts of layouts related to factories running at high-volume and producing a low-variety of products. Where many tasks are called, each with a different task time, it can be difficult to arrange a fixed factory facility in the optimal way. Therefore, we propose a computational method using DNA molecules for designing production systems by determining all the feasible workstation groups in a fixed factory facility, and we show that this computation method can be generally applied to layout decisions.

    DOI

  • Decision making of facility locations based on Fuzzy Probability Distribution Function,

    Pei-Chun Lin, Shuming WANG, Junzo WATADA

    IEEM2010, Industrial Engineering and Engineering Management (IEEM), 2010 IEEE International Conference on Digital Object Identifier: 10.1109/IEEM.2010.5674624, Publication Year:     1911 - 1915  2009

    DOI

  • Biologically Inspired Computation,

    Tutorial, Junzo WATADA

    国際会議ISIS2009, 10th international symposium on Advanced Intelligent Systems,     17  2009

  • A Biologically Intelligent Encoding Approach to a Hierarchical Classification of Relational Elements in a Digraph

    Ikno Kim, Junzo Watada

    KNOWLEDGE-BASED AND INTELLIGENT INFORMATION AND ENGINEERING SYSTEMS, PT II, PROCEEDINGS   5712   174 - 180  2009

     View Summary

    Parallel processing functions using molecules have advantages to be exploited for classifying the given relational elements in a digraph. For instance, hierarchical structural modelling is used for classifying complicated objects into a hierarchical structure. In this paper, we consider the example of a digraph of hierarchical structural modelling that can be transformed to sequences of molecules, and propose a biologically intelligent method of encoding molecular sequences of different types, through the hierarchical classification of hierarchical structural modelling. Moreover, we show that this innovative biologically intelligent encoding method can be applied, not only to hierarchical structural modelling, but also to other relational problems composed of elements from digraphs.

    DOI

  • Shape Design of Products Based on A Decision Support System

    Yung-chin Hsiao, Junzo Watada

    2009 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN AND CYBERNETICS (SMC 2009), VOLS 1-9     4384 - 4390  2009

     View Summary

    From a historical perspective, two fundamental issues are observed for industrial designers: (1) what is the shape design process within the context of a modern product design process, and (2) how shape design theories, methods, tools and computer aided software can be effectively utilized for creating product shapes. A framework is proposed to resolve the issues by describing the relationships of the product design problems, product design processes, shape design processes, shape design methods and tools with consideration of the functional, ergonomic, emotional and manufacturing requirements. The framework implemented here is a new type of decision support system (DSS) - an object-oriented decision support system to assist the designers in designing product shapes.

    DOI

  • Decision Support Systems for Creative City Design: Rethinking Urban Competitiveness

    Lee-Chuan Lin, Junzo Watada

    2009 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN AND CYBERNETICS (SMC 2009), VOLS 1-9     2245 - 2250  2009

     View Summary

    Competitiveness has become one of the important strategies of government and industry in every nation. The concept of the Creative City, proposed by Charles Landry is driving the imagination of city redevelopers. It is essential for researchers to pay more attention to the issue of Creative City design for rethinking urban competitiveness.
    However, Creative City design must be supported with a wide range of knowledge and a diverse database. Concurrent city development requires to empower efficiency with appropriate evaluation and decision tools. Building a decision support system of Creative City development can help decision-makers to solve semi-structured problems by analyzing data interactively.
    The decision support system is based on a new proposed rough set analysis that plays a pivotal role and is employed dynamically. The approach realizes a competent sampling method in rough set analysis that distinguishes whether each subset can be classified in the focal set or not. The algorithm of the rough set model will be used to analyze obtained samples.
    In this paper we will first examine the design rules of Creative City development. Second, we will apply rough set theory to select the decision rules and measure the current status of Japanese cities. Finally, we will initiate a prototype decision support system for Creative City design based on the results obtained from the rough set analysis.

    DOI

  • Measurement of Decision Maker&apos;s Performance in a Fuzzy Multi-attribute Decision Making

    A. Nureize, J. Watada

    2009 IEEE INTERNATIONAL CONFERENCE ON INDUSTRIAL ENGINEERING AND ENGINEERING MANAGEMENT, VOLS 1-4     1598 - 1602  2009

     View Summary

    The performance measurement of decision maker&apos;s evaluation is important to recognize the efficiency of the decision makers in the decision making process. Besides the consideration of attributes, several decision evaluators may involve their preference and judgment. Decision makers tend to provide different evaluation though the same sets of problems are evaluated. Thus, the objectives of this paper are to provide a measure of the decision maker&apos;s performance in the fuzzy multi attribute decision making environment. A numerical example is given to illustrate the computational process of the proposed model.

    DOI

  • Fuzzy portfolio selection based on value-at-risk

    Bo Wang, Shuming Wang, Junzo Watada

    Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics     1840 - 1845  2009

     View Summary

    In this paper, using Value-at-Risk, a new fuzzy portfolio selection model named VaR-FPSM is proposed. The Value-at-Risk is the measure of risk, which describes the greatest loss of an investment with some confidence level. When security returns are same kind of fuzzy variable, we derive two crisp equivalent forms of the VaR-FPSM. Furthermore, in general situations, we designed a fuzzy simulation based particle swarm optimization (PSO) algorithm to find an approximately optimal result. To illustrate the proposed model and hybrid PSO algorithm, a numerical example is provided and some discussions on the results are given. ©2009 IEEE.

    DOI

  • Systematic Construction of Shape Grammars for Form Design of Products,

    Yung-chin HSIAO, Junzo WATADA

    Japan society of Kansei Engineering, ()    2009

    DOI

  • on Support System with Bio-Inspired Interpretive Structural Modelling,

    Ikno KIM, Junzo WATADA

    Decision Support System with Bio-Inspired Interpretive Structural Modelling,Springer Lecturenote, ()    2009

    DOI

  • Reliability Evaluation for Interconnection Planning in North East Asia,

    Junmin Cha, Jaeseok Choi, Dongwook Park, Jaeyoung Yoon, Seungil Moon, Junzo WATADA, Roy Billinton

    International Journal of Innovative Computing, Information and Control, (ISSN 1349-4198)   5 ( 4 ) 6 - 17  2009

  • Pattern Classification Analysis of Corporate Quality in IT Industry,

    Shinya IMAI, Junzo WATADA

    Global Journal of International Business Relations, ()   2 ( 2 ) 50 - 62  2009

  • Real Options Analysis Basedon Fuzzy Random Variables,

    Bo Wang, Shuming WANG, Junzo WATADA

    International Journal of Innovative Computing, Information and Control,, (ISSN 1349-4198)   6 ( 4 ) 1689 - 1698  2009

  • Wise Mining Method through Ant Colony Optimization

    Yang Jianxiong, Junzo Watada

    2009 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN AND CYBERNETICS (SMC 2009), VOLS 1-9     1833 - 1839  2009

     View Summary

    This paper proposes an algorithm for data mining named Pheromone-Miner (ant-colony-based data miner). The algorithm is inspired by both researches on the behavior of real ant colonies and data mining concepts as well as principles. The goal of Pheromone-Miner is to extract more exact knowledge from a database. Pheromone-based mining breaks through limitations of other mining approaches. We compare the performance of pheromone-miner with a general semantic miner. The accident causes discovered by ant-miner are considerably more accurate than those discovered by a general semantic miner. In a word, this evolutionary algorithm is suitable for improving the accuracy of data miners.

    DOI

  • The effect of the operational performance on competitive advantage ? an empiraical study of Taiwan,

    You-Hsin Tsai, Junzo WATADA

    Proceedings, International Conference on Control, Instrumentaion and Mechatoronics Engineering (CIM 2009),    2009

  • On Support System with Bio-Inspired Interpretive Structural Modelling,

    Ikno KIM, Junzo WATADA

    Decision Support System with Bio-Inspired Interpretive Structural Modelling,Springer Lecturenote,    2009

    DOI

  • Wise mining method through ant colony optimization

    Yang Jianxiong, Junzo Watada

    Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics     1833 - 1839  2009

     View Summary

    This paper proposes an algorithm for data mining named Pheromone-Miner (ant-colony-based data miner). The algorithm is inspired by both researches on the behavior of real ant colonies and data mining concepts as well as principles. The goal of Pheromone-Miner is to extract more exact knowledge from a database. Pheromone-based mining breaks through limitations of other mining approaches. We compare the performance of pheromone-miner with a general semantic miner. The accident causes discovered by ant-miner are considerably more accurate than those discovered by a general semantic miner. In a word, this evolutionary algorithm is suitable for improving the accuracy of data miners. ©2009 IEEE.

    DOI

  • Fuzzy Random Multi-attribute Evaluation for Oil Palm Fruit,

    Nureize binti Arbaiy, Junzo WATADA, Xin Liu

    Proceedings, International Conference on Control, Instrumentaion and Mechatoronics Engineering (CIM 2009),    2009

  • Fuzzy Random Facility Location Problems with Recourse

    Shuming Wang, Junzo Watada, Witold Pedrycz

    2009 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN AND CYBERNETICS (SMC 2009), VOLS 1-9     1846 - +  2009

     View Summary

    The objective of this paper is to study facility location problems under a hybrid uncertain environment involving randomness and fuzziness. A two-stage fuzzy random facility location model with recourse is developed in which the demands and the costs are assumed to be fuzzy random variables. As in general the fuzzy random parameters in the model can be regarded as continuous fuzzy random variables with infinite realizations, the computation of the recourse requires solving infinite second-stage programming problems. Owing to this fact, the recourse function cannot be calculated analytically, which implies that the model cannot benefit from the use of methods of classical mathematical programming. In order to solve the location problems of this nature, we first develop techniques of fuzzy random simulation. In the sequel, by combining the fuzzy random simulation, simplex algorithm and binary particle swarm optimization (BPSO), a hybrid algorithm is proposed to solve the two-stage fuzzy random facility location model. Finally, an illustrative numerical example is provided.

    DOI

  • Foreign Exchange Exposure-Based Strategy Building for Non-financial Corporations

    Wen Song, Min-Hsiu Tsai, Junzo Watada

    2009 IEEE INTERNATIONAL CONFERENCE ON INDUSTRIAL ENGINEERING AND ENGINEERING MANAGEMENT, VOLS 1-4     1180 - 1184  2009

     View Summary

    In the presence of deviations from parity conditions, the influence of foreign exchange rate variability beyond the range of financial companies only, and now represents to be an important source of risk for non-financial corporations. This research takes Japan as an example and examines how foreign currency movements affect Japanese manufacturing companies. From firm and industry perspectives, we find 16% of 65 sample companies and three out of six sub sectors of manufacturing industry experienced an economically significant effect from exposure to the U.S. Dollar, the Chinese Yuan, or the European Euro from January 2002 to December 2007. Based on the findings, we propose a hedging method using real options analysis and a binomial decision tree model as a strategy to mitigate the impact of exchange rate exposure, which illustrates that options theory can provide useful financial hedges by introducing adjustment costs or providing faster adjustment procedures.

    DOI

  • Factors on Design of Product Forms from A Historical Persective

    Yung-chin HSIAO, Junzo WATADA, Waseda University, Hsing Hsuan W, Cubic Creativity Corporation

    KEER 2009 Conference, Osaka,    2009

  • Design of the Creative City from a Kansei Engineering Approach, KEER 2009 Conference, Osaka, pp. Febrary 26-29, 2009

    Lee-Chuan Lin, Junzo WATADA, Waseda University

    KEER 2009 Conference, Osaka,    2009

  • A DNA encoding method to determine and sequence all cliques in a weighted graph

    Ikno Kim, Junzo Watada, Jui-Yu Wu

    2009 4th International Conference on Innovative Computing, Information and Control, ICICIC 2009     1532 - 1537  2009

     View Summary

    In many aspects of advanced applied information technology, science, and bioinformatics, having theoretical concepts based on graph theory provides an important way to create or develop new hybrids, combined information, and intelligent techniques or methods. Finding the maximum weighted clique problem can be a significant issue and concept in graph theory. Meanwhile, encoding biological codes, represented as biological sequence information, is an important process in executing biological computations. In this paper, we focus on a way of encoding biological sequences to create a new encoding method particularly designed to solve clique problems in a weighted graph. © 2009 IEEE.

    DOI

  • A DNA-Based Clustering Method Based on Statistics Adapted to Heterogeneous Coordinate Data, Complex, Intelligent and Software Intensive Systems,

    Kim, I, WATADA, Junzo

    2009. CISIS '09. International Conference on,     892 - 897  2009

    DOI

  • A Novel Biological Computation Method for Deriving and Resolving Discernibility Relations

    Ikno Kim, Junzo Watada, Jui-Yu Wu, Yu-Yi Chu

    2009 9TH IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOENGINEERING     9 - +  2009

     View Summary

    Corporate and advanced information and database technologies make it possible to solve potential and hidden problems, such as uncertainty data interactions, disputable resolutions, unclear processes, etc. In this case, a rough set method can be used to grasp characteristics of the classified objects included in those problems. The rough set method is often used for classifying data while figuring out the distinctive features of the given objects in problem solutions. These given object problems that emerge, especially in database handling and resolving discernibility relations with the rough set method, are often computed by electronic computations. On the other hand, in this paper, we basically focus on taking advantage of biological molecular functions to create a novel biological computation method with which we proposed to derive and resolve all the discernibility relations.

    DOI

  • IMPROVED REAL OPTION ANALYSIS BASED ON FUZZY RANDOM VARIABLES

    Bo Wang, Shu-Ming Wang, Junzo Watada

    PROCEEDINGS OF 2009 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS, VOLS 1-6     694 - 699  2009

     View Summary

    The objective of this paper is to build a real options model under hybrid uncertain environment of randomness and fuzziness. In order to well describe the real uncertain situation, we utilize fuzzy random variable as a tool to characterize future cash flows, and propose a new real options analysis approach by combing binomial lattice-based model with fuzzy random variable, as named fuzzy random real options analysis (FR-ROA). Then the proposed FR-ROA is applied to an R&D project problem under fuzzy random environment, and the relations of FR-ROA with the classical ROA and the fuzzy ROA are explicitly discussed, respectively.

    DOI

  • Value-at-Risk-based fuzzy stochastic optimization problems

    Shuming Wang, Junzo Watada

    IEEE International Conference on Fuzzy Systems     1402 - 1407  2009

     View Summary

    A new class of fuzzy stochastic optimization models - two-stage fuzzy stochastic programming with Value-at-Risk (VaR) criteria is established in this paper. An approximation algorithm is proposed to compute the VaR by combining discretization method of fuzzy variable, random simulation technique and bisection method. The convergence theorem of the approximation algorithm is also proved. To solve the twostage fuzzy stochastic programming problems with VaR criteria, we integrate the approximation algorithm, neural network (NN) and particle swarm optimization (PSO) algorithm, and hence produce a hybrid PSO algorithm to search for the optimal solution. A numerical example is provided to illustrate the designed hybrid PSO algorithm. ©2009 IEEE.

    DOI

  • Simulation of Change of Production Efficiency Based on FDI Path Identification,

    Bing XU, Juying Zeng, Junzo WATADA

    ISME2009,     3  2009

  • New Perspectives and Applications of Real-Time Fuzzy Regression

    Azizul Azhar Ramli, Junzo Watada, Witold Pedrycz

    2009 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS, VOLS 1-3     1451 - +  2009

     View Summary

    Fuzzy, regression is one of important methods for data analysis. Fuzzy regression extends the concept of classical regression which has been constructed in the statistical framework. We show that a convex hull method can provide a powerful tool to reduce the computing time, especially for real-time data analysis. The main objective of this study is to propose an efficient real-time fuzzy regression analysis based on the use of convex hull, specifically a Beneath-Beyond algorithm. The reconstruction of convex hull edges depends on incoming vertices while a recomputing procedure can be implemented in real-time. An air pollution data is analyzed by applying the proposed approach. An important role of convex hull is emphasized in particular when dealing with the limitations of linear programming.

    DOI

  • Multi-Camera Tracking System in a Large Area Case

    Zalili Binti Musa, Junzo Watada

    2009 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS, VOLS 1-3     1959 - 1964  2009

     View Summary

    A video tracking system raises a wide range of possibilities in today&apos;s society, particularly in security, monitoring, and robotics. The most important research in tracking systems is to discover and develop an available method and algorithm for tracking an object&apos;s motion. The objective of this paper is to propose a new method that combines a prediction method and particle filter to manage problems in a wide area of observation. The comparative study of the method is provided and its capabilities are evaluated.

    DOI

  • Fuzzy Theory-Based Best Generation Mix Considering Renewable Energy Generators

    Jeongje Park, Liang Wu, Jaeseok Choi, Junmin Cha, A. A. El-Keib, Junzo Watada

    2009 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS, VOLS 1-3     1462 - +  2009

     View Summary

    This paper proposes a fuzzy linear programming (LP)-based solution approach for the long-term multi-stages best generation mix (BGM) problem considering wind turbine generators (WTG) and solar cell generators (SCG), and CO2 emissions constraints. The proposed method uses fuzzy set theory to consider the uncertain circumstances ambiguities associated with budgets and reliability criterion level. The proposed approach provides a more flexible solution compared to a crisp robust plan. The effectiveness of the proposed approach is demonstrated by applying it to solve the multi-years best generation mix problem on the Korean power system, which contains nuclear, coal, LNG, oil, pumped-storage hydro, and WTGs and SCGs.

    DOI

  • Fuzzy Regression Model building through Possibility Maximization and Its Application,

    Yoshiyuki YABUUCHI, Junzo WATADA

    ISME2009,     3  2009

  • Fuzzy approach for assignment problem

    Shamshul Bahar Yaakob, Junzo Watada

    IEEE International Conference on Fuzzy Systems     1408 - 1413  2009

     View Summary

    In workers' evaluation and placement, numerous workers with different skills and expertise may share the same role in an organization, making it hard to select appropriate workers based merely on the assignment relation between role and a job. To bridge the gap between abstract roles and real workers, this paper proposed a workers' placement model capable of evaluating workers' suitability for a specified task according their performance, social and mental factor. For this type of problems, an analysis using a fuzzy number approach promises to be potentially effective. In order to make a more convincing and accurate decision, the relationship among workers is included in the workers' assignment in an industrial environment. Finally candidates are ranked based on their suitability grades to support decision makers in selecting appropriate workers to perform the job. Numerical examples are also presented to demonstrate that the workers' relationship is an important factor and our method is effective for the decision making process. ©2009 IEEE.

    DOI

  • Dilemma of Behavioral Uncertainty of R&D Alliance in Taiwan Machinery Industry

    You-Hsin Tsai, Wen-Hsiang Lai, Pao-Long Chang, Junzo Watada

    2009 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS, VOLS 1-3     1439 - +  2009

     View Summary

    In view of global business competition, industries in Taiwan can no longer made use of its manufacturing advantage to create future businesses without constant research, development (R&D) and innovation as it could survive on the market in the past. As industries confronted with fast changing and ever increasingly competitive environment, the industries can make use of R&D alliance of project to swiftly obtain complementary resources and assets so that they can create knowledge assets for the organization internally and effective transfer of technology. However, the impact of hidden social resources is most significant in the formation and operation of R&D alliance. Therefore, the objective of this paper is to examine the three aspects of industrial environment, trust relationship among working partners and formation motif of R&D alliance and to investigate the current situation and dilemma of the R&D alliance in the Taiwan machinery industry (TMI). This study has employed expert interviews, questionnaires, and their rough sets analysis to clarify influential factors the TMI should confronts.

    DOI

  • Determining Feasible Operating Schedules for a Job Shop Scheduling Problem Based on Bio-Soft Computing

    Ikno Kim, Junzo Watada, Don Jyh-Fu Jeng

    2009 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS, VOLS 1-3     1426 - +  2009

     View Summary

    Meta-heuristic methods, which are often used in order to deal with job shop scheduling problems, are also often applied to combinatorial optimization problems. On another front, bio-soft computing is a new way of computing associated with DNA molecules. It is a new massively parallel computation, compared to micro-soft computing. In this paper, a novel way of determining feasible operating schedules is proposed for a job shop scheduling problem, paying particular attention to machine sequences. This new study shows how the goal of deriving reliable solutions using bio-soft computing can be achieved, for more reliable solutions based on this new bio-soft computing approach. A sample of various types of job shop scheduling problems is selected and its feasible operating schedules are determined using the strengths of our proposed method.

    DOI

  • DC Load Flow Method considering Movement of Electric Trains,

    A. El-keib, Junzo WATADA

    ISME2009,     6  2009

  • Computational Cluster Validation in DNA-Based Computation

    Rohani Binti Abu Bakar, Junzo Watada

    WISP 2009: 6TH IEEE INTERNATIONAL SYMPOSIUM ON INTELLIGENT SIGNAL PROCESSING, PROCEEDINGS     187 - 192  2009

     View Summary

    DNA-based computation is one of new computation paradigms. In this approach, bio-chemical wet experiment plays a central role in obtaining results. However, some errors happen during the wet-experiment procedure is executed. The aim of this study is to examine validity of DNA-based techniques in solving a clustering problem. In the broadest sense, we can define validity as a measure of performance of algorithm in various environments and inputs. We will study how some errors or noises from an input influence on results of the procedures. The comparative study for the different set of errors is discussed at the end of this paper.

    DOI

  • Particle Swarm Optimization for Multi-function Worker Assignment Problem

    Shamshul Bahar Yaakob, Junzo Watada

    KNOWLEDGE-BASED AND INTELLIGENT INFORMATION AND ENGINEERING SYSTEMS, PT II, PROCEEDINGS   5712   203 - 211  2009

     View Summary

    A problem of worker assignment in cellular manufacturing (CM) environment is studied in this paper. The worker assignment problem is an NP-complete problem. In this paper, worker assignment method is modeled based on the principles of particle swarm optimization (PSO). PSO applies a collaborative population-based search, which models over the social behavior of fish schooling and bird flocking. PSO system combines local search method through self-experience with global search methods through neighboring experience, attempting to balance the exploration-exploitation trade-off which determines the efficiency and accuracy of an optimization. An effect of velocity controlled for the PSO's is newly included in this paper. We applied the adaptation and implementation of the PSO search strategy to the worker assignment problem. Typical application examples are also presented: the results demonstrate that the velocity information is an important factor for searching best Solution and our method is a viable approach for the worker assignment problem.

    DOI

  • Evidential Reasoning Based on DNA Computation

    Rohani Binti Abu Bakar, Junzo Watada

    KNOWLEDGE-BASED AND INTELLIGENT INFORMATION AND ENGINEERING SYSTEMS, PT II, PROCEEDINGS   5712   212 - 219  2009

     View Summary

    The objective of this study is to present an alternative approach to solve reasoning problems. DNA computing technique shows to solve evidential reasoning problems in this study. The reasoning is here executed on the basis of the concepts of plausibility and belief function. The evidential reasoning is a process dealing with problems that having both quantitative and qualitative criteria under various uncertainties including ignorance and randomness of information. The procedure to solve reasoning problem by means of DNA computing has been illustrated. An experiment shows the steadfastness of DNA computing to reach the solution of a reasoning problem.

    DOI

  • Evidence Based Similarity Measure for Text Categorization,

    Rozlini Mohamed, Junzo WATADA

    IFMIP 2010, WAC2010,     1 - 6  2009

  • Dynamic Tracking System Using Prediction Formula and Parzen Particle Filter

    Zalili Musa, Junzo WATADA

    KES2009, Sachago, Chili,    2009

  • Determining Workstation Groups in a Fixed Factory Facility Based on Biological Computation

    Ikno Kim, Junzo Watada

    KNOWLEDGE-BASED AND INTELLIGENT INFORMATION AND ENGINEERING SYSTEMS, PT II, PROCEEDINGS   5712   188 - 194  2009

     View Summary

    A strategy for making layout decisions is an important element in developing operating systems in manufacturing factories or other industrial plants. In this paper, we look at fixed factory facilities and propose a method for designing different sorts of layouts related to factories running at high-volume and producing a low-variety of products. Where many tasks are called, each with a different task time, it can be difficult to arrange a fixed factory facility in the optimal way. Therefore, we propose a computational method using DNA molecules for designing production systems by determining all the feasible workstation groups in a fixed factory facility, and we show that this computation method can be generally applied to layout decisions.

    DOI

  • Biologically Inspired Computation,

    Tutorial, Junzo WATADA

    国際会議ISIS2009, 10th international symposium on Advanced Intelligent Systems,     17  2009

  • A Biologically Intelligent Encoding Approach to a Hierarchical Classification of Relational Elements in a Digraph

    Ikno Kim, Junzo Watada

    KNOWLEDGE-BASED AND INTELLIGENT INFORMATION AND ENGINEERING SYSTEMS, PT II, PROCEEDINGS   5712   174 - 180  2009

     View Summary

    Parallel processing functions using molecules have advantages to be exploited for classifying the given relational elements in a digraph. For instance, hierarchical structural modelling is used for classifying complicated objects into a hierarchical structure. In this paper, we consider the example of a digraph of hierarchical structural modelling that can be transformed to sequences of molecules, and propose a biologically intelligent method of encoding molecular sequences of different types, through the hierarchical classification of hierarchical structural modelling. Moreover, we show that this innovative biologically intelligent encoding method can be applied, not only to hierarchical structural modelling, but also to other relational problems composed of elements from digraphs.

    DOI

  • Shape design of products based on a decision support system,

    Hsiao, Yung-chin, WATADA, Junzo

    Systems, Man and Cybernetics, 2009. SMC 2009. IEEE International Conference on,     4384 - 4390  2009

    DOI

  • Keynote Speech,

    Junzo Watada

    CoMM2009,   45 ( 2 ) 101 - 109  2009

    DOI CiNii

  • Decision Support Systems for Creative City Design: Rethinking Urban Competitiveness

    Lee-Chuan Lin, Junzo Watada

    2009 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN AND CYBERNETICS (SMC 2009), VOLS 1-9     2245 - 2250  2009

     View Summary

    Competitiveness has become one of the important strategies of government and industry in every nation. The concept of the Creative City, proposed by Charles Landry is driving the imagination of city redevelopers. It is essential for researchers to pay more attention to the issue of Creative City design for rethinking urban competitiveness.
    However, Creative City design must be supported with a wide range of knowledge and a diverse database. Concurrent city development requires to empower efficiency with appropriate evaluation and decision tools. Building a decision support system of Creative City development can help decision-makers to solve semi-structured problems by analyzing data interactively.
    The decision support system is based on a new proposed rough set analysis that plays a pivotal role and is employed dynamically. The approach realizes a competent sampling method in rough set analysis that distinguishes whether each subset can be classified in the focal set or not. The algorithm of the rough set model will be used to analyze obtained samples.
    In this paper we will first examine the design rules of Creative City development. Second, we will apply rough set theory to select the decision rules and measure the current status of Japanese cities. Finally, we will initiate a prototype decision support system for Creative City design based on the results obtained from the rough set analysis.

    DOI

  • Measurement of Decision Maker&apos;s Performance in a Fuzzy Multi-attribute Decision Making

    A. Nureize, J. Watada

    2009 IEEE INTERNATIONAL CONFERENCE ON INDUSTRIAL ENGINEERING AND ENGINEERING MANAGEMENT, VOLS 1-4     1598 - 1602  2009

     View Summary

    The performance measurement of decision maker&apos;s evaluation is important to recognize the efficiency of the decision makers in the decision making process. Besides the consideration of attributes, several decision evaluators may involve their preference and judgment. Decision makers tend to provide different evaluation though the same sets of problems are evaluated. Thus, the objectives of this paper are to provide a measure of the decision maker&apos;s performance in the fuzzy multi attribute decision making environment. A numerical example is given to illustrate the computational process of the proposed model.

    DOI

  • Fuzzy portfolio selection based on value-at-risk

    Bo Wang, Shuming Wang, Junzo Watada

    Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics     1840 - 1845  2009

     View Summary

    In this paper, using Value-at-Risk, a new fuzzy portfolio selection model named VaR-FPSM is proposed. The Value-at-Risk is the measure of risk, which describes the greatest loss of an investment with some confidence level. When security returns are same kind of fuzzy variable, we derive two crisp equivalent forms of the VaR-FPSM. Furthermore, in general situations, we designed a fuzzy simulation based particle swarm optimization (PSO) algorithm to find an approximately optimal result. To illustrate the proposed model and hybrid PSO algorithm, a numerical example is provided and some discussions on the results are given. ©2009 IEEE.

    DOI

  • Fuzzy Random Renewal Reward Process and Its Application,

    Shuming WANG, Junzo WATADA

    Information Sciences, (ISSN : 0020-0255)   179 ( 23 ) 4057 - 4069  2009

  • Systematic Construction of Shape Grammars for Form Design of Products,

    Yung-chin HSIAO, Junzo WATADA

    Japan society of Kansei Engineering, ()    2009

    DOI

  • on Support System with Bio-Inspired Interpretive Structural Modelling,

    Ikno KIM, Junzo WATADA

    Decision Support System with Bio-Inspired Interpretive Structural Modelling,Springer Lecturenote, ()    2009<