OGAI, Harutoshi



Faculty of Science and Engineering, Graduate School of Information, Production, and Systems

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Research Institute 【 display / non-display

  • 2020

    理工学術院総合研究所   兼任研究員

Degree 【 display / non-display

  • 東京工業大学   博士(工学)

  • Tokyo Institute Technology   Doctor(Engineering)

Professional Memberships 【 display / non-display











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Research Areas 【 display / non-display

  • Environmental load reduction and remediation

  • Database

  • Chemical reaction and process system engineering

  • Safety engineering

  • Social systems engineering

Research Interests 【 display / non-display

  • プロセス制御、プロセス情報処理、情報検索、可視化情報学、環境保全技術、人間生活環境、鉄鋼プロセス制御、微生物制御、廃棄物処理、プロセスモデリング、プロセス解析、橋梁診断、センサーネットワーク、照明制御、自動車エンジン制御、自動車走行制御

Papers 【 display / non-display

  • A Novel Bridge Damage Diagnosis Algorithm Based on Deep Learning with Gray Relational Analysis for Intelligent Bridge Monitoring System

    Haitao Xiao, Wenjie Wang, Limeng Dong, Harutoshi Ogai

    IEEJ Transactions on Electrical and Electronic Engineering   16 ( 5 ) 730 - 742  2021.05

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    In recent years, intelligent structural damage diagnosis algorithms using machine learning have achieved much success. However, because of the fact that in real bridge applications, the working environment (load, temperature, and noise) is changing all the time, degradation of the performance of intelligent structural damage diagnosis methods is very serious. To address these problems, a novel bridge diagnosis algorithm based on deep learning is proposed. Our contributions include: First, we proposed an improved denoising auto-encoder-based deep neural networks, which is optimized by the gray relational analysis. It is able to automatically extract high-level features from raw signals via a multi-layer extraction to satisfy any damage diagnosis objective and thus does not need any time consuming denoising prepossessing. The model can achieve high accuracy under noisy environment. Second, the algorithm does not rely on any domain adaptation algorithm or require information of the target domain. It can achieve high accuracy when working environment is changed. Numerical simulations and experimental investigations on real bridges conducted to present the accuracy and efficiency of the proposed algorithm, comparing with other commonly machine learning-based algorithms. The result shows it is deemed as an ideal and effective method for damage diagnosis of bridge structures. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.


  • Conditional maximum likelihood identification for state space system

    Luo Xiao, Harutoshi Ogai, Wang Jianhong, Ricardo A.Ramirez Mendoza

    Mechatronic Systems and Control   49 ( 1 ) 1 - 8  2021.01

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    In this paper, we investigate the use of conditional maximum likelihood identification in the context of identifying one general state space system, being parametrized by one unknown parameter vector. The process of modifying the common state space system into our general form is presented, and the traditional negative log-likelihood function for identifying unknown parameter vector is constructed with only observed output variables. To combine state variables and output variables simultaneously, the conditional maximum likelihood estimate based on the conditional probability density and the total probability theorem is proposed here. Further, when the prior distribution of that parameter vector is flat, we continue to obtain the joint maximum a posteriori estimate. To maximize a negative log-likelihood function, the classical Robbins- Monro algorithm from stochastic approximation theory is applied to avoid the computation of the second-order derivative of conditional likelihood function.


  • Quasi-Linear SVM with Local Offsets for High-dimensional Imbalanced Data Classification

    Li Yanze, Harutoshi Ogai

    2020 59th Annual Conference of the Society of Instrument and Control Engineers of Japan, SICE 2020     882 - 887  2020.09

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    Imbalanced problems often occur in the classification problem. A special case is within-class imbalance, which worsen the imbalance distribution problem and increase the learning concept complexity. Most methods for solving imbalanced data classification focus on finding a globe boundary to solve between-class imbalance problem. My thesis proposes a effective quasi-linear network with local offsets adjustment for imbalanced classification problems. First, we proposed a gated piecewise linear network, an autoencoder-based partitioning method is modified for imbalanced datasets to divide input space into multiple linearly separable partitions along the potential separation boundary. Construct a quasi-linear SVM based on the gated signal that obtained by autoencoder partitioning information. Then training a neural network that let F-score as loss function to generate the local offsets on each local cluster. Finally a quasi-linear SVM classifier with local offsets is constructed for the imbalanced datasets. Our proposed method avoids calculating Euclidean distance, so it can be applied to high dimensional datasets. Simulation results on different real world datasets that our method is effective for imbalanced data classification especially in high-dimensional data.

  • Spatial Attention for Autonomous Decision-making in Highway Scene

    Shuwei Zhang, Yutian Wu, Harutoshi Ogai

    2020 59th Annual Conference of the Society of Instrument and Control Engineers of Japan, SICE 2020     1435 - 1440  2020.09

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    Automated decision making is still a significant challenge to realize fully autonomous driving. A common method that encoding surrounding vehicles in a grid map is used to describe observation space for decision making algorithm. It preserves vehicles spatial characteristics. But commonly in human driving, distinct position and speed surrounding vehicles contribute differently to make decision. We introduce a spatial attention module to calculate weights for each vehicle and integrate the attention mechanism into Deep Q network to make decision actions. The agent, ego vehicle, is trained in a simulated highway environment. Simulation results show the proposed method can get significant performance gains compared with other deep reinforcement learning methods by using two kinds of metrics.

  • Realtime Single-Shot Refinement Neural Network for 3D Obejct Detection from LiDAR Point Cloud

    Yutian Wu, Harutoshi Ogai

    2020 59th Annual Conference of the Society of Instrument and Control Engineers of Japan, SICE 2020     332 - 337  2020.09

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    3D object detection from point cloud is an important aspect of environmental perception in intelligent systems such as autonomous driving systems and robot systems. However, efficient 3D feature extraction and accurate object localization is challenging for current algorithms. In this paper, we introduce a new single-shot refinement neural network for fast and accurate 3D object detection. Firstly, we simplify the 3D feature extraction network and use single-shot object detector to increase processing speed. Secondly, we exploit self-attention mechanism in main object detection branch to improve object feature representation. Thirdly, an object refinement branch is introduced to produce a finer regression of objects upon the primary estimation from the main detection branch. Both modifications lead to further improvements in performance without additional computational cost. Our approach is tested on KITTI 3D Car detection benchmark and achieves good results in the validation set. The running speed is around 40 frame per second.

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Books and Other Publications 【 display / non-display

  • Pipe Inspection Robots for Structual Health and Condition Monitoring

    Harutoshi Ogai, Bishakh Bhattacharya( Part: Joint author)

    Springer  2018

  • 自動車エンジンのモデリングと制御

    編著者, 申鉄龍, 大畠明

    コロナ社  2011.03 ISBN: 9784339046106

  • 高度知識化社会における情報管理

    村山 博, 大貝晴俊

    コロナ社  2003.04 ISBN: 4339026271

  • プロセス制御


    コロナ社  2003.02 ISBN: 4339033618

Misc 【 display / non-display

  • Deep 3D Object Detection Networks Using LiDAR Data: A Review

    Yutian Wu, Yueyu Wang, Shuwei Zhang, Harutoshi Ogai

    IEEE Sensors Journal   21 ( 2 ) 1152 - 1171  2021.01

    Book review, literature introduction, etc.  

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    As the foundation of intelligent systems, machine vision perceives the surrounding environment and provides a basis for decision-making. Object detection is the core task in machine vision. 3D object detection can provide object steric size and location information. Compared with the 2D object detection widely studied in image coordinates, it can provide more applications of detection systems. Accurate LiDAR data has a stronger spatial capture capability and is insensitive to natural light, which makes LiDAR a potential sensor for 3D detection. Recently, deep neural network has been developed to learn powerful object features from sensor data. However, the sparsity of LiDAR point cloud data poses challenges to the network processing. Plenty of emerged efforts have been made to address this difficulty, but a comprehensive review literature is still lacking. The purpose of this article is to review the challenges and methodologies of 3D object detection networks using LiDAR data. On this account, we first give an outline of 3D detection task and LiDAR sensing techniques. Then we unfold the review of deep 3D detection networks with three kinds of LiDAR point cloud representations and their challenges. We next summarize evaluation metrics and performance of algorithms on three authoritative 3D detection benchmarks. Finally, we provide valuable insights of challenges and open issues.


Industrial Property Rights 【 display / non-display

  • プロセスの状態予測方法

    大貝 晴俊, 小川 雅俊


  • プロセスの状態予測方法

    大貝 晴俊, 小川 雅俊, 葉 怡君


  • プロセスの状態予測方法


    草柳 晃介, 大貝 晴俊, 小川 雅俊, 葉 怡君


  • プロセスの状態予測方法及びそれを用いたプロセス制御装置

    葉 怡君, 大貝 晴俊, 川成 翔, 小川 雅俊


  • エンジン制御パラメータ適合化装置及びプログラム

    大貝 晴俊, 小川 雅俊, 草鹿 仁


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Awards 【 display / non-display

  • 計測自動制御学会論文賞


  • 鉄鋼協会 計測・制御・システム部門研究賞


  • 鉄鋼協会 計測・制御・システム部門研究賞


  • 計測自動制御学会技術賞


  • 日本塑性加工学会会田技術賞


Research Projects 【 display / non-display

  • Real-time SLAM for Dynamic Environments

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  • System development to realize transillumination and functional imaging of animal body using safe light

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  • 無線センサネットワークによる社会基盤の安全・高効率化の基礎研究


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    (1)無線センサモジュールと無線電力伝送システムの設計・試作(大貝) 無線センサモジュールの小型化と省電力化を、低消費RFモジュール、マイコン、加速度センサを選定して試作した。また、無線電力伝送技術の改良を、送信アンテナの指向性向上、受信側のレクチナの改良により行なった。
    (2)橋梁診断方法の拡充(犬島、大貝) 橋梁診断方法について、技術整理を行うとともに新診断技術について研究し診断ソフト試作を行なった。ARMAモデルとPCAを用いた劣化度診断方法、ウェブレット変換とその可視化のよる劣化度の診断方法を検討した。
    (3)照明シミュレータの開発と大規模オフィスの照明最適化制御の開発(大貝) 大規模オフィス照明シミュレータについて検討し、照明装置と照度センサの関係をRBFニューラルネットワークによりモデル化についてする方法を確立した。また照明装置の最適制御方法の改良検討を行なった。また、1センサを用いた制御方法について検討した。実際のビルにて実証試験をおこない、大きな省エネ効果を確認した。
    (4)車々間通信システムの改良、隊列制御実験(大貝) 車々間通信システムについ2台の車車間通信システムの開発と安定化について検討し、試作実験評価した。また、隊列制御の高精度化、安定化について検討し、改良試作を行い、実験により性能評価した。

  • 高炉トータルシミュレータの研究

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  • センサネットによる大型構造物長寿命化技術

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Specific Research 【 display / non-display

  • 小型電気自動車における全天候型自動運転とその活用研究

    2018   Pan Xun, 犬島浩

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  • 磁気センサを用いた高速鉄道のオンライン診断技術研究


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  • 超軽量車両ULVを用いた運転支援・自動運転システムの研究開発

    2016   大貝晴俊

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  • 超軽量車両ULVを用いた運転支援・自動運転システムの研究開発

    2016   大貝晴俊

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  • 高齢者用小型電気自動車の自動運転技術の研究


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    [目的] 本研究では、高齢者用低速・短距離エコ自律走行システムの開発を目的とする。そこで、本研究では、低コストなステレオカメラ・超音波センサにより障害物回避、追越し、車線変更、信号停止などの動作を行いながら目的地まで自律走行させ、高齢運転者の操作を最低限に抑え、より安全、スムーズな走行を実現する。[結果] 自動車学校の模擬公道走行実験における自動運転デモの結果について述べる.自律走行テストは北九州市の若戸自動車学校で行った。走行速度を30Km/hで行った。目的地Eを設定し、スタート地点Sから走らせた。走行ルートはカーブ4つ、信号機1つ、登坂1つと下り坂1つで構成された。走行距離:は約260Mです。COMS車はSの位置から走行し、経路計画に基づく走行制御を行った。走行中に環境認識センサによって、走行方向を維持しながら目的地Eへ走った。走行中に白線認識と道路認識により車線維持走行を行った。信号機認識による停止線で一時停止を行った。カーブでは自動減速(10KM/h)で走行した。目的地Eに到着したら、自動ブレーキをかけ停止した。

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Overseas Activities 【 display / non-display

  • 欧州での自動運転研究調査と危険予知探究(HPI)、インフラ設備の診断技術の研究(IITK)


    イギリス   ホリバミラ

    フランス   ナビア社

    インド   インド工科大学カンプール校

    ドイツ   ブラウンシュビッツ、ダルムシュタット工科大学

    ドイツ   ミュンヘン、アーヘン工科大学


Syllabus 【 display / non-display

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