Smoothness of mobile and vehicle navigation has become relevant to ensure the safety and the comfortability of riding. The robotics community has been able to render smooth trajectories in mobile robots by using non-linear optimization approaches and well-known fairness metrics considering the curvature variations along the path. In this paper, we introduce the possibility of computing smooth paths from observed mobile robot trajectories from higher order non-linear fairness functionals. Our approach is potential to enable the generation of simple and computationally-efficient path planning smoothing for navigation in mobile robots.

Gradient-free stochastic optimization algorithms are well-known for finding suitable parameter configurations over independent runs ubiquitously. Attaining low variability of convergence performance through independent runs is crucial to allow further generalization over distinct problem domains. This paper investigates the performance of a differential particle system in stabilizing a nonlinear inverted pendulum under diverse and challenging initial conditions. Compared to the relevant algorithms in the literature, our experiments show the feasibility of achieving lower convergence variability to stabilize a nonlinear pendulum over independent runs and initial conditions within a reasonable computational load.

Motion planning approaches aided by learning schemes have achieved relevant results in the community, particularly in terms of rendering new paths efficiently and adapting to new environments/situations through encoder-decoder frameworks and latent space configurations. This paper evaluates the feasibility of learning motion planning functions for robot manipulators using a linear transition of the configuration space. Our computational experiments involving a relevant set of learning architectures have shown the feasibility and the efficiency in finding motion planning functions that meet user-defined criteria. Our approach contributes to realizing the practical efficiency to tackle the learning-based motion planning problem. Due to the amenability to parallelization schemes, our approach is potential to tackle larger degrees of freedom.

In recent years, SiC power modules have attracted a lot of attention because they offer higher frequency and density as compared to the conventional Si power module. However high speed switching inevitably lead to the generation of surge voltage which may damage the power module. The design of layout, which composed of copper patterns, power semiconductors and terminals, is one of the factors that is necessary to overcome the problem. In this paper, the layout design of the half-bridge power module is optimized to reduce its internal inductance. The inductance was evaluated by electromagnetic field simulation.

This study focused on the hierarchical modular approach to assembling space structures, which can be scaled up to increasingly larger sizes. This approach is based on dividing a large space structure into multiple modules, each with homogeneous automatic assembly functions. Hence, the overall structure can have diverse shapes and functions depending on the arrangement of modules, regardless of the size of the structure. Conventional approaches use locomotion and docking/release mechanisms installed on each module, but have been limited to 2-D shapes. In this study, a mechanism was developed that uses permanent magnets and electromagnets to assemble the space structure into any shape in three dimensions. The response surface method and downhill simplex method were used to optimize the size and arrangement of the magnets for efficient locomotion. The proposed mechanism was applied to a demonstration experiment to evaluate its effectiveness compared with existing methods.

Among the existing control schemes, Fuzzy Logic (FL) is advantageous in offering robustness to uncertainty and noise in sensor readings. However, the performance of FL-based navigation strategies is well-known to be bounded by localminima risks in polygonal environments with nonconvex configurations. Here, we propose a twin-stage Fuzzy Logic scheme for autonomous navigation of mobile robots. Our exhaustive computational and real-world experiments using Robotino, an omnidirectional mobile robot architecture shows the potential to avoid local-minima and obstacle collisions, enabling the computationally efficient and adaptive FL-based behavioural modules for mobile robots.

Minimal-length Steiner trees in the two-dimensional Euclidean domain are of special interest to enable the efficient coordination of multi-agent and interconnected systems. We propose an approach to compute obstacle-avoiding Steiner trees by using the hybrid between hierarchical optimization of shortest routes through sequential quadratic programming over constrained two-dimensional convex domains, and the gradient-free stochastic optimization algorithms with a convex search space. Our computational experiments involving 3,000 minimal tree planning scenarios in maps with convex and non-convex obstacles show the feasibility and the efficiency of our approach. Also, our comparative study involving relevant classes of gradient-free and nature inspired heuristics has shed light on the robustness of the selective pressure and exploitation abilities of the Dividing Rectangles (DIRECT), the Rank-based Differential Evolution (RBDE) and the Differential Evolution with Successful Parent Selection (DESPS). Our approach offers the cornerstone mechanisms to further advance towards developing efficient network optimization algorithms with flexible and scalable representations.

Proportional-integral-derivative (PID) control is ubiquitous in industrial automation tasks, and the parameter tuning of the gains is challenging due to nonlinearity and stagnation in local optima. In this paper we present a differential particle scheme based on stagnation-based selection mechanism, and evaluate its effectiveness in the stabilization of a nonlinear inverted pendulum and a magnetic levitation system. Our computational experiments show the feasibility to avoid stagnation, the lower variability of convergence over independent runs, and the feasibility to converge to significantly better fitness values compared to relevant heuristics in the literature. We believe our approach offers the building blocks to build stagnation-free nature inspired optimization algorithms useful for adaptive control and tuning.

Addressing the subset sum problem is relevant to study resource management problems efficiently. In this paper, we study a new scheme to sample solutions for the subset sum problem based on swarm-based optimization algorithms with distinct forms of selection pressure, the balance of exploration-exploitation, the multimodality considerations, and a search space defined by numbers associated with subsets of fixed size. Our experiments show that it is feasible to find optimal subsets with few number of fitness evaluations, and that Particle Swarm Optimization with Fitness Euclidean Ratio converges faster to the global optima with zero variability over independent runs. Since the search space is one-dimensional and friendly to parallelization schemes, our work is potential to study further classes of combinatorial problems using swarm-based optimization algorithms and the representation based on numbers.

Curves are essential concepts that enable compounded aesthetic curves, e.g., to assemble complex silhouettes, match a specific curvature profile in industrial design, and construct smooth, comfortable, and safe trajectories in vehicle-robot navigation systems. New mechanisms able to encode, generate, evaluate, and deform aesthetic curves are expected to improve the throughput and the quality of industrial design. In recent years, the study of (log) aesthetic curves have attracted the community's attention due to its ubiquity in natural phenomena such as bird eggs, butterfly wings, falcon flights, and manufactured products such as Japanese swords and automobiles. A (log) aesthetic curve renders a logarithmic curvature graph approximated by a straight line, and polar aesthetic curves enable to mode user-defined dynamics of the polar tangential angle in the polar coordinate system. As such, the curvature profile often becomes a by-product of the tangential angle. In this paper, we extend the concept of polar aesthetic curves and establish the analytical formulations to construct aesthetic curves with user-defined criteria. In particular, we propose the closed-form analytic characterizations of polar log-aesthetic curves meeting user-defined criteria of curvature profiles and dynamics of polar tangential angles. We present numerical examples portraying the feasibility of rendering the logarithmic curvature graphs represented by a straight line. Our approach enables the seamless characterization of aesthetic curves in the polar coordinate system, which can model aesthetic shapes with desirable aesthetic curvature profiles.

A new interconnected translational manipulator is proposed. It is the only interconnected manipulator that makes such motion using revolute joints and three rotary actuators. Rotary joints and actuators are favored practically than their linear counterparts due to their lower price, lower size of installation and higher reliability. The configuration of the proposed manipulator allows it to maintain, to a large extent, the combined merits of serial and parallel manipulators. In contrast to all other existing interconnected manipulators, the proposed manipulator has free-internal-singularity workspace. Using a practical proposed methodology, a balancing system is developed that reduces dramatically the power consumption and facilitates using small-sized-motors. The mobility analysis is carried out using a newly developed methodology suitable for interconnected manipulators. Closed forms for position and velocity kinematics as well as for maximum cuboid workspace are derived. The developed mechanical design is validated by finite element analysis. The controller performance is tested using ADAMS MATLAB/Simulink co-simulation. The results indicate the feasibility of the proposed manipulator and its advantages over existing translational manipulators from engineering as well as economic viewpoints.

In recent years, SiC power modules have attracted a lot of attention because they offer higher frequency and density as compared to the conventional Si power module. However high speed switching inevitably lead to the generation of surge voltage which may damage the power module. The design of layout, which composed of copper patterns, power semiconductors and terminals, is one of the factors that is necessary to overcome the problem. In this paper, the layout design of the half-bridge power module is optimized to reduce its internal inductance. The inductance was evaluated by electromagnetic field simulation.

Due to the demand for miniaturization, further improvement in cooling performance is required for power modules. The spiral-fin heatsink we considered has higher cooling performance than conventional ones. In this paper, we examined the design parameters of the spiral-fin, and found that three parameters of spiral-fin have big effect for the cooling performance

Trees are useful entities allowing to model data structures and hierarchical relationships in networked decision systems ubiquitously. An ordered tree is a rooted tree where the order of the subtrees (children) of a node is significant. In combinatorial optimization, generating ordered trees is relevant to evaluate candidate combinatorial objects. In this paper, we present an algebraic scheme to generate ordered trees with $n$ vertices with utmost efficiency; whereby our approach uses $O$ (n) space and $O$ (1) time in average per tree. Our computational studies have shown the feasibility and efficiency to generate ordered trees in constant time in average, in about one tenth of a millisecond per ordered tree. Due to the 1-1 bijective nature to other combinatorial classes, our approach is favorable to study the generation of binary trees with $n$ external nodes, trees with $n$ nodes, legal sequences of $n$ pairs of parentheses, triangulated n-gons, gambler's sequences and lattice paths. We believe our scheme may find its use in devising algorithms for planning and combinatorial optimization involving Catalan numbers.

A shape of the satellite's solar sail membrane is essential for unloading angular momentum in the three-axis stabilized attitude control system because the three-dimensional solar sail can receive solar radiation pressure from arbitrary directions. In this paper, the objective is the shape optimization of a three-dimensional membrane-structured solar sail using the angular momentum unloading strategy. We modelled and simulated the solar radiation pressure torque, for unloading angular momentum. Using the simulation system, since the unloading angular momentum rate is maximized, the shape of the three-dimensional solar sail was optimized using a Genetic algorithm and Sequential Quadratic Programming. The unloading velocity in the optimized shaped solar sail was greatly improved with respect to a conventional flat or pyramid solar sail.

Elucidating versatile configurations of spiral folding, and investigating the deployment performance is of relevant interest to extend the applicability of deployable membranes towards large-scale and functional configurations. In this paper we propose new schemes to package flat and curved membranes of finite thickness by using multiple spirals, whose governing equations render folding lines by juxtaposing spirals and by accommodating membrane thickness. Our experiments using a set of topologically distinct flat and curved membranes deployed by tensile forces applied in the radial and circumferential directions have shown that (1) the multi-spiral approach with prismatic folding lines offered the improved deployment performance, and (2) the deployment of curved surfaces progresses rapidly within a finite load domain. Furthermore, we confirmed the high efficiency of membranes folded by multi-spiral patterns. From viewpoints of configuration and deployment performance, the multi-spiral approach is potential to extend the versatility and maneuverability of spiral folding mechanisms.

This paper proposes a wheel with a deployable leg that can change the apparent wheel radius to improve the runnability of a rover traversing a lunar surface covered with regolith. The driving force of the wheel was formulated according to terramechanics, and relations for the changing driving force with the different configurations were clarified. The simulated driving forces with the original wheel configuration and extended leg configuration were compared in a single-wheel experiment, and the results confirmed that the proposed extendable leg system exhibited a higher driving force than the original circular wheel. With this system, the rover can use the original wheel state for flat ground surfaces that do not require a high driving force and then switch to the proposed extendable leg system when a high driving force is required, such as escaping from local concave ground or climbing on steep slope. The proposed system is potentially applicable to efficiently traversing irregular surfaces not only on the Moon but also on other planets.

This paper presents a method to inspect the interior of a winding small gas pipe using a hollow guide wire. There is no conventional method to insert an endoscope into an 8-bend 25-mm-diameter gas pipe within 2 hours. In medical practice, a guide wire inserted in advance enables insertion of a catheter into a vessel. However, it is impossible to insert a normal guide wire into a gas pipe because the wire buckles in the pipe. Thus, we designed a hollow guide wire with a small front diameter and large rear diameter, making the front soft and the rear stiff. This guide wire can be inserted without buckling or meandering. First, we measured mechanical properties such as the torsional spring constant and damping coefficient of the wire and the frictional coefficient between the pipe and wire. Second, we conducted an experiment inserting guide wires with various tip pitches, front lengths, front outer diameters, and rear outer diameters. Third, we analyzed the insertion distance by simulating guide wire insertion using the Lagrange method, and optimized the guide wire shape via the response surface method. Finally, the optimized guide wires were tested experimentally to validate the analysis. As a result, an optimized guide wire and an endoscope can both be inserted into a gas pipe and removed within just 3 minutes.

Trees are useful entities allowing to model data structures and hierarchical
relationships in networked decision systems ubiquitously. An ordered tree is a
rooted tree where the order of the subtrees (children) of a node is
significant. In combinatorial optimization, generating ordered trees is
relevant to evaluate candidate combinatorial objects. In this paper, we present
an algebraic scheme to generate ordered trees with $n$ vertices with utmost
efficiency; whereby our approach uses $\mathcal{O}(n)$ space and
$\mathcal{O}(1)$ time in average per tree. Our computational studies have shown
the feasibility and efficiency to generate ordered trees in constant time in
average, in about one tenth of a millisecond per ordered tree. Due to the 1-1
bijective nature to other combinatorial classes, our approach is favorable to
study the generation of binary trees with $n$ external nodes, trees with $n$
nodes, legal sequences of $n$ pairs of parentheses, triangulated $n$-gons,
gambler's sequences and lattice paths. We believe our scheme may find its use
in devising algorithms for planning and combinatorial optimization involving
Catalan numbers.

This research deals with hierarchical modular structure which is noted as the assembly method in view of the recent upsizing of space structures. The idea is dividing a large space structure into multiple modules and homogeneous automatic assembly functions are installed on each of a module. Hence the shape and function of the entire structure has diversity as they can be changed by the arrangement of modules, regardless of the size of the structure. The conventional researches of locomotion and docking / release mechanisms installed on each module and keeping such characteristics are limited to two-dimensional. In this research, we propose a mechanism using an electromagnet that can be assembled into any shape in three dimensions. The size and arrangement of the electromagnet are optimized to maximize its movement performance by using the Response Surface Method (RSM) for making response surface and applied downhill simplex method. On that basis, the demonstration experiment was actually carried out and the success rate was compared with the existing method to demonstrate the effectiveness of the proposed method.

© 2019, The Author(s). Breast cancer diagnosis has been mostly accomplished through imaging. These methods have great advantages in being able to detect the presence and location of breast cancer. However, it is difficult to distinguish between a benign and malignant tumor located in a deep position because both tumor types look similar. In this paper, tissue including the tumor from skin was vibrated using a compression cylinder, to analyze the frequency difference for distinguishing tissue type. Before distinguishing between a benign and malignant tumor, it is necessary to validate that the difference between normal tissue and tumor can be distinguished. The objective of the study is to validate the feasibility to emphasize the frequency differences in a 10.0 mm or greater deep tumors during vibration by pushing a cylinder towards the deep tumor. A phantom model and finite element analysis model were constructed to simulate the breast. In the experiment, air was injected into the phantom and the displacement was measured. The frequency response for distinction of tissue types was analyzed and it was found that the displacement difference rate was over 50% at a frequency of 130 Hz when the cylinder was pushed into the sample as opposed to when not pushed in. Changes in displacement were measured according to the distance between the tumor and vibration point using finite element analysis. When the measurement and vibration points were on the center of the tumor, the difference in the resonance point was at its largest (5.5 Hz). Results show that the position of a tumor could be easily and rapidly detected by vibrations from a cylinder pushed into the diagnostic site.

© 2019, The Author(s). Biomimetics present useful ideas for various product designs. However, most biomimetics only mimic the features of living organisms. It has not been clarified how a given shape is attained through natural selection. This paper presents the design factors that optimize the radula shape of Euhadra peliomphala. Clarifying the important design factors would help designers in solving several problems simultaneously in order to adapt to complicated and multi-functionalized design mechanisms. We measured the radula of Euhadra peliomphala by using a microscope and modeled the grinding/cutting force using the finite element analysis (FEA). We reproduced the natural selection using multi-objective genetic algorithm (MOGA). We compared the solutions when optimizing the radula shape using objective functions of each combination of stress, cutting force, abrasion, or volume. The results show that the solution obtained through two-objective optimization with stress and cutting force was the closest to the actual radula shape.

© 2018 IEEE. Being ubiquitously used as anti-adhesive and wound-covering mechanisms, thin films have potential therapeutic uses as cell sheets to target inner organs while navigating narrow environments. A significant challenge to realize versatile films lies in achieving compact storage and efficient transport while ensuring coherency in curvature-bounded environments. In this paper, we propose a folding mechanism of a curved film by using a spiral approach, enabling efficient unfolding and flexible plasters with curved surfaces. Our experiments using gelatin-based films with curved surfaces shows the superior indwelling ability in terms of chromaticity level compared to the conventional planar films, as well as the efficient unfolding in the order of seconds. Our results presents the theoretical and experimental building blocks to realize a versatile class of films which are able to navigate narrow environments, and unfold efficiently and flexibly.

In recent years, there have been increasing the number of power modules which is required with high performance, miniaturization and weight saving. But these requires cause high heat generation density for power module, which gets junction operation temperature to rise. Cooling unit is thus greatly demanded for high heat dissipation. The simple shaped heatsinks (straight-fin type and pin fin type) were generally used. But they have the limit of cooling performance. In this report, we have developed new heatsink shape to cope with rapidly increasing of the cooling requirement. Cooling performance is shown to thermal resistance and pressure loss. We evaluated them by thermal fluid analysis. In this approach, the spiral-fin heatsink with spiral curved channels has excellent cooling performance. This shape is the unique point in this report. This is because the shape with three-dimensional regular curve has not been studied. The spiral-fin heatsink has many factors (tin thickness, fin pitch, the number of channels, etc.). These factors affect cooler performance. We changed these factors to determine the best shape of spiral-fin. As a result, the best shape is 14.9[%] lower than the straight-fin type in thermal resistance.

<p>In this study we propose the use of spiral folding to deploy automotive airbags, and propose the folding pattern considering creases, which has the potential benefits to diminish friction during storage and bring benefits to faster deployment. Our preliminary experiments show the feasibility to deploy airbags by using the spiral folding approach, and the reasonable consistency between the modeling and real-world deployment. Our results offer preliminary insights for further study on spiral folding mechanisms and safest deployment performance.</p>

<p>This paper presents a surgical robot mechanism optimization method considering of the working error and operator's muscle burden. The virtual surgical system was developed to simulate the visual and haptic feedback. The participants operated the VR surgical simulation system while the authors measured the working error and the participant's joint motion. The histogram was made of the data to show the distribution. The authors estimated the appropriate probabilistic distribution model using Akaike's Information Criterion (AIC) method. As a result, there were many cases most applicable to the Weibull model.</p>

© copyright Environment and Climate Change Canada. In this paper, we proposed a wheel equipped rover with deployable legs that can change the apparent wheel radius in order to improve the runnability of the rover which travels on the ground covered with lunar regolith. Furthermore, we formulated the driving force of the wheel based on Terramechanics and clarified the mechanism of the driving force change using our proposed rover wheel. In addition, the driving force was compared between the original wheel configuration and the leg expanded one in single wheel running experiments, and it was validated that the proposed leg expandable system exhibits higher driving force than the original circular wheel. With this system, in the case of a flat ground surface that does not require a high driving force, the vehicle should use the original wheel state, and when a high driving force is required such like the situation the vehicle need to escape the wheel from the local concaved ground, the proposed expandable leg system can be useful, and then the possibility of traveling on irregular surface efficiently not only on the moon but also on other planets was shown.

© 2019 Design Society. All rights reserved. In recent years, product complexity in terms of function and structure has been driven by technological development in complementary components. Designing unbiased product evaluation metrics being to grasp the complex relationships of product features, and able to capitalize on market needs has become a challenge in industrial practice. In this paper, we propose a hybrid framework in which evaluation models are generated by integrating Interpretive Structural Modeling (ISM), Hierarchical Clustering and Data Envelopment Analysis (DEA). Whereas ISM constructs hierarchical digraphs (skeletons), Hierarchical Clustering reduces dimensionality of pairwise comparisons (correlations) of design variables, and suggests possible evaluation configurations, and DEA computes weights to provide optimal evaluation metrics. Our computational experiments using more than twenty thousand vehicles from 1982 to 2013 confirmed the feasibility and usefulness of DEA with hierarchical concepts to generate the optimal vehicle evaluation metric, and to suggest configurations for vehicle design layouts.

© 2019 by the American Institute of Aeronautics and Astronautics, Inc. All rights reserved. This research formulates the two-dimensionally model of a membrane with crease line which is always added when the thin material is used for deployable space structure like a solar sail. Recent space development is tending to focus on installing a system on a ultra-light weight structure, so when the membrane is chosen as the base of space structure, the keeping accuracy of the membrane surface is the point of interest. This means how to reduce the wrinkle appeared on the membrane becomes the key point. However, the most research on modelling the membrane limit to the one dimension model, or if any, the plastic deformation is not considered. Here in this paper, the authors established the two-dimensionally membrane model considering the plasticity and clarified from analysis and experiment that wrinkles can be reduced by giving crease to a membrane. In the model assuming the actual antenna installed structure and with the actual load condition, the fact is obtained that a crease perpendicular to the loading direction should be applied to a position one fifth between the load application point and the wrinkle generated region. This position can be considered to be able to reduce wrinkle the most from the view point of amount of energy required to eliminate wrinkles.

© 2019, Springer Nature Switzerland AG. Path bundling consists in compounding multiple routes in a polygonal map to minimize connectivity in a network structure. Being closely related to the Steiner Tree Problem, yet with a different scope, path bundling aims at computing minimal trees while preserving network connectivity among origin-destination pairs to allow the joint transport of information, goods, and people. In this paper, we propose a method to tackle the path bundling problem in modular bipartite networks by using a two-layer optimization with a convex representation. Exhaustive computational experiments in diverse polygonal domains considering convex and non-convex geometry show the feasibility and the efficiency of the proposed approach, outperforming the state of the art in generating comparatively shorter trees, and improved scalability as a function of edges in bipartite networks.

© 2018 IEEE. Breast cancer diagnosis has been mostly accomplished by imaging technologies. These methods have the great advantages of detecting the presence and location of breast cancer. However, it's difficult to distinguish between a benign and malignant tumor in a deep position because both tumor types look similar. In this paper, we vibrated the tissue including tumor from skin with a compression cylinder to analyze the frequency difference for distinguishing the tissue type. Before distinguishing a benign and malignant tumor, it's necessary to validate to distinguish between normal tissue and tumor. The objective is to validate the feasibility of using a compression cylinder that emphasizes the differences in frequency between normal tissue and tumor. In two experiments, we measured the displacement on the surface of a breast phantom vibrated by an impulse hammer. We compared the frequency difference with and without a cylinder. We also studied the frequency changes in the relationship between tumor and cylinder position. We found a 5.0 Hz difference in compliance between normal tissue and the simulated tumor using a compression cylinder. The difference in frequency correlated negatively with distance from the simulated tumor to a compression cylinder. We concluded that a compression cylinder would enhance the frequency difference between normal tissue and a simulated tumor with appropriate configuration.

© 2018 IEEE. Laparoscopic surgery has the advantage of the minimally invasive for patients. However, the surgery is technically difficult for surgeon because high dexterity is required for suturing in the narrow patient's body. This paper presents a sealing method to locate the adhesive plaster at the incision instead of suturing. The objective is to optimize the plaster material and structure. We made the plaster with the thermally cross-linked gelatin film in a spiral fold because thermally cross-linked gelatin film has the high biocompatibility and tackiness, and a spiral fold has great storage efficiency. In 3 experiments, we measured expansion rate, expansion tension, peeling force, and sealing pressure in a variety of gelatin volume and concentration, and the films diameter. From these experimental results, we optimized the films using response surface method. As a result, the plaster is optimal at gelatin volume 10 mL, gelatin concentration 4 wt %, and films diameters 75 mm. We concluded that the optimized spiral folded adhesive plaster is sufficient in terms of the expansion, tackiness, and sealing properties.

This paper aims at computing minimal-length tree layouts given an n-star graph in a polygonal map. This problem is strongly related to the edge bundling problem, which consists of compounding the edges of an input graph to obtain topologically compact graph layouts being free of clutter and easy to visualize. Computational experiments using a diverse set of polygonal maps and number of edges in the input graph shows the feasibility, efficiency and robustness of our approach.

Graphs with self-loops enable to represent a large variety of interactions in natural and artificial systems, allowing not only inter-connectivity among heterogeneous entities but also the self-dependence of entities, e.g.The recursive and autonomous nature of dynamical systems. In this paper we present new bijective constructs which enable the numerical representation of graphs with self loops (or loopy graphs). In particular, we study the case of (1) undirected and (2) directed graphs with n nodes and m edges with self-loops. Our proposed approach realizes the succinct representations by using integer numbers in which rigorous computational experiments show the efficiency of our proposed algorithms: The complexity follows a quasi-linear behaviour as a function of the number of edges (which is independent of the number of nodes). Furthermore, as direct consequence of our constructs, we propose list structures having O(m) space complexity, which realize the linear space complexity depending only on the number of edges (the list is independent of n). We believe that our bijective algorithms are useful to tackle problems involving sampling of graphical models, network design as well as process planning by using number theory and sample-based learning.

© 2017 IEEE. Route bundling implies compounding multiple routes in a way that anchoring points at intermediate locations minimize a global distance metric. The result of route bundling is a tree-like structure where the roots of the tree (anchoring points) serve as coordinating locus for the joint transport of information, goods, and people. Route bundling is a relevant conceptual construct in a number of path planning scenarios where the resources and means of transport are scarce/expensive, or where the environments are inherently hard to navigate due to limited space. In this paper we propose a method for searching optimal route bundles based on a self-adaptive class of differential evolution using a convex representation. Computational experiments in scenarios with and without convex obstacles show the feasibility and efficiency of our approach.

Computing hierarchical routing networks in polygonal maps is significant to realize the efficient coordination of agents, robots and systems in general
and the fact of considering obstacles in the map, makes the computation of efficient networks a relevant need for cluttered environments. In this paper, we present an approach to compute the minimal-length hierarchical topologies in polygonal maps by Differential Evolution and Route Bundling Concepts. Our computational experiments in scenarios considering convex and non-convex configuration of polygonal maps show the feasibility of the proposed approach.

Combinations of m out of n are ubiquitous to model a wide class of combinatorial problems. For an ordered sequence of combinations, the unranking function generates the combination associated to an integer number in the ordered sequence. In this paper, we present a new method for unranking combinations by using a gradient-based optimization approach. Exhaustive experiments within computable allowable limits confirmed the feasibility and efficiency of our proposed approach. Particularly, our algorithmic realization aided by a Graphics Processing Unit (GPU) was able to generate arbitrary combinations within 0.571 seconds and 8 iterations in the worst case scenario, for n up to 1000 and m up to 100. Also, the performance and efficiency to generate combinations are independent of n, being meritorious when n is very large compared to m, or when n is time-varying. Furthermore, the number of required iterations to generate the combinations by the gradient-based optimization decreases with m in average, implying the attractive scalability in terms of m. Our proposed approach offers the building blocks to enable the succinct modeling and the efficient optimization of combinatorial structures.

Optimal topologies in networked systems is of relevant interest to integrate and coordinate multi-agency. Our interest in this paper is to compute the root location and the topology of minimal-length tree layouts given n nodes in a polygonal map, assuming an n-star network topology. Computational experiments involving 600 minimal tree planning scenarios show the feasibility and efficiency of the proposed approach.

© Springer International Publishing AG, part of Springer Nature 2018. Huge lava tubes with an approximate diameter of 65–225 m were found on the surfaces of Moon and Mars in the late 2000’s. It has been argued that the interiors of the caves are spacious, and are suitable to build artificial bases with habitable features such as constant temperature, as well as protection from both meteorites and harmful radiation. In line of the above, a number of studies which regard the soft landing mechanisms on the bottom of the lava tubes have been proposed. In this paper, aiming to extend the ability to explore arbitrary surface caves, we propose a mechanism which is able to reach the ceiling of lava tubes. The basic concept of our proposed mechanism consists of a rover connected to an oscillating sample-gatherer, wherein the rover is able to adjust the length of the rope parametrically to increase the deflection angle by considering periodic changes in the pivot, and thus to ease the collection of samples by hitting against the ceiling of the cave. Relevant simulations confirmed our theoretical observations which predict the increase of deflection angle by periodically winding and rewinding the rope according to pivotal variations. We believe the our proposed approach brings the building blocks to enable finer control of exploration mechanisms of lava tubes and narrow environments.

In this paper we aim at tackling the problem of searching for novel and high-performing product designs. Generally speaking, the conventional schemes usually optimize a (multi) objective function on a dynamic model/simulation, then perform a number of representative real-world experiments to validate and test the accuracy of the some product performance metric. However, in a number of scenarios involving complex product configuration, e.g. optimum vehicle design and large-scale spacecraft layout design, the conventional schemes using simulations and experiments are restrictive, inaccurate and expensive.
In this paper, in order to guide/complement the conventional schemes, we propose a new approach to search for novel and high-performing product designs by optimizing not only a proposed novelty metric, but also a performance function which is learned from historical data. Rigorous computational experiments using more than twenty thousand vehicle models over the last thirty years and a relevant set of well-known gradient-free optimization algorithms shows the feasibility and usefulness to obtain novel and high performing vehicle layouts under tight and relaxed search scenarios.
The promising results of the proposed method opens new possibilities to build unique and high performing systems in a wider set of design engineering problems. (C) 2017 Elsevier B.V. All rights reserved.

The differentiation and the integration of products are the essential procedures for product innovation. To understand the product innovation, the approaches using S-curve theory, which explain the evolution of a technological system, have been effective. However, the S-curve theory has the disadvantage that the validity of the analysis depends greatly on the number of data. In this paper, we propose a novel method for measuring and predicting the technological innovation and the product evolution based on the S-curve and wavelet transform to solve the problem. In order to confirm the effectiveness of the proposed method, we will conduct a case study using patents of air purifiers. Furthermore, we will define and support the differentiation and the integration of the mechanical structure using the proposed method. Our analysis shows that the differentiation and the integration of the mechanical structure occur as a life cycle extension after the main technologies enter the declining phase. Therefore, the incidental technologies should be introduced at the beginning of the declining phase of the main technologies.

Route bundling implies compounding multiple routes in a way that anchoring points at intermediate locations minimize a global distance metric to obtain a tree-like structure where the roots of the tree (anchoring points) serve as coordinating locus for the joint transport of information, goods and people. Route bundling is a relevant conceptual construct in a number of path-planning scenarios where the resources and means of transport are scarce/expensive, or where the environments are inherently hard to navigate due to limited space. In this paper we propose a method for searching optimal route bundles based on a self-adaptive class of Differential Evolution using a convex representation. Rigorous computational experiments in scenarios with and without convex obstacles show the feasibility and efficiency of our approach.

© Copyright 2017 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved. Path bundling, a class of path planning problem, consists of compounding multiple routes to minimize a global distance metric. Naturally, a tree-like structure is obtained as a result wherein roots play the role of coordinating the joint transport of information, goods, and people. In this paper we tackle the path bundling problem in bipartite networks by using gradient-free optimization and a convex representation. Then, by using 7,500 computational experiments in diverse scenarios with and without obstacles, implying 7.5 billion shortest path computations, show the feasibility and efficiency of the mesh adaptive search.

Directed graphs encode meaningful dependencies among objects ubiquitously. This paper introduces new and simple representations for labeled directed graphs with the properties of being succinct (space is information-theoretically optimal); in which we avoid exploiting a-priori knowledge on digraph regularity such as triangularity, separability, planarity, symmetry and sparsity. Our results have direct implications to model directed graphs by using single integer numbers effectively, which is significant to enable canonical (generation of graph instances is unique) and efficient (coding and decoding take polynomial time) encodings for learning and optimization algorithms. To the best of our knowledge, the proposed representations are the first known in the literature.

Being a significant construct in a wide range of combinatorial problems, the k-subset sum problem (k-SSP) computes k-element subsets, out of an n-element set, satisfying a user-defined aggregation value. In this paper, we formulate the k-subset sum problem as a search (optimization) problem over the space of integers associated with combination elements. And by using rigorous computational experiments using the search space over more than 10(14) integer numbers, we show that our approach is effective and efficient: it is feasible to find any combination with a user-defined sum within 10(4) function evaluations by using a gradient-free optimization algorithm. Our scheme opens the door to further advance the understanding of combinatorial problems by improved/tailored gradient-free optimization algorithms based on enumerative encoding. Also, our approach realizes the practical building block for combinatorial problems in planning and operations research using k-SSP concepts.

The search for novel and high-performing product designs is a ubiquitous problem in science and engineering: aided by advances in optimization methods the conventional approaches usually optimize a (multi) objective function using simulations followed by experiments. However, in some scenarios such as vehicle layout design, simulations and experiments are restrictive, inaccurate and expensive. In this paper, we propose an alternative approach to search for novel and high-performing product designs by optimizing not only a proposed novelty metric, but also a performance function learned from historical data. Computational experiments using more than twenty thousand vehicle models over the last thirty years shows the usefulness and promising results for a wider set of design engineering problems.

Modularity is vital to engineer complex products and machines. We assert that modularity can emerge in the context of desirable structures constrained to life cycle factors; and propose a method to evaluate machine modularity in the context of life cycle optimization. Experiments using explorit, a new and well suited global optimization algorithm, on fve relevant and divergent machine models show that it is possible to obtain tractable modules within the context of life cycle factors.

Graphs denote useful dependencies among objects ubiquitously. This paper introduces new and simple bijections to the integer grid to enable the succinct, canonical and efficient representations of labeled graphs; whereas previous work has focused on regularities in structure such as triangularity, separability, planarity, symmetry and sparsity. By succinct we imply that space is information-theoretically optimal, by canonical we imply that generation of instances is unique, and by efficient we imply that coding and decoding take polynomial time.
Our results have direct implications to handle labeled graphs by using single numbers efficiently, which is significant to enable the canonical graph encodings in learning and optimization algorithms. Our bijections are the first known in the literature.

How to build optimal vehicular powertrains? We study this question and propose an algorithm inspired by a domain-general design process. The basic idea is to interplay co-biasingly between the local approximations of discrete design and the global refinements of continuous parameters. The proposed method was evaluated to design powertrains of four types of vehicles: Series Hybrid Electric Vehicle(SHEV), Parallel Hybrid Electric Vehicle(PHEV), Fuel Cell(FC) and Electric Vehicle(EV). Simulation results show noticeable improvements on mileage per gas emissions over different study cases. To our knowledge, this is the first study aiming at designing vehicle powertrains considering the holistic point of view. © Springer-Verlag 2013.

One important concept in financial risk management is the diversification process of capital allocation. This paper proposes an evolutionary approach for the optimal diversification when making asset allocation using variable-size genetic relation algorithm (vs-GRA), whose main role is to model and evolve structures toward effective and diversified portfolios through its graph structure. Simulations using heterogeneous and globally located asset classes in the United States, Europe, and Asia show that the proposed scheme offers competitive economic advantages. (C) 2012 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

As global financial innovation opens innumerable risks and opportunities, a global view of the asset allocation brings advantages in risk diversification for investments. We propose a novel framework for asset selection under global diversification principles using genetic network programming. Simulations using the stocks, bonds and currencies from relevant financial markets in USA, Europe and Asia show that the proposed framework is effective and offers competitive advantages against the conventional methods in finance and computational fields. (C) 2011 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

One important question in investment is how to build adaptive asset allocation strategies, i.e. portfolios which adjust to the changing conditions of the economic environments. This paper proposes an evolutionary approach for the adaptive asset allocation by using Guided Genetic Relation Algorithm(GRA-g), whose main role is to model and evolve the optimal adaptive portfolio structures. Simulations using asset classes in USA show that the proposed scheme offers competitive economic advantages. This paper suggests that the use of evolutionary computing techniques is an excellent tool to aid the asset allocation, whose advantages imply the usefulness to manage the exposure to risk. ? 2011 SICE.

Stock selection involves the continuous quest for the margin of safety, or a favorable difference between the stock price and its intrinsic value. Although this variable might not be quantified with exact precision, it may be approximated through the underlying relationships in financial markets and the real economy. We propose Genetic Network Programming with changing structures(GNP-cs), a novel evolutionary based algorithm to approximate these relationships through graph networks, and build asset selection models to identify the prospective stocks in the context of changing environments. GNP-cs uses functionally distributed systems to monitor the change of the economic environment and execute the strategy for stock selection adaptively. The comparison shows that the proposed scheme outperforms the standard stock selection styles using the stocks listed in the Russell 3000 Index. This paper suggests that the use of evolutionary computing techniques is an excellent tool to tackle the stock selection problem, whose advantages imply the usefulness to manage the risk and safeguard investments. © 2011 Authors.

Financial innovation is continuously testing the asset selection models, which are the key both for building robust portfolios and for managing diversified risk. This paper describes a novel evolutionary based scheme for the asset selection using Robust Genetic Network Programming(r-GNP). The distinctive feature of r-GNP lies in its generalization ability when building the optimal asset selection model, in which several training environments are used throughout the evolutionary approach to avoid the over-fitting problem to the training data. Simulation using stocks, bonds and currencies in developed financial markets show competitive advantages over conventional asset selection schemes.

Asset selection is a challenging task in the complex global financial system, whose nature has highlighted the need to rethink conventional practices. The attractive and non-toxic assets must be kept on the eye so that our financial systems sustain building blocks in our economic systems. This paper presents an asset selection framework using Genetic Network Programming(GNP). GNP handles evolvable graph structures that prevent the size expansion for dynamic and complex environments, which in turn make it suitable for dealing with decision processes effectively under uncertainty such as partially observable Markov decision processes. Simulations using stocks, bonds and currencies from relevant financial markets in USA, Europe and Asia show the competitive advantages of the proposed method against relevant selection strategies in the finance literature.

Global financial development have opened innumerable risks and opportunities for investments. A global view of the portfolio allocation through diversification brings advantages for the risk allocation in investments. In this paper, an asset allocation framework under the return, risk and liquidity considerations is proposed for short term investment using Genetic Relation Algorithm. Simulations using the stocks, bonds and currencies from relevant financial markets in USA, Europe and Asia show that the proposed framework is effective and robust. The efficacy of the proposed method is compared against the relevant constructs in finance and computational fields.