bluuebunny/crossref_metadata_2025 huggingface dataset:
Fuzzy Rule Interpolation and Extrapolation Techniques: Criteria and Evaluation Guidelines
Authors: Tikk Domonkos ,   Dep. of Telecommunications and Media Informatics, Budapest University of Technology and Economics, Magyar tudósok krt. 2, H-1117 Budapest, Hungary, Johanyák Zsolt Csaba , Kovács Szilveszter , Wong Kok Wai ,   Institute of Information Technology, Kecskemét College, Izsáki út 10, H-6000 Kecskemét, Hungary,   Department of Information Technology, University of Miskolc, H-3515 Miskolc-Egyetemváros, Hungary,   School of Information Technology, Murdoch University, South St., Murdoch, Western Australia 6150, Australia
Published: April 2016

This paper comprehensively analyzes Fuzzy Rule Interpolation and extrapolation Techniques (FRITs). Because extrapolation techniques are usually extensions of fuzzy rule interpolation, we treat them both as approximation techniques designed to be applied where sparse or incomplete fuzzy rule bases are used, i.e., when classical inference fails. FRITs have been investigated in the literature from aspects such as applicability to control problems, usefulness regarding complexity reduction and logic. Our objectives are to create an overall FRIT standard with a general set of criteria and to set a framework for guiding their classification and comparison. This paper is our initial investigation of FRITs. We plan to analyze details in later papers on how individual techniques satisfy the groups of criteria we propose. For analysis,MATLAB FRI Toolbox provides an easy-to-use testbed, as shown in experiments.
Semantically Enhanced Code Clone Refinement Algorithm Based on Analysis of Multiple Detection Reports
Authors: Sotolongo Ricardo ,   Department of Computational Intelligence and Systems Science, Tokyo Institute of Technology, G3-49, 4259 Nagatsuta, Midori-ku, Yokohama 226-8502, Japan, Dong Fangyan , Hirota Kaoru
Published: April 2016

An algorithm based on semantic analysis of multiple detection tools’ reports using WordNet is proposed oriented on the refinement of code clones. It parses different detection tools’ reports looking for new clone specifications, and refines the location of existing ones using semantic information contained in source code. It is applied to a real and complex software system and is compared to three other well-known detection algorithms, discovering 4888 clone pairs more than the average detected by other tools; also making the code clones 3 lines longer (for a subset of the same system the results are proportional to the size reduction). The objective is to provide higher quantity of code clones, and more appropriated localization to be used in refactoring processes.
A Fuzzy Weights Representation for Inner Dependence AHP
Authors: Ohnishi Shin-ichi ,   Faculty of Engineering, Hokkai-Gakuen University, W11-1-1, S26, Chuo-ku, Sapporo, Hokkaido 064-0926, Japan, Yamanoi Takahiro , Imai Hideyuki ,   Graduate School of Information Science and Technology, Hokkaido University, W9, N14, Kita-ku, Sapporo, Hokkaido 060-0814, Japan
Published: April 2016

The Analytic Hierarchy Process (AHP) proposed by T. L. Saaty has been widely used in decision making. Inner dependence method AHP is used for cases in which criteria are not independent enough. Using the original AHP or inner dependence AHP may cause results to lose reliability because the comparison matrix is not necessarily sufficiently consistent. In such cases, fuzzy representation for weighting criteria using results from sensitivity analysis is useful. We present weights of normal AHP criteria via fuzzy sets, then calculate modified fuzzy weights of inner dependence methods. We also get overall weights of alternatives based on certain assumptions. Results show the fuzziness of inner dependence AHP if the comparison matrix is not sufficiently consistent and individual criterion do not have enough independence.
Autonomous Environmental Sensing System for Human Activity Monitoring
Authors: Fujita Takayuki ,   Dept. of Electrical Engineering and Computer Sciences, University of Hyogo, 2167 Shosha, Himeji, Hyogo 671-2280, Japan, Okada Jun , Okochi Sayaka , Higuchi Kohei , Maenaka Kazusuke ,   ERATO Maenaka Human Sensing Fusion Project, Japan Science and Technology Agency, 2167 Shosha, Himeji, Hyogo 671-2280, Japan
Published: April 2016

Continuous observation of human activities and circumstances are quite important for healthcare applications that collect a lot of data from the various MEMS (Micro Electromechanical Systems) sensors. This study demonstrates the multi-environmental sensing system for human applications that can measure the time-based three-axes acceleration (threeaxes shock), barometric pressure, temperature and relative humidity, simultaneously. The system has battery and large sized memory for autonomous sensing. The measured data are stored in a flash memory via an onboard microcontroller. The detailed configurations of the prototype device and some experimental results are investigated.
Information Presentation Support of Car Robotics Simulator System Based on Humatronics
Authors: Chang Cheng ,   Graduate School of System Design, Tokyo Metropolitan University, 6-6 Asahigaoka, Hino, Tokyo 191-0065, Japan, Ohbayashi Gen , Yamaguchi Toru , Sato-Shimokawara Eri
Published: April 2016

Most traffic accidents are caused by drivers’ carelessness and lack of information about the surrounding objects. In this paper, authors develop information presentation support system of a car robotics system based on humatronics. The main purpose of humatronics is to establish the symmetric interaction between human and electronics system by giving the system the capability of understanding human. Car robotics system based on humatronics is used in this research. Car robotics system is developed on human centered city. In this city, systems judge the situation and autonomously support human. A car recognizes driver’s intention by car robotics system, and acquires environmental information by connecting to the network of the human centered city’s system. The environmental information is divided into two parts: foreground information and background information. Authors develop information presentation support system which presents foreground information and gackground information. Driver is supported by the system which can be used to predict the driving reaction beforehand and send a warning signal to the driver in time.
Using Hybrid MCDM Methods to Assess Fuel Cell Technology for the Next Generation of Hybrid Power Automobiles
Authors: Huang Chi-Yo ,   Department of Industrial Education, National Taiwan Normal University, No.162, He-ping East Road, Section 1, Taipei 106, Taiwan, Hung Yi-Hsuan , Tzeng Gwo-Hshiung ,   Institute of Project Management, Kainan University, No.1, Kainan Road, Luchu, Taoyuan 338, Taiwan,   Institute of Management of Technology, National Chiao Tung University, 1001, Ta-Hsueh Road, Hsinchu 300, Taiwan
Published: April 2016

With their huge consumption of petroleum and creation of a large number of pollutants, traditional vehicles have become one of the major creators of pollution in the world. To save energy and reduce carbon dioxide emissions, in recent years national governments have aggressively planned and promoted energy-saving vehicles that use green energy. Thus, hybrid electric vehicles have already become the frontrunners for future vehicles while fuel cells are considered the most suitable energy storage devices for future hybrid electric vehicles. However, various competing fuel cell technologies do exist. Furthermore, very few scholars have tried to investigate how the development of future fuel cells for hybrid electric vehicles can be assessed so that the results can serve as a foundation for the next generation of hybrid electric vehicle developments. Thus, how to assess various fuel cells is one the most critical issues in the designing of hybrid electric vehicles. This research intends to adopt a framework based on Hybrid Multiple-Criteria Decision Making (MCDM) for the assessment of the development in fuel cells for future hybrid electric vehicles. The analytic framework can be used for selecting the most suitable fuel cell technology for future hybrid electric vehicles. The results of the analysis can also be used for designing the next generation of hybrid electric vehicles.
Knowledge Mining for Project Management and Execution
Authors: Mochida Shinji ,   University of Marketing and Distribution Sciences, 3-1 Gakuen-Nishimachi, Nishiku, Kobe, Hyougo 651-2188, Japan
Published: April 2016

Efficient project management is demanded to solve project management problems. Project success or failure depends on the skill of the project manager. A good project manager must have good knowledge on project management and growth. If knowledge is considered a judgment for effective action, the manager’s action and experience must first be registered. This paper defines knowledge as a group of low-level information and data. Such knowledge must be registered easily. Since it is difficult to find the appropriate timing for registering knowledge, this paper shows how to maximize such timing and registration for the knowledge. Knowledge is information and data for execution performance that is improvement for project management. We constructed a prototype using WEB technology to collect large amounts of knowledge for project management, but such knowledge must be registered manually and it is difficult to find the time that information should be registered on. This paper discusses how to find the best timing for registering knowledge. We tried to take into the change in progress of the project in order to get the knowledge. Earned Value Management (EVM), which is one way to manage project progress, is examined to find change in project progress, since the time that progress changes appears to be the best timing for registration. It was found that knowledge may be able to be registered automatically. It will be necessary to achieve the function to register the knowledge at the just timing in the future.
A Hybrid Particle Swarm Optimization Approach and its Application to Solving Portfolio Selection Problems
Authors: Yaakob Shamshul Bahar ,   Graduate School of IPS, Waseda University, 2-7 Hibikino, Wakamatsu, Kitakyushu 808-0135, Japan, Watada Junzo ,   School of Electrical Systems Engineering, Universiti Malaysia Perlis, 02600 Perlis Malaysia
Published: April 2016

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 proposed method is a viable approach to portfolio selection.
Genetic Network Programming-Sarsa with Subroutines for Trading Rules on Stock Markets
Authors: Yang Yang ,   The Graduate School of Information, Production and Systems, Waseda University, 2-7 Hibikino, Wakamatsu-ku, Kitakyushu, Fukuoka 808-0135, Japan, Mabu Shingo , Li Jianhua , Hirasawa Kotaro
Published: April 2016

The purpose of this paper is to propose a new approach to generate effective subroutines, which are automatically discovered by the GNP-Sarsa programs combining evolution and reinforcement learning. We name it asGNP-Sarsa with subroutines (GNPsb-Sarsa) and apply it to the trading rules on stock markets. In the proposal method, GNPsb-Sarsa offers an alternative population, where individuals are represented by subroutines. Each main program of GNPsb-Sarsa can refer to an individual in the subroutine population, after adding a new kind of node, namely subroutine node, in the graph network structure of GNP-Sarsa. GNPsb-Sarsa containing a main program and a subroutine evolves by natural selection and genetic operations, where the gene of GNPsb-Sarsa is the pair of the main GNP and its subroutine. That is, the genetic operations on GNPsb-Sarsa are constrained by the gene structure on which they can operate. In the simulations, the stock prices of different brands from 2001 to 2004 are used to test the effectiveness of the GNPsb-Sarsa. The results show that the proposed approach can provide reasonable opportunities for evolving complex solutions.
Multi-Order Rules Extraction by Genetic Network Programming with Rule Accumulation and its Application to Stock Trading Problems
Authors: Xing Yafei ,   Graduate School of Information, Production and Systems, Waseda University, 2-7 Hibikino, Wakamatsu-ku, Kitakyushu, Fukuoka 808-0135, Japan, Mabu Singo , Yuzhu Lian , Hirasawa Kotaro
Published: April 2016

As the effectiveness of the trading rules for stock trading problems has been verified, a method of extracting multi-order rules by Genetic Network Programming (GNP) is proposed using the rule accumulation for improving the efficiency of the trading rules in this paper. GNP is one of the evolutionary computations having a directed graph structure. Because of this special structure, the rule accumulation from GNP individuals is more effective for trading the stock than other methods. In this paper, there are two main points: rule extraction and trading action determination. Rule extraction is carried out in the training period, where the rules including the 1st order rules and multi-order rules, are extracted from the best individual and accumulated into the rule pools generation by generation. In the testing period, the trading action is determined by the matching degree of the stock price information with the rules, and the profits of the trading are evaluated. In the simulations, the stock prices of 16 brands in 2004, 2005 and 2006 are used for the training and those in 2007 for the testing. The simulation results show that the multi-order rules perform better than the 1st order rules. So, it is proved that themulti-order rules extracted by GNP is more effective than the 1st order rules for stock trading.
Learning of Obstacle Avoidance with Redundant Manipulator by Hierarchical SOM
Authors: Kobayashi Yuichi ,   Tokyo University of Agriculture and Technology, 2-24-16 Naka-cho, Koganei, Tokyo 184-8588, Japan, Nomura Takahiro
Published: April 2016

This paper proposes a method of obstacle avoidance motion generation for a redundant manipulator with a Self-OrganizingMap (SOM) and reinforcement learning. To consider redundancy, two types of SOMs - a hand position map and a joint angle map - are combined. Multiple joint angles corresponding to the same hand position are memorized in the proposed map. Preserved redundant configuration information is used to generate motions based on tasks and situations, while resolving inverse kinematics problems with a redundant manipulator. The proposed map is applied to planning motion control using reinforcement learning in an unknown environment, where collision with obstacles is detected only directly by tactile sensing. The feasibility of the proposed framework was verified by simulation and experiments with an arm robot with force and a vision sensors.
Dual-Hierarchical Control Mechanism of Interpersonal Embodied Interactions in Cooperative Walking
Authors: Muto Takeshi ,   Department of Computational Intelligence and Systems Science, Tokyo Institute of Technology, G3-822, 4259 Nagatsuta, Midori-ku, Yokohama 226-8502, Japan, Miyake Yoshihiro ,   Department of Integrated Information Technology, College of Science and Engineering, Aoyama Gakuin University (AGU), O-505b, 5-10-1 Fuchinobe, Chuo-ku, Sagamihara, Kanagawa 252-5258, Japan
Published: April 2016

Interpersonal embodied interactions play a significant role as emergent functions in human development and rehabilitation. However, a framework for applying embodied interactions to “human interface systems” to support such emergent functions has not yet been suggested because the details of the motorcontrol mechanism have not yet been clarified. In this study, the interpersonal cooperative walking motions of two humans, as an example of such a mechanism, have been replicated and their motor-control mechanisms analyzed. The results indicate that the hierarchical dynamics were derived from an interpersonal footstep entrainment process and an intrapersonal interaction of arm and footstep motions. We suggest that embodied interactions in cooperative walking are achieved by a dual-hierarchical control structure related to emergence of the phase-control function of interpersonal cooperative walking, based on an automatic control mechanism for interpersonal entrainment of footstep motions and an intrapersonal voluntary-motion-control mechanism.
High Accurate Discrimination Method of Forearm Motions from Surface Electromyogram and its Condition
Authors: Nishikawa Yoshio ,   Graduate School of Engineering, Takushoku University, 815-1 Tatemachi, Hachioji, Tokyo 193-0985, Japan, Kagawa Yoshihito , Kurabayashi Jun ,   Faculty of Health Sciences, Kyorin University, 476 Miyashita-cho, Hachioji, Tokyo 192-8508, Japan
Published: April 2016

We propose high-speed motion discrimination method for three types of motions, pronating, flexing, and grasping without any mistake by acquired surface electromyography (EMG) signals from three locations on the right-forearm. To achieve high-speed and accurate method, we introduce motion discrimination method based on a comparison of features extracted by wavelet transform of EMG signals via a database, and also we examine the places on the forearm where the system acquires surface EMG signals. As a final, we discuss whether the discrimination rate was improved by motion training.
Emotion Recognition Based on ECG Signals for Service Robots in the Intelligent Space During Daily Life
Authors: Rattanyu Kanlaya ,   Graduate School of Functional Control Systems Engineering, Shibaura Institute of Technology, 3-7-5 Toyosu, Koto-ku, Tokyo 135-8548, Japan, Mizukawa Makoto ,   Department of Electrical Engineering, Shibaura Institute of Technology, 3-7-5 Toyosu, Koto-ku, Tokyo 135-8548, Japan
Published: April 2016

This paper presents our approach for emotion recognition based on Electrocardiogram (ECG) signals. We propose to use the ECG’s inter-beat features together with within-beat features in our recognition system. In order to reduce the feature space, post hoc tests in the Analysis of Variance (ANOVA) were employed to select the set of eleven most significant features. We conducted experiments on twelve subjects using the International Affective Picture System (IAPS) database. RF-ECG sensors were attached to the subject’s skin to monitor the ECG signal via wireless connection. Results showed that our eleven feature approach outperforms the conventional three feature approach. For simultaneous classification of six emotional states: anger, fear, disgust, sadness, neutral, and joy, the Correct Classification Ratio (CCR) showed significant improvement from 37.23% to over 61.44%. Our system was able to monitor human emotion wirelessly without affecting the subject’s activities. Therefore it is suitable to be integrated with service robots to provide assistive and healthcare services.
Content-Based Image Retrieval via Combination of Similarity Measures
Authors: Okamoto Kazushi ,   Dept. of Computational Intelligence and Systems Science, Interdisciplinary Graduate School of Science and Engineering, Tokyo Institute of Technology, G3-49, 4259 Nagatsuta, Midori-ku, Yokohama 226-8502, Japan, Dong Fangyan , Yoshida Shinichi , Hirota Kaoru ,   School of Information, Kochi University of Technology, 185 Tosayamada-cho-Miyanokuchi, Kochi 782-8502, Japan
Published: April 2016

A multiple (dis)similarity measure combination framework via normalization and weighting of measures is proposed to find suitable measure combinations in terms of retrieval accuracy and computational cost. In the combination of Manhattan and Hellinger distances, the computational time is more than 12 times faster and the retrieval accuracy improves or remains at the same level, when compared with Minkowski distance, a measure having the best retrieval accuracy in the single measure scenario. These performances are determined on a visual word based image retrieval system by using the Corel collections. Due to the reduction of computational cost and robustness of retrieval accuracy in this combination, applications include retrieval employing large number of images and categories in a database.
Development of an Automatic Landmine Detection and Marking System for the Demining Robot Gryphon
Authors: Kaneko Alex M. ,   Tokyo Institute of Technology, 2-12-1 Ookayama, Meguro-ku, Tokyo 152-8550, Japan, Fukushima Edwardo F.
Published: April 2016

Mechanical systems or robots are designed to support human operators during complex and dangerous tasks such as demining operations. Even though the robot Gryphon was created to automate these operations, some of its tasks still rely greatly on the human operator, who has few or no assisting tools to perform efficient decisions. During the landmine detection and marking task in special, the operator is totally responsible for analyzing the scanned data and pointing the potential targets, which makes the system performance unstable and vulnerable to human factors. This article proposes an automatic method for finding potential targets, which the operator has the simple role of accepting or not the decisions taken by the automatic method. Experimental results showed that time duration, POD and FAR were greatly improved compared to the former methods.
Enhancing Bidding Strategy Using Genetic Network Programming in Agent-Based Multiple Round English Auction
Authors: Yue Chuan ,   Graduate School of Information, Production and Systems, Waseda University, 2-7 Hibikino, Wakamatsu-ku, Kitakyushu, Fukuoka 808-0135, Japan, Mabu Shingo , Hirasawa Kotaro
Published: April 2016

The agent-based auction mechanism widely used in web sites and originally designed for trading goods for customers might not be the most efficient one in the future, while there is a demand of automated auction agents, which are adaptable to the dynamic auction environments. To this end, this paper discusses how to apply Genetic Network Programming (GNP) to automated auction agents in order to make a bid efficiently and effectively at each time step according to the auction environments, and Multiple Round English Auction (MREA) mechanism studied in this paper is based on multi-agent systems, which aims to help the buyer to procure profitable deals as much as possible. GNPbased agent is compared with other agents using conventional strategies in MREA. It has been found from the simulations that the proposed method could help agents to evolve their strategies generation by generation, which shows that GNP has a good performance of helping the agent to find the suitable strategy under various situations and outperform than other strategies.
Adaptive Controller for T-S Fuzzy Model with Modeling Error
Authors: Han Hugang ,   Faculty of Management and Information Systems, Prefectural University of Hiroshima, 1-1-71 Ujina-higashi, Minami-ku, Hiroshima, Hiroshima 734-8558, Japan
Published: April 2016

Modeling error occurs in linearizing the real (nonlinear) system into the (linear) T-S fuzzy model, and the existence of uncertainties in the real system including external disturbances. This paper deals with the T-S fuzzy model with modeling error in order to improve the control performance. As a result, an adaptive controller that consists of two parts: one is obtained by solving certain Linear Matrix Inequalities (LMIs) (fixed part) and the other is acquired by the fuzzy approximator in which the related parameters are tuned by adaptive law (variable part), is proposed. The proposed controller can guarantee all signals involved in the closed-loop system uniformly ultimately bounded.
4W1H and Particle Swarm Optimization for Human Activity Recognition
Authors: Palafox Leon ,   Institute of Industrial Science, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan, Hashimoto Hideki
Published: April 2016

This paper proposes a paradigm in the forensic area for detecting and categorizing human activities. The presented approach uses five base variables, referred to as 4W1H (“Who,” “When,” “What,” “Where,” and “How”) to describe the context in an environment. The proposed system usesself-organizing mapsto classify movements for the “How” variable of 4W1H, as well asparticle swarm optimization clusteringtechniques for the grouping (clustering) of data obtained from observations. The paper describes the hardware settings required for detecting these variables and the system designed to do the sensing.
Dependence-Maximization Clustering with Least-Squares Mutual Information
Authors: Kimura Manabu ,   Department of Computer Science, Tokyo Institute of Technology, 2-12-1 O-okayama, Meguro-ku, Tokyo 152-8552, Japan, Sugiyama Masashi
Published: April 2016

Recently, statistical dependence measures such as mutual information and kernelized covariance have been successfully applied to clustering. In this paper, we follow this line of research and propose a novel dependence-maximization clustering method based on least-squares mutual information, which is an estimator of a squared-loss variant of mutual information. A notable advantage of the proposed method over existing approaches is that hyperparameters such as kernel parameters and regularization parameters can be objectively optimized based on cross-validation. Thus, subjective manual-tuning of hyperparameters is not necessary in the proposed method, which is a highly useful property in unsupervised clustering scenarios. Through experiments, we illustrate the usefulness of the proposed approach.
Nonlinear Active Noise Control via Model-Based Approaches
Authors: Duong Sam Chau ,   Faculty of Engineering, University of the Ryukyus, 1 Senbaru, Nishihara, Okinawa 903-0213, Japan, Kinjo Hiroshi , Oshiro Naoki
Published: April 2016

Active noise control has attracted much research attention due to its several advantages over passive noise control. This paper introduces two model-based noise canceling techniques, that is, using the Moving Average (MA) model and a feedforward Neural Network (NN) to estimate the signal. The Least Mean Square (LMS) algorithm is used to minimize the error in the MA model while a backpropagation algorithm is employed to optimize the NN. Due to its advantages of good robustness and nonlinear processing, the NN is considered to be suitable for nonlinear signals. In order to reduce computational cost, the backpropagation algorithm in the NN is applied once at each time step with only one iteration. To examine the methods, two real-world problems are considered, one being engine noise and the other road traffic noise. A comparison between the two methods is carried out. Results indicate that both the MA and NN processors are effective in reducing the noises and that the NN based approach is superior over the MA model, especially for low frequency band.
Human Pointing Navigation Interface for Mobile Robot with Spherical Vision System
Authors: Takahashi Yasutake ,   Dept. of Human and Artificial Intelligent Systems, Graduate School of Engineering, University of Fukui, 3-9-1 Bunkyo, Fukui, Fukui 910-8507, Japan, Yoshida Kyohei , Hibino Fuminori , Maeda Yoichiro
Published: April 2016

Human-robot interaction requires intuitive interface that is not possible using devices, such as, the joystick or teaching pendant, which also require some trainings. Instruction by gesture is one example of an intuitive interfaces requiring no training, and pointing is one of the simplest gestures. We propose simple pointing recognition for a mobile robot having an upwarddirected camera system. The robot using this recognizes pointing and navigates through simple visual feedback control to where the user points. This paper explores the feasibility and utility of our proposal as shown by the results of a questionnaire on proposed and conventional interfaces.
Multi-Agent Transport Simulation Model for Eco-Commuting Promotion Planning
Authors: Okushima Masashi ,   Dept. of Ecosystem Design, The University of Tokushima, 2-1 Minamijyosanjima, Tokushima 770-8506, Japan, Akiyama Takamasa ,   Kansai University, 3-3-35 Yamate-cho, Suita, Osaka 564-8680, Japan
Published: April 2016

The global warming problem due to greenhouse gas emission has become very serious. Moreover, in the transportation section, the construction of a sustainable transportation system in the environment has been an important subject. Not only improvement of service level of modes, except those of private vehicles, but also consciousness for the environmental problem of an individual trip maker is important for eco-commuting promotion. Eco-consciousness can be changed under the influence of other people. The multi-agent simulation system for planning of ecocommuting promotion is developed using a social interaction model for eco-consciousness in this study. Modal shift is modeled by considering the result of a transport social experiment for eco-commuting promotion as reference. Influence of the promotion plan such as improvement of service level of public transport, promotion of individual eco-consciousness, and strengthening the interaction of individuals can be analyzed using the proposed model. Finally, it is concluded that the proposed multi-agent simulation with social interaction for eco-consciousness is useful for the planning of eco-commuting promotion.
Smart Management of HVDC Interface Flow for Jeju Island System with High Penetration of Wind Energy
Authors: Song Hwachang ,   Electrical Engineering, Seoul National University of Science and Technology, 138 Gongreung-Gil, Nowon-Gu, Seoul 139-743, Korea, Yoon Dong-Hee , Jang Gilsoo , Lee Jang-Ho ,   Electrical Engineering, Korea University, 1 Anam-5Ga, Sungbuk-Gu, Seoul 136-701, Korea,   Mechanical Engineering, Kunsan National University, San 68, Miryong-Dong, Kunsan, Jeonbuk 573-701, Korea
Published: April 2016

The recent concerns over the threat of global climate change and the requirements of national reduction of CO2 emission have led to the diversification of energy resources and a large scale integration of renewable resources. For island systems, however, the penetration level of renewable energy should be limited to maintain the power system security perspectives to frequency and voltage stability. The main interest of this paper is the operation of Jeju island system with a high penetration level of wind energy. As an option to make a breakthrough against the technical constraints, this paper presents a smart algorithm to set the active power of HVDC (High Voltage Direct Current) interties, which are to supply the power deficit and to regulate the frequency of the system.
Neurophysiological and Dynamical Control Principles Underlying Variable and Stereotyped Movement Patterns During Motor Skill Acquisition
Authors: Kudo Kazutoshi ,   Department of Life Sciences, Graduate School of Arts and Sciences, The University of Tokyo, 3-8-1 Komaba, Meguro-ku, Tokyo 153-8902, Japan, Miyazaki Makoto , Sekiguchi Hirofumi , Kadota Hiroshi , Fujii Shinya , Miura Akito , Yoshie Michiko , Nakata Hiroki ,   Research Institute of Kochi University of Technology, 185 Miyanokuchi, Tosayamada, Kochi 782-8502, Japan,   Faculty of Business and Information Sciences, Jobu University,   Division of Physical and Health Education, Graduate School of Education, The University of Tokyo,   Japan Society for the Promotion of Science,   Clinical Imaging Sciences Centre, Brighton and Sussex Medical School, University of Sussex,   Faculty of Sport Sciences, Waseda University
Published: April 2016

While novices who are unfamiliar to a new motor skill typically show variable and unstable movements, highly skilled experts show a stable and accurate performance. These distinct differences in motor control between experts and novices have led researchers to hypothesize that neuromotor noise is reduced in the process of motor skill acquisition. On the other hand, it should be noted that novices’ movements have other characteristics; they are habituated and stereotyped. In this review, we discuss the principles governing spatiotemporal organization of movements in novices and experts while solving specific motor problems under varied conditions, by introducing experimental and theoretical studies that use neurophysiological techniques such as electromyography, functional magnetic resonance imaging, and transcranial magnetic stimulation, and mathematical models such as stochastic and dynamical models. On the basis of the findings from a variety of perceptual-motor skills (e.g., ballthrowing, badminton smash, long-distance running, piano and drum performance, street dance, a popular hand game of rock-paper-scissors, and temporal order judgement task), we argue that the novices’ characteristic movement patterns were organized under specific constraints and typical strategy, without which the variability would increase even more, while experts’ movements were organized with functional and compensatory variability that can drive out erroneous noise variability. We also showed that in a particular type of interlimb coordination, skilled and unskilled movement patterns could be seamlessly described as the time evolution of nonlinear and self-organized dynamical systems, suggesting that the dynamical systems approach is a major candidate for understanding the principle underlying organization of experts’ and novices’ movements.
Accuracy of Synchrony Judgment and its Relation to the Auditory Brainstem Response: the Difference Between Pianists and Non-Pianists
Authors: Aiba Eriko ,   Kwansei Gakuin University, 2-1 Gakuen, Sanda, Hyogo 669-1337, Japan, Kazai Koji , Shimotomai Takayuki , Matsui Toshie , Tsuzaki Minoru , Nagata Noriko ,   Brain Science Institute, Tamagawa University, Japan,   Nara Medical University, Japan,   Kyoto City University of Arts, Japan
Published: April 2016

Synchrony judgment is one of the most important abilities for musicians. Only a few milliseconds of onset asynchrony result in a significant difference in musical expression. Using behavioural responses and Auditory Brainstem Responses (ABR), this study investigates whether synchrony judgment accuracy improves with training and, if so, whether physiological responses are also changed through training. Psychoacoustic experiments showed that accuracy of synchrony judgment of pianists was higher than that of non-pianists, implying that pianists’ ability to perceive tones increased through training. ABRmeasurements also showed differences between pianists and non-pianists. However, cochlear delay, an asymmetric aspect of temporal processing in the human auditory system, did not change with training. It is possible that training improved ability related to temporal tone perception and that training may increase synchrony in auditory nerve firing.
Learning Strategy in Time-to-Contact Estimation of Falling Objects
Authors: Kambara Hiroyuki ,   Precision and Intelligence Lab., Tokyo Institute of Technology, R2-15, 4259 Nagatsuda, Midori-ku, Yokohama 226-8503, Japan, Ohishi Keiichi , Koike Yasuharu ,   Department of Computational Intelligence and Systems Science, Tokyo Institute of Technology, Japan,   Solutions Research Lab., Tokyo Institute of Technology, Japan,   CREST, JST, Japan
Published: April 2016

The ability to estimate the time that remains before contact (Time-To-Contact or TTC) of a falling object is critical in daily life. In this paper, we investigated how the Central Nervous System (CNS) becomes able to estimate the TTC of a ball falling at various accelerations. According to experiments on the human ability to catch a ball falling at various accelerations, we assumed that the CNS can hold multiple TTC estimators each of which is trained for a different acceleration, and one of them is adopted for TTC estimation in a ball-catching trial. Here we made a hypothesis about how each TTC estimator is trained when there is an estimation error. (1) If the estimation error is small, the TTC estimator adopted in the trial is recalibrated. (2) If the estimation error is large, a new TTC estimator is created. To test this hypothesis, we conducted two types of ball-catching experiments in a virtual environment where the acceleration of a virtual ball is changed gradually or suddenly in each experiment. The difference in catching performances in the two experiments supported our hypothesis.
Kicking Motion Imitation of Inverted-Pendulum Mobile Robot and Development of Body Mapping from Human Demonstrator
Authors: Takahashi Sataya ,   Department of Human and Artificial Intelligent Systems, Faculty of Engineering, University of Fukui, 3-9-1 Bunkyo, Fukui, Fukui 910-8507, Japan, Takahashi Yasutake , Maeda Yoichiro , Nakamura Takayuki ,   Department of Human and Artificial Intelligent Systems, Graduate School of Engineering, University of Fukui, 3-9-1 Bunkyo, Fukui, Fukui 910-8507, Japan,   Faculty of Systems Engineering, Wakayama University, 930 Sakaetani, Wakayama 640-8510, Japan
Published: April 2016

This paper proposes a new method for learning the dynamic motion of an inverted-pendulum mobile robot from the observation of a human player’s demonstration. First, an inverted-pendulum mobile robot with upper and lower body links observes the human demonstration with a camera and extracts the human region in images. Second, the robot maps the region to its own two links and estimates link posture trajectories. The robot starts learning kicking based on the trajectory parameters for imitation. Through this process, our robot can learn dynamic kicking shown by a human. The mapping parameter gives an important role for successive imitation. A reasonable and feasible procedure of learning from observation for an inverted-pendulum robot is proposed. Learning performance from observation is investigated, then, the development of body mapping is proposed and investigated.
Non-Euclidean Extension of FCMdd-Based Linear Clustering for Relational Data
Authors: Yamamoto Takeshi ,   Graduate School of Engineering, Osaka Prefecture University, 1-1 Gakuen-cho, Nakaku, Sakai, Osaka, Japan, Honda Katsuhiro , Notsu Akira , Ichihashi Hidetomo
Published: April 2016

Relational data is common in many real-world applications. Linear fuzzy clustering models have been extended for handling relational data based on Fuzzyc-Medoids (FCMdd) framework. In this paper, with the goal being to handle non-Euclidean data, β-spread transformation of relational data matrices used in Non-Euclidean-type Relational Fuzzy (NERF)c-means is applied before FCMdd-type linear cluster extraction. β-spread transformation modifies data elements to avoid negative values for clustering criteria of distances between objects and linear prototypes. In numerical experiments, typical features of the proposed approach are demonstrated not only using artificially generated data but also in a document classification task with a document-keyword co-occurrence relation.
KL-Divergence-Based and Manhattan Distance-Based Semisupervised Entropy-Regularized Fuzzy c-Means
Authors: Kanzawa Yuchi ,   Shibaura Institute of Technology, 3-7-5 Toyosu, Koto, Tokyo 135-8548, Japan, Endo Yasunori , Miyamoto Sadaaki ,   University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki 305-8573, Japan
Published: April 2016

In this paper, two types of semi-supervised fuzzy c-means algorithms are proposed. One feature of proposed algorithms is that they are based on an entropyregularized fuzzy c-means clustering algorithm, while conventional algorithms are based on standard fuzzy c-means. Another feature of proposed algorithms is that the membership updating equation can be obtained explicitly with any fuzzifier parameter value, while in conventional methods, the updating equation must be solved by some numerical method or by a numerically complex refinement with almost all fuzzifier parameters. The influence of supervisor-parameter and fuzzifier parameter on clustering results are discussed based on numerical experiments and compared to the conventional method, demonstrating the feasibility of proposed algorithms.
Extraction of Community Transition Rules from Data Streams as Large Graph Sequence
Authors: Yamaguchi Takehiro ,   Graduate School of Systems Information Science, Future University Hakodate, Niimi Ayahiko ,   Department of Media Architecture, Faculty of Systems Information Science, Future University Hakodate, 116-2 Kamedanakano, Hakodate, Hokkaido 041-8655, Japan
Published: April 2016

In this study, we treat transactional sets of data streams as a graph sequence. This graph sequence represents both the relational structures of data for each period and changes in these structures. In addition, we analyze changes in a community in this graph sequence. Our proposed algorithm extracts community transition rules to detect communities that appear irregularly in a graph sequence using our proposed method combined with adaptive graph kernels and hierarchical clustering. In experiments using synthetic datasets and social bookmark datasets, we demonstrate that our proposed algorithm detects changes in a community appearing irregularly.
A Proposal of Memory and Prediction Based Genetic Algorithm Using Speciation in Dynamic Multimodal Function Optimization
Authors: Ichimura Takumi ,   Faculty of Management and Information Systems, Prefectural University of Hiroshima, 1-1-71 Ujina-Higashi, Minami-ku, Hiroshima 734-8559, Japan, Inoue Hiroshi , Hara Akira , Takahama Tetsuyuki , Mackin Kenneth J. ,   Graduate School of Information Sciences, Hiroshima City University, 3-4-1 Ozuka-higashi, Asaminami-ku, Hiroshima 731-3194, Japan,   Department of Information Systems, Tokyo University of Information Sciences, 4-1 Onaridai, Wakaba-ku, Chiba 265-8501, Japan
Published: April 2016

It is a difficult problem for evolutionary algorithms to search an optimal solution in multimodal functions with dynamic environments, where individuals searchmore than one optimum and their fitness values change over time. In this paper, we propose a method of Memory and Prediction Based Genetic Algorithm Using Speciation. This method is extended with a case based memory and a meta learner for precise prediction of environmental change. Especially, the individuals in a memory consist of 4 kinds of predictors and they can adjust to the change of dynamic environment adaptively. Speciation has shown to be an effective technique for multimodal optimization. A niching method based on speciation can be used to classify a population into groups according to their similarity measured by a distance. In this paper, each group by speciation has a memory and the individuals stored in the memory can respond to the situation according to the dynamic environment. In order to verify the effectiveness, the method is examined to search for an optimal solutions in multimodal functions.
Preservation and Application of Acquired Knowledge Using Instance-Based Reinforcement Learning for Multi-Robot Systems
Authors: Sakanoue Junki ,   Graduate School of Engineering, Hiroshima University, 1-4-1 Kagamiyama, Higashi-Hiroshima, Hiroshima 739-8527, Japan, Yasuda Toshiyuki , Ohkura Kazuhiro
Published: April 2016

We have been developing a reinforcement learning technique called BRL as an approach to autonomous specialization, which is a new concept in cooperative multi-robot systems. BRL has a mechanism for autonomously segmenting the continuous state and action space. However, as in other machine learning approaches, overfitting is occasionally observed after successful learning. This paper proposes a technique to sophisticatedly utilize messy knowledge acquired using BRL. The proposed technique is expected to show better robustness against environmental changes. We investigate the proposed technique by conducting computer simulations of a cooperative carrying task.
A Proposal of Genetic Operations for BSIM Parameter Extraction Using Real-Coded Genetic Algorithm
Authors: Nishiba Ai ,   Graduate School of Engineering, Mie University, 1577 Kurima-Machiya, Tsu, Mie 514-8507, Japan, Kawanaka Hiroharu , Takase Haruhiko , Tsuruoka Shinji ,   Graduate School of Regional Innovation Studies, Mie University, 1577 Kurima-Machiya, Tsu, Mie 514-8507, Japan
Published: April 2016

This paper discusses genetic operations and their effects on evolution of GA in BSIM parameter extraction problems. Generally, Real-Coded Genetic Algorithm (RCGA) using Simplex Crossover (SPX) is often employed to extract BSIM parameter sets. BSIM parameters, however, have recommended operating ranges. There are regarded as constraints, thus all extracted parameters have to be satisfied them. In many cases, when the number of parameters becomes large, the conventional methods generate a lot of infeasible solutions because SPX makes offspring on the simplex plane expanded by ε parameter. This makes search efficiency of GA reduce drastically. Because of these factors, we propose genetic operations considering the constraints to prevent reduction of search efficiency of GA. In this paper, some experiments using actual static characteristic curves of MOS-FET were conducted to validate the proposed method. This paper also discussed the effectiveness of the proposed method.
Deploying Interactive Mission Planning Tools- Experiences and Lessons Learned -
Authors: Cesta Amedeo ,   Consiglio Nazionale delle Ricerche, Istituto di Scienze e Tecnologie della Cognizione, Via S. Martino della Battaglia 44, I-00185 Rome, Italy, Cortellessa Gabriella , Fratini Simone , Oddi Angelo , Bernardi Giulio
Published: April 2016

This article contains a retrospective overview of connected work performed for the European Space Agency (ESA) over a span of 10 years. We have been creating and refining an AI approach to problem solving and have infused a series of deployed planning and scheduling systems which have innovated the agency’s mission planning practice. The goal of this paper is to identify strong features of this experience, comment on general lessons learned and offer guidelines for work practice of the future. Specifically, the work considers some key points that have contributed to strengthening the effectiveness of our approach for the development of an end-to-end methodology to field applications: the attention to domain modeling, the constraint-based algorithm synthesis and the relevance of user interaction services.
Robustness to Bit Inversion in Registers and Acceleration of Program Evolution in On-Board Computer
Authors: Harada Tomohiro ,   The University of Electro-Communications, 3rd Floor in West-6 Building, 1-5-1 Chofugaoka, Chofu, Tokyo 182-8585, Japan, Otani Masayuki , Ichikawa Yoshihiro , Hattori Kiyohiko , Sato Hiroyuki , Takadama Keiki
Published: April 2016

This paper focuses on an on-board computer (OBC) that evolves computer programs through bit inversion and targets analyzing robustness against bit inversion in registers. We also propose a new method that can change the number of computer programs dynamically. Intensive experiments revealed the following: (1) Correct programs can be maintained even in bit inversion in registers in addition to bit inversion in instructions. (2) Our proposal accelerates program evolution by increasing the population size, i.e., the number of programs, within fixed memory size.