bluuebunny/crossref_metadata_2025 huggingface dataset:
Adaptive Anytime Data Transmission of Non-Stationary Signals
Authors: Várkonyi-Kóczy Annamária R. ,   Institute of Mechatronics and Vehicle Engineering, Óbuda University, Népszínház u. 8., H-1081 Budapest, Hungary,   Integrated Intelligent Systems Japanese-Hungarian Laboratory
Published: April 2016

The never unseen information explosion in data transmission and communication called for new methods in signal coding and reconstruction. To minimize the channel capacity needed for the transmission urged researchers to find techniques which are flexible and can adapt to the available space and time. Anytime techniques are good candidates for such purposes. If the signal/data to be transmitted can be characterized as sequence of stationary intervals overcomplete signal representations can be applied. These techniques can be operated in an anytime manner as well, i.e., are excellent tools for handling the capacity problems.

This paper introduces the concept of anytime recursive overcomplete signal representations using different recursive signal processing algorithms. The novelty of the approach is that an on-going set of signal transformations together with appropriate (e.g., L1 norm) minimization procedures can provide optimal and flexible anytime on-going representations, on-going signal segmentations into stationary intervals, and on-going feature extractions for immediate utilization in data transmission, communication, diagnostics, or other applications. The proposed technique may be advantageous if the transmission channel is overloaded and in case of processing non-stationary signals when complete signal representations can be used only with serious limitations because of their relative weakness in adaptive matching of signal structures.
Intelligent Powered Wheelchair Assistance in Daily Use
Authors: Nakamura Atsuhiro ,   Department of Information and Communications Systems Engineering, Tokyo Metropolitan University, 6-6 Asahigaoka, Hino, Tokyo 146-0091, Japan, Fujimoto Yasunari , Nitta Osamu , Yamaguchi Toru ,   Department of Physical Therapy, Tokyo Metropolitan University, 7-2-10 Higashi-Oku, Arakawa-ku, Tokyo 116-8551, Japan
Published: April 2016

In answer to Japan’s rapidly aging society, whose rising number of older people mandates that care requirements for care recipients be minimized, we developed an intelligent powered wheelchair featuring four assistance functions – approach through gesture recognition, assistance in standing, walking assistance, and operational assistance through posture recognition. Experiments demonstrated the usefulness of the four assistance functions.
Selected Papers from IWI 2009
Authors: Yamada Seiji , Murata Tsuyoshi , Takama Yasufumi
Published: April 2016

Various Web systems and services currently provide a great deal of benefits to users, with Web interaction becoming increasingly important in research and business. Such Web interaction has been realized through related technologies as interaction design, interactive information retrieval, interactive intelligent systems, personalization, user interfaces and interactive machine learning. However, each study and development in such different fields has been done independently, which might discourage us from studying Web interaction from an unified view of human-system interaction and making Web interaction more intelligent by applying AI and computational intelligence. Guest Editors (Seiji Yamada, Tsuyoshi Murata, and Yasufumi Takama) organized an Intelligent Web Interaction Workshop 2009 (IWIf09) in Milano, Italy, last year to bring together researchers in diversified fields including Web systems, AI, computational intelligence, humancomputer interaction and user interfaces. Held jointly with 2009 IEEE/WIC/ACM International Conference on Web Intelligence (WI-2009), IWIf09 produced 14 outstanding papers - an acceptance rate of 50%, and active discussions among speakers and participants. A subsequent workshop Intelligent Web Interaction Workshop 2010 (IWIf10) will be held in Toronto, Canada in this September. This special issue presents intelligent Web interaction as a new and promising research field. Speakers selected from among those at IWIf09 were encouraged to submit papers for this issue. The submissions were then reviewed for relevance, originality, significance and presentation based on JACIII review criteria. This special issue consists of five papers which describe excellent studies on Web interface, Web systems, Web credibility, constrained clustering for interactive Web application and graph analysis on the Web. The acceptance rate was 56%. All papers introduce promising approaches and interesting results that readers will find inspiring. We strongly believe intelligent Web interaction has tremendous potential as a new, active field of research, and we hope this issue will motivate researchers to expand studies on intelligent Web interaction.
A Tool for Visualizing the Behavior of Fuzzy Constraint Satisfaction Solvers
Authors: Yanagida Takuto ,   Graduate School of Information Science and Technology, Hokkaido University, Kita 14, Nishi 9, Kita-ku, Sapporo 060-0814, Japan, Kurihara Masahito , Nonaka Hidetoshi
Published: April 2016

This paper presents a software tool to intuitively comprehend and analyze fuzzy constraint satisfaction problems (FCSPs) through effective visualization with animation. FCSPs are an extension of constraint satisfaction problems (CSPs), which are used for formulating problems in real-world information systems. Once we formulate a problem as an FCSP, it can be solved (in principle) by any existing general-purpose FCSP solvers developed so far. However, the formulation is sometimes difficult because it requires high abstraction of real-world problems and affects the performance of the solvers. The authors believe that the tool will improve this situation by increasing the visibility of the behavior of the solvers.
Effective Cycle Time: A Real World Balancing Index for Paced Assembly Lines
Authors: Genikomsakis Konstantinos N. ,   Department of Production Engineering and Management, School of Engineering, Democritus University of Thrace, GR-67100, Kimmeria, Xanthi, Greece, Tourassis Vassilios D.
Published: April 2016

Assembly Line Balancing (ALB) aims at optimally assigning the work elements required to assemble a product to an ordered sequence of workstations, while satisfying precedence constraints. Notwithstanding the advances and developments in ALB over the years, recent and thorough surveys on this field reveal that only a small percentage of companies employ ALB procedures to configure their assembly lines. This paradox may be attributed, to some extent, to the fact that ALB is addressed mostly under ideal conditions. Despite the time variability inherent in manufacturing tasks, there is a strong research trend towards designing and implementing algorithms that consider ALB on a deterministic basis and focus on the optimality of the proposed task assignments according to existing ALB performance measures. In this paper, the need to assess the performance of the proposed solutions of various algorithms in the literature is corroborated through simulation experiments on a benchmark ALB problem under more realistic conditions. A novel ALB index, namely the Effective Cycle Time, ECT, is proposed to assess the quality of alternative assembly line configurations in paced assembly lines operating under task times variations.
Task Allocation in Multistate Systems
Authors: Mizuguchi Tsuyoshi ,   Department of Mathematical Sciences, Osaka Prefecture University, 1-1 Gakuen-cho, Naka-ku, Sakai 599-8531, Japan, Sugawara Ken , Kazama Toshiya ,   PRESTO, Japan Science and Technology Agency (JST), 4-1-8 Honcho Kawaguchi, Saitama 332-0012, Japan,   Department of Information Science, Tohoku Gakuin University, 2-1-1 Tenjinzawa, Izumi-ku, Sendai 981-3193, Japan,   Department of Mathematical and Life Sciences, Hiroshima University, 1-3-1 Kagami-yama, Higashi-Hiroshima 739-8526, Japan
Published: April 2016

Multistate task allocation inspired by social-insect polyethism is treated from the viewpoint of proportionally regulating a population between different states, i.e., the system is homeostatic against external disturbance and environmental change. Using a dynamical model consisting of identical elements and external variable “stock materials,” adaptability to external disturbances is studied numerically. Experiments are performed with actual robots moving in virtual pheromone fields simulated by computer graphics and video camera feedback demonstrating task allocation.
Inference Based on α-Cut and Generalized Mean in Representing Fuzzy-Valued Functions
Authors: Uehara Kiyohiko ,   Ibaraki University, Hitachi 316-8511, Japan, Koyama Takumi , Hirota Kaoru ,   Tokyo Institute of Technology, Yokohama 226-8502, Japan
Published: April 2016

It is mathematically proved that inference based on α-cuts and generalized mean (α-GEMII) deduces consequences converging to fuzzy sets mapped by linear fuzzy-valued functions, to be represented with α-GEMII, as the number of fuzzy rules increases. The proof indicates that α-GEMII satisfies axiomatic properties and can contribute to presenting interpretability in designing fuzzy systems in the rule base. Such properties do not hold in conventional methods based on the compositional rule of inference. Simulation results show that the difference between deduced consequences and fuzzy sets mapped by linear fuzzyvalued functions is smaller as the number of fuzzy rules increases, in which the difference is evaluated by mean square errors. The discussions may lead to improvements of the interpretability in representing nonlinear fuzzy-valued functions by using α-GEMII.
Posture Estimation of Human Body Based on Connection Relations of 3D Ellipsoidal Models
Authors: Hayase Mitsuhiro ,   Graduate School of Computer and Cognitive Science, Chukyo University, 101 Tokodachi, Kaizu-cho, Toyota, Aichi 470-0393, Japan, Shimada Susumu ,   School of Information Science and Technology, Chukyo University, 101 Tokodachi, Kaizu-cho, Toyota, Aichi 470-0393, Japan
Published: April 2016

We propose new method of estimating human body posture from connection relations of threedimensional (3D) ellipsoidal models. First, 3D ellipsoidal models with enlargement and reduction transformations are constructed. Next, two-dimensional (2D) appearance models are constructed from 2D projected images of the 3D model. The appearance models are related to each other by employing a network data structure. They are then matched with an image of the body made from actual thermal images. By using the connection relations between the head and body, the head and the body can be recognized. Differences between individuals can be simply treated by using different sized parts. Moreover, this method can be applied also to recognition of arms and legs.
Conversation System with State Information
Authors: Glina Elyse Marie ,   University of Tasmania, Hobart, Tasmania 7001, Australia, Kang Byeong-Ho
Published: April 2016

Most current approaches to conversation system development invoke a complex set of language parsing rules or development tools difficult for novices to handle and are unable to convincingly simulate advanced natural language features such as topic awareness or conversation thread involvement. This study proposes an alternate approach based on the Ripple Down Rules (RDR) algorithm, presently used to enable more effective maintenance of expert systems. This tree-based algorithm enables a conversation system to travel incrementally deeper into a particular topic, then to switch based on context-dependent information to the correct previously discussed topic – resulting in a highly reusable method of developing conversation systems based around a variety of topics.
An Integrated Perceptual System of Different Perceptual Elements for an Intelligent Robot
Authors: Masuta Hiroyuki ,   Department of System Design, Tokyo Metropolitan University, 6-6 Asahigaoka, Hino-shi, Tokyo 191-0065, Japan, Kubota Naoyuki
Published: April 2016

This paper discusses an integrated perceptual system for intelligent robots. Robots should be able to perceive environments flexibly enough to realize intelligent behavior. We focus on a perceptual system based on the perceiving-acting cycle discussed in ecological psychology. The perceptual system we propose consists of a retinal model and a spiking-neural network realizing the perceiving-acting cycle concept. We apply our proposal to a robot arm with a threedimensional (3D)-range camera. We verified the feasibility of the perceptual system using a single input such as depth or luminance information. Our proposal integrates different perceptual elements for improving the accuracy of perception. Experimental results showed that our proposal perceives the targeted dish accurately by integrating different perceptual elements using the 3D-range camera.
Toward Strategic Human Skill Development Through Human and Agent Interaction: Improving Negotiation Skill by Interacting with Bargaining Agent
Authors: Otaki Atsushi ,   The University of Electro-Communications, 1-5-1 Chofugaoka, Chofu, Tokyo 182-8585, Japan, Hattori Kiyohiko , Takadama Keiki
Published: April 2016

This paper focuses on developing human skills through interaction between a human player and a computer agent, and explores its strategic method through experiments on the bargaining games where human players negotiate with computer agents. Specifically, human players negotiate with three types of agents: (a) strong/weak attitude agents making aggressive/defensive proposals in advantageous/disadvantageous situations; (b) fair agents making fair proposals; and (c) the “human-like” agents making mutually agreeable proposals as the number of games increases. Analysis of the human subject experiments has revealed the three major implications: (1) human players negotiating with the strong/weak attitude agents obtain the largest profit overall; (2) human players negotiating with “human-like” agents win many games; and (3) no relationship exists between profit maximization and a win of the games.
Behavior Generation and Evaluation of Negotiation Agent Based on Negotiation Dialogue Instances
Authors: Katagami Daisuke ,   Department of Applied Computer Science, Faculty of Engineering, Tokyo Polytechnic University, 1583 Iiyama, Atsugi, Kanagawa 243-0297, Japan, Ikeda Yusuke , Nitta Katsumi ,   Interdisciplinary Graduate School of Science and Engineering, Tokyo Institute of Technology, 4259 Nagatsuta, Midori-ku, Yokohama, Kanagawa 226-8502, Japan
Published: April 2016

This study focuses on gestures negotiation dialogs. Analyzing the situation/gesture relationship, we suggest how to enable agents to conduct adequate human-like gestures and evaluated whether an agent’s gestures could give an impression similar to those by a human being. We collected negotiation dialogs to study common human gestures. We studied gesture frequency in different situations and extracted gestures with high frequency, making an agent gesture module based on the number of characteristics. Using a questionnaire, we evaluated the impressions of gestures by human users and agents, confirming that the agent expresses the same state of mind as the human being by generating an adequately human-like gesture.
Efficient Optimization Using Experimental Queries: A Peak-Search Algorithm for Efficient Load-Pull Measurements
Authors: Baylis Charles ,   Department of Electrical and Computer Engineering, Baylor University, Waco, TX, USA, Dunleavy Lawrence , Lardizabal Steven , II Robert J. Marks , Rodriguez Alberto ,   Department of Electrical Engineering, University of South Florida, Tampa, FL, USA,   Raytheon RF Components, Andover, MA, USA,   ITT Corporation, Palm Harbor, FL, USA
Published: April 2016

In the process of hardware optimization, physical queries requiring laboratory experiments are often necessary. This is similar to optimization using software where queries are made to a computer model. In both the laboratory optimization and optimization using computer models, queries come at a cost: laboratory time or computer time. Finding efficient searches using a small number of queries on average is therefore motivated. In this paper, techniques used in computer search are shown to be transparently applicable to certain instances of hardware optimization. The hardware example presented is a load-pull peaksearch algorithm for power amplifier load-impedance optimization. The successful search shown in this paper allows high-resolution measurement of the maximum power with a significant reduction in the number of measured reflection-coefficient states. The use of computationally intelligent procedures for reducing time costs in design optimization using hardware has significant potential applications in a number of iterative experimental procedures performed in the laboratory.
Semi-Supervised Fuzzyc-Means Algorithm by Revising Dissimilarity Between Data
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

We propose two approaches for semi-supervised FCM withsoftpairwise constraints. One applies NERFCM to the revised dissimilarity matrix by pairwise constraints. The other applies K-FCM with a dissimilarity-based kernel function, revising the dissimilarity matrix based on whether data in the same cluster may be close to each other or the data in the different clusters may be apart from each other. Propagating given pairwise constraints to unconstrained data is done when given constraints are not sufficient to obtain the desired clustering result. Numerical examples show that the proposed algorithms are valid.
A Proposal of Stock Price Predictor Using Associated Memory
Authors: Nagaya Shigeki ,   Imaging Science and Engineering Laboratory, Tokyo Institute of Technology, 4259 Nagatsuta, Midori-ku, Yokohama 226-8503, Japan, Chenli Zhang , Hasegawa Osamu
Published: April 2016

The novel method [1] we propose for predicting stock prices is a case-based reasoning predictor based on associative stock price data memory using Self-Organizing and IncrementalNeural Networks (SOINN) [2]. When a user inputs stock price data, the predictor outputs the most likely prediction based on statistically summarizing similar stock price pattern. It also outputs all cases included in the prediction. Our method has following advantages: (a) our predictor gives users grounds by giving all cases consisting of the prediction using associative memory. Users thereby recognize and are ready for prediction risk. (b) Our predictor avoids large prediction failures because it modifies itself through online learning and continues to learn without its learning parameters being reassigned. This makes it much safer where investment loss may be large. (c) Our predictor is as profitable as previous work while realizing unique, useful functions, as shown by experimental results using actual stock price data from the US and Japan markets between 1998 and 2005.
Effects of Information Diffusion in OnlineWord-of-Mouth Communication Among Consumers
Authors: Okada Isamu ,   Faculty of Business Administration, Soka University, 1-236 Tangi, Hachioji, Tokyo 192-8577, Japan, Yamamoto Hitoshi ,   Faculty of Business Administration, Rissho University, 4-2-16 Osaki, Shinagawa, Tokyo 141-8602, Japan
Published: April 2016

The effects of online word-of-mouth communication among consumers were investigated using an agentbased model. In order to explain consumers’ purchasing behaviours from the view of consumer behavioral theory, we installed heterogeneity on consumers based on individual informative actions. Consumers were assumed to communicate with other consumers selectively using one of three policies: random selection, similar level selection, and higher level selection. Simulation showed that the most effective policy for selecting communication partners depends on the characteristics of goods under consideration. It also showed that increasing the number of communication partners and changing distribution of consumers positively affects purchasing behaviour while increasing consumer memory through such technologies as blogs does not. These findings help clarify how consumers deal with their cognitive limitations in the face of the massive amount of information now available.
Simulation of Futures and Spot Markets by Using an Agent-Based Multi-Market Model
Authors: Ohi Tomoko ,   Graduate School of Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan, Hashimoto Yasuhiro , Chen Yu , Ohashi Hirotada
Published: April 2016

The agent-based multi-market model we propose simulates futures and spot markets. On the basis of trading strategies in real markets, four kinds of agents - arbitragers, hedgers, speculators, and noise traders - are included in our model. Interactions of the two markets are generated through various agent trading behavior. We also statistically analyzed futures and spot prices of the Nikkei 225 index, where we found a large positive correlation between the two prices and a fat-tail distribution of the basis. Simulations results show that, instead of the conventional single-market model, only the two-market model reproduces both statistical properties of futures prices.
Risk Evaluation by Human Trajectory Simulation Based on Real Data
Authors: Izumi Kiyoshi ,   School of Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan, PRESTO, JST, Japan, Nishida Yoshifumi , Motomura Yoichi ,   Digital Human Research Center, National Institute of Advanced Industrial Science and Technology, 2-3-26 Aomi, Koto-ku, Tokyo 135-0064, Japan, CREST, JST, Japan
Published: April 2016

This paper proposes a new approach integrating the modeling of moving persons from sensor data and agent-based simulation for indoor layout design viewed from preventing children’s accidents. Our model focuses on interaction between indoor objects and children to estimate the risk of indoor accidents. We discuss the agent-based simulation of multiple persons moving in public spaces and its application to evaluating information presentation for guidance.
Massive Multiagent-Based Urban Traffic Simulation with Fine-Grained Behavior Models
Authors: Hattori Hiromitsu ,   Graduate School of Informatics, Kyoto University, Yoshida-Honmachi, Sakyo-ku, Kyoto 606-8021, Japan, Nakajima Yuu , Yamane Shohei
Published: April 2016

As it is getting easier to obtain reams of data on human behavior via ubiquitous devices, it is becoming obvious that we must work on two conflicting research directions for realizing multiagent-based social simulations; creating large-scale simulations and elaborating fine-scale human behavior models. The challenge in this paper is to achievemassively urban traffic simulations with fine-grained levels of driving behavior. Toward our objective, we show the design and implementation of a multiagent-based simulation platform, that enables us to execute massive but sophisticated multiagent traffic simulations. We show the capability of the developed platform to reproduce the urban traffic with a social experiment scenario. We investigate its potential to analyze the traffic from both macroscopic and microscopic viewpoints.
Unnormalized Interval Type-2 TSK Fuzzy Logic System Design Based on Convexity and Sample Data
Authors: Wang Tiechao ,   Institute of Automation, Chinese Academy of Sciences, 95 Zhongguancun East Road, Haidian District, Beijng 100190, China, Yi Jianqiang ,   Liaoning University of Technology, Jinzhou, Liaoning 121001, China
Published: April 2016

Prior knowledge of convexity is encoded into a Single-Input Single-Output (SISO) unnormalized interval type-2 Takagi-Sugeno-Kang (TSK) Fuzzy Logic System (FLS) such that the system converges to a given convex target function. After giving sufficient conditions to guarantee convexity with respect to inputs, we show how to combine convexity with Unnormalized Interval Type-2 TSK FLSs (UIT2FLSs) to design convex fuzzy systems enabling derived systems to approach the target function. A simulation example demonstrates the usefulness of convexity and the advantages of UIT2FLSs in the presence of noise.
Adaptive Cruise Control Based on Reinforcement Leaning with Shaping Rewards
Authors: Hu Zhaohui ,   Institute of Automation, Chinese Academy of Sciences, No.95, Zhongguancun East Road, Beijing 100190, China, Zhao Dongbin
Published: April 2016

This paper proposes a Supervised Reinforcement Learning (SRL) algorithm for the Adaptive Cruise Control system (ACC) to comply with human driving habit, which can be thought of as a dynamic programming problem with stochastic demands. In short, the ACC problem can be deemed as the host vehicle adopting different control parameters (accelerations in the upper controller, brakes and throttles in the bottom controller) in the process of following or other driving situations according to the driver’s behavior. We discretize the relative speed as well as the relative distance to construct the two-dimensional states and map them to a one-dimensional state space; discretize the acceleration to generate the action set; and design additional speed improvement shaping reward and distance improvement shaping reward to construct the supervisor. We apply the SRL algorithm to the ACC problem in different scenarios. The results show the higher robustness and accuracy of the SRL control policy compared with traditional control methods.
Finding Communities Using User Preference in Web Structure Mining
Authors: Yoshikawa Takeshi ,   Graduate School of Information Science and Technology, Hokkaido University, Kita 14, Nishi 9, Kita-ku, Sapporo 060-0814, Japan, Nonaka Hidetoshi
Published: April 2016

Web structure mining is the method based on the graph structure of hyperlinks, and it does not use the information of web contents. HITS algorithm and PageRank algorithm are popular methods for web structure mining. In this study, we deal with the finding algorithm of web communities in web structure mining. This algorithm receives some URLs of user’s known web pages, and proposes to the user the candidates of pages in the web community by using the structure of bipartite graph. We investigate the effect of introduction of user preference to each known pages, and discuss the way to improve the finding algorithm of web communities.
Statistical Analysis for Human Preference to Colors
Authors: Kanda Taki ,   Department of Service Management, Bunri University of Hospitality, 311-1 Kashiwabarashinden, Sayama, Saitama 350-1336, Japan
Published: April 2016

This paper discusses human preference of colors. There are many statistical methods to analyze human preference. In sensory evaluation method of rank order or various methods of paired comparisons are used. Here for methods to analyze human preference to colors the method of rank order and Thurstone’s method of paired comparisons are taken up, for colors red, blue, yellow, black, and white are taken up, and it is studied how those colors are preferred using both methods and same subjects aiming to find out whether equal results are obtained. In the method of rank order, order statistics from the normal distribution are used for evaluation, and in the Thurstone’s method of paired comparisons, it is assumed that human sensations follow the normal distribution. In both methods, the units of evaluation values become the standard deviations of the standard normal distribution, that is, σ = 1, hence the evaluation values obtained by both methods are useful to compare the results among many objects. In this paper, the method of rank order and Thurstone’s method of paired comparisons are described, results of evaluation for preference to colors by both methods using the same subjects are shown, and it is discussed how the results of evaluation differ for both methods.
Trustees’ and Investors’ Behavior in the First Two Rounds of a Trust Game: Overview and a (Partial) Explanation Based on Cooperative Game Theory
Authors: Koshelev Misha ,   Human Neuroimaging Lab, Division of Neuroscience, Baylor College of Medicine, Houston, TX 77030, USA,   W. M. Keck Center for Interdisciplinary Bioscience Training, Houston, TX 77030, USA
Published: April 2016

To study social behavior, researchers studied simple games like the multi-round trust game. Based on experimental data, several models have been proposed to explain how players behave in the third and following rounds depending on their previous behavior in this game. However, none of these models explain the behavior in the first rounds. In this paper, we show that cooperative game theory can lead to a partial explanation of the trustees’ (and investors’) behavior in the first rounds.
Development of Foot Gait Simulator for Presenting Environment to Each User
Authors: Hashimoto Tomomi ,   Saitama Institute of Technology, 1690 Fusaiji, Fukaya, Saitama 369-0293, Japan, Takakura Yasuyuki , Hamada Toshimitsu , Akazawa Toshiko , Yamamoto Mitsuru ,   Saitama Medical University, 981 Kawakado, Moroyamamachi, Saitama 350-0496, Japan,   Tsukuba Gakuin University, 3-1 Azuma, Tsukuba-shi, Ibaraki, 305-0031, Japan,   Kitasato University, 1-15-1 Kitasato, Minami, Sagamihara, Kanagawa 252-0373, Japan,   Saitama Medical Center, Saitama Medical School, 1981 Kamoda, Kawagoe, Saitama 350-8550, Japan
Published: April 2016

This article develops gait rehabilitation that presents the subject’s living environment in Virtual Reality (VR). This simulator is shaped similar to a walking frame and consists of a PC, a monitor, a Wii Balance Board of Wii Fit, a handle, and a cage. The subject steps on the Wii Balance Board tomove forward in VR and operates the handle left and right to turn around. Walking quickly on the Wii Balance Board, the subject moves quickly in VR, and walking slowly, moves slowly. In VR, an avatar in front of the subject enables the subject to operate the simulator to follow the avatar. As an example of living environment, a virtual town was made with roads, houses, nursing homes, traffic lights, trees, etc. Our system was tested by ten older subjects and 15 younger subjects from November 2009 to June 2010. Our system features a variable reference speed for each subject to move in VR, which realizes an appropriate amount of motion. In a low-difficulty gait task, all finished in almost the same walking time and enjoyed appropriate exercise. In a highly-difficult gait task, some older subjects had difficulty in gait training due to multiple attention requirements.
Multimodal Gesture Recognition for Mascot Robot System Based on Choquet Integral Using Camera and 3D Accelerometers Fusion
Authors: Tang Yongkang ,   Department of Computational Intelligence and Systems Science, Tokyo Institute of Technology, G3-49, 4259 Nagatsuta, Midori-ku, Yokohama, Kanagawa 226-8502, Japan, Vu Hai An , Le Phuc Q. , Masano Daisuke , Thet OoHan , Fatichah Chastine , Liu Zhentao , Yamaguchi Masashi , Tangel Martin Leonard , Dong Fangyan , Yamazaki Yoichi , Hirota Kaoru ,   Department of Electrical, Electronic & Information Engineering, Faculty of Engineering, Kanto Gakuin University, 1-50-1 Mutsuura-higashi, Kanazawa-ku, Yokohama, Kanagawa 236-8501, Japan
Published: April 2016

A multimodal gesture recognition method for mascot robot system is proposed based on Choquet integral by fusing camera and 3D accelerometer data. The optimization of two fuzzy measures in the training phase for two recognition units, i.e., camera-based and accelerometer-based units, obtains enough recognition rate of 92.7% for 8 types of gestures by improving the recognition rate approximate 20% compared with that of each unit. The proposed method targets casual communication from humans to robots by integrating nonverbal gesture messages and verbal messages.
Data Mining Using Human Motions for Intelligent Systems
Authors: Ho Yihsin ,   Graduate School of System Design, Tokyo Metropolitan University, 6-6 Asahigaoka, Hino, Tokyo 191-0065, Japan, Shibano Tomomi , Sato-Shimokawara Eri , Yamaguchi Toru
Published: April 2016

In recent years, robot systems have come to play an important assisting role in providing information to human beings. The great progress made in Internet technology is also greatly beneficial as a means of collecting information. However, amassing too much information also gives rise to the problem about how to offer the most suitable data. In this paper, the authors present a system that can use users’ intentions to provide information to them. We focus on applying a series of “Time logs” constructed by using explicit human motions and motion time to provide base data for data mining, using the datamining to ascertain human intentions from the Time logs series, and then offering information to people. To achieve our research targets, we constructed our systembased on the Kukanchi concept, which allows every part of a system to provide and receive data. Moreover, our system uses cameras to collect human motion data without any need to place devices on users’ bodies. Our work verifies the possibility of using the Kukanchi concept to construct a system and the ability of such a system to apply human motions as mining data without disturbing users in any way. It also demonstrates the possibility of using a series of Time logs to detect human intentions so as to offer useful data to users.
Ant Colony Optimization for Feature Selection Involving Effective Local Search
Authors: Kabir Md. Monirul ,   Department of System Design Engineering, Graduate School of Engineering, University of Fukui, 3-9-1 Bunkyo, Fukui 910-8507, Japan, Shahjahan Md. , Murase Kazuyuki ,   Department of Electrical and Electronic Engineering, Khulna University of Engineering and Technology, Building no-13E, KUET Campus, Khulna 9203, Bangladesh,   Research and Education Program for Life Science, University of Fukui, Japan
Published: April 2016

This paper proposes an effective algorithm for feature selection (ACOFS) that uses a global Ant Colony Optimization algorithm (ACO) search strategy. To make ACO effective in feature selection, our proposed algorithm uses an effective local search in selecting significant features. The novelty of ACOFS lies in its effective balance between ant exploration and exploitation using new pheromone update and heuristic information computation rules to generate a subset of a smaller number of significant features. We evaluate algorithm performance using seven real-world benchmark classification datasets. Results show that ACOFS generates smaller subsets of significant features with improved classification accuracy.
Fuzzy Genetic Network Programming with Noises for Mobile Robot Navigation
Authors: Sendari Siti ,   Graduate School of Information, Production and Systems, Waseda University, 2-7 Hibikino, Wakamatsu-ku, Kitakyushu, Fukuoka 808-0135, Japan, Mabu Shingo , Tjahjadi Andre , Hirasawa Kotaro
Published: April 2016

Genetic Network Programming (GNP) has been proposed as one of the evolutionary algorithms, which is represented by graph structures. It was extended to GNP with Reinforcement Learning (GNP-RL) which combines online learning and evolution. GNP-RL succeeded in implementing the wall following behaviors of a Khepera robot. The objective of this paper is to improve the robustness of GNP-RL by introducing fuzzy GNP with noises. Fuzzy GNP overcomes the sharp boundary problem using the probabilistic transition on fuzzy judgment nodes, which improves the exploration ability. Furthermore, the robustness of fuzzy GNP can be improved by adding Gaussian noises to the sensors during the training phase. In order to evaluate the robustness of fuzzy GNP with noises, the wall following of a Khepera robot is simulated. Simulation results show that fuzzy GNP with noises is superior to GNP-RL.
Selected Papers from SCIS & ISIS 2010 – No.1
Authors: Watanabe Keigo , Ohkura Kazuhiro , Izumi Kiyotaka
Published: April 2016

SCIS & ISIS is a biennial international joint conference on soft computing and intelligent systems, with research ranging from fuzzy systems, neural networks, and evolutionary computation to multi-agent systems, artificial intelligence, and robotics. SCIS & ISIS 2010 consisted of the 5th International Conference on Soft Computing and Intelligent Systems (SCIS) and the 11th International Symposium on Advanced Intelligent Systems (ISIS), held at Okayama Convention Center on December 8-12, 2010. Original presentations numbered 302 and participants 322. After preliminary selection by SCIS & ISIS 2010 session chairs, we listed over 70 papers to be published in extended form in the Special Issue of the Journal of Advanced Computational Intelligence and Intelligent Informatics. After inviting these authors to submit papers for this special issue, we had two referees to review them and accepted 27 for publication in Vol.15, Nos.7 and 8 in 2011. This special issue presents 15 of these papers covering most conference topics, including fuzzy theory, learning methods, neural networks, and evolutionary computation, with a focus on reinforcement learning, multi-agent system, nonlinear estimation, and real-world applications to visual system, robotics and energy. We thank the authors and reviewers for their invaluable contributions toward making this special issue possible. We are also grateful to Editors-in-chief Prof. Toshio Fukuda of Nagoya University and Prof. Kaoru Hirota of the Tokyo Institute of Technology for inviting us to serve as Guest Editors.
Merging with Extraction Method for Transfer Learning in Actor-Critic
Authors: Takano Toshiaki ,   Graduate School of Engineering, Mie University, 1577 Kurima-Machiya, Tsu, Mie 514-8507, Japan, Takase Haruhiko , Kawanaka Hiroharu , Tsuruoka Shinji ,   Graduate School of Regional Innovation Studies, Mie University, 1577 Kurima-Machiya, Tsu, Mie 514-8507, Japan
Published: April 2016

This paper aims to accelerate learning process of actor-critic method, which is one of the major reinforcement learning algorithms, by a transfer learning. Transfer learning accelerates learning processes for the target task by reusing knowledge of source policies for each source task. In general, it consists of a selection phase and a training phase. Agents select source policies that are similar to the target one without trial and error, and train the target task by referring selected policies. In this paper, we discuss the training phase, and the rest of the training algorithm is based on our previous method. We proposed the effective transfer method that consists of the extractionmethod and the mergingmethod. Agents extract action preferences that are related to reliable states, and state values that lead to preferred states. Extracted parameters are merged into the current parameters by taking weighted average. We apply the proposed algorithm to simple maze tasks, and show the effectiveness of the proposed method: reduce 16% episodes and 55% failures without transfer.
Experimental Evaluations of Approaching Hand/Eye-Vergence Visual Servoing
Authors: Yu Fujia ,   Graduate School of Natural Science and Technology, Okayama University, 3-1-1 Tsushimanaka, Kita-ku, Okayama 700-8530, Japan, Song Wei , Minami Mamoru , Yanou Akira , Deng Mingcong ,   University of Shanghai, 99 Shangda Road, BaoShan District, Shanghai 200444, China
Published: April 2016

We focus on controlling a robot’s end-effector to track a moving object meanwhile approaching an object with a desired tracking pose for grasping the object - a process we call Approaching Visual Servoing (AVS). AVS using binocular cameras requires inheritable eyevergence to keep a target in camera images at the center of the camera frame because approaching motion narrows the camera’s visual field or may even lose sight of the object. Experiments using our proposed hand and eye-vergence dual control involved full 6-degree-of-freedom AVS to a moving object by using a 7-link manipulator with a binocular camera, confirming the feasibility of hand and eye-vergence control.
Abductive Reasoning as an Integrating Framework in Skill Acquisition
Authors: Furukawa Koichi ,   Graduate School of Business Innovation, Kaetsu University, Masuda Toshiki , Kobayashi Ikuo ,   Cellist,   SFC Research Institute, Keio University
Published: April 2016

Skill acquisition includes many issues, such as finding explanations for skillful performance, finding missing links in skill explanations, diagnosing performance malfunctions, identifying the role of “surprising facts” in skill discovery, and accommodating new skills. Interestingly, most of these issues are treated appropriately in an abductive reasoning framework. In this article, we focus on the skill of cello playing. A cellistmay find musical passages not playable using previously acquired methods alone. In such a case, we must invent a new skill, which we call an abduced skill. A simple case of skill abduction can be realized by adding a factual hypothesis, whereas in some cases we need rule abduction. In acquiring new skills, an instructor’s suggestion may improve a learner’s skill. This indicates the importance of such suggestions, which are called “surprising facts” in abduction. We point out the importance of surprising facts by showing two examples of dramatic improvement.
Decomposition of Limb Movement Based on Muscular Coordination During Human Running
Authors: Iimura Taiki ,   Graduate School of Engineering Science, Osaka University, 1-3 Machikaneyama-cho, Toyonaka, Osaka 560-8531, Inoue Keita , Pham Hang T. T. , Hirai Hiroaki , Miyazaki Fumio
Published: April 2016

The study of decomposing movement into units of motor function is evolving in neuroscience. Meanwhile, in robotics, there is a problem with redundant Degrees Of Freedom (DOF) in the motor control of humanlike robots. We attempt to achieve fewer-DOF control of a human-like musculoskeletal robot by using our knowledge of the units of motor function. In this paper, we introduce “the agonist-antagonistmuscle pairs (A-A) ratio” and “A-A activity,” which are defined by using ElectroMyoGraphic (EMG) data and which describe the coordination between the agonist and antagonist muscles. Human running is decomposed into two units of motor function from the point of view of muscle coordination using Principal Component Analysis (PCA) of these biological signals. The kinematic meanings of the extracted patterns of muscle coordination are visualized by human-like musculoskeletal leg robot.
An Approximation Algorithm with Factor Two for a Repetitive Routing Problem of Grasp-and-Delivery Robots
Authors: Karuno Yoshiyuki ,   Department of Mechanical and System Engineering, Kyoto Institute of Technology, Matsugasaki, Sakyo-ku, Kyoto-shi, Kyoto 606-8585, Japan, Nagamochi Hiroshi , Shurbevski Aleksandar ,   Department of Applied Mathematics and Physics, Kyoto University, Yoshida-honmachi, Sakyo-ku, Kyoto-shi, Kyoto 606-8501, Japan
Published: April 2016

In this paper, we consider a routing problem for a single grasp-and-delivery robot used on a printed circuit (PC) board assembly line. The robot arranges n identical pins from their current configuration to the next required configuration by transferring them one by one in a transition. The n pins support a PC board from underneath to prevent it from overbending as an automated manipulator embeds electronic parts in the PC board from above. Each PC board has its own circuit pattern, and required configurations for PC boards all differ. Given an initial configuration of n pins and a sequence of m required configurations, the problem asks to find a transfer route of the robot that minimizes the route length over all m transitions. By applying a weighted matroid intersection algorithm, we show the repetitive routing problem to be 2-approximable in polynomial time.
Spiral Dynamics Inspired Optimization
Authors: Tamura Kenichi ,   Tokyo Metropolitan University, 1-1 Minamiosawa, Hachioji, Tokyo 192-0397, Japan, Yasuda Keiichiro
Published: April 2016

We recently proposed a new multipoint search method for 2-dimensional continuous optimization problems based on an analogy of spiral phenomena called 2-dimensional spiral optimization. Focused spiral phenomena, which appear frequently in nature, are approximated to logarithmic spirals. Two-dimensional spiral optimization used a feature of logarithmic spirals. In this paper, we proposen-dimensional spiral optimization by extending the 2-dimensional one. The n-dimensional spiral model is constructed based on rotation matrices defined inn-dimensional space. Simulation results for different benchmark problems show the effectiveness of our proposal compared to other metaheuristics.
WIN Algorithm for Discrete Online TSP
Authors: Wu Yonghua ,   School of Computer Science and Engineering, Guilin University of Electronic Technology, No.1 Jinji Road, Guilin 541004, China, Zhu Guohun , Chen Huaying , Qin Jucun ,   Department of Math and Computer, University of Southern Queensland, Toowoomba, QLD, Australia,   Graduate School of Science, Department of Information System, University of Melbourne, Melbourne, Australia
Published: April 2016

Traveling Salesman Problem (TSP) which is proved as an NP-Complete problem is solved by many algorithms. In this paper, we propose online TSP which is based on general discrete metric space. A Waiting-If-Necessary (WIN) algorithm is proposed that involves with increasing cost caused by zealous algorithms and unnecessary waiting caused by cautious algorithms. We measure the performance of the WIN algorithm using competitive analysis and found that it is a 2-competitive algorithm. The competitive ratio of theWIN algorithm can be improved by setting parameterT0.
Document Analysis System Based on Awareness Learning
Authors: Ji Jie ,   System Intelligence Laboratory, The University of Aizu, Tsuruga, Ikki-machi, Aizu-Wakamatsu, Fukushima 965-8580, Japan, Chen Rung-Ching , Zhao Qiangfu ,   College of Informatics, Chaoyang University of Technology, 168 Jifeng E. Rd., Wufeng District, Taichung City, Taiwan, R.O.C.
Published: April 2016

The rapid growth of the Internet has naturally encouraged users to handle and process documents as online information rather than hard-copies, e.g., on paper. Dealing with large amounts of information efficiently requires classifying data into meaningful categories. Many machine-learning-based algorithms have been proposed for document classification, yielding a variety of applications such as spam filters, patent analyzers, and hot-topic retrieval systems. Different applications having different goals require different teacher signals even for the same dataset. It is not an easy task. In this study, we describe human-behavior-inspired awareness system for analyzing documents. This system starts learning with few or even no teacher signals, learning and understanding user intent through interaction with the user. We describe the structure of our proposed system and the basic steps required for analyzing documents.
An Improvement of Fuzzy Association Rules Mining Algorithm Based on Redundancy of Rules
Authors: Watanabe Toshihiko ,   Department of Electrical and Electronic Engineering, Faculty of Engineering, Osaka Electro-Communication University, 18-8 Hatsu-cho, Neyagawa, Osaka 572-8530, Japan
Published: April 2016

In data mining approach, quantitative attributes should be appropriately dealt with as well as Boolean attributes. This paper presents an essential improvement for extracting fuzzy association rules from a database. The objective of this paper is to improve the computational time of mining and to prune extracted redundant rules simultaneously for an actual data mining application. In this paper, we define the redundancy of fuzzy association rules as a new concept for mining and prove essential theorems concerning the redundancy of fuzzy association rules. Then, we propose a basic algorithm based on the Apriori algorithm for rule extraction utilizing the redundancy of the extracted rules. The essential performance of the algorithmis evaluated through numerical experiments using benchmark data. Fromthe results, themethod is found to be promising in terms of computational time and redundant-rule pruning.