bluuebunny/crossref_metadata_2025 huggingface dataset:
Traffic Flow Prediction with Genetic Network Programming (GNP)
Authors: Zhou Huiyu ,   Graduate School of Information, Production and Systems, Waseda University, Hibikino 2-7, Wakamatsu-ku, Kitakyushu, Fukuoka 808-0135, Japan,, Mabu Shingo , Wei Wei , Shimada Kaoru , Hirasawa Kotaro
Published: April 2016

In this paper, a method for traffic flow prediction has been proposed to obtain prediction rules from the past traffic data using Genetic Network Programming (GNP). GNP is an evolutionary approach which can evolve itself and find the optimal solutions. It has been clarified that GNP works well especially in dynamic environments since GNP is consisted of directed graph structures, creates quite compact programs and has an implicit memory function. In this paper, GNP is applied to create a traffic flow prediction model. And we proposed the spatial adjacency model for the prediction and two kinds of models forN-step prediction. Additionally, the adaptive penalty functions are adopted for the fitness function in order to alleviate the infeasible solutions containing loops in the training process. Furthermore, the sharing function is also used to avoid the premature convergence.
Auction-Based Consensus Mechanism for Cooperative Tracking in Multi-Sensor Surveillance Systems
Authors: Elmogy Ahmed M. ,   University of Waterloo 200 University Avenue, West, Waterloo, Ontario, Canada, Karray Fakhreddine O. , Khamis Alaa M.
Published: April 2016

This paper presents an auction-based consensusmechanism for cooperative targets tracking using minimum numbers of mobile sensors in order to reduce energy consumption due to sensor mobilization. After targets are detected, they are clustered using hybrid subtractive K-means clustering technique to reduce the number of trackers needed to track these detected targets. The proposed target tracking process is based on an Extended Kohonen neural network. In order to decrease the network sensitivity to initial conditions, a supervised learning technique is used to get the initial weights of unsupervised Extended Kohonen Map instead of random initialization. An auction-based consensus mechanism is used as a cooperation methodology between trackers during tracking. Monitoring sensors either remain stationary or begin following their targets is based on this mechanism. The simulation results confirms that the proposed approach outperforms other approaches in energy saving and achieves better coverage.
Special Issue on Advances on Intelligent Multimedia Processing
Authors: Kawamoto Kazuhiko
Published: April 2016

As computer and sensor technology advances make increasing amounts of multimedia data available, multimedia processing methodologies are needed for multimodal data fusion, efficient data processing, information extraction, and added-value data generation. This special issue introduces the following latest practical developments in image processing, acoustical signal processing, pattern recognition, data mining, and visualization: S. Kobashi et al. propose a robust algorithm for reconstructing total knee arthroplasty implants from X-ray cone-beam images. Y. Hatakeyama et al. propose an algorithm for classifying ultrasonic abdominal images with the help of reports in text format. T. Miyazaki et al. analyze breathy and rough speech by the elderly. M. Nii et al. present a genetic algorithm for classifying nursing-care text. These papers provide useful insights into medical diagnosis and nursing care in the aging society coming to dominate 21st century. T. Toyota et al. visualize Japanese law networks based on granular computing. K. Sawase et al. present a management system for large databases of tagged images. These graphical user interface techniques will be helpful to those who are not computer experts. H. Kawano et al. propose a classification algorithm for segmenting range data into multiple quadric surfaces. Y. Arai et al. propose a nearest-neighbor method for personal authentication. H. Kawano et al. present an algorithm for extracting the structure of decorative characters based on graph spectral decomposition. These classification and discriminant algorithms provide a basis for multimedia data processing. G. Tanaka et al. propose a color transfer algorithm. H. Orii et al. present an image completion algorithm. G. Tanaka et al. present an image enhancement algorithm to noisy images. M. Mizumachi et al. propose a stochastic algorithm for estimating sound source direction. These image and acoustical processing algorithms improve the quality of digital data and will provide new applications in these areas. We thank the authors and referees whose invaluable work and kind comments have made this special issue possible and improved overall paper quality.
A Classification Algorithm of Abdominal Ultrasound Images in Medical Practice for Secondary Uses
Authors: Hatakeyama Yutaka ,   Center of Medical Information Science, Kochi University, Kohasu, Oko-cho, Nankoku, Kochi 783-8506, Japan, Kataoka Hiromi , Nakajima Noriaki , Watabe Teruaki , Okuhara Yoshiyasu
Published: April 2016

A classification algorithm for abdominal organs in ultrasonic test images based on the operator’s knowledge is proposed. This is in order to use the medical images included in medical charts for secondary uses, e.g., medical data analysis. It makes a correlation between target organs in test images and search unit information on the body mark region. In the central region of abdominal images, target organs are uniquely determined through recognition of the liver region and in consideration of the location of the diaphragm. A classification experiment, done using 600,000 real test images taken at the Kochi Medical School Hospital from 2004 to 2008, was carried out to evaluate the performance of the proposed system in terms of accuracy rate of detection of the body mark region and diaphragm region. The proposed algorithm constitutes an essential classification system for the secondary use of a large database of ultrasound images taken in the course of medical practice.
Analysis and Visualization of Japanese Law Networks Based on Granular Computing -Visual Law: Visualization System of Japanese Law-
Authors: Toyota Tetsuya ,   Department of Intelligent Interaction Technologies, University of Tsukuba, 1-1-1 Tenoudai, Tsukuba Science City, Ibaraki 305-8573, Japan, Nobuhara Hajime
Published: April 2016

In order to grasp a perspective of the over 7,000 laws in Japan, and to find the relationships between law and laws, a method of creating a hierarchical network of laws using granular computing, is proposed. The proposed method analyze hierarchical networks by using an index of network science such as degree distribution and closeness centrality. Furthermore, it visualizes the hierarchical structure within the setting of granular computing. Using the JAVA-based language ‘Prefuse,’ a law network visualization system ‘Visual Law’ is implemented, and it is confirmed that users can easily analyze/understand the law network structure using our proposal.
Selected Papers from IWACIII 2009
Authors: Hatakeyama Yutaka , Kawamoto Kazuhiko , Dong Fangyan
Published: April 2016

The International Workshop on Advanced Computational Intelligence and Intelligent Informatics (IWACIII), honoring Prof. Kaoru Hirota on his 60th birthday, was held at the Tokyo Institute of Technology in November 2009. The workshop featured 8 invited talks and 30 papers published in IWACIII 2009 proceedings. This special JACIII issue features 10 papers from these proceedings, dealing with data mining, robotics, fuzzy systems, signal and image processing, and industrial applications. A paper by Prof. Koczy was chosen as the best paper of this workshop. This workshop is chaired by Professor Toshihiro Kaino, Professor Yasufumi Takama, and Professor Hajime Nobuhara. The workshop is sponsored by Fuji Technology Press Co., Ltd., Suzhou KTI Semiconductor Manufacturing Machine Co., Ltd., R&D Center of VEST, Mycom, Inc., Computing, Designing, and Integration Co., Ltd., Japan Association of Service Science (JASS), TOKYO TECH Pursuing Excellence, Hirota Laboratory, Tokyo Institute of Technology. As guest editor of this special issue, we thank all of the contributors and referees for their fine work and candid reviews. The second IWACIII will be held in 2011 in Suzhou, China. We invite you to submit your research papers and to plan to take participate in IWACIII 2011.
Data Cleaning for Classification Using Misclassification Analysis
Authors: Jeatrakul Piyasak ,   School of Information Technology, Murdoch University, South Street, Murdoch, Western Australia 6150, Australia, Wong Kok Wai , Fung Chun Che
Published: April 2016

In most classification problems, sometimes in order to achieve better results, data cleaning is used as a preprocessing technique. The purpose of data cleaning is to remove noise, inconsistent data and errors in the training data. This should enable the use of a better and representative data set to develop a reliable classification model. In most classification models, unclean data could sometime affect the classification accuracies of a model. In this paper, we investigate the use of misclassification analysis for data cleaning. In order to demonstrate our concept, we have used Artificial Neural Network (ANN) as the core computational intelligence technique. We use four benchmark data sets obtained from the University of California Irvine (UCI) machine learning repository to investigate the results from our proposed data cleaning technique. The experimental data sets used in our experiment are binary classification problems, which are German credit data, BUPA liver disorders, Johns Hopkins Ionosphere and Pima Indians Diabetes. The results show that the proposed cleaning technique could be a good alternative to provide some confidence when constructing a classification model.
Enhancing the Largest Set Rule for Assembly Line Balancing Through the Concept of Bi-Directional Work Relatedness
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

The process of optimally assigning the timed tasks required to assemble a product to an ordered sequence of workstations is known as the Assembly Line Balancing (ALB) problem. Typical approaches to ALB assume a strict mathematical posture and mostly treat it as a combinatorial optimization problem with the objective of minimizing the idle time across the workstations, while satisfying precedence constraints. The actual nature of the tasks assigned is seldom taken into consideration. While this approach may yield satisfactory cycle time results on paper, it often leads to inconvenient task assignments in an actual work environment. It has been postulated in the literature that assigning groups of related tasks to the same workstation may lead to assembly lines that exhibit increased robustness in real-world situations at the expense of a slightly increased cycle time. The prototypical example of such an approach, Agrawal’s Largest Set Rule (LSR), utilizes backward work relatedness to assign a set of cohesive tasks to the proper workstation. In this paper, we enhance the performance of the original LSR algorithm through the concept of bi-directional work relatedness, where backward and forward relationships are taken into consideration for task assignments. The proposed concept leads to comparable cycle time and improved work relatedness. Applying this novel concept to a benchmark ALB problem demonstrates the feasibility and applicability of the proposed approach.
Color Quantization Based on Hierarchical Frequency Sensitive Competitive Learning
Authors: Zhang Jun ,   Graduate School of Information, Production and Systems, Waseda University, 2-7 Hibikino, Wakamatsu-ku, Kitakyushu, Fukuoka 808-0135, Japan, Hu Jinglu
Published: April 2016

In this paper, we propose a Hierarchical Frequency Sensitive Competitive Learning (HFSCL) method to achieve Color Quantization (CQ). In HFSCL, the appropriate number of quantized colors and the palette can be obtained by an adaptive procedure following a binary tree structure with nodes and layers. Starting from the root node that contains all colors in an image until all nodes are examined by split conditions, a binary tree will be generated. In each node of the tree, a Frequency Sensitive Competitive Learning (FSCL) network is used to achieve two-way division. To avoid over-split, merging condition is defined to merge the clusters that are close enough to each other at each layer. Experimental results show that the proposed HFSCL has desired ability for CQ.
Natural Language Questions and Answers for RDF Information Resources
Authors: Akita Chie ,   School of Science and Technology, Kwansei Gakuin University, Mase Motohiro , Kitamura Yasuhiko ,   Graduate School of Science and Technology, Kwansei Gakuin University, 2-1 Gakuen, Sanda, Hyogo 669-1322, Japan
Published: April 2016

We propose Questions and Answers (Q&A) method responding natural language questions about RDF information resource. When a natural language question and an RDF graph are given, keywords are extracted from the question using morphological analysis and keywords are converted to key elements referencing lexica describing correspondence relationships between keywords and elements. A question subgraph containing all key elements corresponding to keywords in the question are extracted from the RDF graph and the question subgraph is searched for an answer. We evaluate performance using an RDF information resource that describes our laboratory.
An Efficient Algorithm for Optimizing Bipartite Modularity in Bipartite Networks
Authors: Liu Xin ,   Graduate School of Information Science and Engineering, Tokyo Institute of Technology, 2-12-1 Ookayama, Meguro, Tokyo 152-8552, Japan, Murata Tsuyoshi
Published: April 2016

Modularity evaluates the quality of a division of network nodes into communities, and modularity optimization is the most widely used class of methods for detecting communities in networks. In bipartite networks, there are correspondingly bipartite modularity and bipartite modularity optimization. LPAb, a very fast label propagation algorithm based on bipartite modularity optimization, tends to become stuck in poor local maxima, yielding suboptimal community divisions with low bipartite modularity. We therefore propose LPAb+, a hybrid algorithm combining modified LPAb, or LPAb’, and MSG, a multistep greedy agglomerative algorithm, with the objective of using MSG to drive LPAb out of local maxima. We use four commonly used real-world bipartite networks to demonstrate LPAb+ capability in detecting community divisions with remarkably higher bipartite modularity than LPAb. We show how LPAb+ outperforms other bipartite modularity optimization algorithms, without compromising speed.
A Double-Deck Elevator Systems Controller with Idle Cage Assignment Algorithm Using Genetic Network Programming
Authors: Mabu Shingo ,   Graduate School of Information, Production and Systems, Waseda University, 2-7 Hibikino, Wakamatsu-ku, Kitakyushu, Fukuoka 808-0135, Japan, Yu Lu , Zhou Jin , Eto Shinji , Hirasawa Kotaro
Published: April 2016

So far, many studies on Double-Deck Elevator Systems (DDES) have been done for exploring more efficient algorithms to improve the system transportation capacity, especially in a heavy traffic mode. The main idea of these algorithms is to decrease the number of stops during a round trip by grouping the passengers with the same destination as much as possible. Unlike what occurs in this mode, where all cages almost always keep moving, there is the case, where some cages become idle in a light traffic mode. Therefore, how to dispatch these idle cages, which is seldom considered in the heavy traffic mode, becomes important when developing the controller of DDES. In this paper, we propose a DDES controller with idle cage assignment algorithm embedded using Genetic Network Programming (GNP) for a light traffic mode, which is based on a timer and event-driven hybrid model. To verify the efficiency and effectiveness of the proposed method, some experiments have been done under a special down-peak pattern where passengers emerge especially at the 7th floor. Simulation results show that the proposed method improves the performance compared with the case when the cage assignment algorithm is not employed and works better than six other heuristic methods in a light traffic mode.
Special Issue on ISIS 2009, Dong-A University, Busan, Korea
Authors: Lee John-Tark , Park Gyei Kark
Published: April 2016

The 10th International Symposium on Advanced Intelligent Systems 2009 (ISIS2009) held on August 17-19, 2009, at the Bumin Campus of Dong-A University (http://www.donga.ac.kr/) in Busan, Korea, was sponsored by the Korean Institute of Intelligent System Society (KIIS) and cosponsored technically by the Japan Society for Fuzzy Theory and Intelligent Informatics (SOFT) and the Taiwanese Association for Artificial Intelligence (TAAI). The international symposium focused on state-of-art accomplishments, innovations, and potential directions in intelligent systems. It also marked an epoch of innovation and the dissemination of research into many interesting fields. Its broad theme covered the latest in technical fields, including artificial intelligence, intelligent systems, Ambient Intelligence (AmI), bioinformatics, information technology, and their wide-ranging applications, from basic theoretical work to practical engineering applications. The 80 featured papers were presented by 120 participants. With so many papers submitted to JACIII, this special issue consists of just two strictly selected papers. The first, deals with emerging research trends in robotics, proposing a new trajectory generation using the univariate Dynamic Encoding Algorithm for Searches (uDEAS) in the turning of a biped walking robot. The second paper, presenting the latest findings in AmI, details a newly designed and implemented robust capacitive sensor with parasitic parameter modeling over a range of high 200 KHz frequencies based on an Unscented Kalman Filter (UKF) algorithm. I would like to thank Mr. Kunihiko Uchida, Mr. Shinya Wakai, Ms. Reiko Ohta, and Mr. Shinji Isokawa as editorial staff of Fuji Technology Press for editing these complex manuscripts into their final form. And I really thank to Prof. Kaoru Hirota, Editor-in-Chief of JACIII for inviting me to direct this special issue on ISIS
FPGA-Based Relative Distance Estimation for Indoor Robot Control Using Monocular Digital Camera
Authors: Yu Ying-Hao ,   School of Electrical, Mechanical and Mechatronic Systems, University of Technology Sydney, PO Box 123, Broadway, NSW 2007, Australia, Vo-Ky Chau , Kodagoda Sarath , Ha Quang Phuc ,   Faculty of Electrical and Electronics Engineering, HoChiMinh University of Technology, 268 Ly Thuong Kiet St., Distr. 10, HoChiMinh City, Vietnam
Published: April 2016

Distance measurement methodologies based on the digital camera usually require time-consuming calibration procedures, some are even derived from complicated image processing algorithms resulting in low picture frame rates. In a dynamic camera system, due to the unpredictability of intrinsic and extrinsic parameters, odometric results are highly dependent on the quality of extra sensors. In this paper, a simple and efficient algorithm is proposed for relative distance estimation in robotic active vision by using a monocular digital camera. Accuracy of the estimation is achieved by judging the 2D perspective projection image ratio of the robot labels obtained on a TFT-LCD (Thin Film Transistor – Liquid Crystal Display) monitor without the need of any additional sensory cost and complicated calibration effort. Further, the proposed algorithm does not contain any trigonometric functions so that it can be easily implemented on an embedded system using the Field Programmable Gate Array (FPGA) technology. Experimental results are included to demonstrate the effectiveness of the technique.
Development of Automatic Pattern Generation System Based on User Impressions
Authors: Akiguchi Shunsuke ,   Toyama National College of Technology, 1-2 Ebie-Neriya, Imizu, Toyama 933-0293, Japan
Published: April 2016

In recent years, many researches trying to make relation between multimedia and information of user’s impression using the soft computing methods has been developed. But most of these researches aimed at developing multimedia search system, classify things into groups, and so on. In this research, we developed automatic pattern generation system based on user impression and suggest to the new application of the soft computing methods.
Role of Pre-Operation in Experiencing Differently Sized Hands
Authors: Terabayashi Kenji ,   Department of Precision Mechanics, Faculty of Science and Engineering, Chuo University, 1-13-27 Kasuga, Bunkyo-ku, Tokyo 112-8551, Japan, Miyata Natsuki , Umeda Kazunori , Ota Jun ,   Digital Human Research Center, National Institute of Advanced Industrial Science and Technology (AIST), 2-3-26 Aomi, Koto-ku, Tokyo 135-0064, Japan,   Research into Artifacts, Center for Engineering (RACE), The University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa, Chiba 277-8568, Japan
Published: April 2016

In this paper, we investigate pre-operations intended to adapt one to hands of controlled sizes when experiencing differently sized hands. Pre-operations are categorized into four types based on the relationship between a hand and an object. To quantify the degree of adaptation to differently sized hands, an index, called “Degree Of Immersion (DOI),” is defined. The index indicates the appropriateness of the observed behavior to the presented hand size. The DOI was measured to compare pre-operations when changing hands in size variously. The experimental comparisons led to two main points: (i) no pre-operation is required with decreasing hand size due to easy adaptation, (ii) a pre-operation, touching and controlling an object in position, is sufficiently effective for adapting larger hands. These points are important for design aid applications to assess usability of designing products by various users.
Robot Group Adaptation Gestures Based on Utterance Content and Social Position
Authors: Katagami Daisuke ,   Department of Applied Computer Science, Faculty of Engineering, Tokyo Polytechnic University, 1583 Iiyama, Atsugi, Kanagawa 243-0297, Japan, Ogawa Ken , Nitta Katsumi ,   Interdisciplinary Graduate School of Science and Engineering, Tokyo Institute of Technology
Published: April 2016

We suggest adaptive gestures for robots adapting to groups based on utterance situations and social position and study the differences in type and number of teaching gestures based on the difference in groups in equivalence utterance. Gesture data is collected in which group members are directly taught to a robot through utterance situations and social position to make adaptation rule sets. Such rule sets are built by gestures to work in the group and group utterances to become common knowledge in the group. The degree of gesture adaptation is higher in a group when gestures are generated from a new text as input, confirming goodness of fit for the rule sets of individual groups through experiments.
Relationship Between Mechadroid Type C3 and Human Beings Based on Physiognomic Features
Authors: Ando Teruaki ,   School of Information Science and Technology, Chukyo University, 101 Tokodachi, Kaizu-cho, Toyota 470-0393, Japan, Araki Atsushi , Kanoh Masayoshi , Tomoto Yutaro , Nakamura Tsuyoshi ,   Graduate School of Engineering, Nagoya Institute of Technology, Gokiso-cho, Showa-ku, Nagoya 466-8555, Japan
Published: April 2016

In this paper, we created random facial expressions for the Mechadroid Type C3, a robot equipped with a high degree-of-freedom facial expression mechanism and which is intended to serve a receptionist function. Investigating the morphological characteristics and physiognomy features of these facial expressions, we evaluated what personality characteristics could be expressed by the face of the C3 and what impressions those facial expressions made on people. As a result, it was found that a baby-schema-cute face, modest face, and smiley face are the most suitable as the physiognomy of a reception robot.
A Fuzzy Multiobjective Particle Swarm Optimized TS Fuzzy Logic Congestion Controller for Wireless Local Area Networks
Authors: Nyirenda Clement N. ,   Department of Computational Intelligence and Systems Science, Tokyo Institute of Technology, G3-49, 4259 Nagatsuta, Midori-ku, Yokohama 226-8502, Japan, Dawoud Dawoud S. , Dong Fangyan , Negnevitsky Michael , Hirota Kaoru ,   School of Electrical, Electronics and Computer Engineering, University of KwaZulu-Natal, King George V Avenue, Durban 4041, South Africa,   School of Engineering, University of Tasmania, Private Bag 65, Hobart, Tasmania 7001, Australia
Published: April 2016

A Takagi-Sugeno Fuzzy Logic Congestion Detection (TSFLCD) mechanism is proposed for IEEE 802.11 wireless Local Area Networks. A Fuzzy Preference based Multi-Objective Particle Swarm Optimization (FPMOPSO) mechanism, for tuning the input membership functions and the output scalars, is also proposed. An online adaptation mechanism that finetunes the output scalars based on system dynamics is implemented. Compared to the Adaptive Random Early Detection (ARED) and the Mamdani inference based Fuzzy Logic Congestion Detection (FLCD) mechanisms, simulation results show that the TSFLCD mechanism leads to more than 40% reduction in packet loss rate. It also leads to more than 25% and up to 14% reductions in jitter and delay respectively for real time traffic. This work lays a foundation for the development of simple multiobjective fuzzy congestion controllers in wireless LANs.
Ranking of DMUs Based on Efficiency Intervals
Authors: Entani Tomoe ,   Kochi University, 2-5-1 Akebono, Kochi, Japan
Published: April 2016

The efficiency of the Interval Data Envelopment Analysis (Interval DEA), we proposed, obtains its bounds from optimistic and pessimistic viewpoints. Intervals represent the uncertainty of given input-output data and the intuitive evaluation of decision makers. The partial order relation that intervals give elements may be complex, especially when elements are numerous. The efficiency measurement we propose combining optimistic and pessimistic efficiency in Interval DEA is comparable because both represent the difference of the analyzed Decision Making Unit (DMU) from the most efficient one. The efficiency measurement is defined as their minimum and determined mainly by pessimistic efficiency. Optimistic efficiency is considered if it is inadequate compared to pessimistic efficiency. Pessimistic efficiency based evaluation resembles natural evaluation and DMUs are arranged linearly.
Fuzzyc-Means Clustering for Data with Clusterwise Tolerance Based onL2- andL1-Regularization
Authors: Hamasuna Yukihiro ,   Research Fellow of the Japan Society for the Promotion of Science, Endo Yasunori , Miyamoto Sadaaki ,   Department of Risk Engineering, Faculty of Systems and Information Engineering, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki 305-8573, Japan
Published: April 2016

Detecting various kinds of cluster shape is an important problem in the field of clustering. In general, it is difficult to obtain clusters with different sizes or shapes by single-objective function. From that sense, we have proposed the concept of clusterwise tolerance and constructed clustering algorithms based on it. In the field of data mining, regularization techniques are used in order to derive significant classifiers. In this paper, we propose another concept of clusterwise tolerance from the viewpoint of regularization. Moreover, we construct clustering algorithms for data with clusterwise tolerance based onL2- andL1-regularization. After that, we describe fuzzy classification functions of proposed algorithms. Finally, we show the effectiveness of proposed algorithms through numerical examples.
Heuristic Algorithm for Attribute Reduction Based on Classification Ability by Condition Attributes
Authors: Kudo Yasuo ,   Graduate School of Engineering, Muroran Institute of Technology, 27-1 Mizumoto, Muroran 050-8585, Japan, Murai Tetsuya ,   Graduate School of Information Science and Technology, Hokkaido University, Kita 14, Nishi 9, Kita-Ku, Sapporo 060-0814, Japan
Published: April 2016

The heuristic algorithm we propose to compute a relative reduct candidate is based on evaluating classification ability of condition attributes. Considering the discernibility and equivalence of objects for condition attributes in relative reducts, we introduce evaluation criteria for condition attributes and relative reducts. The computational complexity of the proposed algorithm isO(|U|2|C|2). Experimental results indicate that our algorithm often generates a relative reduct producing a good evaluation result.
Mean Local Trend Error and Fuzzy-Inference-Based Multicriteria Evaluation for Supply Chain Demand Forecasting
Authors: Dan Jingpei ,   Department of Computational Intelligence and Systems Science, Tokyo Institute of Technology, G3-49, 4259 Nagatsuta, Midori-ku, Yokohama 226-8502, Japan, Xie Fuding , Dong Fangyan , Hirota Kaoru ,   Department of Computer Science, Chongqing University, Chongqing 400044, P. R. China,   Department of Computer Science, Liaoning Normal University, Liaoning Dalian 116029, P. R. China
Published: April 2016

To overcome the inefficiency arising from the separate use of conventional forecast accuracy measures that suffer from the bullwhip effect, especially in uncertain and vague supply chain environments, a forecast accuracy measure, Mean Local Trend Error (MLTE) and a fuzzy-inference-based multicriteria evaluation method are proposed. In contrast to conventional measures, MLTE survives the bullwhip effect by evaluating forecasts based on local trend error. The proposed evaluation method applies fuzzy inference to deal with the uncertainty and vagueness in supply chains and makes a comprehensive evaluation by using an aggregated forecast accuracy index (ACCURACY), which is developed based on fuzzy inference by integrating the proposed MLTE and a conventional measure MAPE, thereby enhancing its efficiency for evaluating supply chain demand forecasts. The proposed MLTE and evaluation method are confirmed by comparative experiments with MAPE based on evaluating four typical forecasting methods – a simple moving average, single exponential smoothing, autoregressive, and autoregressive moving average – on an actual manufacturing-order dataset. The results show that MLTE yields a triple and ACCURACY a quadruple improvement in terms of average distinguishability compared to MAPE. The proposal has potential applications in stock market forecast evaluations.
Analyses of Compound Structures of Groups that Produce Intellectual Property
Authors: Inoue Hiroyasu ,   Osaka Sangyo University, 3-1-1 Nakagaito, Daito-shi, Osaka 574-0013, Japan
Published: April 2016

This paper focuses on collaborations between scientists and engineers and investigates their mutual benefits. More concretely, multi-layered networks separated into four scientific/technological areas are investigated. The areas are life sciences (Bio), nanotechnology/materials (Nano), information and telecommunications (IT), and environmental sciences (Env), and they are mentioned in the third science and technology basic plan issued by the Government of Japan. The networks were then analyzed by using p*models to find compound structures. Logistic regression analysis was conducted, and the compound structures were expressed by explanatory variables. In all four areas, joint authorship and joint application tend to overlap. A role interlocking structure is only found in Bio, and itmeans that a gatekeeper exists between scientific knowledge and technical knowledge. A transitivity structuremeans three-person groups emerge such that a central person publishes papers (or patents) with two other people, and the two other people publish the other outcomes, and patents (or papers). It is found that transitivity is generally not reversible. In Bio and Nano, there is no eminent difference in significance of the two different types of transitivity, but in IT and Env, segregations with a joint application expert and joint authorship support emerge more strongly than the other types of segregations.
Bounded Rationality on Consumer Purchase Decisions and Product Diffusion Under Network Externalities: A Study Using Agent-Based Simulation and Experiments with Human Subjects
Authors: Nishino Nariaki ,   The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan, Oda Sobei H. , Ueda Kanji ,   Kyoto Sangyo University, Motoyama, Kamigamo, Kyoto 603-8555, Japan,   National Institute of Advanced Industrial Science and Technology, 1-1-1 Higashi, Tsukuba, Ibaraki 305-8561, Japan,   The University of Tokyom, 5-1-5 Kashiwanoha, Kashiwa, Chiba 277-8568, Japan
Published: April 2016

This study addresses mechanisms of purchase decision-making under network externalities. In particular, we examine how bounded rationality affects product diffusion in markets where network externalities are present. In response to rapid development of information communication technology, markets with network externalities have been expanding recently. If network externalities are present, then a consumer’s utility depends on the number of same product users. In other words, the product value is determined through consumers’ interaction in society. Therefore, the elucidation of the mechanism is required. For this purpose, this study adopts an integral approach with agent-based simulations and experiments with human subjects. We use a simple decision-making model constructed by Ueda et al. [1] to conduct experiments with human subjects based on the method of experimental economics. Additionally, we construct agents who make the bounded rational actions observed in the experiments and examine how product diffusion emerges through interaction of such bounded rational agents. Results show that bounded rationality of subsequent decision-makers affects the purchase behavior of preceding consumers. Accordingly, the output of product diffusion is varied.
Analyzing the Emergency Restoration Processes of an Electric Power Distribution Network by a Multi-Agent Simulator
Authors: Sagai Shigeo ,   Central Research Institute of Electric Power Industry, 2-11-1 Iwado-Kita, Komae, Tokyo 201-8511, Japan, Mori Toshikatsu , Terano Takao ,   Kozo Keikaku Engineering Inc., 4-38-13 Honcho, Nakano-ku, Tokyo 164-0012, Japan,   Tokyo Institute of Technology, 4259 Nagatsuta-cho, Midori-ku, Yokohama, Kanagawa 226-8503, Japan
Published: April 2016

We have developed a multi-agent simulator, the objective of which is to analyze emergency restoration processes damaged by regional disasters such as earthquakes or typhoons. The restoration simulator facilitates our comparison of multiple emergency restoration procedures to cope with various circumstances of disaster-related damage. In this article, we evaluate the effects of changes of mobile speeds of each worker caused by the road blockades and/or traffic jams during disaster relative to the total emergency restoration time. The main findings are 1) we confirmed that the sensitivity analysis of the total completion time relative to the change of the mobile speed become executable via the use of the restoration simulator, 2) the delay of the emergency restriction could be mitigated by proper restrictions of the road.
Special Issue on Fuzzy Rule Interpolation
Authors: Szilveszter Kov?cs
Published: April 2016

Fuzzy Rule Interpolation (FRI) methods are well known tools for reasoning in case of insufficient knowledge expressed as sparse fuzzy rule-bases. It also provides a simple way to define fuzzy functions. Despite these advantages, FRI techniques are relatively rarely applied in practice. Enabling sparse fuzzy rule-bases, FRI dramatically simplifies rule-base creation. Regardless of whether the rule-base is generated by a human expert, or automatically from input-output data, the ability to provide reasonable interpolated conclusions even if no rule fires for a given observation, help to concentrate on cardinal actions alone. This reduces the number of rules needed, speeds up parameter optimization and validation steps, and hence simplifies rule-base creation itself. This special issuefs six papers take six different directions in current FRI research. The first introduces the FRI concept and sets up a unified criteria and evaluation system. This work collects the main properties an FRI method generally has to fulfill. The next two papers are related to the constantly important mainstream research on the more and more sophisticated FRI methods, the endeavor of finding the best way for defining a fuzzy valued fuzzy function based on data given in the form of the relation of fuzzy sets, i.e., in fuzzy rules. The second paper introduces a novel FRI method that is able to handle fuzzy observations activating multiple rule antecedents applying the concept of nonlinear fuzzy-valued function. The third paper presents a novel ganalogical-basedh FRI method that rather fits into the traditional FRI research line, improving the n-rule-based gscale and move transformationh FRI to ensure continuous approximate functions. The fourth paper addresses the issue of defining a distance function between fuzzy sets on a domain that is not necessarily Euclidean metric space. In FRI, this takes on the importance if antecedent or consequent domains are non-Euclidean metric spaces. The last two papers discuss direct FRI control applications. One is an example proving that the sparse fuzzy rule-base is an efficient knowledge representation in intelligent control solutions. The last deals with the computational efficiency of implemented FRI methods applied to direct control area, clearly showing that the sparse fuzzy rule-base is not only a convenient way for knowledge representation, but also makes FRI methods possible devices for direct embedded control applications.
Compound Distance Function for Similarity Measurement Between Fuzzy Sets
Authors: Kovács László ,   Department of Information Technology, University of Miskolc, H-3515 Miskolc-Egyetemváros, Hungary
Published: April 2016

Distance is usually used to measure the similarity of different objects. The distance function has a key role in clustering, pattern recognition, in information retrieval. The compound real valued distance function on fuzzy sets we propose considers both key aspects of fuzzy sets – membership function and support set.
Performance Optimization of the Fuzzy Rule Interpolation Method “FIVE”
Authors: Vincze Dávid ,   Department of Information Technology, University of Miskolc, Miskolc, Hungary, Kovács Szilveszter
Published: April 2016

Fuzzy Rule Interpolation (FRI) methods are efficient structures for knowledge-representation with relatively few rules. In spite of their good knowledge representation efficiency, their high computational demand makes the FRI methods hardly suitable for embedded real-time applications, for which short reasoning time has a high importance. On the other hand, the fact that currently available devices have increased computational power gives the FRI methods an opportunity to appear in real-time embedded applications. Therefore, the need for a low-computation and lowresource-demand FRI method is emerging. The goal of this paper is to introduce some implementation details of such an FRI method, together with its brief time and space complexity analysis. The paper also gives some hints for further performance optimization possibilities.
Consistent Conjectural Variations Equilibrium in a Mixed Duopoly
Authors: Kalashnykova Nataliya I. ,   FCFM, UANL, San Nicolás de los Garza, N.L. 66450, Mexico, Bulavsky Vladimir A. , Kalashnikov Vyacheslav V. , Pérez Felipe J. Castillo ,   Sumy State University, Sumy, Ukraine 40007,   Central Economics & Mathematics Institute (CEMI), Moscow 117418, Russia,   ITESM, Campus Monterrey, Monterrey, N.L. 64849, Mexico
Published: April 2016

In this paper, we consider a model of mixed duopoly with Conjectured Variations Equilibrium (CVE). The agents’ conjectures concern the price variations depending on the increase or decrease in their production outputs. We establish existence and uniqueness results for the conjectured variations equilibrium (called an exterior equilibrium) for any set of feasible conjectures. To introduce the notion of an interior equilibrium, we develop a consistency criterion for the conjectures (referred to as influence coefficients) and prove the existence theorem for the interior equilibrium (understood as a CVE with consistent conjectures). To prepare the base for the extension of our results to the case of non-differentiable demand functions, we also investigate the behavior of the consistent conjectures in dependence upon a parameter representing the demand function derivative with respect to the market price.
Chanel’s Strategy of Communication Tools and Packaging for its Beauty Products
Authors: Nagasawa Shin’ya ,   Graduate School of Commerce, Waseda University, 1-6-1 Nishi-Waseda, Shinjuku-ku, Tokyo 169-8050, Japan, Sugimoto Kana
Published: April 2016

This paper views Chanel as a technology management company in order to point out its distinguishing characteristics. Technology management refers to business management conducted around sophisticated technological expertise. Although Chanel was a latecomer to the cosmetic business, Chanel’s commitment to technology and innovation is apparent in its establishment of its own laboratory. This commitment will be considered through its cosmetics business as follows. (1) Communication tools: counseling by Beauty Advisors (BA), Customer Relationship Management (CRM), and development of sampling devices (blotter and olfactive bar). (2) Packaging: unified packaging worldwide, use of new materials, and packages exuding a luxury feel.
RTM (Robot Technology Middleware) Based Dynamic Interrupt System for Communication Between Humans and Robots
Authors: Vu Hai An ,   Department 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 , Hirota Kaoru
Published: April 2016

A dynamic fuzzy interrupt system is proposed to optimize responsiveness of robots in humans-robots communication. The system guarantees a continuous and efficient human-robot interaction by assigning priority to the given instruction and it remains immune to distractions caused by conflicting instructions. The system is embedded in the robot technology components that are able to implement by different programming languages, under the different operating system. Experiments to simulate actions of mascot robot system such as the eye robot, mobile robot, and information retrieval engine involving 10,000 instructions show a 14% improvement in the system responsiveness and a delay time of less than 2 seconds. The implementation of home party scenario between four humans (a host and three guests) and five eye robots shows the affective communication, and confirms to facilitate casual communication between humans and robots with limited interruption.
Special Issue on Human-Robot Interaction Systems
Authors: Hashimoto Tomomi , Kagawa Yoshihito
Published: April 2016

Opportunities for human beings and robots to work together are increasing. The objective of this special issuefs ten articles is to activate and expand top-quality research. In the first, T. Muto and Y. Miyake explore interpersonal cooperative walking replicated for two human beings and the motor-control mechanisms involved. In the second, Y. Nishikawa, Y. Kagawa, and J. Kurabayashi study control of healthcare equipment and a manipulator using electromyography (EMG) signals. The third contribution, by T. Hashimoto, Y. Takakura, T. Hamada, T. Akazawa, and M. Yamamoto, details a walking simulator using virtual reality. The fourth, by Y. Tang, H. A. Vu, P. Q. Le, D. Masano, O. Thet, C. Fatichah, Z. Liu, M. Yamaguchi, M. L. Tangel, F. Dong, Y. Yamazaki, and K. Hirota, proposes casual communication between humans and robots by integrating nonverbal gestures and verbal messages. Y. Ho, T. Shibano, E. Sato-Shimokawara, and T. Yamaguchi present a system in the fifth article that uses data mining to detect human intent collecting, processing, and analyzing human motion. In the sixth, K. Rattanyu and M. Mizukawa introduce emotion recognition based on electrocardiography (ECG) signals. In the seventh article, K. Ogawa, S. Nishio, K. Koda, G. Balistreri, T. Watanabe, and H. Ishiguro discusses two field tests using the Telenoid android. The eighth contribution, by H. Suzuki and H. Nishi, shows how animal gaits are implemented by quadrupedal robots. K. Zhang, Y. Maeda, and Y. Takahashi propose multiagent reinforcement learning in the ninth article. The closing contribution, by B. Piriyanont, N. Uchiyama, and S. Sano, proposes collision avoidance control for a quadrocopter based on the social force model concept. We thank the referees for their comprehensive reviews and the Fuji Technology Press Ltd. staff for its encouragement and advice.
Generation Method of Quadrupedal Gait Based on Human Feeling for Animal Type Robot
Authors: Suzuki Hidekazu ,   Department of Electronics and Mechatronics, Faculty of Engineering, Tokyo Polytechnic University, 1583 Iiyama, Atsugi, Kanagawa 243-0297, Japan, Nishi Hitoshi ,   Department of Electronics and Information Engineering, Fukui National College of Technology, Geshi, Sabae, Fukui 916-8507, Japan
Published: April 2016

Characterizing animal movement appropriately is important in pet robot design and Robot-Assisted Therapy (RAT). In studying how to generate animal gaits in quadruped robots, we used the canine AIBO robot. We began by optimizing a single leg path outputting propulsion efficiently and imitating canine walking using a genetic algorithm and a zoological basis. We administered a questionnaire to help determine subjective feelings in observers for choosing the optimum AIBO gait. We corrected minor deviations in joint parameters to stabilize walking on the ground.
Adaptive Control Using an Oscillator Network with Capacitive Couplers
Authors: Moriyama Takuro ,   Tokyo Institute of Technology, 2-12-1 Ookayama, Meguro-ku, Tokyo, Japan, Kurabayashi Daisuke
Published: April 2016

We focus on the oscillatory aspects of neural components and their roles in neural system functionality such as adaptability. This study provides an implementation of adaptive control by using a network system consisting of oscillators and capacitive couplers. The functionality of oscillators alone is fairly limited, but capacitive couplers change interaction between oscillators, enabling logical operations, absolute function, gain variable amplifiers, and adaptive control. We provide basic functional networks of oscillators and capacitive couplers and demonstrate adaptive control implementation based on these functions.
Particle Filter with Gaussian Weighting for Vehicle Tracking
Authors: Siradjuddin Indah Agustien ,   Faculty of Engineering, Trunojoyo University, Telang Raya Street, Kamal Bangkalan, Madura Island, East Java, Indonesia, Widyanto Muhammad Rahmat ,   Faculty of Computer Science, University of Indonesia, Depok Campus, West Java, Indonesia
Published: April 2016

To track vehicle motion in data video, particle filter with Gaussian weighting is proposed. This method consists of four main stages. First, particles are generated to predict target’s location. Second, certain particles are searched and these particles are used to build Gaussian distribution. Third, weight of all particles is calculated based on Gaussian distribution. Fourth, particles are updated based on each weight. The proposed method could reduce computational time of tracking compared to that of conventional method of particle filter, since the proposed method does not have to calculate all particles weight using likelihood function. This method has been tested on video data with car as a target object. In average, this proposed method of particle filter is 60.61% times faster than particle filter method meanwhile the accuracy of tracking with this newmethod is comparable with particle filter method, which reach up to 86.87%. Hence this method is promising for real time object tracking application.