bluuebunny/crossref_metadata_2025 huggingface dataset:
Spatial Memory for Augmented Personal Working Environments
Authors: Song Young Eun ,   The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8654, Japan, Kovacs Peter , Niitsuma Mihoko , Hashimoto Hideki ,   Computer and Automation Research Institute, Hungarian Academy of Sciences, Kende u. 13-17, Budapest 1111, Hungary,   Department of Precision Mechanics, Chuo University, 1-13-27 Kasuga, Bunkyo-ku, Tokyo 112-8551, Japan,   Department of Electrical, Electronic and Communication Engineering, Chuo University, 1-13-27 Kasuga, Bunkyo-ku, Tokyo 112-8551, Japan
Published: April 2016

Augmented Personal Working Environments (APWEs) are 3D environments in which the physical surroundings of the user are overlaid with representations of a virtual reality. With the rapid technological evolution of personal informatics devices as well as a growing demand for more comfortable and efficient working environments, the partial virtualization of resources used in our everyday work settings is expected to gradually become inevitable. Irrespective of whether someone is working in an office environment or in industrial settings, this trend in virtualization is expected to lead to more collaborative working environments in which the available resources and the interfaces for dealing with those resources can be both physical and virtual in nature. SpatialMemory, which is a memory system embedded in 3-dimensional physical reality, may without doubt be a central subsystem of future APWEs. In this paper, our goal is to contribute to the development of a theoretical background for Spatial Memory from a cognitive infocommunications perspective, and to outline the future research directions of Spatial Memory in APWEs based on some key applications.
Investigation About Applicability of Personal Values for Recommender System
Authors: Hattori Shunichi ,   Tokyo Metropolitan University, 6-6 Asahigaoka, Hino, Tokyo 191-0065, Japan, Takama Yasufumi
Published: April 2016

A recommender systemis a fundamental technique for finding information that is likely to be preferred by users among vast amounts of information. While existing recommender systems usually employ user preference or attributes of items to make recommendations, marketing fields have been taking notice of personal values, because that such values are significantly related to user preference. This paper investigates the applicability of personal values in modeling items and users. The results of questionnaires show the feasibility of a recommender system based on personal values.
The Analysis of Consumer Purchasing Behavior on Cosmetics
Authors: Wu Ya-Ling ,   Department of Applied English, National Chin-Yi University of Technology, 57 Zhongshan Rd. Sec. 2, Taiping District, Taichung 41170, Taiwan, Chen You-Shyang ,   Department of Information Management, Hwa Hsia Institute of Technology, 111 Gong Jhuan Rd., Chung Ho District, New Taipei City 235, Taiwan
Published: April 2016

Consumers have different purchasing behaviors. In order to understand their differences, the individual characters and background are the main factors to be analyzed. This study investigated the consumer purchasing behaviors on cosmetics in two different consumer groups: the counter cosmetic consumers and the open-stack cosmetic consumers. The participants were selected from a famous commercial district. The results revealed the differences on fashion, brand, service, quality and price between the two consumer groups. In addition, the demographic analysis was provided.
Coalition Formation Based Staffing Strategy Development
Authors: Miyata Mayuko ,   Aoyama Gakuin University, 5-10-1 Fuchinobe, Chuo-ku, Kanagawa 252-5258, Japan, Sung Shao-Chin
Published: April 2016

In this paper, we propose game theoretical models for developing staffing strategies, i.e., strategies which support managers’ decision making on hiring, head hunting, staff reassignment, and implementation of staff development policy in enterprises. Our staffing models are hedonic coalition formation games with newly proposed stability concepts calledinvitation based stabilities, in which players’ activities of changing their coalitions are motivated based on invitation from other coalitions. These stabilities capture behaviors of managers, employees, and contractors depending on the balance of power in business situations. We analyze the existence of stable coalition structures under invitation based stabilities, and provide several sufficient conditions with natural interpretations as staffing strategies.
Tournament Evaluation System Applying Win-Lose Result Presumption Considering Kansei Evaluation by Multiple People
Authors: Takenouchi Hiroshi ,   Graduate School of Science and Engineering, Kansai University, 3-3-35 Yamate-cho, Suita-shi, Osaka 564-8680, Japan, Tokumaru Masataka , Muranaka Noriaki ,   Faculty of Engineering Science, Kansai University, 3-3-35 Yamate-cho, Suita-shi, Osaka 564-8680, Japan
Published: April 2016

We describe an Interactive Genetic Algorithm (IGA) with tournament evaluation for applying win-lose results obtained from an evaluation using multiple people. In our previous study, we developed an IGA with tournament evaluation as a basic model for evaluating candidate solutions using votes from multiple people [13]. However, tournament evaluation requires that IGA users evaluate the same candidate solution multiple times. Therefore, our previous method can reduce a user’s motivation for evaluating the solution. In addition, the number of users participating in a vote may decrease because of the decreasedmotivation to evaluate. To overcome this difficulty, we propose the application of a win-lose result presumption based on the tournament evaluation records of multiple people. When a system-based presumption is possible, the win-lose result presumption automatically determines the preferred and non-preferred candidates in each round. This method can reduce the number of times that users need to evaluate the same candidate solution. The effectiveness of the proposed method is verified using a numerical simulation that employs multiple numerical evaluation agents instead of human evaluators. The simulation results show an initial convergent improvement with the proposed method.
Division of Iterative-Transportation Based on Local Observation by Multiple Mobile Robots
Authors: Kobayashi Yuichi ,   Tokyo University of Agriculture and Technology, 2-14-16 Naka-cho, Koganei, Tokyo 184-8588, Japan, Sato Yuta , Gouko Manabu ,   The Tokyo Electric Power Company, Incorporated, 1-1-3 Uchisaiwaicho, Chiyoda-ku, Tokyo, Japan,   Tohoku Gakuin University, 1-13-1 Chuo, Tagajo, Miyagi 985-8537, Japan
Published: April 2016

This paper deals with a framework of decentralized approach to division of labor by multiple mobile robots. An iterative-transportation task by multiple robots with multiple sets of starts (pick-up place of the object) and goals (put down place) is considered as the task. On each route between a start and a goal, the efficiency of transportation improves when the number of robots increases. Due to jams, however, excessive number of robots on the same route causes inefficiency. We propose a control law of each robot to choose an appropriate route so as to optimize the total efficiency of the transportation, where each robot utilizes information which can be obtained only by local observation (without any explicit communication among robots). The proposed control is based on the estimation of the number of robots on the routes in the future. In simulation, it was verified that the proposed control law realized 96% efficiency of the fully centralized control by appropriately choosing the route, compared with the case where global information can be utilized.
Semi-Qualitative Trend Analysis for the Monitoring of Process Control Loops
Authors: Yamashita Yoshiyuki ,   Department of Chemical Engineering, Tokyo University of Agriculture and Technology, 2-24-16 Naka-cho, Koganei, Tokyo 184-8588, Japan
Published: April 2016

This paper first introduces a semi-qualitative methodology to analyze a time trend based on a qualitative description with the addition of quantitative attributes. Using this methodology, it further defines a method to diagnose and qualify a control valve problem in a process control loop. Finally, the defined method of diagnosis successfully applied to several actual data sets from industrial chemical plants.
360 Degree Selective Laser Trabeculoplasty in Mexican Population
Authors: Roberto-Lozano Josué ,   Global Glaucoma Institute, 2428 Hidalgo st., Obispado, Monterrey, Nuevo León, Mexico, Kalashnikova Mariya V. , Velasco-Gallegos Gustavo , Watada Junzo ,   Hospital San José, 2428 Hidalgo st., Obispado, Monterrey, Nuevo León, Mexico,   Graduate School of Information, Production and Systems, Waseda University, 2-7 Hibikino, Wakamatsu-ku, Kitakyushu, Fukuoka 808-0135, Japan
Published: April 2016

The principal objective of the paper was to determine the safety and effectiveness of Selective Laser Trabeculoplasty (SLT) as applied to Mexican patients with Open Angle Glaucoma (OAG) according to the degree of Intraocular Pressure (IOP) reduction and the patient’s required medication. We used the following materials and methods: patients with OAG who underwent 360° SLT and either were under maximum tolerated topical medical therapy or had unwanted side effects were accepted as objects. The selected patients were subject to IOP measurement with 2 different tonometry methods prior to the SLT procedure and to the follow-up measurements on the third, sixth, and twelfth month after the procedure. In order to evaluate the efficiency of the SLT procedure, statistical analysis making use of the Repeated Measures One-Sample t-test, Paired-Samples t-test + Pearson Correlation Coefficient test, and Wilcoxon Signed-Rank test, was implemented. The main result of our research is: the desired criteria were met by twenty seven patients, who represent a sample of 50 eyes. Based upon this data, we can conclude that the treatment with SLT applied to Mexican patients with OAG reduces IOP and the need for topical medical therapy, which can lead to better control of the disease and compliance with treatment.
The Cluster-Weighted DEMATEL with ANP Method for Supplier Selection in Food Industry
Authors: Shen Jung-Lu ,   Central South University, 932 Lushan South Road, Changsha, Hunan 410083, China, Liu Yong-Mei , Tzeng Yi-Lin ,   National Taichung University of Education, 140 Min-Shen Road, TaiChung 40306, Taiwan
Published: April 2016

In terms of keeping material costs low and increasing competitive advantage, supplier selection is one of the most important functions of a business. This paper uses an effective solution based on a combined Analytic Network Process (ANP) and Decision-Making Trial and Evaluation Laboratory (DEMATEL) approach to find the criteria that are key to performance improvement. This paper surveys the multicriteria supplier selection approaches and reveals the most popular criteria and sub-criteria. Based on experts’ suggestions and the most popular criteria, the key factors in the selection of food industry suppliers are identified. Then, the causal relationships and relative importance weights of the criteria in the system are computed using the cluster-weighted DEMATEL with ANP method. This paper compares the results of experts and purchasing managers and discovers a cognitive gap. The managers agree that cost and delivery are very important; they need to keep material costs down and increase the company’s competitive advantage. The experts value customer satisfaction and Corporate Social Responsibility (CSR), so they feel that quality and service are the most important criteria.
Japanese Economic Analysis by Possibilistic Regression Model Building Through Possibility Maximization
Authors: Yabuuchi Yoshiyuki ,   Faculty of Economics, Shimonoseki City University, 2-1-1 Daigaku-cho, Shimonoseki, Yamaguchi 751-8510, Japan, Watada Junzo ,   Graduate School of Information, Production and Systems, Waseda University, 2-4 Hibikino, Wakamatsu-ku, Kitakyushu, Fukuoka 808-0196, Japan
Published: April 2016

A possibilistic regression model illustrates the potential possibilities inherent in the target system by including all data in the model. Tanaka and Guo employ exponential possibility distribution to build a model, while Inuiguchi et al. and Tajima are independently working on coinciding between the center of a possibility distribution and the center of a possibilistic regression model. Typically, samples influence and distort the shape of the model if they are far from the center of data. Yabuuchi and Watada have developed a model for describing the system possibility using the center of a possibilistic fuzzy regression model and an approach that mends the distortion of the model. The objective of this paper is to analyze the Japanese economy using our model, and to show the usefulness of our model by analysis results.
Topic Tracking Based on Identifying Proper Number of the Latent Topics in Documents
Authors: Serizawa Midori ,   Advanced Sciences, Graduate School of Humanities and Sciences, Ochanomizu University, 2-1-1 Ohtsuka, Bunkyo-ku, Tokyo 112-8610, Japan, Kobayashi Ichiro
Published: April 2016

In this paper, we propose a method for detecting and tracking topics of newspaper articles based on the latent semantics of the documents. We use Latent Dirichlet Allocation (LDA) to extract latent topics. In using LDA, we have to provide the number of latent topics in target documents in advance. To do so, perplexity is widely used as a metric for estimating the number of latent topics in documents. As a solution, we estimate the number of latent topics without any prior information in the case of using Hierarchical Dirichlet Process LDA (HDP-LDA). We propose a method to estimate the number of latent topics in target documents based on calculating the similarity among extracted topics, and conduct an experiment with three data sets to compare the method with the above two representative methods, i.e., HDP-LDA and LDA using perplexity. From experimental results, we confirmed that our method can provide results similar to that of HDP-LDA. We also detect and track topics by means of our proposed method and confirm that our method is useful.
Process Estimation of Word-of-Mouth Information Spread Based on Ad Hoc Communications
Authors: Katagami Daisuke ,   Faculty of Engineering, Tokyo Polytechnic University, 1583 Iiyama, Atsugi, Kanagawa 243-0297, Japan, Takei Mizuki , Nitta Katsumi ,   Interdisciplinary Graduate School of Science and Engineering, Tokyo Institute of Technology, J2-53, 4259 Nagatsuta-cho, Midori-ku, Yokohama 226-8502, Japan
Published: April 2016

We focus on the information spread in ad hoc communications, and propose a method of estimating process of word-of-mouth information spread based on analysis of the human network generated by using a contact history among people. This method extracts the cluster structure of people which changes according to the time-series and identifies the clusters including the people which transmitted information. The results of the experiments which applied the proposal method to the data generated by using an agent based simulation method shows that it becomes possible to estimate the information spread process from a connection among the clusters in the human network.
A Combined Method Based on SVM and Online Learning with HOG for Hand Shape Recognition
Authors: Shimada Kazutaka ,   Department of Artificial Intelligence, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502, Japan, Muto Ryosuke , Endo Tsutomu
Published: April 2016

In this paper, we propose a combined method for hand shape recognition. It consists of Support Vector Machines (SVMs) and an online learning algorithm based on the perceptron. We apply HOG features to each method. First, our method estimates the hand shape of an input image by using SVMs. Here, an online learning method with the perceptron uses an input image as new training data if the image is effective in relearning in the recognition process. Next, we select a final hand shape from the outputs of SVMs and perceptrons by using the score from SVMs. The combined method with the online perceptron is robust against unknown users because it contains a relearning process for the current user. Therefore applying the online perceptron leads to an improvement in accuracy. We compare the combined method with a method that uses only SVMs. Experimental results show the effectiveness of the proposed method.
Human-Machine Cooperation in an iSpace Robot Room
Authors: Tusor Balázs ,   Integrated Intelligent Systems Japanese-Hungarian Laboratory, Óbuda University, Népszínház u.8., H-1081 Budapest, Hungary, Várkonyi-Kóczy Annamária R. , Klie Gábor , Kocsis Gábor ,   Institute of Mechatronics and Vehicle Engineering, Óbuda University, Népszínház u.8., H-1081 Budapest, Hungary
Published: April 2016

Nowadays, with the never unseen spreading of computer controlled applications, the usage of smart environment systems that aim to improve the living conditions and quality of everyday life are gaining more and more importance. Intelligent Space (iSpace) based systems are good examples: they strive to create an intelligent, comfortable environment for higher quality, natural and easy to follow lifestyle. The goal of this paper is to present a research that focuses on developing a new Intelligent Space application that is able to comprehend and execute commands given by human users. The presented solution is also able to learn commands that are given periodically under specific conditions and execute them if the conditions occur.
An Adaptation System in Unknown Environments Using a Mixture Probability Model and Clustering Distributions
Authors: Phommasak Uthai ,   Division of Information and Electronics, Graduate School of Engineering, Muroran Institute of Technology, 27-1 Mizumoto, Muroran, Hokkaido 050-8585, Japan, Kitakoshi Daisuke , Shioya Hiroyuki ,   Department of Information Engineering, Tokyo National College of Technology, 1220-2 Kunugida-machi, Hachioji-shi, Tokyo 193-0997, Japan
Published: April 2016

Adaptation to dynamic environments is required in an agent system using Reinforcement Learning (RL). A mixture model of Bayesian network was introduced into the learning system for quickly adapting to such environments. This increases the computational complexity for training the parameters of the system. Therefore, reducing such complexities is necessary when there are limitations in the processing resources. In this paper, we introduce a mixture probability into RL for allowing an agent to adjust to environmental changes. We also introduce a new clustering method that enables one to select fewer elements of the mixture probability in order to reduce the computational complexity and simultaneously maintain the system’s performance. Computer simulations are presented to investigate the effectiveness of our proposed method.
Localization with a Mobile Anchor Using ABC-GA Hybrid Algorithm in Wireless Sensor Networks
Authors: Qi Rongbin ,   Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai 200237, China, Li Sijin , Ma Tianyi , Qian Feng ,   Department of Computer Science and Technology, Shanghai Business School, Shanghai 200235, China
Published: April 2016

Nodes localization plays an important role in applications of wireless sensor networks. In this paper, a localization scheme with a mobile anchor using a hybrid algorithm (ABC-GA) which combines Artificial Bee Colony (ABC) algorithm with the advantages of Genetic Algorithm (GA) is proposed. The localization scheme determines location of unknown node by the mobile anchor. It has high accuracy without any additional requirements for the hardware of unknown node. The core problem of the scheme is to find the shortest path to traverse all unknown nodes by the proposed hybrid algorithm. Simulation results show that ABC-GA hybrid algorithm has high convergence rate and strong global search capability, and the accuracy of localization scheme is satisfactory.
Rebo: A Pet-Like Strokable Remote Control
Authors: Kobayashi Kazuki ,   Graduate School of Science and Technology, Shinshu University, 4-17-1 Wakasato, Nagano City 380-8553, Japan, Yamada Seiji , Nakagawa Shinobu , Saito Yasunori ,   National Institute of Informatics, 2-1-2 Hitotsubashi, Chiyoda, Tokyo 101-8430, Japan,   SOKENDAI, Shonan Village, Hayama, Kanagawa 240-0193, Japan,   Tokyo Institute of Technology, 4259 Nagatsuta-cho, Midori-ku, Yokohama, Kanagawa 226-8503, Japan,   Design Department, Osaka University of Arts, 469 Higashiyama, Kanan-cho, Minami Kawachi-gun, Osaka 585-8555, Japan,   Faculty of Engineering, Shinshu University, 4-17-1 Wakasato, Nagano City 380-8553, Japan
Published: April 2016

This paper describes a pet-like remote control called Rebo for home appliances and TVs. Rebo has three new advantages over conventional remote controls: user-friendliness, function awareness, and functional manipulation by stroking touch panels. Its pet-like presence and facial expressions make it seem friendly to users. Its function awareness makes users easily aware of its functions through expressive feedback that informs them of the meaning of their manipulations by showing part of the function that is to be executed. The ability to manipulate its functions by stroking it like one would do a pet also enables users to use Rebo without having to look for buttons to push. We conducted experiments in which we monitored the eye movements of users while they operated Rebo and another remote control and administered questionnaires to users afterwards. The experimental results revealed significant aspects of Rebo and confirmed its advantages.
Entropy-Regularized Fuzzy Clustering for Non-Euclidean Relational Data and Indefinite Kernel Data
Authors: Kanzawa Yuchi ,   Shibaura Institute of Technology, 3-7-5 Toyosu, Koto, Tokyo 135-8548, Japan
Published: April 2016

In this paper, an entropy-regularized fuzzy clustering approach for non-Euclidean relational data and indefinite kernel data is developed that has not previously been discussed. It is important because relational data and kernel data are not always Euclidean and positive semi-definite, respectively. It is theoretically determined that an entropy-regularized approach for both non-Euclidean relational data and indefinite kernel data can be applied without using a β-spread transformation, and that two other options make the clustering results crisp for both data types. These results are in contrast to those from the standard approach. Numerical experiments are employed to verify the theoretical results, and the clustering accuracy of three entropy-regularized approaches for non-Euclidean relational data, and three for indefinite kernel data, is compared.
A Dynamic Risk Allocation of Value-at-Risks with Portfolios
Authors: Yoshida Yuji ,   Faculty of Economics and Business Administration, University of Kitakyushu, 4-2-1 Kitagata, Kokuraminami, Kitakyushu 802-8577, Japan
Published: April 2016

A mathematical dynamic portfolio model with uncertainty is discussed by use of value-at-risks. The risk criterion is composed by the sum of unexpected shortterm risks which occur suddenly in each period. By dynamic programming approach, we derive an optimality condition for the optimal value-at-risk portfolio in a stochastic decision process. It is shown that the optimal value-at-risk is a solution of the optimality equation under a reasonable assumption, and an optimal trading strategy is obtained from the equation. A numerical example is given to illustrate our idea.
A Variable Step Size Incremental Conductance Direct MPPT Method for Stand-Alone PV Systems
Authors: Cho Jae-Hoon ,   Smart Logistics Technology Institute, Hankyong National University, 327 Chungang-ro, Anseong-si, Gyeonggi-do 456-749, Korea, Park Jin-Il , Hong Won-Pyo , Chun Myung-Geun ,   Department of Building Science and Plant Engineering, Hanbat National University, 16-1 Dukmyung-dong, Yuseong-gu, Daejeon 305-719, Korea,   College of Electrical and Computer Engineering, Chungbuk National University, 410 Seongbong-ro, Cheongju, Chungbuk 361-763, Korea
Published: April 2016

This paper presents a variable step size incrementalconductance direct Maximum Power Point Tracking (MPPT) method using fuzzy membership for a standalone photovoltaic (PV) system under rapidly changing irradiation. MPPT techniques have been widely applied in PV systems to make a PV array generate maximum power, which depends on solar irradiation. In most applications of MPPT technologies, MPPT algorithm design methods are performed and tested under slowly changing atmospheric conditions such as irradiation and temperature. The short-term effect under rapidly changing irradiation should be considered, however, to improve the dynamic performance of PV system. Our proposed MPPT method is based on an incremental conductance algorithm with a direct control scheme that can directly adjust the duty cycle for the PI controller. A fuzzy membership function is adopted to determine the variable step size according to rapidly changing irradiation. The proposed methods thus has not only faster dynamic performance but also high tracking accuracy. In order to show the effect of the proposed method, the simulation model and proposed MPPT is designed with MATLAB/Simpower and simulated with MATLAB/Stateflow.
Improved Approach for Maximizing Reliability in Fault Tolerant Networks
Authors: Kaushik Baijnath ,   Punjab Technical University, PTU-Kapurthala Highway, Near Pushpa Gujral Science City, Kapurthala-144601, India, Kaur Navdeep , Kohli Amit Kumar ,   Computer Science Department, Chandigarh Engineering College, Landran, Mohall, Punjab, India,   Electronics & Communication Engineering (ECED), Thapar University, P.O. Box 32, Patiala, Pin-147004, India
Published: April 2016

The objective of this paper is to present a novelmethod for achievingmaximumreliability in fault-tolerant optimal network design when networks have variable size. Reliability calculation is a most important and critical component when fault-tolerant optimal network design is required. A network must be supplied with certain parameters that guarantee proper functionality and maintainability in worse-case situations. Many alternative methods for measuring reliability have been stated in the literature for optimal network design. Most of these methods, mentioned in the literature for evaluating reliability, may be analytical and simulation-based. These methods provide significant ways for computing reliability when a network has a limited size. Significant computational effort is also required for growing variable-sized networks. A novel neural network method is therefore presented to achieve significant high reliability in fault-tolerant optimal network design in highly growing variable networks. This paper compares simulation-based analytical methods with improved learning rate gradient descent-based neural network methods. Results show that improved optimal network design with maximum reliability is achievable by a novel neural network at a manageable computational cost.
Infinite-Level Interpolation for Inference with Sparse Fuzzy Rules: Fundamental Analysis Toward Practical Use
Authors: Uehara Kiyohiko ,   Ibaraki University, 4-12-1 Nakanarusawa-cho, Hitachi 316-8511, Japan, Hirota Kaoru ,   Tokyo Institute of Technology, G3-49, 4259 Nagatsuta, Midori-ku, Yokohama 226-8502, Japan
Published: April 2016

Infinite-level interpolation is proposed for inference with sparse fuzzy rules. It is based onmulti-level interpolation where fuzzy rule interpolation is performed at a number of multi-level points. Multi-level points are defined by the bounds of α-cuts of each given fact. As a feasibility study, fundamental analysis is focused on in order to theoretically derive convergent consequences in increasing the number of the levels of α for the α-cuts. By increasing the number of the levels, nonlinear mapping by the inference is made more precise in reflecting the distribution forms of sparse fuzzy rules to consequences. The convergent consequences make it unnecessary to examine the number of the levels for improving the mapping accuracy. It is confirmed that each of the consequences deduced with simulations converges to one theoretically derived with an infinite number of the levels of α. It is thereby proved that the fundamental analysis has its validity. Toward the practical use of the convergent consequences, further discussions may be possible to extend the fundamental analysis, considering practical conditions.
A Mechanism Design for Managing Emissions in Energy Supply Sector
Authors: Nagasaka Ken ,   Department of Electrical and Electronics Engineering, Tokyo University of Agriculture and Technology, 2-24-16 Nakamachi, Koganei-shi, Tokyo 184-8588, Japan, Takamori Hiroshi , Go Eiroku ,   Waseda University Environmental Research Center, 2-7-10 Misaki-cho, Chiyoda-ku, Tokyo 101-0061, Japan,   CSD Corporation, 3-2-1 Sakato, Takatsu-ku, Kawasaki-shi, Kanagawa, Japan
Published: April 2016

The strategic design for a program for managing the commons involves devising a scheme where those regulated are not trapped into inefficient equilibrium of moral hazardous behavior. This paper studies the auctionmechanism for allocating allowance or licenses for CO2 emission and also pricing transferable allowance. For the sake of clear presentation, we use a behavioral model of an electricity supplier firm to define key ingredients in the auction mechanism. Program implementation is evaluated by simulating the firm’s response to various levels of constraint under the program.
Incorporating Renewable Energy Resources in a Smart Grid with Power Line Communication Using Matlab/Simulink
Authors: Sarker Md. Atikur Rahman ,   Department of Electrical and Electronics Engineering, Tokyo University of Agriculture and Technology, 2-24-16 Nakacho, Koganei, Tokyo 184-8588, Japan, Ueno Yuki , Chu Chi-Hung Kelvin , Nagasaka Ken ,   Department of Energy Management, Electric Research Planning, Con Edison, Inc., 116 Persimmon Lane, Holmdel, NJ 07733, USA
Published: April 2016

This paper proposes smart grid configurations that incorporate renewable resources, such as solar and wind power, using Power Line Communication (PLC). PLC and renewable energy connected to the grid are two promising applications for power system. Smart grid combines modern information communication technologies with existing power grid to enable power flow and information in all directions. The implementation of smart grid technology to a rural area promotes the utilization of renewable resources such as photovoltaic (PV) and wind turbines. In this paper, a grid-connected smart grid was implemented for a small community in Sitakunda, Bangladesh. The developed model is simulated using the Matlab/Simulink program. Finally, the energy flows between the renewable energy system and local supplying network are analyzed and optimized.
Proposal of a New Recommendation System that Addresses “Personalizability”
Authors: Yoshikawa Tomohiro ,   Nagoya University, Furo-cho, Chikusa-ku, Nagoya 464-8601, Japan, Mori Takafumi , Furuhashi Takeshi ,   Brother Industries, Ltd., 15-1 Naeshiro-cho, Mizuho-ku, Nagoya 467-8561, Japan
Published: April 2016

Users’ accessibility to a tremendous amount of information has increased because of the recent development of various media platforms and the resulting spread of the Internet. Alternatively, however, users are finding it increasingly difficult to locate meaningful data. “Recommending” information has become a popular field of study. A variety of recommendation systems are currently in use. “Accuracy” is the main performance index used to assess these systems. However, other evaluation indexes have been proposed. One particular index, “Serendipity,” provides a description of each recommended item’s newness and states whether each item contains an element of surprise. In this paper, we provide a definition of “Personalizability,” one of several factors that must be considered during an evaluation of “Serendipity.” In addition, we propose a recommendation method that employs “Personalizability,” and we apply the proposed method to benchmark data. We demonstrate that “Personalizability” can be used to tweak “Accuracy.” We also demonstrate how this method improves on conventional methods in “Personalizability.”
Diagnosis System for Predicting the Centrosome Hyperamplification in Bladder Cancer by Using DNA Microarray Data
Authors: Tokunaga Kazuhiro ,   Fuzzy Logic Systems Institute, 680-41 Kawazu, Iizuka, Fukuoka 820-0067, Japan, Kubosaka Fumiya , Suetake Noriaki , Uchino Eiji , Matsuyama Hideyasu ,   Graduate School of Science and Engineering, Yamaguchi University, 1677-1 Yoshida, Yamaguchi 753-8512, Japan,   Graduate School of Medicine, Yamaguchi University, 1-1-1 Minami-kogushi, Ube 755-8505, Japan
Published: April 2016

In this paper, we propose a Computer Aided Diagnosis (CAD) system using a DNA microarray data for an early detection of a bladder cancer. In previous works, it is reported that one of the generating factors of the cancer cells is a centrosome hyperamplification. The centrosome hyperamplification is caused by a damage of DNA. Therefore, it is possible to detect the cancer by using the DNA microarray data. In this paper, the CAD system implemented based on a stochastic approach is proposed. The effectiveness of the present system is verified by the actual experiments.
Conditions to Diffuse Green Management into SMEs and the Role of Knowledge Support: Agent-Based Modeling
Authors: Zaima Keiko ,   Kyoto Sangyo University, Kamigamo-motoyama, Kita-ku, Kyoto 603-8555, Japan
Published: April 2016

Spreading green management among Small and Medium-sized Enterprises (SMEs) is an important environmental policy issue. The objective herein is to design an agent-based model to analyze the conditions that need to be met for the diffusion of green management into SMEs. Our model belongs to the agentbased models related to the diffusion of innovation. One remarkable feature of the basic model is that it includes two related markets for a product and its component, in which there are transactions between consumers and Large Enterprises (LEs) and between LEs and SMEs. Although the basic model is not concerned with a specific product, settings of the model are based on the existing empirical researches on SMEs in machine & metal and plastic processing industries. Simulation results of the basic model reveal effective strategies of enterprises. LEs which raise eco-levels faster than others can acquire profits. SMEs which lower prices faster than others can also improve financial resource. The sensitivity analysis shows that the cost condition is an important factor for the diffusion of green management into SMEs. We suggest that knowledge support to SMEs can take a role in order to reduce environmental cost, based on the results of previous empirical research. An applied model concerning knowledge support is also presented. Simulation results of the applied model reveal that information provision from the external organizations to SMEs could be effective for the diffusion of green management.
Distance Measure for Symbolic Approximation Representation with Subsequence Direction for Time Series Data Mining
Authors: Li Tianyu ,   Department of Computational Intelligence & Systems Science, Tokyo Institute of Technology, G3-49, 4259 Nagatsuta, Midori-ku, Yokohama 226-8502, Japan, Dong Fang-Yan , Hirota Kaoru
Published: April 2016

A distance measure is proposed for time series data mining based on symbolic aggregate approximation (SAX) with direction representation. It aims at increasing lower bound tightness to Euclidean distance and decreasing the error rate of time series data mining tasks by adding the time series subsequence direction factor to original SAX. Experiments on public University of California, Riverside (UCR) time series datasets, which contain various time series data with diverse type, length, and size, demonstrate that the tightness of the proposed distance measure increases 17.54% on average when compared with that of original SAX, and classification error rates on SAX with direction representation are reduced by 16.22% in comparison with that of results obtained by original SAX. The proposed approach lowers the classification error rate and could be applied to other time series data mining tasks, such as clustering, query by content, and motif discovery.
Adapting Multi-Robot Behavior to Communication Atmosphere in Humans-Robots Interaction Using Fuzzy Production Rule Based Friend-Q Learning
Authors: Chen Lue-Feng ,   Department of Computational Intelligence and Systems Science, Tokyo Institute of Technology, G3-49, 4259 Nagatsuta, Midori-ku, Yokohama, Kanagawa 226-8502, Japan, Liu Zhen-Tao , Dong Fang-Yan , Yamazaki Yoichi , Wu Min , Hirota Kaoru ,   Department of Electrical, Electronic, and Information Engineering, Kanto Gakuin University, 1-50-1 Mutsuura-higashi, Kanazawa-ku, Yokohama, Kanagawa 236-8501, Japan,   School of Information Science and Engineering, Central South University, Yuelu Mountain, Changsha, Hunan 410083, China
Published: April 2016

A behavior adaptation mechanism in humans-robots interaction is proposed to adjust robots’ behavior to communication atmosphere, where fuzzy production rule based friend-Q learning (FPRFQ) is introduced. It aims to shorten the response time of robots and decrease the social distance between humans and robots to realize the smooth communication of robots and humans. Experiments on robots/humans interaction are performed in a virtual communication atmosphere environment. Results show that robots adapt well by saving 44 and 482 learning steps compared to that by friend-Q learning (FQ) and independent learning (IL), respectively; additionally, the distance between human-generated atmosphere and robot-generated atmosphere is 3 times and 10 times shorter than the FQ and the IL, respectively. The proposed behavior adaptation mechanism is also applied to robots’ eye movement in the developing humans-robots interaction system, calledmascot robot system, and basic experimental results are shown in home party scenario with five eye robots and four humans.
Development of a Nutritional Management System for a Healthy Eating Habits Support System
Authors: Tokumi Yuta ,   Graduate School of Kansai University, 3-3-35 Yamate-cho, Suita-shi, Osaka 564-8680, Japan, Hakamata Junki , Tokumaru Masataka ,   Kansai University, 3-3-35 Yamate-cho, Suita-shi, Osaka 564-8680, Japan
Published: April 2016

In this study, we propose a Nutritional Management System (NMS) that optimizes nutritional balance using a tabu search. Contemporary recipe retrieval systems generally retrieve a recipe either by using a keyword or by recommending a popular recipe. However, these recipe retrieval systems yield the same retrieval results for different users, and thus, the results do not necessarily reflect an individual user’s tastes. In addition, the search results delivered by many recipe retrieval systems do not always describe the nutritional details of the recipes. Therefore, we developed a Healthy Eating Habits Support System for resolving these issues. This system is composed of an NMS and a Kansei Retrieval System (KRS). The NMS retrieves nutritionally balanced menus using a tabu search. The KRS learns a user’s taste preferences, and selects menus suitable for a user’s tastes from among the menus retrieved by the NMS. The KRS needs multiple nutritionally balanced menus to learn a user’s taste preferences. Thus, in this study, we simulated scenarios to examine whether the NMS can retrieve multiple nutritionally balanced menus in the long-term without duplication, using quasi recipe data and real recipe data. When we used quasi recipe data in a simulation with a very large number of recipes, the NMS could retrieve a sustained quantity of menus in the long-term. In addition, when we used real recipe data, the NMS could quickly retrieve menus over the long-term. Therefore, the effectiveness of the NMS was confirmed.
Fuzzy Control of Target Approaching and Object-Grabbing for a Four-Wheeled Vision-Based Mobile Robot
Authors: Wu Chih-Hung ,   Department of Electrical Engineering, National University of Kaohsiung, 700 Kaohsiung Univ. Road, Nan-Tzu, Kaohsiung 811, Taiwan, Lin I-Sheng , Wei Ming-Liang
Published: April 2016

This paper presents practical experiences in deploying a fuzzy controller on a vision-based fourwheeled mobile robot for target-approaching and object-grabbing. The robot senses the environment using a simple CCD camera in its patrol. One of the robot’s missions is to actuate a mechanical grabber for picking up specific objects detected in its patrol routine. To overcome the control errors caused by physical friction and inertia, a fuzzy controller is designed and implemented for wheel-driving and objectgrabbing. Definitions are presented for fuzzy sets and control rules that consider hardware specifications. The feasibility of the method has been verified under various ground friction. Experiments in the performance of the proposed method are presented and analyzed.
Discovering Emotion-Inducing Music Features Using EEG Signals
Authors: Cabredo Rafael ,   The Institute of Scientific and Industrial Research, Osaka University, 8-1 Mihogaoka, Ibaraki, Osaka 567-0047, Japan, Legaspi Roberto , Inventado Paul Salvador , Numao Masayuki ,   Center for Empathic Human-Computer Interactions, De La Salle University, 2401 Taft Avenue, Manila 1004, Philippines
Published: April 2016

Music induces different kinds of emotions in listeners. Previous research on music and emotions discovered that different music features can be used for classifying how certain music can induce emotions in an individual. We propose a method for collecting electroencephalograph (EEG) data from subjects listening to emotion-inducing music. The EEG data is used to continuously label high-level music features with continuous-valued emotion annotations using the emotion spectrum analysis method. The music features are extracted fromMIDI files using a windowing technique. We highlight the results of two emotion models for stress and relaxation which were constructed using C4.5. Evaluations of the models using 10-fold cross validation give promising results with an average relative absolute error of 6.54% using a window length of 38.4 seconds.
Characterization of Multiple-Valued Logic for Dealing with Ambiguity
Authors: Takagi Noboru ,   Department of Intelligent Systems Design Engineering, Toyama Prefectural University, 5180 Kurokawa Imizu-shi, Toyama 939-0398, Japan
Published: April 2016

This paper focuses onr-valued functions monotonic in a partial order relation, which can be interpreted as a relation expressing something about ambiguity. First, necessary and sufficient conditions for anr-valued function to be monotonic in the relation are clarified. After that, minimal and maximal information loss functions are defined as a special part ofr-valued functions monotonic in the relation. We describe a practical application of minimal information loss functions that correct input failures in multiple-valued logic circuits.
Improved Stabilization for Robust Fixed Point Transformations-Based Controllers
Authors: Várkonyi Teréz A. ,   Doctoral School of Applied Informatics, John von Neumann Faculty of Informatics, Óbuda University, 96/B Bécsi út, Budapest H-1034, Hungary, Tar József K. , Rudas Imre J. ,   Doctoral School of Computer Science, Department of Computer Science, Universitá degli Studi di Milano, Crema Campus, 65 Via Bramante, Crema (CR) I-26013, Italy,   Institute of Intelligent Engineering Systems, John von Neumann Faculty of Informatics, Óbuda University, 96/B Bécsi út, Budapest H-1034, Hungary
Published: April 2016

Nowadays, dynamical systems are getting increasingly complex because they have to fulfill more expectations and further they have to handle more and more uncertainties. The controllers built in these systems have to face with the same difficulties. Luckily, the applied new controllers are designed to take into account the grown claims and they are able to fit the extreme conditions. The method called Robust Fixed Point Transformations (RFPT) is used to ameliorate existing controllers’ results when an approximate model is used to predict the dynamical system’s behavior. To achieve the stability of RFPT many efforts have been taken in the recent past. In this paper, a new algorithm is proposed that prevents the occurrence of short temporal unstable periods of the RFPT-based controllers, i.e., ensures the continuous stability.
Nearest Prototype and Nearest Neighbor Clustering with Twofold Memberships Based on Inductive Property
Authors: Takumi Satoshi ,   Department of Risk Engineering, School of Systems and Information Engineering, University of Tsukuba, Ibaraki 305-8573, Japan, Miyamoto Sadaaki
Published: April 2016

The aim of this paper is to study methods of twofold membership clustering using the nearest prototype and nearest neighbor. The former uses theK-means, whereas the latter extends the single linkage in agglomerative hierarchical clustering. The concept of inductive clustering is moreover used for the both methods, which means that natural classification rules are derived as the results of clustering, a typical example of which is the Voronoi regions inK-means clustering. When the rule of nearest prototype allocation inK-means is replaced by nearest neighbor classification, we have inductive clustering related to the single linkage in agglomerative hierarchical clustering. The former method usesK-means or fuzzyc-means with noise clusters, whereby twofold memberships are derived; the latter method also derives two memberships in a different manner. Theoretical properties of the both methods are studied. Illustrative examples show implications and significances of this concept.
Relational Fuzzy c-Means and Kernel Fuzzy c-Means Using an Object-Wise β-Spread Transformation
Authors: Kanzawa Yuchi ,   Shibaura Institute of Technology, 3-7-5 Toyosu, Koto, Tokyo 135-8548, Japan
Published: April 2016

Clustering methods of relational data are often based on the assumption that a given set of relational data is Euclidean, and kernelized clustering methods are often based on the assumption that a given kernel is positive semidefinite. In practice, non-Euclidean relational data and an indefinite kernel may arise, and a β-spread transformation was proposed for such cases, which modified a given set of relational data or a give a kernel Gram matrix such that the modified β value is common to all objects. In this paper, we propose an object-wise β-spread transformation for use in both relational and kernelized fuzzy c-means clustering. The proposed system retains the given data better than conventional methods, and numerical examples show that our method is efficient for both relational and kernel fuzzy c-means.
Ordered Weighted Averages on Intervals and the Sub/Super-Additivity
Authors: Yoshida Yuji ,   Faculty of Economics and Business Administration, University of Kitakyushu, 4-2-1 Kitagata, Kokuraminami, Kitakyushu 802-8577, Japan
Published: April 2016

This paper deals with continuous Ordered Weighted Averages (OWA) on a closed interval and investigates their fundamental properties. In this paper, we focus on OWA with a truncation weight and derive the subadditivity of a top-concentrated average. We then deal with OWA from the bottom and investigate their relations. The subadditivity for OWA with monotone weights is also discussed, then OWA based on probability are demonstrated and value-at-risks are explained as an example.
Comparison of Baselines in Extraction of F-Responses
Authors: Fukuda Ryoji ,   Oita University, 700 Dan-noharu, Oita 870-1192, Japan, Honda Aoi , Hachisuka Akiko , Hachisuka Kenji ,   Kyushu Institute of Technology, 680-2 Kawazu, Iizuka, Fukuoka 820-8502, Japan,   University of Occupational and Environmental Health, 1-1 Iseigaoka, Yahatanishi-ku, Kitakyushu-city, Fukuoka 807-8555, Japan
Published: April 2016

The objective of this study is to eliminate slow responses that displace baselines positively. This is a key component of an automated technique for determining parameters of F-waves in an evoked potential obtained by limb stimulation. We investigate five methods and compare them to F-wave repeater data through visual observation by experts.