bluuebunny/crossref_metadata_2025 huggingface dataset:
CapRe: a Gaze Tracking System in Man-machine Interaction
Authors: Collet Christophe ,   LSV-CNRS & ENS Cachan, 61,av du Pdt Wilson F-94235 Cachan Cedex, France, Finkel Alain , Gherbi Rachid ,   LIMSI-CNRS, Bât. 508, BP 133 F-91403 Orsay, France
Published: April 2016

We present a real-time camera-based system designed for gaze tracking focused on human-computer communication. We aim to equip computer systems with a tool that provides visual information on the user. This tool must satisfy interaction constraints and be nonintrusive, so we use a CCD camera placed between the keyboard and the screen. The system detects the user's presence, locates and tracks the face, nose, and eyes. Detection combines image processing and pattern recognition techniques.
The ECOS Framework and the ECO Learning Method for Evolving Connectionist Systems
Authors: Kasabov Nikola ,   Department of Information Science, University of Otago P.O Box 56, Dunedin, New Zealand
Published: April 2016

This paper introduces a paradigm of connectionist architectures and an approach to building on-line, adaptive intelligent systems. This approach is called evolving connectionist systems (ECOS). EGOS evolve through incremental, on-line learning, both supervised and unsupervised. They can accommodate new input data, including new features, new classes, etc. The ECOS framework is presented and illustrated on a particular type of evolving neural networks - evolving fuzzy neural networks. The task of voice recognition and person identification is used as a case study.
Fuzzy Control Stability Analysis Using a Generalized Fuzzy Petri Net Model
Authors: Furuhashi Takeshi ,   Dept. of Information Electronics, Nagoya University Furo-cho, Chikusa-ku, Nagoya 464-8603, Japan, Yamamoto Hidehiro , Peters James F. , Pedrycz Witold ,   Dept. of Electrical Engineering, Univ. of Manitoba 15 Gillson Street, Winnipeg, Manitoba, Canada R3T 5V6
Published: April 2016

Fuzzy inference is used to describe nonlinear input- output relationships using fuzzy if-then rules. Continuous values of input and output are converted into granules by fuzzy sets, and each granule is labeled with a symbol. Fuzzy inference has a multigranular architecture consisting of continuous values and symbols that has worked well incorporating expert knowledge into fuzzy control. An important issue in fuzzy control is guaranteeing fuzzy control stability. We applied Petri nets to fuzzy control stability analysis and derived a theory on asymptotic stability for symbolic representation of control. We present new bridging between symbolic stability analysis and actual control behavior numerically. We use a generalized fuzzy Petri net model and its neural network representation. Conditions for validity of granularized control stability analysis are derived from the movement of tokens in neural network representation. Simulations are used to study derived conditions.
Hierarchical Fuzzy Intelligent Controller for Gymnastic Bar Actions
Authors: Nishino Junji ,   Department of Information Science, Faculty of engineering, Fukui University 3-9-1 Bunkyou, Fukui-shi, Fukui 910-8507, Japan, Tagawa Akihiro , Shirai Haruhiko , Odaka Tomohiro , Ogura Hisakazu
Published: April 2016

We introduce hierarchical fuzzy intelligent control for high-level navigation tasks in a unstable system. Control uses linguistic expressed knowledge and consists of three subsystems - planning, navigation, and regulation. System performance is demonstrated in a gymnastic action behavior control problem. Gymnastic action is a difficult task for control, because it has many difficult-to-handle properties such as nonlinearity, numerous degrees of freedom, and unstable behavior. We show the controller realize gymnastic action sequences with linguistic knowledge in 5-link dynamic model computer simulation.
Conceptual Fuzzy Matching and the Realization of Search Agents
Authors: Takagi Tomohiro ,   Department of Computer Science, Meiji University 1-1-1 Higashi-Mica, Tama-ku, Kawasaki-shi, Kanagawa-ken 214-8571, Japan, Kasuya Shinichi , Mukaidono Masao , Yamaguchi Toru ,   Department of Information Science, Faculty of Engineering, Utsunomiya University 2753 Ishii-cho, Utsunomiya-shi 321-8585, Japan
Published: April 2016

We propose a method for representing common concepts by fusing conceptual fuzzy sets (CFSs) with ontology. We also describe a method of conceptual matching for retrieving information that meets a user’s intentions. The matching is done based on the region of the ontological structure covered by the CFS representing the user’s intention as well as on the original keywords. We apply the proposed conceptual-matching method to two types of agents. One selects music matching the tone of a text composition. Selection of music for two email notes shows that the proposed system can select music that matches human feelings. The other agent recommends TV programs to watch. The recommended programs have EPGs (Electronic Program Guide) similar to those of previously watched programs or that contain words matching the learning data. Practical examples demonstrate that the proposed system can recommend TV programs matching the user’s tastes.
Compensatory Multicriteria Aggregation Algorithm
Authors: Simoes Marcelo Godoy ,   Engineering Division Colorado School of Mines Golden CO, 80401-1887, USA
Published: April 2016

Performance aggregation in decision processes is easy to implement, amplified by clarity with varying degrees of compensation that are a measure of the decision-maker’s willingness to consider all goals and constraints globally. We extend this concept, suggesting that the notion of compensation should be applied to another step of the decision process. We presented a mathematical function for this -- the DI-operator - that complies with the notion of degrees of compensation. A query interface is used with a personal computer and the algorithm is applied in purchase decisions of mechanical materials, partner selection, and urban vehicle delivery scheduling.
SIRMs Connected Fuzzy Inference Model Applied to Process Control -- Automatic Tuning Using a Genetic Algorithm Carla Cavalcante
Authors: Koike  ,   Departamento de Engenharia Mecanica e Engenharia, Universidadl de Brasilia, Hirota Kaoru ,   Department of Computational Intelligence and Systems Science Interdisciplinary Graduate School of Science and Engineering Tokyo Institute of Technology
Published: April 2016

A tuning process proposed for the single input rule modules (SIRMs) connected inference model is applied to process control. Using a genetic algorithm (GA), the importance degree is adjusted to improve process response. Simulation using first-order processes with dead time verified the validity of this approach.
LimNet-Flexible Learning Network Containing Linear Properties
Authors: Hu Jinglu ,   Department of Electrical and Electronic Systems Engineering, Kyushu University 6-10-1 Hakozaki, Higashi-ku, Fukuoka 812-8581, Japan, Hirasawa Kotaro , Kumamaru Kousuke ,   Department of Control Engineering and Science, Kyushu Institute of Technology 680-4 Kawatsu, lizuka 812, Japan
Published: April 2016

We propose a flexible learning network of a class of linear models. A nonlinear black box system is transformed into a network of known and unknown nodes (node functions), where a linear model is introduced. Unknown nodes are parameterized using neurofuzzy models. The resulting learning network is interpreted as a linear model network (LimNet) and features useful linear properties and universal approximation ability.
Fuzzy Rule Acquisition from Trained Artificial Neural Networks
Authors: Geczy Peter ,   Department of Information and Computer Sciences Toyohashi University of Technology Hibarigaoka, Toyohashi 441-8580, Japan, Usui Shiro
Published: April 2016

We approach the problem of rule extraction in its primary form. That is, given a trained artificial neural network, we extract rules classifying data set as correctly as possible. Attention is oriented toward extraction of fuzzy rules. The choice of fuzzy rules underlines the aim of balancing rule comprehensibility and complexity. To achieve higher comprehensibility of extracted rules, the formulated theoretical material is an extension of crisp rule extraction 1). A rule extraction algorithm is introduced. The presented algorithm for fuzzy rule extraction implies from the derived theoretical results rather than from heuristics. The rule extraction algorithm incorporates a ’built-in’ rule simplification mechanism. This feature is beneficial in cases when trained neural network structure is overdetermined for a given task. The rule extraction algorithm is experimentally demonstrated. Demonstrations incorporate both structure modification training and fixed structure training.
Emotional Robotic System with Psychological Model
Authors: Ishihara Hidenori ,   Department of Intelligent Mechanical Systems Engineering, Kagawa University 2217-20 Hayashi, Takamatsu, Kagawa 761-0396, Japan TEL: +81-87-864-2334, FAX: +81-87-864-2369, Fukuda Toshio ,   Center for Cooperative Research in Advanced Science and Technology, Nagoya University Furo-cho, Chikusa-ku, Nagoya 464-8603, Japan
Published: April 2016

Authors propose a new concept of a group robotic system, which decides motion based on mass psychology. Mass psychology deal with the behavior of a crowd of organisms, focusing on the effect of emotion on the behavior of a group of organisms. In a group of organisms, emotion has an effect on motion and telecommunication between them. We apply this principle of behavior based on emotion to a group robotic system and propose an algorithm to control it using this theory. Simulation shows the effect of algorithms using emotion.
Null Queries with Compound Fuzzy Attributes
Authors: Wang Shyue-Liang ,   Department of Information Management I-Shou University, Kaoshiung, Taiwan, R.O.C. 1, Section 1, Hsueh-Cheng Road, Ta-Hsu Hsiang, Kaohsiung, Taiwan 84008, R. O. C, Tsai Yu-Jane ,   National University of Kaohsiung in Preparatation No. 416, Lan-Chang road, Nan-Tzu District, Kaohsiung, Taiwan, R. O. C
Published: April 2016

We present a generalized approach for handling null queries that contain compound fuzzy attributes. Null queries elicit a null answer from the database. Compound fuzzy attributes are ambiguous attributes not defined in the original database schema but derived from multiple rigid attributes in a schema. Compound fuzzy attributes derived from simple numbers were studied by Nomura11). We extend compound fuzzy attributes so they can be derived from numbers, interval values, scalars, and sets of all these data types. Database management systems that handle this type of ambiguous attributes in null queries both reduce occurrences of null answers and provide an improved user-friendly query environment.
Quality Function for Unsupervised Classification and its Use in Graphic Arts
Authors: Gelzinis Adas ,   Kaunas University of Technology, Studentu 50, Verikas Antanas , Malmgvist Kerstin ,   Centre for Imaging Sciences and Technologies, Halmstad University Box 823, S-301 18 Halmstad, Sweden
Published: April 2016

In this paper, we propose quality function for an unsupervised neural classification. The function is based on the third order polynomials. The objective of the quality function is to find a place of the input space sparse in data points. By maximising the quality function, we find decision boundary between data clusters instead of centres of the clusters. The shape and place of the decision boundary are rather insensitive to the magnitude of the weight vector established during the maximisation process. A superiority of the proposed quality function over other similar functions as well as conventional clustering algorithms tested has been observed in the experiments. The proposed quality function has been successfully used for colour image segmentation.
A Rule Discovery by Fuzzy Classifier System Utilizing Symbolic Information
Authors: Fujii Makoto ,   Department of Information Electronics, Graduate School of Eng., Nagoya University Furo-cho, Chikusa-ku, Nagoya 464-01, Japan, Furuhashi Takeshi
Published: April 2016

This paper presents a new fuzzy classifier system (FCS) that can discover effective fuzzy rules efficiently. The system incorporates human knowledge in the form of symbolic information, and effectively limits its search space for fuzzy rules by using knowledge. The system also extracts symbolic information from acquired fuzzy rules for efficient exploration of other new fuzzy rules. Simulations are done to demonstrate the feasibility of the proposed method.
Temporal-Hierarchical Emergency-Degree Inference System for Running Vehicles Using Image and Navigation Data
Authors: Koizumi Satoshi ,   Department of Computational Intelligence and Systems Science, Matsushita Masayuki , Takama Yasufumi , Takahashi Hiroshi , Hirota Kaoru ,   Nissan Research Center, Nissan Motor Co., Ltd. 1 Natsusima, Yokosuka 237-8523, Japan
Published: April 2016

A fuzzy inference system of emergency-degrees for operating vehicles is proposed. Three situations of joining, cutting-in, and approaching the vehicle ahead are selected as target situations. The system has a hierarchical structure with temporal effects, using vagueness concept and a sigmoid function, to achieve the continuity of inference values and to suppress the influence of noise. The proposed system is tested using video data, taken by CCD stereo cameras mounted on a car and navigation data. The difference between the emergency-degree and driver’s perception of danger is evaluated by an expert for safety in an automobile company. Finally the validity of the fuzzy inference with temporal effects for the proposed system is shown through comparison with four other inference methods.
Fuzzy Logic Based Speed Control of an IPM Synchronous Motor Drive
Authors: Uddin M. N. ,   Faculty of Engineering and Applied Science, Memorial University of Newfoundland St. John’s, NF, Canada, Rahman M. A.
Published: April 2016

This paper presents the on-line implementation of a novel fuzzy logic based speed controller for an interior permanent magnet synchronous motor drive. The fundamentals of fuzzy logic algorithms relating to motor control applications are illustrated. A new fuzzy speed controller for the IPMSM drive is designed. The complete vector control scheme incorporating the fuzzy logic controller (FLC) is successfully implemented in real-time using a digital signal processor board DS 1102 in a laboratory 1 hp interior permanent magnet synchronous motor (IPMSM). The efficacy of the proposed fuzzy logic controller (FLC) based IPMSM drive is verified by simulation as well as experimental results at different dynamic operating conditions. The fuzzy logic controller is found to be more robust for application in the IPMSM drive.
Similarity Relations in Diagnosis Fuzzy Systems
Authors: Vascák Ján ,   Department of Cybernetics and Artificial Intelligence Faculty of Electrical Engineering and Informatics, Technical University of Kosice, Letna 9, 041 20 Kosice, Slovakia, Madarász Ladislav
Published: April 2016

This paper deals with the use of similarity relations and coefficients in fuzzy logic. Fundamental similarity relations and their use for three possible applications in diagnosis systems are described. They are shown on an example of a system for failure detection of rolling-mill transmissions.
Emerging Intelligent Technologies in Computer-Aided Engineering
Authors: Horváth László ,   Faculty of Information Technology, Institute of Applied Informatics, Rudas Imre J. ,   Faculty of Information Technology Institute of Mathematical and Computing Science, Budapest Polytechnic Nepszinhaz u.8,Budapest, H-1081 Hungary
Published: April 2016

Competition generated the requirement of quick decisions at engineering activities. As a consequence, application of advanced computer modeling in engineering design needs application of intelligent computer methods to assist human decision making. A powerful CAD/CAM system with a comprehensive range of sophisticated modeling tools for describing engineering objects and programming tools for creating modeling procedures constitutes an appropriate environment to accept intelligent methods. The only way of survival for companies producing mechanical products on the competitive edge seems application of advanced modeling together with intelligent decision making. Much modeling, problem solving, database handling, visualization and other methods are involved in a typical computer-based engineering process. This process recently relies upon an integrated set of modeling tools and an integrated product database. Involving intelligent computer methods is a great challenge in this field. This paper surveys advanced modeling from the point of view of application of intelligent methods. It is organized as follows. A characterization of state of the art in advanced engineering modeling reveals important issues to be discussed in this paper. Following this, worldwide network-based group work of engineers is discussed. Human computer interaction (HCI) and network communication methods as important aspects of computer-aided engineering are outlined. Then recent development results in modeling of mechanical systems with special emphasis on integrated modeling of mechanical products, especially well-engineered shapes, are introduced. Finally, virtual manufacturing as an area of involving intelligent methods in CAD/CAM technology is discussed.
Simultaneous Optimization of the External Loop Parameters in an Adaptive Control Based on the Co-operation of Uniform Procedures
Authors: Tar József K. ,   John von Neumann Faculty of Informatics Institute of Mathematical and Computational Sciences, Budapest Polytechnic, H-1081 Budapest, Népszínház utca 8.Hungary, Rudas Imre J. , Madarász Ladislav , Bitó János F. ,   Department of Cybernetics and Artificial Intelligence, Technical University of Kosice, 042 00 Kosice, Letná9/b, Slovak Republic,   Centre of Robotics and Automation (CRA), John von Neumann Faculty of Informatics Institute of Mathematical and Computational Sciences, Budapest Polytechnic, H-1081 Budapest, Nápszínház utca 8.Hungary
Published: April 2016

Control approaches based on fuzzy controllers, artificial neural networks that is on "soft computing" (SC) are generally constrained by standardized formal procedures and structural restrictions. In general these restrictions guarantee the solution for a quite wide class of prolems. However, "orthodox" application of SC may result in overcomplicated control not necessarily exploiting the peculiarities of the given task. This often manifests itself in a huge number of unknown parameters to be identified or tuned. In this paper non-conventional integration of simple elements as classic PID/ST, regression analysis, saturated sigmoids, fuzzy sets and uniform structures obtained from the Lagrangian Mechanics is presented. The great majority of the parameters applied are either independent of the particular problem to be solved or have simple rigid tuning rules expressed in the language of the conventional fuzzy controllers. Due to the relatively simple structure of reduced number of the free parameters real time tuning can be carried out. In this paper the simultaneous optimization of the free parameters is considered in details. It is concluded that via this non-conventional integration simple and efficient adaptive control can be constructed for robots of very approximately known dynamic properties and being involved in unknown environmental dynamic interaction.
Learning M-of-N Concepts for Medical Diagnosis Using Neural Networks
Authors: Hayashi Yoichi ,   Department of Computer Science, Meiji University, 1-1-1 Higashimita, Tama-ku, Kawasaki 214-8571, Japan, Setiono Rudy , Yoshida Katsumi ,   School of Computing, National University of Singapore Lower Kent Ridge Road, Singapore 119260,   Department of Preventive Medicine, St. Marianna University School of Medicine, 2-16-1 Sugao, Miyamae-ku, Kawasaki 216-8511, Japan
Published: April 2016

Records in a medical dataset may be best characterized by M-of-N concepts. For example, a patient showing at least 2 of the 4 symptoms is likely to be diagnosed as having a certain illness. In this paper, we describe how feedforward neural networks can be used to learn such concepts. We train a network where each input in the data can only have one of two possible values, -1 or 1 and apply the hyperbolic tangent function to each connection from the input layer to the hidden layer of the network before the hidden unit activations are computed. By applying this squashing function, the activation values at the hidden units are effectively computed as the hyperbolic tangent (or the sigmoid) of the weighted inputs, where the weights have magnitudes that are near one. By restricting the inputs and the weights to binary ’ values either -1 or 1, the extraction of the M-of-N concepts from networks becomes trivial. We show how this approach can be used to learn concise and accurate the M-of-N concepts for the diagnosis of hepatobiliary disorders.
Kansei Information Processing in Multimedia Applying Intelligent Soft Computing Techniques
Authors: Onisawa Takehisa ,   Institute of Engineering Mechanics and Systems, University of Tsukuba, 1-1-1, Tennodai, Tsukuba, 305-8573 Japan
Published: April 2016

This paper mentions the concept of Kansei information that must be dealt with in multimedia. Kansei information has subjectivity, ambiguity, vagueness and situation dependence. This piece of information is not dealt with by the conventional natural science techniques. This paper also introduces soft computing techniques such as a neural network model, fuzzy set theory, a fuzzy measures and fuzzy integrals model, and the interactive genetic algorithm approach that are applied to Kansei information processing or some related problems.
Image Retrieval using Conceptual Fuzzy Sets
Authors: Takagi Tomohiro ,   Department of Computer Science, Meiji University, 1-1-1 Higashi-Mita, Tama-ku, Kawasaki-shi, Kanagawa 214-8571, Japan, Kawase Kazushi , Otsuka Kazuhiko ,   Department of Electronics and Information Science, Tsukuba College of Technology, 4-3-15 Amakubo, Tsukuba-shi, Ibaragi 305-0005, Japan
Published: April 2016

An algorithm is described that uses fuzzy sets to handle word ambiguity, the main cause of vagueness in the meaning of a word. It is based on conceptual fuzzy sets (CFSs), which represent the meaning of words by linking other related words. A trial application of this algorithm to image retrieval showed that it can retrieve images that conceptually fit the meanings of the entered keyword based on the context understood from the characteristics of images. Experimental results showed that the proposed algorithm works well to represent various meanings of a keyword by linking it to other words and to connect words directly to image data. In addition, image retrieval starting with a sample image also worked well. First, a selected sample image was translated into abstract concepts, and images fitting the concepts were chosen.
Speech Noise Cancellation Based on a Neuro-Fuzzy System: Further Improvements
Authors: Esposito Anna ,   International Institute for Advanced Scientific Studies (IIASS) Via G. Pellegrino 19, 84019, Vietri sul Mare, SA, Italy, Ezin Eugene C. , Reyes-Garcia Carlos A. ,   INFM Salerno University,   Institut de Mathematiques et de Sciences Physiques (IMSP) B.P. 613 Porto-Novo, Benin,,   Instituto Tecnologico de Apizaco-COSNET Apizaco, Tlaxcala, Mexico 90300
Published: April 2016

This work reports on an experimental system based upon the Adaptive Neuro-Fuzzy Inference System (ANFIS) architecture, which is employed for identifying a nonlinear model of the unknown dynamic characteristics of the noise transmission paths. The output of this model is used to subtract the noisy components from the received signal. The novelty of the system described in the present paper, with respect to our previous work, consists in a different set up, which requires more fuzzy rules, generated by seven trapezoidal membership functions, and uses a second order it sinc function to generate the nonlinear distortion of the noise. Once trained for few epochs (only three) with a long sentence corrupted with babble noise, the FIS obtained, has the ability to clean speech sentences corrupted by babble and also by car, traffic, and white noise, in a computational time almost close to realtime. The average improvement, in terms of SNR, was 37 dB without further training.
Fast Iterative Solving Method of Various Types of Fuzzy Relational Equations and its Application to Image Reconstruction
Authors: Nobuhara Hajime ,   Tokyo Institute of Technology, 4259 Nagatsuta, Midori-ku Yokohama 226-8502 Japan, Takama Yasufumi , Hirota Kaoru
Published: April 2016

A fast iterative solving method of various types of fuzzy relational equations is proposed. This method is derived by eliminating a redundant comparison process in the conventional iterative solving method (Pedrycz, 1983). The proposed method is applied to image reconstruction, and confirmed that the computation time is decreased to 1/39 - 1/45 with the compression rate of 0.0625. Furthermore, in order to make any initial solution converge on a reconstructed image with good quality, a new cost function is proposed. Under the condition that the compression rate is 0.0625, it is confirmed that the root mean square error of the proposed method decreases to 24.00% and 86.03% compared with those of the conventional iterative method and a non iterative image reconstruction method (Nobuhara, 2001), respectively.
Approximate Reasoning for Processing Uncertainty
Authors: Seridi Hamid ,   LERI, Universite de Reims Rue des Crayères, BP 1035 51687 Reims cedex 2, France, Akdag Herman ,   LIP6, Université P. & M. Curie 4, Place Jussieu Boîte 169 75687 Paris Cedex 05 France
Published: April 2016

In Bayesian networks as well as in knowledge-based systems, uncertainty in propositions can be represented by various degrees of belief encoded by qualitative values. In this paper, we present a qualitative approach of classical probability theory in the particular case where the set of probability degrees is replaced by a totally ordered set of symbolic values. We first define the four elementary operations (addition, subtraction, multiplication and division) allowing to manipulate these symbolic degrees of uncertainty, then we propose an axiomatic. The properties obtained from this axiomatic allows to show that our theory constitutes a qualitative approach for processing uncertain statements of natural language. The obtained results are usable in inferential processes as well as in Bayesian networks.
Intelligent Consultant
Authors: Buckley James J. ,   Department of Mathematics, University of Alabama at Birmingham Birmingham, Alabama, 35294, USA, Feuring Thomas , Hayashi Yoichi ,   Department of Electrical Engineering and computer Science, University of Siegen Holderlinstr. 3, 57068 Siegen, Germany,   Department of Computer Science, Meiji University, 1-1-1 Higashimita, Tama-ku, Kawasaki, Kanagawa, 214-8571, Japan
Published: April 2016

This paper discusses the design features of an intelligent consultant which is to interact with the user(s) accepting verbal, and non-verbal inputs, and producing verbal, and nonverbal outputs, as suggestions (commands) to the user. The "ears" of the system is a trained fizzy neural net, the "brain" a fuzzy expert system, and its "mouth" is a trained neural net.
Application of SOM-Based Fuzzy Systems in Voltage Security Margin Estimation
Authors: Su Mu-Chun ,   Department of Computer Science and Information Engineering, National Central University, Taiwan, R. O. C., Lai Eugene , Tew Chee-Yuen , Liu Chih-Wen , Chang Chen-Sung ,   Department of Electrical Engineering, Tamkang University, Taiwan, R. O. C.,   Department of Electrical Engineering, National Taiwan University, Taiwan, R. O. C.
Published: April 2016

In recent years, many significant research efforts have been devoted to voltage security margins which show how close the current operating point of a power system is to a voltage collapse point as assessment of voltage security. In this paper we propose a technique based on the SOM-based fuzzy systems for voltage security margin estimation. The SOM-based fuzzy systems use the Kohonen’s self-organizing feature map (SOM) algorithm, not only for its vector quantization feature, but also for its topological property. The vector quantization feature of feature maps is used to search a good supply of most representative cluster centers. Then the topology-preserving feature is fully utilized to select a set of most influential rules so as to contribute to the computation of system outputs. The proposed technique was tested on 1604 simulated data randomly generated from operating conditions on the IEEE 30-bus system to indicate its high efficiency.
Multiagent Architecture Combined with a Multicontract Protocol for FMS Control
Authors: Khoukhi Amar ,   Ls 14 Rae Amirat Abdelkader Ouled-Moussa Boumendes. Algeriea. (Current Address: 3865 # 4 Beambien E Montreal, PQ. Canada HlX 1H3), Moualek Adlene ,   USTHB, BP32 El Alia Bab. Ettouar Algerie
Published: April 2016

This paper describes a new Multiagent architecture for control of flexible manufacturing. In this architecture, agents coordinate their actions following a new negotiation protocol used for scheduling and rescheduling of tasks. The proposed protocol, MultiContract-Net, is an innovation of Contract-Net protocol enabling several tasks to be negotiated concurrently in real time with optimal results. Thus, the multicontract-Net protocol enables both dynamic task allocation and optimization of opportunities provided by manufacturing flexibility by handling knowledge uncertainty characterizing the negotiation process. This paper stresses the efficiency of distributed implementation.
Applying AHP to Preference Analysis by Dynamic Judgment-A Study of Adaptation and Identification of Odor
Authors: Ueda Yoshihiro ,   Industrial Research Institute of Ishikawa Kanazawa, 920-0223 Japan, Kato Naotaka , Kimura Haruhiko , Furukawa Shinji , Oyabu Takashi ,   Faculty of Engineering Kanazawa University Kanazawa, 920-8667 Japan,   Hitachi Electronics Services Co.,Ltd. Yokohama, 244-0801 Japan,   Dpt. of Economics, Kanazawa Seiryo University Kanazawa, 920-8620 Japan
Published: April 2016

In this paper we focus on dynamic judgment involving the concept of elapsed time incorporated into AHP This concept is useful for preference analysis because both factors of human environment and factors of human sensory organs change with time and they affect human preferential judgment, making it important to analyze preferential changes as a function of time. First, we propose 2 models for odor One is on olfactory adaptation and the other is on odor identifiability. Second, in these models, we analyze the consistency index, which is the degree of consistency of human subjective judgment, and proposed a method for improving the consistency index. Lastly, the following parameters are estimated using proposed models. (1) Odor preference of the individual person and its change with time. (2) The adaptation-time until the person adapts to odor in low, constant concentrations. (3) The identification-time until the person identifies odor in increasing concentrations. Because these 3 parameters are important for many odor problems, we expect that proposed models will be effective in intelligent process control for odor; which can deodorize and modulate odor.
Virtual Technology Based Associative Integration of Modeling of Mechanical Parts
Authors: Horváth László ,   John von Neumann Faculty of Informatics, Institute of Applied Informatics, Budapest Polytechnic, N6pszinhdz u. 8. Budapest, H-1081 Hungary, Rudas Imre J. ,   John von Neumann Faculty of Informatics, Institute of Mathematical and Computing Science, Budapest Polytechnic, N6pszinhdz u. 8. Budapest, H-1081 Hungary
Published: April 2016

Advanced application of powerful computer tools at engineering design of advanced products related activities means creating integrated product models in advanced computer systems. Leading application oriented master model concepts and standards as STEP serve this purpose. Engineers are working simultaneously on product design. Results of engineering decisions are analyzed for loads, aesthetics and manufacturability. Product design, results of analyses, processes of manufacturing of parts and much more are described in integrated product model. This model is accessed by several engineers simultaneously and is communicated with other engineering design environments. Because most of prototype development activities are being moved into computer systems, advanced modeling is often cited as virtual pro-, totyping. This paper summarizes and relates some advanced modeling approaches and methods to emphasize main trends in fulfilling demands for modeling tools in the early 21th century. As one of the key issues a manufacturing process model associative with form feature based part model is introduced. This model was proposed by the authors. The motivation is that low level integration of design and manufacturing of mechanical parts is still a main drawback of efficient application of expensive modeling systems. The proposed method ’allows for creating part models simultaneously with their analysis of machineability. In this paper chapter 2 outlines the scene of modeling based decision making of engineers as well as levels of product model creation. Chapter 3 explains the prevailing modeling principles, while chapter 4 emphasizes structure descriptions. In chapter 5 the proposed associative, Petri net model representation based manufacturing process modeling and its potential applications are explained are detailed. Two additional key subject areas are discussed in chapter 6 and chapter 7, namely effects of globalization on modeling and product data management. The main aspect of discussion is model and communication based group work of engineers.
Toward a Bioinspired Fusion of Color and Infrared Textural Image Information
Authors: Ruiz-del-Solar Javier ,   University of Chile, Dept. of Electrical Engineering, Casilla 412-3, 6513021 Santiago, Chile, Soria-Frisch Aureli ,   Fraunhofer I.P.K., Dept. of Pattern Recognition, Pascalstr. 8-9, D-10587 Berlin, Germany
Published: April 2016

Simultaneous progress in sensor and signal processing technologies stimulates the implementation of more refined pattern recognition systems in order to solve problems of increasing complexity. The progress on both technologies induced the implementation of the here presented framework for the fusion of infrared and color textural information. The framework is based on different aspects of the processing of visual information in the human brain. Some organizational principles of multisensorial information fusion in higher associative areas are also reflected in it. Preliminary results, realized in a simplified framework, show the validity of the biological-based approach in the resolution of multisensorial image fusion.
Effective Linear Calculational Method for Nonlinear Optimization with a Convex Polyhedral Objective Function and Linear Constraints
Authors: Phruksaphanrat Busaba ,   Graduate school of Engineering, Nagaoka University of Technology, 1603-1, Kami-Tomioka, Nagaoka, Niigata, 940-2188 Japan, Ohsato Ario ,   Department of Management and Information Systems Science, Nagaoka University of Technology, 1603-1, Kami-Tomioka, Nagaoka, Niigata, 940-2188 Japan
Published: April 2016

This research proposes an effective linear calculational method based on convex cone concept for solving non-linear optimization problems with a convex polyhedral objective function and linear constraints. One familiar type of convex polyhedral objective functions is a triangular type, which can be normally solved by weighted goal programming (WGP). The necessary preference information of WGP is weights of positive and negative deviational variables. Alternatively, the linear calculational method prefers the other operational way in designing of an objective function by deviational constants, which is practical for the decision maker. For convex polyhedral type objective function problems, conventionally separable convex programming and goal programming (GP) are applied. By separable convex programming, it needs to separate the objective function into line segments before solving the problem, which means increasing of variables and constraints. In case of GP each breakpoint is determined as a goal so the number of constraints and the deviational variables are drastically increased. By the effective linear calculational method proposed in this paper, the problem could be simply formulated to the linear programming problem, which is easy for the decision maker to apply. Moreover this method has lower number of constraints and variables than existing methods so the calculational time can also be reduced.
Leaning Causal Models with Conditional Causal Probabilities from Data
Authors: Yamada Koichi ,   Department of Management and Information Systems Science, Nagaoka University of Technology, 1603-1, Kamitomioka, Nagaoka, Niigata, 940-2188 Japan
Published: April 2016

We propose a way to lean probabilistic causal models using conditional causal probabilities (CCPs) to represent uncertainty of causalities. The CCP is a probability devised by Peng and Reggia representing the uncertainty that a cause actually causes an effect given the cause. The main advantage of using CCPs is that they represent exact probabilities of causalities that people recognize mentally, and that the number of probabilities used in the causal model is far smaller than that of conditional probabilities by all combinations of possible causes. Thus, Peng and Reggia assumed that CCPs are given by human experts as subjective ones, and did not discuss how to calculate them from data when a dataset was available. We address this problem, starting from a discussion about properties of data frequently given in practical problems, and shows that prior probabilities that should be learned may differ from those derived by counting data. We then discuss and propose how to learn prior probabilities and CCPs from data, and evaluate the proposed method through numerical experiments and analyze results to show that the precision of leaned models is satisfactory.
Decentralized Robust Fuzzy Sliding Mode Control Design of Interconnected Uncertain System
Authors: Vinh Thai Quang ,   Institute of Information Technology, National Center for Natural Science and Technology, Hoang Quoc Viet road, Hanoi, Vietnam, Hirota Kaoru ,   Department of Computational Intelligence and Systems Science Inter-disciplinary Graduate School of Science and Technology, Tokyo Institute of Technology, 4529, Nagatsuta, Midori, Yokohama, 226-8502 Japan
Published: April 2016

We present a fuzzy approach to design a robust suboptimal control for decentralized interconnected system. We denote the non-linear interactions between the sub-systems are uncertain and in many real-life situation it can be represented by the differences of actual behavior from nominal description of sub-systems (e, é). Fuzzy sliding mode control is designed hr sub-system to track the prespecified trajectory in phase plane (e, é) according it’s states (x,x´). Fuzzy adaptation is proposed for tuning closed-related control subsystem on the sliding surface toward new equilibrium points. The adaptation strategy is determined by rule bases based on considering controller as PID controller with variable structure, switching between different PID settings with different inputs (e, é).
Application of Fuzzy Mathematical Morphology with Adaptive Structuring Elements to Seal Defect Testing
Authors: Kikuchi Takuo ,   Department Of Electronic Technology, Institute of Tokyo, Polytechnic University, 2-32-1, Ogawa-nishimachi, Kodaira, Tokyo, 187-0035 Japan, Murakami Shuta ,   Department Of EECE, Faculty of Engineering, Kyushu Institute of Technology, 1-1, Sensui-cho, Tobata, Kitakyushu, Fukuoka, 804-8550 Japan
Published: April 2016

Fuzzy mathematical morphology has been proposed as a new method of image processing, especially in the analysis of features from an ambiguous image. Fuzzy morphological operators work with 2 images: an original image to be processed and a structuring element. Generally, we must decide on the shape and value of the structuring element before applying fuzzy morphology. A problem arises when the applied image changes largely by the selection of the structuring element, so it is difficult to apply fuzzy morphological operators to inspection for defect testing. In this paper, we propose a new structuring element for fuzzy morphology, called an adaptive structuring element. The adaptive structuring element determines shapes and values of structuring elements dynamically from an input image by searching for the local features in the image. Consequently, the adaptive structuring element is sensitive to ambiguous images, useful for automatic inspection using fuzzy morphology, and effective for extraction. Performance evaluations via simulations show that the adaptive structuring element efficiently extracts features from an ambiguous image. The adaptive structuring element also shows a higher performance in package defect testing than other filters. We attained experimental results (more than 95.5%) by applying the adaptive structuring element to seal defect testing.
Online Neofuzzy Neuron Flux Observer for Induction Motor Drives
Authors: Landim Regis P. ,   DEESP-UFPE, Av. Academico H. Ramos, S/N, Cidade Universitaria, Recife-PE - Brasil, Neves Francisco A. S. , Silva Selenio R. , Walmir M. Caminhas**  , Benjamim R. Menezes**  ,   Departainento de Engenharia Eletronica da UFMG, Av. Pres, Antonio Carlos, 6627 - Campus Parnpulha 31.270-901 Belo Horizonte MG - Brasil
Published: April 2016

This paper presents an algorithm for three-phase induction motors rotor flux observation based on a neofuzzy neuron (NFN) network with real-time training featuring quick accurate convergence, great adaptability to system dynamics, good performance over a wide speed range, and low sensitivity to parameter variations (rotor and mutual inductances and rotor resistance) and disturbances. The observer requires only rotor speed and stator current measurements as input signals and. its fuzzy-neural network does not need previous training. Convergence in one step is demonstrated and the influence of the learning rate on performance is analyzed. Both digital simulation and experimental results are presented and show the observer good overall performance.
Implementing Fuzzy Learning Algorithms in a 6 DOF Hydraulic Parallel Link Manipulator: Control with Actuators’ Forces Fuzzy Compensation
Authors: Zyada Zakarya ,   Department of Micro System Engineering, Nagoya University, Furo-cho, Chikusa-ku, Nagoya, 464-8603 Japan, Hasegawa Yasuhisa , Fukuda Toshio
Published: April 2016

This paper proposes fuzzy compensation for actuators’ motion forces, (dynamics, gravity and friction) in a force/motion control algorithm for the assembly of segments of a shield tunnel excavation applying a 6 DOF hydraulic parallel link manipulator. First, we introduce the feedback force/motion control algorithm with fuzzy forces compensation. Then, we introduce a rule-base fuzzy compensating model and its real-time implementation for every hydraulic actuator of a Stewart Platform so that we can reduce the effect of friction forces and hence improve the quality of force control and assembly. The experimental results of the control system with and without fuzzy compensation are presented, which show a good achievement in contact force estimation and manipulator motion utilizing the proposed fuzzy compensation.
Effective Use of Learning Knowledge by FEERL
Authors: Hoshino Yukinobu ,   Computer Science, Ritsumeikan University, 1-1-1, Noji-Higashi, Kusatsu, Shiga 525-8577, Japan, Kamei Katsuari
Published: April 2016

The machine learning is proposed to learning techniques of spcialists. A machine has to learn techniques by trial and error when there are no training examples. Reinforcement learning is a powerful machine learning system, which is able to learn without giving training examples to a learning unit. But it is impossible for the reinforcement learning to support large environments because the number of if-then rules is a huge combination of a relationship between one environment and one action. We have proposed new reinforcement learning system for the large environment, Fuzzy Environment Evaluation Reinforcement Learning (FEERL). In this paper, we proposed to reuse of the acquired rules by FEERL.
Algorithms of Hard c-Means Clustering Using Kernel Functions in Support Vector Machines
Authors: Miyamoto Sadaaki ,   Institute of Engineering Mechanics and Systems, University of Tsukuba, Ibaraki 305-8573, Japan, Nakayama Youichi ,   Graduate School of Systems and Information Engineering, University of Tsukuba, Ibaraki 305-8573, Japan
Published: April 2016

We discuss hard c-means clustering using a mapping into a high-dimensional space considered within the theory of support vector machines. Two types of iterative algorithms are developed. Effectiveness of the proposed method is shown by numerical examples.
Fuzzy c-Means Clustering Using Kernel Functions in Support Vector Machines
Authors: Miyamoto Sadaaki ,   Institute of Engineering Mechanics and Systems, University of Tsukuba, Ibaraki 305-8573, Japan, Suizu Daisuke ,   Graduate School of Systems and Information Engineering, University of Tsukuba, Ibaraki 305-8573, Japan
Published: April 2016

We studied clustering algorithms of fuzzy c-means using a kernel to represent an inner product for mapping into high-dimensional space. Such kernels have been studied in support vector machines used by many researchers in pattern classification. Algorithms of fuzzy c-means are transformed into kernel-based methods by changing objective functions, whereby new iterative minimization algorithms are derived. Numerical examples show that clusters that cannot be obtained without a kernel are generated.
A Fuzzy Inference System for Identifying Tissue Elasticity Using Ultrasound
Authors: Kimura Tadashi ,   Graduate School of Engineering, Himeji Institute of Technology, Nagamune Kouki , Kobashi Syoji , Kondo Katsuya , Hata Yutaka , Taniguchi Kazuhiko ,   KINDEN Corporation
Published: April 2016

This paper proposes a fuzzy rule-based approach for identifying tissue elasticity using ultrasound. The purpose of this paper identifies automatically tissue elasticity. Information of tissue elasticity helps us to diagnose several diseases. Elastography was able to estimate tissue elasticity. However, this measurement range is limited due to the need of pressure. To avoid this limitation, this paper proposes the identification system without pressure. This inference system consists of two stages. In the first stage, fuzzy membership functions are constructed by known data of elasticity. The second stage identifies elasticity of unknown data by using the membership functions. We used five different phantoms (total 5×10 = 50) of elasticity as known data and applied this system into nine different phantoms (total 9×10 = 90) of elasticity as unknown data. As a result, the correlation coefficient between actual value and identified value was 0.789 and the error of means was 0.646. This system thus acquired smaller error ratio than that of the statistical method.
An Agent System Using Basic Emotions as Communication Method
Authors: Dendo Yosuke ,   Graduate School of Science and Engineering, Ritsumeikan University, 1-1-1, Noji-Higashi, Kusatsu, Shiga 525-8577, Japan, Kamei Katsuari ,   Computer Science, Ritsumeikan University, 1-1-1, Noji-Higashi, Kusatsu, Shiga 525-8577, Japan
Published: April 2016

It is difficult to let computer express feelings because of the ambiguity and nonlinearity. But it is important for people to communicate with computers and to built a society where people live with many robots. And it is said that expressing emotions is usuful for high quality data processing. In this paper, we construct an environment where an agent expresses its emotions to convey its intentions to other agents. First, we survey emotion models from the view point of psychology. Next, we propose an agent system that he can control other agents using his emotion expression as their communication method. Finally, we show that the agent could achieve his goal and that the agent had a kind of personality.