bluuebunny/crossref_metadata_2025 huggingface dataset:
Adaptive Fuzzy Control of One Linear Actuator Hopping Robot
Authors: Kuswadi Son ,   Department of Mechanical and Environmental Informatics, Tokyo Institute of Technology, 2-12-1 Ookayama, Meguro-ku, Tokyo, 152-8552 Japan, Sampei Mitsuji , Nakaura Shigeki ,   Electronic Engineering Polytechnic Institute of Surabaya, Kampus ITS Keputih Sukolilo Surabaya 60111, Indonesia,   Department of Mechanical and Control Engineering, Tokyo Institute of Technology, 2-12-1 Ookayama, Meguro-ku, Tokyo, 152-8552 Japan
Published: April 2016

This work demonstrates the applicability of adaptive fuzzy control approach to control one linear actuator hopping robot. Our simple approach uses linearized model to design a state feedback servo controller. Afterward, by using fuzzy networks we developed self-tuning adaptive control to handle nonlinearity of the robot. The continuous hopping gait was realized by simulation.
Processing Technical Daily Reports in Offshore Petroleum Engineering - An Experience
Authors: Miura Kazuo ,   Petrobras, Campos Basin Business Unit, Macae, Rio de Janeiro, Brazil, Guilherme Ivan Rizzo , Morooka Celso Kazuyuki , Mendes José Ricardo Pelaquim ,   Sao Paulo State University - UNESP, Department of Statistics, Applied Mathematics and Computer Science, Rio Claro, SP, Brazil,   The State University of Campinas - UNICAMP, Petroleum Engineering Department, Campinas, SP, Brazil
Published: April 2016

This paper reports ten years experience with the use of a methodology to extract and to consolidate expert knowledge from technical reports in Petroleum Engineering. The methodology here presented is based on the context of sensitive text analysis. The experience acquired from the present study is discussed and benefits obtained for the petroleum industry are highlighted.
Special Issue on Intelligent Technologies, Fuzzy Systems and Applications
Authors: Phuong Nguyen Hoang ,   Institute of Information Technology National Centre for Natural Sciences and TechnologyHoang Quoc Viet Road, Can Giay, Hanoi, VietnamTu Bao Ho School of Knowledge Science Japan Advanced Institute of Science and TechnologyTatsunokuchi, Ishikawa, 923-1292 Japan
Published: April 2016

In this issue, we are featuring fifteen papers devoted to intelligent technologies, fuzzy systems and their applications as a special issue of the journal. The papers are selected from papers accepted and presented at the joint Third International Conference on Intelligent Technologies and Third Vietnam-Japan Symposium on Fuzzy Systems and Applications (InTech/VJFuzzy'2002) that was held in Hanoi, Vietnam on December 3-5, 2002. In InTech/VJFuzzy'2002, there was a wide spectrum of research topics on artificial intelligence, fuzzy systems, soft computing, and related fields such as"fuzzy logic", "fuzzy set theory", "fuzzy systems", "AI techniques", "Bayesian networks", "genetic algorithms", "neural networks", "knowledge discovery and data mining", "speech recognition", "soft computing in medicine", among others. More than 60 papers were accepted and presented by authors from many countries, including Australia, China, India, Korea, Germany, France, Thailand, Taiwan, Japan, Vietnam, and U.S.A. Fifteen papers that received outstanding recommendations from its reviews were selected in this special issue. The topics addressed by these selected papers include fuzzy rule systems, fuzzy inference methods, fuzzy and rough models, problem solving with equivalent transformation, genetic algorithms, reinforcement learning, non–monotonic reasoning, support vector machines, Hedge algebra, intelligent control, natural language understanding, self–organizing map learning, soft computing and data mining in medicine. As editors of this special issue, we would like to express our sincere gratitude to paper's authors in this issue. Our special thanks go to the anonymous referees for their excellent job, Ms. Kumiko Sato for her help in coordinating the publication of the issue, the Editorial Board of JACIII, especially Professor Kaoru Hirota for his great support and encouragement. Finally, we wish to thank Professors Hung T. Nguyen, Michio Sugeno and Pratit Santiprabhob for their help and contribution to InTech/VJFuzzy'2002. Without their support, the InTech/VJFuzzy'2002 and this issue would not be possible.
A Recursively Axiomatizable Subsystem of Levesque’s Logic of Only Knowing
Authors: Duc Ho Ngoc ,   Institute for Information Systems, University of Luebeck, Ratzeburger Allee 160, 23562 Luebeck, Germany
Published: April 2016

A complete, recursive axiomatization of a subsystem of Levesque’s only-knowing logic is given. The sublogic is obtained by relaxing some of Levesque’s semantic conditions while keeping the most important definitions unchanged. The axiom system is obtained by adding an axiom of Humberstone to a subset of the set of Levesque’s axioms. The completeness result is proved using the subordination method of Hughes and Cresswell.
Fuzzy Inference Methods Employing T-norm with Threshold and Their Implementation
Authors: Cuong Bui Cong ,   Institute of Mathematics, National Center of Natural Science and Technology, 18 Hoang Quoc Viet Rd., 10307 Hanoi, Vietnam, Phuong Nguyen Hoang , Le Ho Khanh , Son Bui Truong , Yamada Koichi ,   Institute of Information Technology, National Center of Natural Science and Technology, 18 Hoang Quoc Viet Rd., Cau Giay, Dist., Hanoi, Vietnam,   Post and Telecommunication Institute of Technology, 122 Hoang Quoc Viet Rd. Cau Giay Dist., Hanoi, Vietnam,   Dept. of Management and Information Systems Science, Nagaoka University of Technology, 1603-1, Kami-tomioka, Nagaoka, Niigata, 904-2188, Japan
Published: April 2016

The fuzzy inference engine is an important part of reasoning systems. Among the many different types of inference, MATLAB is a powerful tool including many useful toolboxes, one of which is the Fuzzy Logic Toolbox. To improve toolbox capacity, we programmed and installed several new inference methods.
Special Issue on Pattern Recognition
Authors: Hasegawa Osamu ,   Associate Professor, Imaging Science and Engineering Laboratory, Tokyo Institute of Technology, Yokohama 226-8503, Japan
Published: April 2016

We are pleased to publish this special JACIII issue on pattern recognition featuring 3 invited and 14 regular papers. The first and second concern support vector machines and Bayesian networks by authors who are field experts, and should serve as an introduction to beginners and a resource for researchers. In the third paper, the authors propose an artificial neural network for pattern recognition using "living" neural cells. This paper was invited because the research it deals with is considered an example of the interfield research expected to develop in the 21st century. The remaining 14 regular papers are reviewed and selected from 19 submitted papers. In reviewing and selecting the 14 regular papers, covering a broad field range from basic theory to applied systems, we focused on the originality of each paper and the viewpoints of the authors toward problems and experimental results. This wide-ranging selection should prove both innovative and enlightening to interested readers. We thank Professors Kaoru Hirota and Toshio Fukuda, editors-in-chief of this journal, for their support of this special issue. We also thank the staff of Fuji Technology Press for its invaluable assistance.
A Growing Neural Network for Online Unsupervised Learning
Authors: Furao Shen ,   Department of Computational Intelligence and Systems Science, Tokyo Institute of Technology (TIT), R2-52 Imaging Science and Engineering Lab., 4259 Nagatsuda, Midori-ku, Yokohama 226-8503, Japan, Hasegawa Osamu ,   Imaging Science and Engineering Lab., Tokyo Institute of Technology (TIT)
Published: April 2016

New online learning is proposed for unsupervised classification and topology representation. The combination of similarity threshold and local accumulated error suits the algorithm for nonstationary data distribution. A novel online criterion for removal of nodes is proposed to classify the data set well and eliminate noise. The use of a utility parameter, error-radius, is able to judge if insertion is successful and control the increase of nodes. As shown in experiment results, the system can represent the topological structure of unsupervised online data, report the reasonable number of clusters, and give typical prototype patterns of every cluster without priori conditions such as a suitable number of nodes or a good initial codebook.
Human Limb Extraction Based on Motion Estimation Using Optical Flow and Image Registration
Authors: Tamaki Toru ,   Graduate School of Science and Technology, Niigata University, 8050 Ikarashi2nocho, Niigata City, Niigata Pref. 950-2181, Japan
Published: April 2016

We propose a method for extracting human limb regions by the combination of optical flow-based motion segmentation and nonlinear optimization-based image registration. First, rotating limb regions with rough boundaries are extracted and motion parameters are estimated for an approximated model. Then the extracted region and estimated parameters are used as initial values for nonlinear optimization that minimizes residuals of two successive frames and estimates motion parameters. Combining the two steps reduces computational cost and avoids the initial state problem of optimization. According to estimated parameters, the limb region is extracted by a Bayesian classifier to obtain accurate region boundaries. Experimental results on real images are shown.
Automatic Video Recording of Lecture’s Audience with Activity Analysis and Equalization of Scale for Students Observation
Authors: Nishiguchi Satoshi ,   Graduate School of Law, Kyoto University, Yoshidahonmachi, Sakyoku, Kyoto 606-8051, Japan, Kameda Yoshinari , Kakusho Koh , Minoh Michihiko ,   Institute of Engineering Mechanics and Systems, University of Tsukuba, 1-1-1 Tennoudai, Tsukuba, Ibaraki 305-8573, Japan,   Academic Center for Computing and Media Studies, Kyoto University, Yoshidanihonmatsucho, Sakyoku, Kyoto 606-8051, Japan
Published: April 2016

This paper propose a method for automatic recording of audience during the lectures based on the results from conventional studies of teaching process improvements. In the presented approach we propose the audience analysis features which are later used for proper camera settings in the lecture hall with the multiple cameras installed. We focus on the analysis of student activities during the lectures in order to select the regions with different features representing the cues related to the estimated lecture attendance levels. The experimental results of the application of the proposed system together with user comments are presented in order to evaluate the validity of our approach.
Printed Japanese Character Recognition Using Multiple Commercial OCRs
Authors: Miyao Hidetoshi ,   Shinshu University, 4-17-1, Wakasato, Nagano 380-8553, Japan, Nakano Yasuaki , Tani Atsuhiko , Tabaru Hirosato , Hananoi Toshihiro ,   Kyushu Sangyo University, 2-3-1 Matsukadai, Higashi-ku, Fukuoka 813-8503, Japan,   Hitachi Software Engineering Co., Ltd., 5030 Totsuka-cho, Totsuka-ku, Yokohama 244-8555, Japan,   Fuji Xerox Co., Ltd., 2-3-1 Matsukadai, Higashi-ku, Fukuoka 813-8503, Japan
Published: April 2016

This paper proposes two algorithms for maintaining matching between lines and characters in text documents output by multiple commercial optical character readers (OCRs). (1) a line matching algorithm using dynamic programming (DP) matching and (2) a character matching algorithm using character string division and standard character strings. The paper proposes a method that introduces majority logic and reject processing in character recognition. To demonstrate the feasibility of the method, we conducted experiments on line matching recognition for 127 document images using five commercial OCRs. Results demonstrated that the method extracted character areas with more accuracy than a single OCR along with appropriate line matching. The proposed method enhanced recognition from 97.61% provided by a single OCR to 98.83% in experiments using the character matching algorithm and character recognition. This method is expected to be highly useful in correcting locations at which unwanted lines or characters occur or required lines or characters disappear.
Fuzzy Descriptor System Modeling and Control of Lagrange Dynamics with Regional Pole-Placement Constraint
Authors: Kang Jin-Shig ,   Faculty of Telecommunication and Computer Eng., Cheju National University, Ara 1, Jeju 690-756, Korea
Published: April 2016

We present a descriptor fuzzy model for Lagrange dynamics and a controller design algorithm based on state feedback pole placement. The fuzzy descriptor system (FDS) model is a simple extension of the original Takagi-Sugeno fuzzy model for which a new controller is designed based on the linear matrix inequality (LMI) theory. We show that LMI-based regional pole-placement design for the FDS is easily solved. Two examples explain the controller’s simplicity and easy design.
A Fuzzification of Morphological Wavelets Based on Fuzzy Relational Calculus and its Application to Image Compression/Reconstruction
Authors: Nobuhara Hajime ,   Department of Computational Intelligence and Systems Science, Tokyo Institute of Technology, 4259 Nagatsuta, Midori-ku, Yokohama 226-8502, Japan, Hirota Kaoru
Published: April 2016

A new style of fuzzy wavelets is proposed by the fuzzification of morphological wavelets. Due to the correspondence of the morphological wavelets operations and fuzzy relational ones, wavelets analysis/synthesis schemes can be formulated based on fuzzy relational calculus. To enable efficient image compression/reconstruction, the concept of the alpha-band which is an alpha-cut generalization, is also proposed for thresholding wavelets. In an image compression/reconstruction experiment using test images extracted from the Standard Image DataBAse (SIDBA), it is confirmed that the root mean square error (RMSE) of the proposed soft thresholding is decreased to 87.3% of conventional hard thresholding, when the original image is "Lenna."
Fault Detection of Induction Motors Using Fourier and Wavelet Analysis
Authors: Bae Hyeon ,   Research Institute of Computer Information and Communication, Pusan National University, 30 Jangjeon-dong, Geumjeong-gu, Busan 609-735, Korea, Kim Youn-Tae , Kim Sungshin , Lee Sang-Hyuk , Wang Bo-Hyeun ,   School of Electrical and Computer Engineering, Pusan National University, 30 Jangjeon-dong, Geumjeong-gu, Busan 609-735, Korea,   Faculty of Electrical Engineering and Information Technology, Kangnung National University, 120 Gangneung Daehangno, Gangneung City, Gangwon Province 210-702, Korea
Published: April 2016

The motor is the workhorse of industries. The issues of preventive and condition-based maintenance, online monitoring, system fault detection, diagnosis, and prognosis are of increasing importance. This paper introduces fault detection for induction motors. Stator currents are measured by current meters and stored by time domain. The time domain is not suitable for representing current signals, so the frequency domain is applied to display signals. The Fourier Transform is employed to convert signals. After signal conversion, signal features must be extracted by signal processing such as wavelet and spectrum analysis. Features are entered in a pattern classification model such as a neural network model, a polynomial neural network, or a fuzzy inference model. This paper describes fault detection results that use Fourier and wavelet analysis. This combined approach is very useful and powerful for detecting signal features.
New Method of Program Selection in Digital TV Receivers and its Evaluation by Users
Authors: Weissar Petr ,   Department of Applied Electronics, University of West Bohemia, Pinker Jiri , Hrichova Miloslava , Firt Jaroslav ,   Department of Psychology, University of West Bohemia
Published: April 2016

A novel type of a user-friendly "man-to-TV set" interface has been designed for the new generation of digital TV sets with many hundreds of channels. It consists of an optimised remote-control device and a system of menus appearing on the TV screen. Response of potential users has been tested on the groups of students of different age and education, including seniors. Positive results have shown that the new TV control system can be used in practice.
Kalman-Filter Based Control and Performance Monitoring Systems
Authors: Bencsik Attila L. ,   Budapest Polytechnic, Bánki Donát Faculty of Mechanical Engineering, H-1081 Budapest, Népszínház utca 8
Published: April 2016

This paper addresses the problem to design control and performance monitoring systems for robotic manipulators. Basically three control schemes are proposed. The first one is a modification of the computed torque method where the input torques necessary to drive the robotic manipulator are on-line computed as functions of the optimal – unbiased, minimum variance – estimates of the joint coordinates and velocities, and the corrected accelerations. The optimal state estimator based on a stochastic discrete-time linear dynamic model and previous observations is generated the discrete Kalman filter. The idea of the second control system is to use a better reference trajectory for linearization in each working cycle. Two ways are given to perform this relinearization process: the relinearization is performed around the estimated trajectory, and the extended Kalman filter. The third novel control method addresses the problem to design a nonlinear feedback controller in case of uncertain dynamical parameters and other disturbances. Monitoring schemes for real-time detection and isolation of failures in robotic manipulators are outlined in the second part of the paper. The problem of fault detection is formulated as a problem in Hypothesis Testing by regarding the normal operation of the robot manipulator as the null hypothesis.
A Fast Dynamic Full-Text Search Method Using Efficient Block Management Structure
Authors: Fuketa Masao ,   Department of Information Science and Intelligent Systems, University of Tokushima, Atlam El-Sayed , Ghada Elmarhomy , Aoe Jun-ichi
Published: April 2016

Full-text search is widely used for various services of the Internet. A more high-speed and a more efficient full-text search technology are necessary because of the amount of increasing handled document and corresponding document data every day. This paper proposes an adaptive block management algorithm that is efficient for dynamic data management method. This algorithm is applied for inverted file searching. The new method is speeding up character string retrieval by first making the full-text search of Uni-gram and by the full-text search of Bi-gram. This paper proposes a method of enhancing the static full-text search system of Bi-gram to the dynamic full-text search system of Bi-gram. Moreover, this paper presents an efficient achievement method of the dynamic full-text search system of Bi-gram using effectiveness of the adaptive block management structure.
Fuzzy Querying of Evolutive Situations: Application to Driving Situations
Authors: Yahia Sabiha Ould ,   ISTIT laboratory, University of Technology of Troyes, 12 rue Marie Curie, BP 2060 10010 Troyes, France, Loriette-Rougegrez Sophie
Published: April 2016

The study of the human behavior in driving situations is of primary importance for the improvement of drivers security. This study is complex because of the numerous situations in which the driver may be involved. The objective of the CASSICE project (Symbolic Characterization of Driving Situations) is to elaborate a tool in order to simplify the analysis task of the driver's behavior. In this paper, we will mainly take an interest in the indexing and querying of a multimedia database including the numerical data and the video sequences relating to a type of driving situations. We will put the emphasis on the queries to this database. They are often complex because they are formulated according to criteria depending on time, space and they use terms of the natural language.
Fuzzy Rationality in Quantitative Decision Analysis
Authors: Nikolova Natalia ,   Technical University of Varna, Dept. of Economics and Management, 1 Studentska Str., 9010 Varna, Bulgaria, Shulus Aleksei , Toneva Daniela , Tenekedjiev Kiril ,   Academy of Labor and Social Relations, Moscow, Russia, 117454 Lobachevskogo 90,   Eley K&K, Ltd., Varna, Bulgaria, Mladost, bl. 139, entr. 1, app. 19
Published: April 2016

The paper presents a discussion on fuzzy rationality in the elicitation of subjective probabilities and utilities. In addition to previous research, two functions, measuring the degree of preference of the real decision maker on both sides of the uncertainty interval are introduced, and their relationship with the indifference function, measuring the degree of indifference of the decision maker over gambles, is analyzed and graphically interpreted. A new relation -- hesitation -- is introduced to give a better description of the actual process of subjective elicitation by real decision makers. The influence of the preference-hesitation combination in an elicitation process is presented graphically, and it is argued that the resulting uncertainty interval is much tighter than the one, resulting from the preference-indifference based elicitation.
Fractal Image Coding with Simulated Annealing Search
Authors: Furao Shen ,   Department of Computational Intelligence and Systems Science, Tokyo Institute of Technology (TIT), Hasegawa Osamu ,   Imaging Science and Engineering Lab., Tokyo Institute of Technology, 4259 Nagatsuta, Midori-ku, Yokohama, Kanagawa 226-8503, Japan
Published: April 2016

The main shortcomings of fractal image coders are (1) the slow speed for searching domain block pool, and (2) known fast algorithms leading to a loss of image quality. We propose efficient fractal image coding using simulated annealing method. Compared to previous schemes, our proposal greatly increases the search speed of domain block pool with almost no image quality loss. Experimental results indicate the high feasibility of the proposed method, which is, furthermore, extendable to other fractal coders.
Special Issue on Selected Papers in SCIS & ISIS 2004 - No.1
Authors: Onisawa Takehisa ,   Professor in Graduate School of Systems and Information Engineering University of Tsukuba, Ibaraki 305-8573, Japan
Published: April 2016

The Joint Conference of the 2nd International Conference on Soft Computing and Intelligent Systems and the 5th International Symposium on Advanced Intelligent Systems (SCIS & ISIS 2004) was held at Keio University in Yokohama, Japan, on September 21-24, 2004. Over 300 papers in various fields, for example, mathematics, urban and transport planning, entertainment, intelligent control, learning, image processing, clustering, neural networks application, evolutionary computation, system modeling, fuzzy measures, and robotics were submitted to the conference. The Program Committee required reviewers in SCIS & ISIS 2004 to select excellent papers considering publication in a special issue of the Journal of Advanced Computational Intelligence and Intelligent Informatics (JACIII). Some 45 papers were selected and some of them accepted by other reviewers will be published in a two-part special issue of SCIS & ISIS 2004. In this, the first part, 13 papers have been classified into six groups — papers 1-3 under intelligent control, paper 4 under robotics, papers 5 and 6 under neural network applications, papers 7-9 under evolutionary computation applications, paper 10 under human behavior analysis, and papers 11-13 under image processing. Remaining papers currently under review will be published in the next volume. We thank the reviewers for their time and effort in making these special issues possible so quickly, and thank the JACIII editorial board, especially Profs. Hirota and Fukuda, the Editors-in-Chief and Managing Editor Kenta Uchino for their invaluable aid and advice in putting these special issues together. This issue is dedicated to the late Prof. Toshiro Terano, who passed away on February 15, 2005. He will be greatly missed.
Simulation of Developmental Process of Organism and Application to Structural Design
Authors: Inoue Masato ,   School of Integrated Design Engineering, Graduate School of Science and Technology, Keio University, 3-14-1 Hiyoshi, Kohoku-ku, Yokohama 223-8522, Japan, Matsuoka Yoshiyuki
Published: April 2016

In the early processes of design called conceptual and preliminary design, diverse design ideas must be obtained under the constraint of unclear design conditions. Since ancient times, designers have been referred to form and structure of natural organism to create new artifacts. Design method referencing organisms could develop new designs and improve design capabilities. This paper describes a design system referencing emergent organism evolution, diversity, and environmental adaptation to obtain diverse design solutions. We simulate developmental process of organism, "induction" and "apical dominance" for diverse organism morphogenesis and propose form-generation system for obtaining diverse design solutions in a self-organizing manner using cellular automata. Generated organisms are adapted to the environment. We add a process in which forms are structurally adapted, optimizing diverse design ideas. To verify effectiveness proposal, we apply our proposal to the structural design of chairs. We discuss the effectiveness of our proposal applying organism morphogenesis for obtaining diverse design solutions.
Improved ANN Based Tap-Changer Controller Using Modified Cascade-Correlation Algorithm
Authors: Islam M. Fakhrul ,   Gippsland School of Computing & Information Technology, Monash University, Churchill 3842, Australia, Kamruzzaman Joarder , Lu Guojun
Published: April 2016

Artificial Neural Network (ANN) based tap changer control of closed primary bus and cross network connected parallel transformers has demonstrated potential use in power distribution system. In those research works the proposed ANN for application in this control were developed using various algorithms and concluded that a network trained by Bayesian Regularization (BR) backpropagation algorithm produced the best performance measured in terms of correct tap changing decisions. However, further improvement of ANN based transformer tap changer operation is always desirable. A general rule for obtaining good generalization is to use the smallest network that solves the problem. In this paper, we show that a small sized ANN is obtainable for further improvement of transformer tap changer operation by modifying the standard Cascade-Correlation algorithm. The modification incorporates weight smoothing of output layer weights in Cascade-Correlation learning using Bayesian frame work. Experimental results demonstrate that significant improvement in performance is achieved when an ANN is trained by modified Cascade-Correlation algorithm instead of standard Cascade-Correlation or Bayesian Regularization backpropagation algorithm. A comparison of performances of different algorithms in application to transformer tap changer operation is analyzed and the results are presented.
Analysis of Symbol Generation and Integration in a Unified Model Based on a Neural Network
Authors: Yamauchi Yukari ,   Division of Natural Science, International Christian University, 3-10-2 Osawa, Mitaka, Tokyo 181-8585, Japan, Tano Shun'ichi ,   Graduate School of Information Systems, University of Electro-Communications, 1-5-1 Chofugaoka, Chofu, Tokyo 182-8585, Japan
Published: April 2016

The computational (numerical information) and symbolic (knowledge-based) processing used in intelligent processing has advantages and disadvantages. A simple model integrating symbols into a neural network was proposed as a first step toward fusing computational and symbolic processing. To verify the effectiveness of this model, we first analyze the trained neural network and generate symbols manually. Then we discuss generation methods that are able to discover effective symbols during training of the neural network. We evaluated these through simulations of reinforcement learning in simple football games. Results indicate that the integration of symbols into the neural network improved the performance of player agents.
A New Electronic Dictionary with Meaning Description of Case Frame
Authors: Shimizu Kouhei ,   Department of Information and Computer Science, Keio University, 3-14-1 Hiyoshi, Kohoku-ku, Yokohama 223-8522, Japan, Hagiwara Masafumi
Published: April 2016

In this paper, we propose a new electronic dictionary that helps computers to understand languages. In the field of natural language processing, meaning analysis is a difficult problem. The research has been quite few. On the other hand, meaning comprehension is quite interesting and expected to contribute to many fields. For meaning analysis, knowledge about words is indispensable. Thesauruses provide such knowledge by classifying words by their concepts. They can be used as electronic dictionaries. However, the contents are merely the clusters of words. They have little knowledge about the relationships of multiple clusters, resulting in no capacity for grasping relative meaning of words. For this reason, thesauruses are insufficient for meaning analysis. A novel approach is presented in this paper for improving the capability of electronic dictionaries. The contents of the new electronic dictionary are set of nodes and links, the same style as thesauruses. Our method enables computers to treat concretely multiple concepts of words by deriving nodes from another node. Each node consists of a verb and some nouns. Thus, a node-itself can describe meanings as a form of sentence, which differs from thesauruses. Meaning comprehension is achieved by routing nouns through nodes connected with links. Experimental results indicate the excellent ability to analyze meaning of a whole sentence. It is concluded that such ability of the dictionary is quite useful not only as an electronic dictionary itself but also in many fields such as composition support system.
Utilization of Neural Networks for Error Reduction of Elastomagnetic Sensors
Authors: Vojtko Jozef ,   Dept. of Theoretical Electrotechnics and Electrical Measurement, Technical University of Košice, Park Komenského 3, FEI TU, 042 00 Košice, Slovak Republic, Kovácová Irena , Madarász Ladislav , Kovác Dobroslav ,   Dept. of Electrical Drives and Mechatronics, Technical University of Košice, Letná 9, FEI TU, 042 00 Košice, Slovak Republic,   Dept. of Cybernetics and Artificial Intelligence, Technical University of Košice, Letná 9, FEI TU, 042 00 Košice, Slovak Republic
Published: April 2016

This paper deals with an elastomagnetic sensor of a pressure output metrological parameter improving to achieve linear sensor output. The designed solution is based on using neural networks as a universal approximator that enables conversion from sensor output to measured force and suppresses error.
Analogy Based Methodology for Complex Adaptive Production Network Modelling
Authors: Pátkai Béla ,   Product and Production System Design Laboratory, Institute of Production Engineering, Tampere University of Technology, P.O.Box 589, 33101 Tampere, Finland
Published: April 2016

The investigation (i.e. modelling, simulation, understanding and control) of complex adaptive systems is a problematic issue mainly due to the immaturity of systems scientific modelling paradigms. The apparent analogies between complex adaptive systems and production networks provide the opportunity to formulate the latter one in terms of the first, and to integrate the knowledge and the way of thinking of both fields. Through the application of the method of analogy and agent-based modelling and the development of a methodology a disciplined way of thinking is presented that involves the formulation of natural scientific concepts in terms of simulation models.
Statistical Image Analysis of Psychological Projective Drawings
Authors: Takemura Kazuhisa ,   Department of Psychology, Waseda University, 1-24-1 Toyama, Shinjuku-Ku, Tokyo 162-8644, Japan, Takasaki Iyuki , Iwamitsu Yumi ,   Department of Medical Psychology, Graduate School of Medical Sciences, Kitasato University, 1-15-1 Kitasato, Sagamihara City, Kanagawa 228-8555, Japan
Published: April 2016

We propose statistical image analysis for psychological projective drawings to facilitate assessing the reliability of the projective drawing making the determination of its validity difficult. Standard analysis involves (1) drawing a picture, (2) scanning the drawing, (3) dividing the drawing, (4) analyzing the gray level histogram moment (GLHM), (5) applying spatial gray level dependence method (SGLDM), (6) applying the gray level difference method (GLDM) for the drawing, and (7) interpreting the drawing. To demonstrate the proposed procedure, we used the tree test (Baum test). Three adults were presented with blank A4 paper and asked to draw a picture of a tree with fruit on it. Drawings were analyzed by statistical image analysis and results interpreted clinically.
From Basic Research to Applications
Authors: Mattila Jorma K. ,   Department of Information Technology, Laboratory of Applied Mathematics, Lappeenranta University of Technology, Lappeenranta, Finland
Published: April 2016

Forty years have passed since Prof. Lotfi A. Zadeh introduced fuzzy set theory in his known article “Fuzzy Sets” in Information and Control, 8, 1965, sparking new development in information technology and automation. This article also formed the roots of the Fuzzy Systems Research Group, an active part of the Laboratory of Applied Mathematics, Lappeenranta University of Technology. Rough set theory, evolutionary computing, and neural computing followed, together with their combinations. This Special Issue presents 10 papers representing these areas. Many of the contributors of this Special Issue belong to the Fuzzy Systems Research Group and others work in close co-operations with this group. The first paper considers the use of linguistically expressed objectives in multicriteria decision-making in selection processes based on topological similarity M-relations between L-sets. The second presents basic ideas and fundamental concepts of rough set theory and considers properties of rough approximations. The third combines Lukasiewicz logics and modifier algebras based on Zadeh algebras, i.e., quasi-Boolean algebras of membership functions. The fourth applies Mö{o}bius transformations, known in complex analysis, to fuzzy subgroups in a topological point of view. The fifth discusses the stability of a classifier based on the Lukasiewicz structure and tests Schweizer and Sklar's implications with an extension to generalized mean to a classification task. The sixth deals with the interpretability problem of first-order Takagi-Sugeno systems and interpolation issues, developing a special two-model configuration. The seventh describes an expert system for defining an athlete's aerobic and anaerobic thresholds that successfully mimics decision-making by sport medicine professionals, with system functionality based on fuzzy comparison measures, generalized means, fuzzy membership functions, and differential evolution. The eighth applies a differential evolution algorithm-based method to training radial basis function networks with variables including centers, weights, and widths. The ninth compares two floating-point-encoded evolutionary algorithms – differential evolution and a generalized generation gap model – using a set of problems with different characteristics. The tenth proposes a new approach for monitoring break tendency of paper webs on modern paper machines, combining linguistic equations and fuzzy logic in a case-based reasoning framework. As the Guest Editor of this Special Issue, I thank the contributors and reviewers for their time and effort in making this special issue possible. I am also grateful to the JACIII editorial board, especially Prof. Kaoru Hirota, the Editors-in-Chief and Managing Editor Kenta Uchino, and the staff of Fuji Technology Press for the opportunity to participate in this work. I also thank Prof. Kaoru Hirota for organizing the reviewing of my paper.
A Selection Model with Linguistically Expressed Objectives
Authors: Kortelainen Jari ,   Laboratory of Applied Mathematics, Lappeenranta University of Technology, P. O. Box 20, FIN-53851 Lappeenranta, Finland
Published: April 2016

We present and generalize some ideas studied previously in [14]. We express objectives by natural language and use L-sets, binary relations, and topologies to model this knowledge. We define topological similarity M-relation between L-sets and apply multiple criteria decision making methods when selecting alternatives. As an application, we present the process of selecting a member for a project team.
Properties of Rough Approximations
Authors: Järvinen Jouni ,   Turku Centre for Computer Science (TUCS), Lemminkäisenkatu 14 A, FIN-20520 Turku, Finland
Published: April 2016

In this paper, we present the basic ideas and fundamental concepts of rough set theory, focusing on properties of rough approximations.
A Comparison of Differential Evolution and Generalized Generation Gap Model
Authors: Rönkkönen Jani ,   Laboratory of Information Processing, Department of Information Technology, Lappeenranta University of Technology, P.O. Box 20, FIN-53851, Lappeenranta, Finland, Kukkonen Saku , Lampinen Jouni
Published: April 2016

We compared two floating-point-encoded evolutionary algorithms (EA) – differential evolution (DE) and the generalized generation gap (G3) – using a set of problems with different characteristics. G3 is reported to offer superior performance with unimodal functions, which are, however, often solved more efficiently using derivative-based optimization for example and it is interesting to know, how these algorithms perform in multimodal global optimization problems. Our results suggest that G3 converges fast but is prone to converge prematurely rather than finding the global optimum in high-dimensional multimodal problems. DE, in contrast, appears to handle multimodal problems better but cannot match convergence speed of G3 in unimodal problems.
Plant Recognition by Integrating Color and Range Data Obtained Through Stereo Vision
Authors: Takizawa Hotaka ,   University of Tsukuba, Ibaraki 305-8573, Japan, Ezaki Nobuo , Mizuno Shinji , Yamamoto Shinji ,   Toba National College of Maritime Technology, Mie 517-8501, Japan,   Toyohashi University of Technology, Aichi 441-8580, Japan,   Chukyo University, Aichi  470-0393, Japan
Published: April 2016

We present a new method for recognizing plants automatically and nondestructively by measuring plants three-dimensionally, including height, width, and leaf area, which are important clues for determining a plant condition. We use only two cameras and small plant preparation. A pair of color images for a plant is obtained by a binocular stereo camera, then leaf and stalk areas are extracted from color images, range data is calculated from stereo images, three-dimensional reconstruction for the plant is generated by using the Delaunay triangulation and the plant is measured in the generated reconstruction. Two experimental results are shown for actual plants.
Knowledge Representation of Architectural Design by Using IBIS-FRS System
Authors: Kiatake Marly ,   LabCAD - Dept. Civil Construction Engineering, Escola Politécnica, University of São Paulo, Av. Prof. Almeida Prado, Trav. 2 n.83 Ed. Eng. Civil - Cidade Universitária - CEP 05508-900 - São Paulo, Brasil, Cheng Liang-Yee , Petreche João R. D.
Published: April 2016

IBIS-FRS is a framework of design information that can be used to represent the reasoning process of designers in order to assist them in the decision-making processes. It is a combination of two systems - IBIS (Issue-Based Information Systems) and FRS (Fuzzy Reasoning System). This paper presents an application of IBIS-FRS to architectural design: the development of systems for evaluation of living's configuration in dwelling designs. These systems are used to analyse two cases of study and the results show the potential of IBIS-FRS in organizing the design information and in evaluating and improving the design.
Machine Learning: Automated Knowledge Acquisition Based on Unsupervised Neural Network and Expert System Paradigms
Authors: Elfadil Nazar ,   College of Engineering, Sultan Qaboos University, Muscat, Sultanate of Oman
Published: April 2016

Self-organizing maps are unsupervised neural network models that lend themselves to the cluster analysis of high-dimensional input data. Interpreting a trained map is difficult because features responsible for specific cluster assignment are not evident from resulting map representation. This paper presents an approach to automated knowledge acquisition using Kohonen's self-organizing maps and k-means clustering. To demonstrate the architecture and validation, a data set representing animal world has been used as the training data set. The verification of the produced knowledge base is done by using conventional expert system.
Switching Model Construction and Stability Analysis for Nonlinear Systems
Authors: Ohtake Hiroshi ,   Department of Mechanical Engineering and Intelligent Systems, The University of Electro-Communications, 1-5-1 Chofugaoka, Chofu, Tokyo 182-8585, Japan, Tanaka Kazuo
Published: April 2016

This paper presents switching model construction and stability analysis for a class of nonlinear systems. A switching fuzzy model newly developed in this paper is employed to represent the dynamics of a nonlinear system. A key feature of the switching fuzzy model construction is to find the so-called minimum distance sector by solving a nonlinear optimization problem. Next, we discuss the stability of a switching fuzzy model. To take advantage of the switching fuzzy model, we introduce a piecewise Lyapunov function that mirrors its structure. We show that the piecewise Lyapunov function approach provides less conservative results for the typical quadratic Lyapunov function approach. Illustrative examples demonstrate the utility of the switching model construction and the stability analysis.
Genetically Optimized Multi-Layer Fuzzy Polynomial Neural Networks: Analysis and Design
Authors: Oh Sung-Kwun ,   Department of Electrical Engineering, The University of Suwon, San 2-2, Wau-ri, Bongdam-eup, Hwaseong-si, Gyeonggi-do 445-743, South Korea, Pedrycz Witold , Park Ho-Sung ,   Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB T6G 2G6, Canada,   Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland,   Department of Electrical Electronic and Information Engineering, Wonkwang University, 344-2, Shinyong-Dong, Iksan, Chon-Buk, 570-749, South Korea
Published: April 2016

In this study, we introduce a new category of neurofuzzy networks – Fuzzy Polynomial Neural Networks and develop a comprehensive design methodology involving mechanisms of genetic optimization, and genetic algorithms, in particular. The augmented genetically optimized FPNN (referred to as gFPNN) is a structurally optimized architecture which comes with a higher level of flexibility in comparison to the one we have encountered in the conventional FPNN. The GA-based design procedure being applied to each layer of FPNN leads to the selection of preferred nodes (or FPNs) available within the FPNN. In the sequel, two general optimization mechanisms are explored. First, the structural optimization is realized via GAs whereas for the ensuing detailed parametric optimization is carried out in the setting of a standard least square method-based learning. The performance of the gFPNN is quantified through experimentation where we use a number of modeling benchmarks – synthetic and experimental data already experimented with in fuzzy or neurofuzzy modeling.
Combining the Global and Partial Information for Distance-Based Time Series Classification and Clustering
Authors: Zhang Hui ,   School of Knowledge Science, Japan Advanced Institute of Science and Technology, Nomi, Ishikawa 923-1292, Japan, Ho Tu Bao , Lin Mao-Song , Huang Wei ,   School of Computer Science, Southwest University of Science and Technology, Mianyang, Sichuan 621002, China
Published: April 2016

Many time series representation schemes for classification and clustering have been proposed. Most of the proposed representation focuses on the prominent series by considering the global information of the time series. The partial information of time series that indicates the local change of time series is often ignored. Recently, researches shown that the partial information is also important for time series mining. However, the combination of these two types of information has not been well studied in the literature. Moreover, most of the proposed time series representation requires predefined parameters. The classification and clustering results are considerably influenced by the parameter settings, and, users often have difficulty in determining the parameters. We attack above two problems by exploiting the multi-scale property of wavelet decomposition. The main contributions of this work are: (1) extracting features combining the global information and partial information of time series (2) automatically choosing appropriate features, namely, features in an appropriate wavelet decomposition scale according to the concentration of wavelet coefficients within this scale. Experiments performed on several benchmark time series datasets justify the usefulness of the proposed approach.
Robust Face Detection for Low-Resolution Images
Authors: Hayashi Shinji ,   Tokyo Institute of Technology, R2-52, 4259 Nagatsuta, Midori-ku, Yokohama 226-8503, Japan, Hasegawa Osamu ,   PRESTO, Japan Science and Technology Agency (JST)
Published: April 2016

Face detection, one of the most actively researched and progressive computer vision fields, has been little studied in low-resolution images. Using the AdaBoost-based face detector and MIT+CMU frontal face test set – the standard detector and images for evaluation in face detection – we found that face detection rate falls to 39% from 88% as face resolution decreases from 24×24 pixels to 6×6 pixels. We discuss a proposal using “portrait images,” “image expansion,” “frequency-band limitation of features” and “two-detector integration” and show that 71% of face detection rate is obtained for 6×6 pixel faces of MIT+CMU frontal face test set. Note that each of the above detections involves 100 false positives for 112 evaluation images.