bluuebunny/crossref_metadata_2025 huggingface dataset:
Fingerprint Matching Algorithm Using String-Based MHC Detector Set
Authors: Jeong Jae-Won ,   School of Electrical and Electronics Engineering, Chung-Ang University, 221, Heukseok-Dong, Dongjak-Ku, Seoul 156-756, Korea, Jang In-Hoon , Sim Kwee-Bo
Published: April 2016

Fingerprints have been widely used in the biometric authentication because of its performance, uniqueness and universality. Lately, the speed of identification has become a very important aspect in the fingerprint-based security applications. Also, the reliability still remains the main issue in the fingerprint identification. A fast and reliable fingerprint matching algorithm based on the process of the "self-nonself" discrimination in the biological immune system was proposed. The proposed algorithm is organized by two-matching stages. The 1st matching stage utilized the self-space and MHC detector string set that are generated from the information of the minutiae and the values of the directional field. The 2nd matching stage was made based on the local-structure of the minutiae. The proposed matching algorithm reduces matching time while maintaining the reliability of the matching algorithm.
Strategy Acquisition for Games Based on Simplified Reinforcement Learning Using a Strategy Network
Authors: Kanakubo Masaaki ,   Tokyo University of Technology, 1404-1 Katakura-cho, Hachioji-City, Tokyo 192-0982, Japan, Hagiwara Masafumi ,   Keio University, 3-14-1 Hiyoshi, Kohoku-ku, Yokohama 223-8522, Japan
Published: April 2016

We propose a simplified form of reinforcement learning (RL) for game strategy acquisition using a strategy network. RL has been applied to a number of games, such as backgammon, checkers, etc. However, the application of RL to Othello or Shogi, which have very large state spaces, is more difficult because these games take a very long time to learning. The proposed strategy network is composed of N lines from N nodes on the game board with a single evaluation node as a 2-layer perceptron. These nodes denote all possible states of every square on the game board and can easily represent the evaluation function. Moreover, these nodes can also denote imaginary states, such as pieces that may exist in the next step, or denote every positional relation of two arbitrary pieces or other various board phases. After several thousands of games had been played, the strategy network quickly acquired a better evaluation function than that using a normalized Gaussian network. The computer player employing the strategy network beat a heuristic-based player that evaluates the values of pieces or places on the game board. The proposed strategy network was able to acquire good weightings of various features of game states. In addition, the player employing the strategy network for a 4×4 Othello task after co-evolutionary training acquired a winning strategy.
Special Issue on Selected Papers in SCIS & ISIS 2004 – No.2
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) held at Keio University in Yokohama, Japan, on September 21-24, 2004, attracted over 300 papers in fields such as mathematics, urban and transport planning, entertainment, intelligent control, learning, image processing, clustering, neural networks applications, evolutionary computation, system modeling, fuzzy measures, and robotics. The Program Committee requested reviewers in SCIS & ISIS 2004 to select papers for a special issue of the Journal of Advanced Computational Intelligence & Intelligent Informatics (JACIII), with 27 papers accepted for publication in a two-part SCIS & ISIS 2004 special – Vol.9, No.2, containing 13 and the second part containing 14. Paper 1 details tap-changer control using neural networks. Papers 2-5 deal with image processing and recognition – Paper 2 proposing a model of saliency-driven scene learning and recognition and applying its model to robotics, paper 3 discussing breast cancer recognition using evolutionary algorithms, paper 4 covering a revised GMDH-typed neural network model applied to medical image recognition, paper 5 presenting how to compensate for missing information in the acquisition of visual information applied to autonomous soccer robot control. Paper 6 details gene expressions networks for 4 fruit fly development stages. Paper 7 proposes an α-constrained particle swarm optimized for solving constrained optimization problem. Paper 8 develops a fuzzy-neuro multilayer perceptron using genetic algorithms for recognizing odor mixtures. Paper 9 discusses how to integrate symbols into neural networks for the fusion of computational and symbolic processing and its effectiveness demonstrated through simulations. Paper 10 proposes an electric dictionary using a set of nodes and links whose usefulness is verified in experiments. Paper 11 presents a multi-agent algorithm for a class scheduling problem, showing its feasibility through computer simulation. Paper 12 proposes inductive temporal formula specification in system verification, reducing memory and time in the task of system verification. Paper 13 applies an agent-based approach to modeling transport using inductive learning by travelers and an evolutionary approach. The last paper analyzes architectural floor plans using a proposed index classifying floor plans from the user's point of view. We thank reviewers for their time and effort in making these special issues available so quickly, and thank the JACIII editorial board, especially Editor-in-Chief Profs. Hirota and Fukuda and Managing Editor Kenta Uchino, for their invaluable aid and advice in putting these special issues together.
A Fully Automated Breast Cancer Recognition System Using Discrete-Gradient Based Clustering and Multi Category Feature Selection
Authors: Ghosh Ranadhir ,   School of Information Technology and Mathematical Sciences, niversity of Ballarat, PO Box 663, Ballarat, Victoria 3353, Australia, Ghosh Moumita , Yearwood John
Published: April 2016

Advances in machine intelligence have provided a whole new window of opportunities in medical research. Building a fully automated computer aided diagnostic system for digital mammograms is just one of them. Given some success with semi-automated systems earlier, a fully automated CAD system is just another step forward. A proper combination of a feature selection model and a classifier for those areas of a mammogram marked by radiologists has been very successful. However a fully automated system with only two modules is a time consuming process as the suspicious areas in a mammogram can be quite small when compared to the whole image. Thus an additional clustering process can help in reducing the time complexity of the overall process. In this paper we propose a fast clustering process to identify suspicious areas. Another novelty of this paper is a multi-category feature selection approach. The choice of features to represent the patterns affects several aspects of pattern recognition problems such as accuracy, required learning time and the required number of samples. In this paper we propose a hybrid canonical based feature extraction technique as a combination of an evolutionary algorithm based classifier with a feed forward MLP model.
Revised GMDH-Type Neural Networks Using AIC or PSS Criterion and Their Application to Medical Image Recognition
Authors: Kondo Tadashi ,   School of Health Sciences, The University of Tokushima, 3-18-15 Kuramoto-cho, Tokushima 770-8509, Japan, Ueno Junji , Kondo Kazuya ,   Institute of Health Biosciences, The University of Tokushima, 3-18-15 Kuramoto-cho, Tokushima 770-8503, Japan
Published: April 2016

This study deals with the revised Group Method of Data Handling (GMDH)-type neural network algorithm using prediction error criterion defined as Prediction Sum of Squares (PSS) or Akaike's Information Criterion (AIC). The revised GMDH-type neural network algorithm generates optimum multilayered neural network architectures fitting the complexity of nonlinear systems using heuristic self-organization. The revised GMDH-type neural networks self-select the number of layers, optimum neuronal architectures, and useful input variables to minimize prediction error criterion defined as PSS or AIC. This algorithm is applied to the identification problem of the nonlinear complex system and results are compared to those obtained by the revised GMDH algorithm and conventional multilayered neural networks. The revised GMDH-type neural network algorithm is also applied to medical image recognition and it is shown that this algorithm is useful for medical image recognition.
Extraction of Developmentally Important Genes from Microarray Data
Authors: Han A Reum ,   Department of BioSystems, Korea Advanced Institute of Science and Technology, 373-1, Guseong-dong, Yuseong-gu, Daejeon, Korea, Kahng Dong-Soo , Ryu Tae Woo , Moon Hyun S. , Lee Kwang Hyung , Lee Doheon ,   Department of ComputerScience, Korea Advanced Institute of Science and Technology, 373-1, Guseong-dong, Yuseong-gu, Daejeon, Korea
Published: April 2016

Using microarray data on 4,028 genes during the lifecycle of Drosophila melanogaster, we constructed gene expression networks for 4 developmental stages of the fruitfly: egg and early embryo, embryo, larva, and pupa. The network for each stage showed a scale-free property with 0.85 < γ < 1.85 showing one or two giant clusters and many small clusters. Since hubs are thought to bring relatively dramatic changes in a network, we assumed hubs can be effective targets of medical treatments and/or drugs. Accordingly, we analyzed hub genes with a high degree for all networks. We found many previously studied genes that play crucial roles in each stage. We also assigned the biological process of gene ontology (GO) to genes in local dense regions of a cluster centered on hubs and found several enriched functions that are the keys to understand the effects of hubs.
Dynamic Mode Choice of Commuters in an Agent-Based Simulation Model with Inductive Learning Machines
Authors: Sunitiyoso Yos ,   Centre for Transport and Society, University of the West of England, Frenchay Campus, Coldharbour Lane, Bristol, BS16 1QY, United Kingdom, Matsumoto Shoji ,   Dept. of Civil and Environmental Engineering, Nagaoka University of Technology, 1603-1 Kamitomioka-machi, Nagaoka, Niigata 940-2188, Japan
Published: April 2016

This study applies an agent-based approach to modeling a transport system. Using the advantage of agent-based models of being validated at an individual level, a social dilemma of travel mode choice is modeled and viewed as a complex system. An inductive learning machine is combined with an evolutionary approach to simulate traveler learning. A user-equilibrium point predicted by conventional analysis is reached and stabilized. The stable situation is produced by interaction among agents and by behavioral change of each agent, without a central or external rule that organizes objectives of the system. The study shows conditions that may produce other stable situations besides the user equilibrium point. An emergent situation combined with traveler sensitivity to payoff differences is influential.
Left Ventricle Wall Motion Analysis Using MRI Tagging
Authors: Morito Tsuneharu ,   Okayamasaiseikai General Hospital (Okayama University Medical School), 1-17-18 Ihukutyo, Okayama 700-8511, Japan, Kanazawa Susumu , Sano Tetsuya , Yamamoto Hideki ,   Okayama University, Medical School, 2-5-1 Shikatacho, Okayama 700-8558, Japan,   System Enterprise Co., Ltd., 3203 Tomiyoshi, Okayama 701-1133, Japan,   Okayama University, 3-1-1 Tsushima, Okayama 700-8530, Japan
Published: April 2016

In this paper, we developed a left ventricular (LV) wall motion analysis method and a display method of the LV wall motion. To know precise wall motion such as rotation or deformity, identifiable landmarks must be located on the heart. The MRI tagging makes it possible to estimate the regional wall motion noninvasively. Tagging is achieved by selective saturation pulse method. We tried to estimate the precise LV wall motion by the MRI tagging. The difference of the signal intensity between tagged and nontagged regions persisted for 400-700 milliseconds (ms). We displayed the tag point movement of the LV wall as animation. Animation display enables us to understand heart wall motion clearly. To display the base and the apex of the LV, the difference of the wall motion became obvious. And comparison of short axis and long axis, comparison of another person, and comparison of another period of same person become possible.
An Island-Based Evolution Algorithm for Discrete-Continuous Scheduling with Continuous Resource Discretisation
Authors: Jedrzejowicz Piotr ,   Chair of Information Systems, Gdynia Maritime University, ul. Morska 83F, 81-225 Gdynia, Poland, Skakovski Aleksander ,   Computer Science Department, Gdynia Maritime University, ul. Morska 83F, 81-225 Gdynia, Poland
Published: April 2016

A problem of scheduling nonpreemtable tasks on parallel identical machines under an additional continuous resource to minimize the schedule length is considered in the paper. A continuous resource is divisible continuously and is allocated to tasks from given intervals in amounts unknown in advance. Task processing rate depends on the allocated amount of the continuous resource. The considered problem can be solved in two steps. The first step involves generating all possible task schedules and second - finding an optimal schedule among all schedules with optimal continuous resource allocation. The second step can be formulated as a convex programming problem with linear constraints that can be solved by specialized solvers. To eliminate time consuming optimal continuous resource allocation a problem ΘZ with continuous resource discretisation is introduced. Because ΘZ is NP-hard an island-based evolution algorithm (IBEA) is proposed to tackle the problem. Experimental results proved IBEA to be competitive with known algorithms for solving the considered problem.
Human Intent Description in Environment Adaptive Product Model Objects
Authors: Horváth László ,   John von Neumann Faculty of Informatics, Budapest Tech Polytechnical Institution, Népszínház u. 8, H-1081 Budapest, Hungary, Rudas Imre J.
Published: April 2016

This paper discusses a method for intelligent computer modeling of engineering objects by extended application of advanced effective entities as features for intent of engineers, behavior of modeled objects, and adaptivities. These and other descriptions are organized in engineering process oriented environment adaptive model objects. Knowledge is defined for engineering activities by using intent of humans. Model objects apply embedded, integrated, and referred knowledge representations according to the source and nature of design intent. Behaviors of the modeled engineering objects are simulated for typical sets of circumstances called as situations. Results of a simulation are evaluated then applied at adaptive actions by the model objects. In this way, a solution has been stablished for human intent driven control of decision assistance. This paper starts with an introduction of a multilevel modeling in which knowledge represents human intent. Following this, process of modeling by human intent as well as essential approaches for creation and application of design intent description in decision making are outlined. Next, application of integrated and environment adaptive model object is proposed and a four leveled modeling by behavior and associativity features is explained. Finally, selected issues for implementation of the proposed modeling in CAD/CAM environments are concluded.
Genetic Network Programming for Automatic Program Generation
Authors: Mabu Shingo ,   Graduate School of Information, Production and Systems, Waseda University, 2-7 Hibikino, Kitakyushu, Fukuoka 808-0135, Japan, Hirasawa Kotaro , Matsuya Yuko , Hu Jinglu ,   Graduate School of Information Science and Electrical Engineering, Kyushu University, 6-10-1 Hakozaki, Higashi-ku, Fukuoka 812-8581, Japan
Published: April 2016

In this paper, a recently proposed Evolutionary Computation method called Genetic Network Programming (GNP) is applied to generate programs such as Boolean functions. GNP is an extension of Genetic Algorithm (GA) and Genetic Programming (GP). It has a directed graph structure as gene and can search for solutions effectively. GNP has been mainly applied to dynamic problems and has shown better performances compared to GP. However, its application to static problems has not yet been studied well. Thus in this paper, GNP is applied to generate programs as its extension to solving static problems. In order to apply GNP to generating static problems, we introduced a new element, memory. In the proposed method, a GNP individual consists of a directed graph and a memory, while one in conventional GNP consists only of a directed graph. In the simulations, GNP succeeded in solving Even-n-Parity problem and Mirror Symmetry problem.
Stability Issues with Classifier Using Lukasiewicz Similarity and Modified Schweizer & Sklar Equations
Authors: Luukka Pasi ,   Laboratory of Applied Mathematics, Lappeenranta University of Technology  P.O. Box 20, FIN-53851 Lappeenranta, Finland, Sampo Jouni
Published: April 2016

In this article we have tested the stability of a classifier based on Lukasiewicz similarity in the generalized Lukasiewicz structure. We have also tested Schweizer & Sklar's implications with an extension to generalized mean to classification task. We will show that classification results are not so sensitive to p values with Schweizer & Sklar's measures, which indicates a generalized form of equations. In this article we have also tested the stability of these measures. Two different tests for stability were made: In one test the stability was checked with respect to weight parameters and the other test was carried out for idealvectors. The tests were done with five different classification problems.
Identification of Numerically Accurate First-Order Takagi-Sugeno Systems with Interpretable Local Models from Data
Authors: Riid Andri ,   Department of Computer Control, Tallinn University of Technology, Ehitajate tee 5, Tallinn 19086, Estonia, Rüstern Ennu
Published: April 2016

The paper deals with the interpretability problem of 1st order Takagi-Sugeno systems and interpolation issues in particular. Interpolation is improved by a corrective secondary model – essentially a black box – complementing the primary (interpretable) model. Optimization technique for this two-model configuration is developed. Experimental results indicate that the approach achieves a better accuracy-interpretability tradeoff than methodologies currently in use.
Traces of Fuzzy Relations Under Dual Operations
Authors: Hashimoto Hiroshi ,   Faculty of Economics, Yamaguchi University, Yamaguchi 753-8514, Japan
Published: April 2016

We examined dual properties of traces of a fuzzy relation using fuzzy matrices. Traces of a fuzzy relation are reflexive and transitive fuzzy relations obtained from the given fuzzy relation. Under standard operations on fuzzy matrices, fuzzy matrix inequalities and equalities are deduced by applying the well-known equivalent transformation of a fuzzy matrix inequality. We present, in particular, propositions on the construction of transitive fuzzy relations.
Improving Text Categorization by Multicriteria Feature Selection
Authors: Doan Son ,   Graduate School of Information Science, Japan Advanced Institute of Science and Technology, 1-1 Asahidai, Nomi, Ishikawa 923-1292, Japan, Horiguchi Susumu ,   Graduate School of Information Science, Tohoku University, 6-3-09 Aoba, Sendai 980-8579, Japan
Published: April 2016

Text categorization involves assigning a natural language document to one or more predefined classes. One of the most interesting issues is feature selection. We propose an approach using multicriteria ranking of eatures, a new procedure for feature selection, and apply these to text categorization. Experimental results dealing with Reuters-21578 and 20Newsgroups benchmark data and the naive Bayes algorithm show that our proposal outperforms conventional feature selection in text categorization performance.
Selected Papers from ISCIIA'04
Authors: Kang Yaohong ,   *Hainan University, Renmin Road, Haikou, Hainan 570228, China**Guangdong University of Technology, 7290 East Dongfeng Road, Guangzhou, Guangdong, China***Faculty of Engineering, Kyushu Institute of Technology, 1-1 Sensui-cho, Tobata-ku, Kitakyushu-shi 804-8550, Japan, Zhao Shibin , Kawamoto Kazuhiko
Published: April 2016

This special issue contains 14 papers selected from the first International Symposium on Computational Intelligence and Industrial Applications (ISCIIA'04), held in Haikou, China, December 20-24, 2004. Of the 82 papers from 8 countries submitted to the symposium, 62 were accepted for the proceedings. Based on reviewer's recommendations and guest editor's careful consideration, the authors of 14 papers have revised and extended their symposium papers for this issue. Computational intelligence is the study of the design of "intelligent" systems, which is flexible in changing environments and changing goals with uncertainty, and covers artificial intelligence, neural networks, fuzzy systems, evolutionary computation, and hybrid systems. The objective of this special issue is to reveal current challenges, research topics, and technology solutions critical to algorithms and applications involving computational intelligence. These 14 papers cover such important research areas as neural networks, image processing, control, financial engineering, robotics, and related technologies in computational intelligence. We believe that the information in this issue will become a valuable new resource for the computational intelligence community. We thank the authors and referees whose selfless work and valuable comments have made this special issue possible and improved the overall quality of the papers.
Improved MLP Learning via Orthogonal Bipolar Target Vectors
Authors: Nomura Shigueo ,   Graduate School of Informatics, Kyoto University, 606-8501 Kyoto, Japan, Yamanaka Keiji , Katai Osamu , Kawakami Hiroshi , Shiose Takayuki ,   Faculty of Electrical Engineering, Federal University of Uberlândia, 38400-902 Uberlândia, Brazil
Published: April 2016

In this paper, we present an approach to improve "Multilayer Perceptron" (MLP) learning by adopting orthogonal bipolar vectors as expectation values. These vectors differ from conventional vectors in two main ways. First, since they are larger than conventional ones, they use more synaptic connections among hidden and output neurons. Second, since they are orthogonal vectors with bipolar representation, the Euclidean distance for these vectors (in a Euclidean space Rn) increases when their number (n) of components increases. We present an algorithm for constructing orthogonal bipolar vectors used as target vectors. These target vectors are used in experiments for training by backpropagation algorithm with an MLP model that classifies characters extracted from degraded license plate images. Experimental results are obtained by using both orthogonal bipolar and conventional target vectors. Comparisons of results lead to conclusions that recognition of degraded characters are considerably improved with the proposed expectation values for MLP learning.
Competitive Learning with Fast Neuron-Insertion
Authors: Shigei Noritaka ,   Kagoshima University, Kagoshima-shi 890-0065, Japan, Miyajima Hiromi , Maeda Michiharu ,   Kurume National College of Technology, Kurume-shi 830-8555, Japan
Published: April 2016

Adaptive Vector Quantization (AVQ) is to find a small set of weight vectors that well approximates a larger set of input vectors. This paper presents a fast AVQ method Competitive Learning with Approximate Neuron-Insertion (CLANI). Though neuron-insertion techniques can much enhance the accuracy in AVQ, a naive implementation requires a large computational cost proportional to the number of input vectors. Approximate neuron-insertion has an advantage that its computational cost is independent of the number of input vectors. We theoretically estimate the computational costs of CLANI and the other conventional methods. The effectiveness of CLANI is demonstrated in vector quantization simulations and an image compression application.
SONIA-Based Decision Neural Network for Preference Assessment with Incomplete Comparisons
Authors: Widyanto Muhammad R. ,   Department of Computational Intelligence and Systems Science, Tokyo Institute of Technology, G3-49, 4259 Nagatsuta, Midori-ku, Yokohama-city 226-8502, Japan, Kawamoto Kazuhiko , Kusumoputro Benyamin , Hirota Kaoru ,   Faculty of Computer Science, University of Indonesia, Depok Campus, West Java, Indonesia
Published: April 2016

To deal with the problem of incomplete comparisons in decision maker preference assessment, a SONIA (Self-Organized Network inspired by Immune Algorithm)-based Decision Neural Network (DNN) is proposed. B cell mutation of SONIA creates hidden units having diverse data characteristics that improve generalization capability. This mutation deals with a limited number of training data resulting from incomplete pair-wise comparisons by decision maker. Experiments on linear, Euclidean, and Lp-metric function as underlying decision maker preference show that SONIA-based DNN outperforms conventional DNN for decision maker preference assessment with incomplete comparisons.
Improved Debt Rating Model Using Choquet Integral
Authors: Kaino Toshihiro ,   School of Business Administration, Aoyama Gakuin University,       4-4-25 Shibuya, Shibuya-ku, Tokyo 150-8366, Japan, Urata Ken , Yoshida Shinichi , Hirota Kaoru ,   DENTSU INC. 1-8-1 Higashi-shimbashi, Minato-ku, Tokyo 105-7001, Japan,   Information Science Research Center, Aoyama Gakuin University, 5-10-1 Fuchinobe, Sagamihara-shi, Kanagawa 229-8558, Japan,   Department of Computational Intelligence and Systems Science, Interdisciplinary Graduate School of Science and Engineering, Tokyo Institute of Technology, Midori-ku, Yokohama 226-8502, Japan
Published: April 2016

Improved long-term debt rating model using Choquet integral is proposed, where the input is qualitative and quantitative data of the corporations, and the output is the Moody's long-term debt ratings. The fuzzy measure, which is given as the importance of each qualitative and quantitative data, is derived from a neural net method. Moreover, differentiation of the Choquet integral is applied to the long-term debt ratings, where this differentiation indicates how much evaluation of each specification influence to the rating of the corporation. A long-term debt rating model using Choquet integral was proposed by Kaino and Hirota [1]. Under the ambiguous information which couldn't be expressed by the statistics model, this Kaino and Hirota model [1] enabled analysis of the amount of influences of a specific variable, and showed the new possibility in the field of credit risk measurement. However, in order to develop a practical system for small and medium-sized corporations with many needs, this model must be improved so that it may correspond to the changing market or many types of industry. Moreover, this model is modified by the implementation of actual rating provider's similar process to raise the relevance ratio. The advanced model proposed herein corporations than the model is more precise than conventional model using 2-layer type neural network model.
Evolutionary Optimisation for Obstacle Detection and Avoidance in Mobile Robotics
Authors: Pauplin Olivier ,   INRIA, IMARA and COMPLEX Teams, BP 105, 78153 Le Chesnay Cedex, France, Louchet Jean , Lutton Evelyne , Fortelle Arnaud de La
Published: April 2016

This paper presents an artificial evolution-based method for stereo image analysis and its application to real-time obstacle detection and avoidance for a mobile robot. It uses the Parisian approach, which consists here in splitting the representation of the robot's environment into a large number of simple primitives, the "flies", which are evolved according to a biologically inspired scheme. Results obtained on real scene with different fitness functions are presented and discussed, and an exploitation for obstacle avoidance in mobile robotics is proposed.
Robust Tuning of PID Controller Using Bacterial-Foraging-Based Optimization
Authors: Kim Dong Hwa ,   Dept. of Instrumentation and Control Eng., Hanbat National University, 16-1 San Duckmyong-Dong Yuseong-Gu, Daejon City Seoul 305-719, Korea, Cho Jae Hoon
Published: April 2016

We propose a design approach to PID controllers with resistance to external disturbance in motor-controlled systems using a bacterial foraging-based optimal algorithm. PID controllers are used to operate AC motor drives because of their practical implementation and simple structure. Inexperienced personnel find it difficult, however, to achieve optimal PID gain because this is manually tuned by trial and error in industrial environments full of disturbances. To design disturbance-resistance tuning, we use disturbance-resistance conditions based on H∞ and calculcate response the performance based on bacterial foraging for the PID controller as an integral of time-weighted squared error. Hence, parameters for the PID controller are selected by our bacterial foraging-based optimal algorithm to obtain the required response.
Voting-Based Approach to Nullspace Search for Correspondence Matching and Shape Recovery
Authors: Kawamoto Kazuhiko ,   Faculty of Engineering, Kyushu Institute of Technology, 1-1 Sensui-cho, Tobata-ku, Kitakyushu 804-8550, Japan, Imiya Atsushi , Hirota Kaoru ,   Institute of Media and Information Technology, Chiba University, 1-33 Yayoi-cho, Inage-ku, Chiba 263-8522, Japan,   Dept. of Computational Intelligence and Systems Science, Tokyo Institute of Technology, Mail-Box G3-49, 4259 Nagatsuta, Midori-ku, Yokohama 226-8502, Japan
Published: April 2016

A simultaneous search, callednullspace search, for matching correspondences among images and recovering 3-D objects is proposed by using a voting-based method to circumvent erroneous recovery of 3-D objects arising from wrong matched correspondences among images. It is able to avoid occlusion problems and cope with remarkable changes in visibility in a long image sequence. An experiment is done with synthetic and real image sequences, consisted of 30 images of a sphere and 10 images of a toy house, under the condition that 3-D points are occluded at most 50% of the sequence and the camera moves with rotational as well as translational motions. The proposed method gives a basis for organizing multiple dynamic images where occlusion occurs frequently.
A Hybrid Neuro-Symbolic Approach for Arabic Handwritten Word Recognition
Authors: Souici-Meslati Labiba ,   Department of Informatics, LRI Laboratory, Badji Mokhtar University, BP 12, 23000, Annaba, Algeria, Sellami Mokhtar
Published: April 2016

In this article, we suggest a system that automatically constructs knowledge based artificial neural networks (KBANN) for the holistic recognition of handwritten Arabic words in limited lexicons. To build a neuro-symbolic KBANN classifier for a given vocabulary, ideal samples of its words are first submitted to a structural feature extraction module. The analysis of the presence and possible occurrence numbers for these features in the considered lexicon enables to generate a symbolic knowledge base reflecting a hierarchical classification of the words. A rules-to-network translation algorithm uses this knowledge to build a multilayer neural network. It determines precisely its architecture and initializes its connections with specific values rather than random values, as is the case in classical neural networks. This construction approach provides the network with theoretical knowledge and reduces the training stage, which remains necessary because of styles and writing conditions variability. After this empirical training stage using real examples, the network acquires a final topology, which allows it to recognize new handwritten words. The proposed method has been tested on the automated construction of neuro-symbolic classifiers for two Arabic lexicons: literal amounts and city names. The application of this approach to the recognition of handwritten words or characters in different scripts and languages is also considered.
Chain Restaurant Work Scheduling Based on Genetic Algorithm with Fuzzy Logic
Authors: Watanabe Makoto ,   Department of Computational Intelligence and Systems Science, Tokyo Institute of Technology, G3-49, 4259 Nagatsuta, Midori-ku, Yokohama 226-8502, Japan, Nobuhara Hajime , Kawamoto Kazuhiko , Dong Fangyan , Hirota Kaoru
Published: April 2016

A quasi-optimization algorithm to generate chain restaurant work scheduling (WS) is proposed based on a genetic algorithm with fuzzy logic, where the whole weekly chain restaurant WS problem is fuzzily decomposed into 7 daily WS problems and a combined weekly WS problem. The proposed algorithm expresses the requirements of individual members by membership functions in fuzzy logic and finds a near-optimal solution using the genetic algorithm. Experimental results verified that a 24-hour 7-day schedule for 15 workers at a chain restaurant is produced in 6 minutes using the proposed algorithm implemented with C++ and executed on a PC. A professional expert evaluated WS quality as satisfactory.
Experimentally Constructing Semantic Models Based on DNA Computing
Authors: Tsuboi Yusei ,   Institute of Applied DNA Computing, Graduate School of Science & Technology, Meiji University, 1-1-1 Higashimita, Tama-ku, Kawasaki-shi, Kanagawa 214-8571, Japan, Ibrahim Zuwairie , Ono Osamu
Published: April 2016

We propose a new DNA-based semantic model, constructed of DNA molecules, called asemantic model based on molecular computing(SMC). It is structured as a graph formed by the set of all (attribute, attribute value) pairs contained in the set of represented objects, plus a tag node for each object. Each path in the network, from an initial object-representing tag node to the terminal node, represents the object named on the tag. Inputting a set of input strands the forms object-representing dsDNAs via parallel self-assembly from encoded ssDNAs representing both attributes and attribute values (nodes), as directed by ssDNA splitting strands representing relations (edges) in the network. The success of experiments in constructing a small test model demonstrates that our proposed model suitably represents knowledge to storing vast amounts of information at high density.
FCAPS: Fuzzy Controller with Approximated Policy Search Approach
Authors: Naba Agus ,   Graduate School of Systems and Information Engineering, University of Tsukuba, 1-2-1 Namiki, Tsukuba, Ibaraki, Japan, Miyashita Kazuo ,   National Institute of Advanced Industrial Science and Technology (AIST), 1-2-1 Namiki, Tsukuba, Ibaraki, Japan
Published: April 2016

A fuzzy controller requires an engineer to tune its rules for controlling a given plant. To reduce the burden, we develop a gradient-based tuning method for the fuzzy controller. The developed method is closely related to a theory of reinforcement learning, but takes advantages of a practical assumption made for faster learning. In reinforcement learning, values of problem states need to be acquired through lots of trial-and-error interactions between the controller and the plant. And the plant dynamics should also be learned by the controller. In this research, we assume that an approximated value function of the problem states can be represented as a function of a Euclidean distance from a goal state and an action executed at the state. And, we propose to use it for the gradient search as an evaluation function. Our experimental results on a pole-balancing problem show that the proposed method can tune the fuzzy controller to have an optimal policy for reaching the goal state despite an unknown plant dynamics in not only a set-point problem but also a tracking problem.
An Inference Algorithm for Electronic System Diagnostics at the Block Diagram Level
Authors: Co Celso Bation ,   Electronics, Computer and Communication Engineering Department
Published: April 2016

Mathematical processes are designed to enable computers to emulate human inference in troubleshooting. Crisp set operations facilitate the interactive handling of incomplete but precise input information. Fuzzy set operations handle imprecise but complete information. The algorithm design we discuss here is limited to crisp set operations. We use diagnostics at the electronic system block diagram level to illustrate inference algorithm methodology. The algorithm deduces root causes and excludes intermediary causes in its conclusions. It also provides information for anticipating subsequent moves. We also consider results providing no conclusion, as is normally experienced by human troubleshooters under certain conditions.
A Speedup Algorithm for Repetition of Hypothetical Reasoning
Authors: Kimura Haruhiko ,   Dept. of Information and System Engineering, Kanazawa University, Kakumamachi, Kanazawa 920-1192, Japan, Misawa Tadanobu , Abe Koji , Ogoshi Yasuhiro ,   School of Management, Tokyo University of Science, 500 Shimokiyoku, Kuki, Saitama 346-8512, Japan,   Dept. of Information and Computer Science, Kanazawa Institute of Technology, 7-1 Ohgigaoka, Nonoichi, Ishikawa 921-8501, Japan,   Faculty of Engineering, Fukui University, 3-9-1 Bunkyo, Fukui 910-8507, Japan
Published: April 2016

This paper presents performance evaluations of a speedup method for hypothetical reasoning to a first-order predicate logic knowledge base in the case when reasoning is executed repeatedly replacing hypothetical knowledge and keeping background knowledge and the goal intact. The proposed method consists of substituting hypotheses for predicate knowledge which has recursive structures, deriving bit patterns of solutions from background knowledge and the goal in advance when all the hypotheses are true, and finding actual solutions from inclusion relation with bit patterns of hypothetical knowledge.
Fuzzy Ratings and Crisp Feedback in Fuzzy AHP for Supporting Human Decision Making
Authors: Yamashita Toshiyuki ,   Graduate School of Engineering, Tokyo Metropolitan University, 6-6 Asahigaoka, Hino, Tokyo 191-0065, Japan
Published: April 2016

Analytic Hierarchy Process (AHP) is one of the most popular tools for supporting human decision making, and several fuzzy extensions of AHP have been proposed. The present study investigated psychological effects of both fuzzy ratings in fuzzy AHP and crisp feedback of the results from fuzzy AHP. The results suggest that fuzzy ratings could incorporate the fuzziness of a person’s feelings in his/her decision making. The results also suggest that crisp feedback, which exaggerates the superiority of only one alternative or the differences among the alternatives, could help a person to make his/her decision, especially when being deeply puzzled about his/her choice.
Object Reconfiguration with Dextrous Robot Agents
Authors: Vass Gábor ,   Department of Control Engineering and Information Technology, Budapest University of Technology and Economics, H-1117 Magyar Tudosok krt. 2, Budapest, Hungary, Lantos Béla , Payandeh Shahram ,   Experimental Robotics Laboratory, School of Engineering Science, Simon Fraser University, 8888 University Drive, Burnaby, British Columbia, Canada V5A 1S6
Published: April 2016

This paper addresses an object manipulation planning algorithm for dextrous robot systems consisting a multifingered hand and a robotic manipulator. A method has been developed for object reconfiguration design. The result is a new algorithm using artificial intelligence based on simulated annealing and A*search. The upper level of the manipulation system, the global planner generates the motion of the object. The lower level, the local planner deals with the motion of the agents relative to the object and the design of the contact forces. The local planner is based on simulated annealing, thus the the local minima can be avoided in the energy function of the motion with high probability. Application of the algorithm has been discussed for three robot arms.
Design of Nondeterministic Program Termination Based on the Equivalent Transformation Computation Model
Authors: Takarajima Itaru ,   Department of Commerce, Nagoya Gakuin University, 1350 Kami-Shinano, Seto, Aichi 480-1298, Japan, Akama Kiyoshi , Imani Ikumi , Mabuchi Hiroshi ,   Information Initiative Center, Hokkaido University, Sapporo, Japan,   Department of Foreign Studies, Nagoya Gakuin University, Seto, Japan,   Faculty of Software and Information Science, Iwate Prefectural University, Iwate, Japan
Published: April 2016

We studied the termination of nondeterministic programs, which play an important, basic role in program synthesis, and provide a foundation for proving program termination. The class of programs we consider here is based on the equivalent transformation (ET) computation model. Computation with this model involves the succesive application of rules to sets of clauses. Since the ET model has more expressive terms and rules than other programming languages, program termination is difficult to prove: we must take into account all possible changes of terms made by various rules. We propose a sufficient condition of ET program termination that proves termination by checking each rule in a given program. We also provide an algorithm for this check.
Generic Database for Hybrid Bayesian Pattern Recognition
Authors: Tenekedjiev Kiril I. ,   Department of Economics and Management, Technical University of Varna, 1 Studentska Str., 9010 Varna, Bulgaria, Kobashikawa Carlos A. , Nikolova Natalia D. , Hirota Kaoru ,   Department of Computational Intelligence and Systems Science, Tokyo Institute of Technology, G3-49, 4259 Nagatsuta, Midori-ku, Yokohama 226-8502, Japan
Published: April 2016

A Bayesian pattern recognition system is proposed, that processes information encoded by four types of features: discrete, pseudo-discrete, multi-normal continuous and independent continuous. This hybrid system utilizes the combined frequentist-subjective approach to probabilities, uses parametric and nonparametric techniques for the conditional likelihood estimation, and relies heavily on the fuzzy theory for data presentation, learning, and information fusion. The information for training, recognition, and prediction of the system is organized in a database, which is logically structured into three interconnected hierarchical sub-databases. A software tool is created under MATLAB that assures consistency, integrity, and maintenance of the database information. Three application examples from the fields of technical and medical diagnostics are presented, which illustrate the types of problems and levels of complexity that the database tool can handle.
Special Issue on the Sixth International Conference on Fuzzy Systems (AFSS' 2004)
Authors: Phuong Nguyen Hoang ,   VFSS President and Director of CHIT, MoH of Vietnam
Published: April 2016

This special issue features five papers devoted to fuzzy systems and their applications. Papers were selected from those accepted and presented at the Sixth International Conference on Fuzzy Systems (AFSS' 2004) held in Hanoi, Vietnam on December 15-17, 2004. AFSS' 2004 and Tutorials held in Hue city on December 18-19, 2004, included a wide spectrum of research topics on "fuzzy set theory", "intelligent technology", "fuzzy logic and approximate reasoning", "neural networks", "genetic algorithms", "hybrid systems" and "soft computing". Over 40 papers were accepted and presented by researchers from countries including Brazil, Canada, Taiwan, India, Korea, Malaysia, Japan and Vietnam. Five papers receiving outstanding recommendations in reviews have been selected for this issue. The topics they address include fuzzy logic for robots, data mining, neural networks in medicine, Fuzzy Constraint Satisfaction Problems, and hybrid systems. As editors of this special issue, we are sincerely grateful to the authors. Special thanks also go to the referees for their excellent work, to Mr. Kazuki Ohmori for his aid in coordinating the issue's publication, and to the JACIII Editorial Board, especially Professor Kaoru Hirota for his invaluable support and encouragement. Finally, we thank Professors Masao Mukaidono and Witold Pedrycz for their contributions to AFSS' 2004. Without their support, AFSS' 2004 and this issue would not have been possible.
Dual Scaling in Data Mining from Text Databases
Authors: Watada Junzo ,   Graduate School of Information, Production and Systems, Waseda University, 2-2 Hibikino, Wakamatsu-ku, Kitakyushu, Fukuoka 808-0135, Japan, Aoki Keisuke , Kawano Masahiro , Hitam Muhammad Suzuri ,   AlaxaIA, New Kawasaki Mitsui BLDG West 13 F, 890 Kashimata, Saiwai, Kawasaki, Kanagawa 212-0058, Japan,   University College of Science and Technology Malaysia, 21030 Mengabang Telipot, Kuala Terengganu, Malaysia
Published: April 2016

The availability of multimedia text document information has disseminated text mining among researchers. Text documents, integrate numerical and linguistic data, making text mining interesting and challenging. We propose text mining based on a fuzzy quantification model and fuzzy thesaurus. In text mining, we focus on: 1) Sentences included in Japanese text that are broken down into words. 2) Fuzzy thesaurus for finding words matching keywords in text. 3) Fuzzy multivariate analysis to analyze semantic meaning in predefined case studies. We use a fuzzy thesaurus to translate words using Chinese and Japanese characters into keywords. This speeds up processing without requiring a dictionary to separate words. Fuzzy multivariate analysis is used to analyze such processed data and to extract latent mutual related structures in text data, i.e., to extract otherwise obscured knowledge. We apply dual scaling to mining library and Web page text information, and propose integrating the result in Kansei engineering for possible application in sales, marketing, and production.
A Proposed Model of Diagnosis and Prescription in Oriental Medicine Using RBF Neural Networks
Authors: Thang Cao ,   Graduate School of Science and Engineering, Ritsumeikan University, 1-1-1 Noji-Higashi, Kusatsu, Shiga 525-8577, Japan, Cooper Eric W. , Hoshino Yukinobu , Kamei Katsuari , Phuong Nguyen Hoang ,   21,   Century Center of Excellence Program, Ritsumeikan University, 1-1-1 Noji-Higashi, Kusatsu, Shiga 525-8577, Japan,   College of Information Science and Engineering, Ritsumeikan University, 1-1-1 Noji-Higashi, Kusatsu, Shiga 525-8577, Japan,   Center of Health Information Technology, Ministry of Health of Vietnam, The 9 floors Building, 134 Nui Truc Lane, Ba Dinh Dist., Hanoi, Vietnam
Published: April 2016

In this paper, we present a computing model for diagnosis and prescription in oriental medicine. Inputs to the model are severities of symptoms observed on patients and outputs from the model are a diagnosis of disease states and treatment herbal prescriptions. First, having used rule inference with a Gaussian distribution, the most serious disease state in which the patient appears to be infected is determined. Next, an herbal prescription written in suitable herbs with reasonable amounts for treating the infected disease state is given by RBF neural networks. Finally, we show some experiments and their evaluations, and then describe our future works.