bluuebunny/crossref_metadata_2025 huggingface dataset:
Solving Truck Delivery Problems Using Integrated Evaluation Criteria Based on Neighborhood Degree and Evolutionary Algorithm
Authors: Dong Fangyan ,   Department of Computational Intelligence and Systems Science, Tokyo Institute of Technology, G3-49, 4259, Nagatsuta, Midori-ku, Yokohama 226-8502, Japan, Chen Kewei , Iyoda Eduardo Masato , Nobuhara Hajime , Hirota Kaoru ,   Computing Technology Driving Laboratory, Fujimi 1-6-14-605, Kawasaki-ku, Kawasaki 210-0011, Japan
Published: April 2016

To solve a real-world truck delivery and dispatch problem (TDDP) that involves multiple mutually conflicting objectives, such as running and loading costs, a concept of neighborhood degree (ND) and an integrated evaluation criteria (IEC) of the solution based on ND are proposed. The IEC makes the weight setting easier than by using conventional methods. To find a high-quality solution to a TDDP in practical computational time, an evolutionary algorithm is proposed. It involves 3 components: (i) a simulated annealing (SA)-based method for finding an optimal or a suboptimal route for each vehicle; (ii) an evolutionary computation (EC)-based method for finding an optimal schedule for a group of vehicles; and (iii) threshold-based evolutionary operations, utilizing the ND concept. The TDDP viewed from real-world application is formulated and the proposed algorithm is implemented on a personal computer using C++. The proposed algorithm is evaluated in 2 experiments involving real-world data representative of the TDDP, and applied to food product delivery to a chain of 46 convenience stores in Saitama Prefecture. In the 2 experiments, our proposed algorithm resulted in a better schedule (with 80%-90% shorter computational time) than a schedule produced by an expert. By incorporating application-specific evaluation criteria, the proposed algorithm is applied to problems such as home-delivery of parcels or mail, and to problems of multidepot delivery and dispatch.
Special Issue on Papers Selected in ISIS & SCIS 2003
Authors: Kim Yong-Soo ,   Division of Computer Engineering, Daejeon University, 96-3 Yongun-dong, Dong-gu, Daejeon 300-716, Korea, Sim Kwee-Bo ,   School of Electrical and Electronic Engineering, Chung-Ang University, 221 Heukseok-Dong, Dongjak-Gu, Seoul 156-756, Korea
Published: April 2016

This special issue of journal covers a broad field ranging from intelligent systems to robotics. These papers were selected among the papers that were presented at the Joint 4th International Symposium on Advanced Intelligent Systems and 2nd International Conference on Soft Computing and Intelligent Systems which was held in Jeju, Korea on September 25-28, 2003. In the above symposium, there was a wide spectrum of intelligent systems and related topics, including sessions: intelligent systems, intelligent control, fuzzy sets, fuzzy systems, neural networks, robotics, genetic algorithms, image processing, soft computing, artificial life, etc. Many interesting results were presented at the symposium. Among these various papers, this special issue offers a selection of sixteen papers that contribute to advances of intelligent systems in various aspects. The topics that the selected papers deal with are fuzzy controller for the mobile robot control, neural networks and their application to image processing, intelligent control for a robot, intelligent system for probe detection, fuzzy image processing, genetic algorithms, fuzzy clustering for incomplete categorical data, predictive fuzzy controller for an electric four-wheeled vehicle. As guest editors of this special issue, we would like to express our thanks to authors for their contribution, the anonymous referees for their review, and Prof. Kaoru Hirota for his giving the opportunity to publish this special issue.
Intelligent Soft Driving System for a Four-Wheeled Electric Vehicle Eluding Dynamic Obstacles
Authors: Inoue Masaki ,   Institute of Engineering Mechanics and Systems, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki 305-8573, Japan, Yasunobu Seiji
Published: April 2016

Four-wheeled electric vehicles effectively assist users, such as the elderly, in their movement but the dynamic characteristic of these vehicles is too complex to move abeam, making them difficult inexperienced users to drive. To predict the future state of dynamic obstacles and how to elude such obstacle safely, we used a predictive fuzzy controller (evaluates the future states which several kinds of operation candidates were done and chooses the best one) that modeling human algorithms in the system. Human predict the future states of dynamic obstacles and choose a driving operation to safely elude them. To do so, we incorporated expert knowledge of safe driving into this controller. In this paper, we propose and develop the intelligent soft driving system by a controller that eludes dynamic obstacles safely, and confirmed its effectiveness in experiments.
Design and Analysis of Probe Detection Systems for TCP Networks
Authors: Lee Se-Yul ,   Dept. of Computer Science, Chungwoon University, San29 Namjang-Ri, Hongseong-Eup, Hongseong-Gun, Chungnam 350-701, Korea, Kim Yong-Soo ,   Division of Computer Engineering, Daejeon University, 96-3 Yongun-Dong, Dong-Gu, Daejeon 300-716, Korea
Published: April 2016

Advanced computer network technology enables the connectivity of computers in an open network environment. Despite the growing numbers of security threats to networks, most intrusion detection identifies security attacks mainly by detecting misuse using a set of rules based on past hacking patterns. This pattern matching has a high rate of false positives and cannot detect new hacking patterns, making it vulnerable to previously unidentified attack patterns and variations in attack and increasing false negatives. Intrusion detection and prevention technologies are thus required. We propose a network-based intrusion detection model using fuzzy cognitive maps (FCM) that detects intrusion by Denial of Service (DoS) attack detection using packet analysis. A DoS attack typically appears as a Probe and Syn Flooding attack. Syn Flooding Preventer using Fuzzy cognitive maps (SPuF) model captures and analyzes packet information to detect Syn flooding attacks. Using the result of decision module analysis, which used FCM, the decision module measures the degree of danger of the DoS and trains the response module to deal with attacks. The result of simulation using the "KDD’99 Competition Data Set" for the SPuF model shows that Probe detection exceeded 97%.
Safe Mobility System Cooperating with Human in Collaboration with Cyber City
Authors: Yamaguchi Toru ,   Department of Electronic System Engineering, Tokyo Metropolitan Institute of Technology, 6-6 Asahigaoka, Hino, Tokyo 191-0065, Japan, Kawakatsu Jun , Jing Jianping , Kurosaki Ryuji , Harashima Fumio ,   Department of Electrical Engineering, Tokyo Denki University
Published: April 2016

Recently, the aging society has become a serious problem. Aged people have difficulty to handle much information; we paid attention to the welfare and a disaster prevention cyber city. In this research, we propose the validity of Safe Mobility System cooperating with Human in Collaboration with Cyber City, and show Vehicle Garage Parking Support System as an example.
Expert-Knowledge Gating Mechanism in the Hierarchical Modular System
Authors: Shim Jeong-Yon ,   Division of General Studies, Computer Science, Kangnam University, San 6-2, Kugal-ri, Kihung-up, YongIn Shi, KyeongKi Do, Korea
Published: April 2016

To maximize the efficiency of knowledge learning, it is essential that the knowledge system itself be well structured. Well designed knowledge systems make easy to access for knowledge acquisition and extraction. Expert knowledge plays a role controlling. We propose a Hierarchical modular system with an expert-knowledge gating mechanism that consists of mechanisms for acquiring knowledge, constructing associative memory and enabling knowledge inference and extraction based on expert-knowledge gating. We applied this to medical diagnostics for classifying Viruses (coxackie, echovirus and cold virus), Rhinitis (Nonallergic and allergic) and tested using symptom data.
Translated Multiplicative Neuron: An Extended Multiplicative Neuron that can Translate Decision Surfaces
Authors: Iyoda Eduardo Masato ,   Department of Computational Intelligence and Systems Science, Interdisciplinary Graduate School of Science and Engineering, Tokyo Institute of Technology, Nobuhara Hajime , Hirota Kaoru
Published: April 2016

A multiplicative neuron model called translated multiplicative neuron (πt-neuron) is proposed. Compared to the traditional π-neuron, the πt-neuron presents 2 advantages: (1) it can generate decision surfaces centered at any point of its input space; and (2) πt-neuron has a meaningful set of adjustable parameters. Learning rules for πt-neurons are derived using the error backpropagation procedure. It is shown that the XOR and N-bit parity problems can be perfectly solved using only 1 πt-neuron, with no need for hidden neurons. The πt-neuron is also evaluated in Hwang's regression benchmark problems, in which neural networks composed of πt-neurons in the hidden layer can perform better than conventional multilayer perceptrons (MLP) in almost all cases: Errors are reduced an average of 58% using about 33% fewer hidden neurons than MLP.
Learning from Examples and Comparing Models of Human Motion
Authors: Kulbacki Marek ,   Systems Research Institute, Polish Academy of Sciences, 6 Newelska St, 01-447 Warsaw, Poland, Jablonski Bartosz , Klempous Ryszard , Segen Jakub ,   Institute of Engineering Cybernetics, Wroclaw University of Technology, 11/17 Janiszewskiego St, 50-372 Wroclaw, Poland,   Consultant, 337 Third St, Fair Haven NJ 07704, USA
Published: April 2016

This paper addresses a problem of creating character animations from motion capture clips. The main problem we want to solve is partition set of primitive motions into appropriate groups according to similarity between motions. We construct motion models to easier extract features of given motions and make animation process more flexible. Using these models we propose measure of discrepancy between motions. Moreover it normalizes length of motions and decreases high dimension of considered motion data, so clustering may take place in dimensionally reduced space. In addition we examine different motion representations for the sake of the best clustering results.
Microstatistic Multi-User Detection Receiver
Authors: Kocur Dusan ,   Department of Electronics and Multimedia Communications, Faculty of Electrical Engineering and Informatics, Technical University of Kosice, Park Komenskeho 13, 041 20 Kosice, Slovak Republic, Cízová Jana , Marchevský Stanislav
Published: April 2016

In this paper, the microstatistic multi-user detection receiver (MSF-MUD) will be introduced. The output of the MSF-MUD is taken as the sign of the multi-channel nonlinear transformation of the output of a bank of matched filters (BMF). In the case of the MSF-MUD, this transformation is done by multi-channel conventional microstatistic filter (M-CMF). Because the M-CMF theory was not available, the method of the M-CMF design and adaptive least mean square M-CMF are developed in this paper, too.
Energy-Conservative Algorithm for the Numerical Solution of Initial-Value Hamiltonian System Problems
Authors: Miletics Edit ,   Department of Mathematics, Széchenyi István University Györ, Hungary
Published: April 2016

The numerical treatment of ODE initial-value problems has been intensively researched. Energy-conservative algorithms are very important to dynamic systems. For the Hamiltonian system the symplectic algorithms are very effective. Powerful computers and algebraic software enable the creation of efficient numerical algorithms for solving ODE initial-value problems. In this paper, we propose an adaptive energy-conservative numerical-analytical algorithm for Hamiltonian systems. This algorithm is adaptable to initial-value problems where some quantities are preserved. The algorithm and its efficiency are presented for solving two-body and linear oscillator problems.
Application of Kernel Trick to Fuzzy c-Means with Regularization by K-L Information
Authors: Ichihashi Hidetomo ,   Graduate School of Engineering, Osaka Prefecture University, 1-1 Gakuen-cho, Sakai, Osaka 599-8531, Japan, Honda Katsuhiro
Published: April 2016

Support vector machines (SVM), kernel principal component analysis (KPCA), and kernel Fisher discriminant analysis (KFD), are examples of successful kernel-based learning methods. By the addition of a regularizer and the kernel trick to a fuzzy counterpart of Gaussian mixture models (GMM), this paper proposes a clustering algorithm in an extended high dimensional feature space. Unlike the global nonlinear approaches, GMM or its fuzzy counterpart is to model nonlinear structure with a collection, or mixture, of local linear sub-models of PCA. When the number of feature vectors and clusters are n and c respectively, this kernel approach can find up to c × n nonzero eigenvalues. A way to control the number of parameters in the mixture of probabilistic principal component analysis (PPCA) is adopted to reduce the number of parameters. The algorithm provides a partitioning with flexible shape of clusters in the original input data space.
Accelerated Genetic Programming for Intelligent Fuzzy Robots
Authors: Murai Yasuyuki ,   Department of Information and Computer Sciences, Kanagawa Institute of Technology, 1030 Shimoogino, Atsugi, Kanagawa 243-0292, Japan, Matsumura Koki , Tatsumi Hisayuki , Tsuji Hiroyuki , Tokumasu Shinji ,   Department of Information and Knowledge Engineering, Tottori University, 4-101 Koyama-Minami, Tottori, Tottori 680-8550, Japan,   Department of Computer Science, Tsukuba College of Technology, 4-12-7 Kasuga, Tsukuba, Ibaraki 305-0821, Japan
Published: April 2016

We previously proposed a new way of automatically generating membership functions that play an important role in improving the accuracy of fuzzy control by using genetic programming (GP). In this paper, we report 2 improvements for achieving better fuzzy robot intelligence. First, we made the mutation rate change dynamically based on a coupled chaotic system. Secondly, we introduced population partitioning using deme by parallel processing. The effectiveness of these improvements is demonstrated through computer simulation.
Weighted Similarity Classifier Using Differential Evolution and Genetic Algorithm in Weight Optimization
Authors: Luukka Pasi ,   Laboratory of Applied Mathematics, Lappeenranta University of Technology, P.O. Box 20, FIN-53851 Lappeenranta, Finland, Sampo Jouni
Published: April 2016

We have compared the differential evolution and genetic algorithms in a study of weight optimization for different similarity measures in a task of classification. In a study of high dimensional data weighting similarity measures become of great importance and efforts to study suitable optimizers is needed. In this article we have studied proper weighting of similarity measures in the classification of high dimensional and large scale data. We will show that in most cases the differential evolution algorithm should be used in finding the weights instead of the genetic algorithm.
Hierarchical Concept Structures Based Data Retrieval/Mining by Fuzzy Document Ordering System
Authors: Ohashi Tadashi ,   Tokyo Institute of Technology, G3-49, 4259 Nagatsuta, Midori-ku, Yokohama 226-8502, Japan, Nobuhara Hajime , Hirota Kaoru
Published: April 2016

A shrink operation for FOCUS (Fuzzy dOCUment ordering System) is proposed as a tool for changing user’s viewpoint in concept structures of documents. The FOCUS produces the user’s preference vector space and its concept hierarchical structure based on CSD (Concept System Dictionary) produced by EDR (Japan Electronic Dictionary Research Institute). Through document ordering experiments by 5 examinees using 20 given documents and 40 unknown ones extracted from IMDB (Internet Movie Data Base), the effectiveness of shrink operations, i.e., shrinkage of user’s preference vector space, is confirmed in the way that it avails to do fuzzy document matching in user’s rough point of view by using the hierarchical structure.
Remembering What You Forget in an Online Shopping Context
Authors: Halvey Martin ,   Adaptive Information Cluster, Department of Computer Science, University College Dublin, Dublin 4, Ireland, Keane Mark T.
Published: April 2016

Problems occur for users when they attempt to navigate or purchase items from a virtual shopping environment that has to correlate in the physical world. These problems arise in part, because online shoppers do not have the benefit of external memory provided by the physical world. The memory zones idea attempts to solve these problems by providing an online parallel to this external memory. The system reported uses a profile of previous purchases and a representation of the physical environment of the actual shop to recommend items to shoppers, that they may have otherwise forgotten because of the lack of suitable external memory.
Block-Based Change Detection in the Presence of Ambient Illumination Variations
Authors: Alexandropoulos Theodoros ,   Multimedia Technology Laboratory, School of Electrical and Computer Engineering, National Technical University of Athens, 9 Heroon Polytechneiou st, 15773, Zographou Campus, Athens, Greece, Loumos Vassili , Kayafas Eleftherios
Published: April 2016

The efficiency of change detection methods, in terms of content discrimination, is degraded by the presence of noise. Furthermore, illumination changes tend to cause further degradation in change detection accuracy by swamping content changes of similar magnitude. This fact imposes the application of a method which detects illumination variations in the presence of occlusions. This paper proposes the application of a block clustering method which aims to separate content changes from noise-level changes. The algorithm is performed in conjunction with a brightness normalization technique for the correction of ambient illumination variations.
Modified Axiomatic Basis of Subjective Probability
Authors: Tenekedjiev Kiril ,   Technical University of Varna, Dept. of Economics and Management, 1 Studentska Str., 9010 Varna, Bulgaria, Krushev Anastas , Nikolova Natalia ,   Technical University of Varna, Dept. of Navigation, 1 Studentska Str., 9010 Varna, Bulgaria
Published: April 2016

The paper aims at making a contribution to the axiomatic approach to subjective uncertainty by introducing a modified set of six axioms to subjective probabilities. Those are considered sufficient to guarantee adequacy and rationality in the expression of beliefs in future uncertain events by the observer of the system. The axioms combine mathematical correctness and unambiguous justification. They draw on existing axiomatic bases, but include some additional features and dependencies. A modified theorem for existence and uniqueness of subjective probabilities that uses the axioms discussed is also stated in the paper, and some explanation is given regarding the way to acquire its strict mathematical justification.
Distributed Route Guidance Systems with Self-Organized Multi-Layered Vector Fields
Authors: Sekiyama Kosuke ,   Department of Human and Artificial Intelligence Systems, University of Fukui, 3-9-1 Bunkyo, Fukui 910-8507, Japan, Ohashi Yasuhiro
Published: April 2016

This paper deals with novel distributed route guidance that cooperates with self-organizing control of traffic signal networks. Self-organizing control of traffic signals provides a fully distributed approach to coordinate a number of signals distributed in a wide area based on local information of traffic flows so that split and offset control parameters between traffic signals are adjusted for efficient traffic flow. The self-organizing route guidance systems (SRGS) concept is introduced for efficient route guidance to facilitate offset adjustment of the self-organizing control of signal networks by self-organizing multilayered vector fields. Simulation demonstrates the effectiveness of the proposal under nonstationary traffic conditions.
Studies on an Electronic Analog of a Recurrent Neural Network with Retrieval Phase Weight Adaptations
Authors: Mohan Vishwanathan ,   Dept. of Electronics and Telecommunication Engineering, SGGS Institute of Engineering and Technology, Vishnupuri, Nanded, Maharashtra State 431606, India, Joshi Yashwant V. , Itagi Anand , Gangadhar Garipelli
Published: April 2016

It is argued that weight adaptations even during retrieval phase can greatly enhance the performance of a neurodynamic associative memory. Our simulations with an electronic implementation of an associative memory showed that extending the Hopfield dynamics with an appropriate adaptive law in retrieval phase could give rise to significant improvements in storage capacity and computational reliability. Weights, which are supposed to encode the information stored in the Hopfield neural network, are usually held constant once training/storage is complete. In our case, weights also change during retrieval, hence losing information in the process, but resulting in much better retrieval of stored patterns. We describe and characterize the functional elements comprising the network, the learning system, and include the experimental results obtained from applying the network for character recognition in various noisy conditions. Stability issues emerging as a consequence of retrieval phase weight adaptation and implications of weights being used as transitory, intermediary variables are briefly discussed.
A Competitive Learning Algorithm with Controlling Maximum Distortion
Authors: Miura Takeshi ,   Graduate School of Information Sciences, Tohoku University, 6-6-01 Aramaki Aza Aoba, Aoba-ku, Sendai 980-8579, Japan, Sano Kentaro , Suzuki Kenichi , Nakamura Tadao
Published: April 2016

Vector quantization with an optimal codebook is attractive for lossy data compression. So far, a number of codebook design algorithms have been proposed to minimize the mean square error, MSE. However, these algorithms have a problem that MSE minimization sometimes causes an unacceptable maximum-distortion, which is very important in several applications. This paper proposes a competitive learning algorithm with controlling maximum distortion that designs a codebook giving a maximum distortion within a given error bound. The proposed algorithm assigns a code vector to an input vector with a too large distortion. Experimental results showed that the algorithm has both maximum-distortion control and MSE minimization capability.
Fuzzy Image Matching for Pose Recognition of Occluded Knee Implants Using Fluoroscopy Images
Authors: Kobashi Syoji ,   Graduate School of Engineering, University of Hyogo, 2167 Shosha, Himeji, Hyogo 671-2201, Japan, Tomosada Toshihiko , Shibanuma Nao , Yamaguchi Motoi , Muratsu Hirotsugu , Kondo Katsuya , Yoshiya Shinichi , Hata Yutaka , Kurosaka Masahiro ,   Graduate School of Medicine, Kobe University, 7-5-1 Kusunokicho, Chuo-ku, Kobe, Hyogo 650-0017, Japan
Published: April 2016

Fluoroscopy images have been widely used for evaluating kinematics of the knee implant in vivo after the total knee Arthroplasty, TKA in short. The knee implant mainly consists of tibial tray, tibial insert and femoral component. Because a fluoroscopy image is a 2-D projection image of the 3-D knee implant, the tibial tray often overlaps with the femoral component on the projection image, or a part of the knee implant is outside of the field of view (FOV). In order to analyze such occluded images, this article introduces fuzzy logic into image matching of the given 2-D fluoroscopy image and 3-D geometric models of the knee implant. Based on the proposed fuzzy image matching algorithm, we present a novel computer-aided diagnosis (CAD) system for estimating 3-D kinematics of the knee implant with 2-D fluoroscopy dynamic images. To quantitatively evaluate our system, it was applied to computer-simulated images and phantom images that took the knee implant in vitro fixed with arbitrary pose by a jig. The experimental results denoted that this system could estimate the pose of the knee implant within the error of 0.86° with non-occluded images, and within the error of 1.28° with 15% occluded images. Also, the proposed system was applied to two patients after TKA to demonstrate the clinical application.
Integrated Intelligence Control Based on Fuzzy and AI for Reheating Furnace
Authors: Liao Yingxin ,   School of Information Science and Engineering, Central South University, Changsha, 410083, China, Wu Min , Hirota Kaoru , Dong Fangyan , Cao Weihua ,   School of Electron and Information Engineering, Central South Forestry University, Changsha, 410004, China,   Department of Computational Intelligence and Systems Science, Tokyo Institute of Technology, G3-49, 4259 Nagatsuta, Midori-ku, Yokohama 226-8502, Japan
Published: April 2016

An integrated intelligence control method based on fuzzy and artificial intelligence (AI) is proposed aiming at combustion control of reheating furnace in steel rolling mill. Both fuzzy and AI strategies are used to solve problems of the bigger overshoot and the slower response in conventional hearth temperature control when gas pressure and heat value are frequently and acutely varied. The fuel-air ratio optimization and flux tracking modules based on AI respectively decrease fuel consumption and prolong the lifetime of actuators. The proposed method is implemented on an intelligence controller and distributed controllers, and the field test on two reheating furnaces in Lianyuan Iron and Steel Group Co. Ltd., Loudi, Hunan, China confirms that hearth temperature standard deviation, fuel consumption, and high temperature oxidation of billet are respectively decreased by 50%, 12%, and 10% over the current manual method. The proposed method delivers superior performance for reheating furnace in steel industry, but also it can be applied for other type furnaces in steel industry and in other industries, where further performance improvement might be achieved by adding a self-organizing capability to the fuzzy logic and AI control.
Recognizing Odor Mixtures Using Optimized Fuzzy Neural Network Through Genetic Algorithms
Authors: Kusumoputro Benyamin ,   Faculty of Computer Science, University of Indonesia, Depok Campus, Jakarta Indonesia, PO.Box 3443 Jakarta 10002, Arsyad Teguh P.
Published: April 2016

Recognizing odor mixtures is rather difficult in artificial odor recognition system, especially when the number of sensors is limited. Classification is further hampered if the number of unlearned odor mixtures classes is increased. We developed a fuzzy-neuro multilayer perceptron as a pattern classifier and compared its recognition with that of the Probabilistic Neural Network and Back-propagation Neural Network. To enhance the recognition capability of the system, we then optimized fuzzy-neuro multilayer perceptron topology by deleting its weak weight connections using Genetic Algorithms. Experimental results show that the optimized fuzzy-neuro multilayer perceptron has the highest recognition in 18 classes of two-mixture odors with almost 98.2% when using hardware with 16 sensors, compared to 83.3% when using 8 sensors.
Applying Multi-Agent Algorithm to a Class Scheduling System
Authors: Nunohiro Eiji ,   Department of Information Systems, Tokyo University of Information Sciences, 1200-2 Yatoh-cho, Wakaba-ku, Chiba 265-8501, Japan, Mackin Kenneth J.
Published: April 2016

We propose a multi-agent algorithm to solve a scheduling problem. The proposed application takes into account various restrictions relative to Japanese university class scheduling in particular. A multi-agent system consisting of agents representing the requirements and restrictions for professors and departments are introduced. Scheduling is solved using negotiation between agents together with the hill-climbing method. Through a software simulation, we were able to show that our proposed method successfully applies the self-organizing nature of agents to solve the scheduling problem.
Characteristics Analysis of Two-Dimensional Configuration Using Modified Box-Count Method
Authors: Sato Yusuke ,   Department of Computer Science, Nihon University, Funabashi-shi, Chiba 274-8501, Japan, Shingu Kiyoshi ,   Department of Computer Science and Department of Oceanic Architecture and Engineering, Nihon University, Funabashi-shi, Chiba 274-8501, Japan
Published: April 2016

An architectural floor plan includes elements that determine movement of people within the architectural space and that play an important role in relating people and architecture. Especially since modern times, various shapes that deviate and escape from a floor plan by means of right-angled constitution have arisen and have been used as elements of architecture. Therefore, it is necessary to consider "a floor plan". We propose an index that classifies floor plans from a person's view, analyzing them by the modified box-count method comparing results, and proposing consideration.
Special Issue on Computational Cybernetics
Authors: Elmenreich Wilfried ,   Vienna University of Technology, Institut für Technische Informatik, Treitlstrasse 1-3/182-1, A-1040 Vienna, Austria, Rudas Imre J. ,   Budapest Polytechnic,  Doberdó út 6, H-1034 Budapest, Hungary
Published: April 2016

This issue contains selected papers from the International IEEE Conference on Computational Cybernetics that took place in Vienna 2004 in Austria at the Vienna University of Technology. Computational Cybernetics is the synergetic integration of Cybernetics and Computational Intelligence techniques. Cybernetics was defined by Wiener as "the science of control and communication, in the animal and the machine". The word "cybernetics" itself stems from the Greek "kybernetes" that means pilot or governor. While the roots of cybernetics go back to the time when James Watt equipped his steam engine with a Governor, that is a simple feedback mechanism for regulation of steam flow, the computational component was a child of the 20th century with the rise of information processing machines. The science of cybernetics and the science of computer science have in common, that both infiltrated many fields of application such as mathematics, telecommunication, regulated engines, living systems/medicine, social systems, and economical systems. Thus, on the one hand, the science of computational cybernetics encompasses a wide field, like the comparative study of automatic control systems, mechanical, biological (living), social and economical systems, communication theory, signal processing, information technology, control theory, the theory of adaptive systems, and the theory of complex systems (game theory, operational research). On the other hand, this research allows for finding common roots and common behavior among this broad field. This dichotomy between a broad overarching topic and the focus on computational cybernetics establishes the basis for interesting talks and discussions between scientists of different disciplines. We have selected 11 papers from the conference covering the fields of system design and modeling, neural networks, control theory, robotics and pattern recognition, which resemble the great variety of computational cybernetics. After the conference, each of these papers has undergone another peer review cycle in which the papers had been improved in order to fit this journal's topic and quality. It is our hope that the papers in this issue will inspire and help our readers in the development of advanced intelligent systems at the service of mankind.
Fault-Tolerant Compensation of the Propagation Delay for Hard Real-Time Systems
Authors: Losert Thomas ,   Institute of Computer Engineering, Vienna University of Technology, Vienna, Austria, Schlager Martin , Elmenreich Wilfried
Published: April 2016

In control systems the jitter is a relevant problem since the majority of theoretical results for analysis and design of time-invariant systems do not apply for time-variant systems. Reducing the jitter increases the stability of the closed control-loop thus leading to enhanced reliability. This paper presents a general model that can be applied to bus topologies as well as to star topologies. Based on this model an algorithm is presented that allows to improve the precision of a set of distributed clocks by measuring the propagation delay of the communication lines in a fault-tolerant way and compensating the jitter introduced by the propagation delay. Some fault-tolerant architectures already provide means for coping with propagation delays but require manually entering the values in a configuration-tool. With this algorithm the system supports this error-prone task by providing validity checks for the entered values or measuring these values automatically thus rendering this maintenance step obsolete.
A Unified Framework for Dynamics and Lyapunov Stability of Holonomically Constrained Rigid Bodies
Authors: Melhem Khoder ,   Département de Génie Mécanique, École Polytechnique de Montréal, Montréal, H3C 3A7, Canada, Liu Zhaoheng , Loría Antonio ,   Département de Génie Mécanique, École de Technologie Supérieure, Montréal, H3C 1K3, Canada,   CNRS-LSS, Supélec, Plateau de Moulon, 91192, Gif sur Yvette, France
Published: April 2016

A new dynamic model for interconnected rigid bodies is proposed here. The model formulation makes it possible to treat any physical system with finite number of degrees of freedom in a unified framework. This new model is a nonminimal realization of the system dynamics since it contains more state variables than is needed. A useful discussion shows how the dimension of the state of this model can be reduced by eliminating the redundancy in the equations of motion, thus obtaining the minimal realization of the system dynamics. With this formulation, we can for the first time explicitly determine the equations of the constraints between the elements of the mechanical system corresponding to the interconnected rigid bodies in question. One of the advantages coming with this model is that we can use it to demonstrate that Lyapunov stability and control structure for the constrained system can be deducted by projection in the submanifold of movement from appropriate Lyapunov stability and stabilizing control of the corresponding unconstrained system. This procedure is tested by some simulations using the model of two-link planar robot.
About Fractional Calculus of Singular Lagrangians
Authors: Baleanu Dumitru ,   Department of Mathematics and Computer Sciences, Çankaya University, Balgat 06530, Ankara, Turkey,   Institute of Space Sciences, P.O. BOX MG-23, R-76900 Magurele, Bucharest, Romania
Published: April 2016

In this paper the solutions of the fractional Euler-Lagrange quations corresponding to singular fractional Lagrangians were examined. We observed that if a Lagrangian is singular in the classical sense, it remains singular after being fractionally generalized. The fractional Lagrangian is non-local but its gauge symmetry was preserved despite complexity of equations in fractional cases. We generalized four examples of singular Lagrangians admitting gauge symmetry in fractional case and found solutions to corresponding Euler-Lagrange equations.
Roby-Go, a Prototype for Cooperating MiroSOT Soccer-Playing Robots
Authors: Novak Gregor ,   Vienna University of Technology, Institute of Computer Technology, Vienna, Austria
Published: April 2016

Perfectly working robot are basics to competitive robot soccer teams. The mobile minirobot we developed is two-wheeled and differentially driven (2WDD), featuring simple, compact, modular construction. The robot's open architecture enables it to be used both as a MiroSOT soccer player and as a mobile platform in such tasks as an independent test bed for multi agent systems (MAS).
Semiatoms in Choquet Integral Models of Multiattribute Decision Making
Authors: Murofushi Toshiaki ,   Department of Computational Intelligence and Systems Science, Tokyo Institute of Technology, 4259-G3-47 Nagatsuta, Midori-ku, Yokohama 226-8502, Japan
Published: April 2016

The semiatom is characterized in terms of the multiattribute preference relation represented by a Choquet integral. It is concluded from the characterization that the proper semiatom, the semiatom which is not an atom, is pathological in multiattribute decision making.
On Lukasiewicz Modifier Logic
Authors: Mattila Jorma K. ,   Laboratory of Applied Mathematics, Lappeenranta University of Technology, P.O. Box 20, FIN-53851 Lappeenranta, Finland
Published: April 2016

Algebras of so-called simple modifiers are considered, we create a logical system of modifiers based on Lukasiewicz' many-valued logic together with modifier algebras, then we find connections to graded modalities.
Case-Based Reasoning in Web Break Sensitivity Evaluation in a Paper Machine
Authors: Ahola Timo ,   University of Oulu, Department of Process and Environmental Engineering, P.O.Box 4300, FIN- 90014 University of Oulu, Finland, Leiviskä Kauko
Published: April 2016

This paper proposes a new approach for monitoring the the paper web break tendency in modern paper machines. The approach combines linguistic equations and fuzzy logic in a case-based reasoning framework. The development is based on actual mill data and simulations, and early prototyping is used to validate the approach in practice. The system has been tested on two Finnish paper machines with encouraging results showing good possibility for actual mill-scale application.
Adaptive Vector Quantization with Creation and Reduction Grounded in the Equinumber Principle
Authors: Maeda Michiharu ,   Kurume National College of Technology, Kurume 830-8555, Japan, Shigei Noritaka , Miyajima Hiromi ,   Kagoshima University, Kagoshima 890-0065, Japan
Published: April 2016

This paper concerns the constitution of unit structures in neural networks for adaptive vector quantization. Partition errors are mutually equivalent when the number of inputs in a partition space is mutually equal, and average distortion is asymptotically minimized. This is termed the equinumber principle, in which two types of adaptive vector quantization are presented to avoid the initial dependence of reference vectors. Conventional techniques, such as structural learning with forgetting, have the same number of output units from start to finish. Our approach explicitly changes the number of output units to reach a predetermined number without neighboring relations equalling the numbers of inputs in a partition space. First, output units are sequentially created based on the equinumber principle in the learning process. Second, output units are sequentially deleted to reach a prespecified number. Experimental results demonstrate the effectiveness of these techniques in average distortion. These approaches are applied to image data and their feasibility was confirmed in image coding.
An Interactive System with Facial Expression Recognition
Authors: Shang Yuyi ,   Graduate School of Engineering, Utsunomiya University, 7-1-2 Yoto, Utsunomiya, Tochigi 321-8585, Japan, Sato Mie , Kasuga Masao
Published: April 2016

To make communication between users and machines more comfortable, we focus on facial expressions and automatically classify them into 4 expression candidates: “joy,” “anger, ” “sadness,” and “surprise.” The classification uses features that correspond to expression-motion patterns, and then voice data is output based on classification results. When we output voice data, insufficiency in classification is taken into account. We choose the first and second expression candidates from classification results. To realize interactive communication between users and machines, information on these candidates is used when we access a voice database. The voice database contains voice data corresponding to emotions.
Adaptive Touch Panel User Interface by Type-Based Approach Using Particle Filters
Authors: Ikoma Norikazu ,   Faculty of Engineering, Kyushu Institute of Technology, Fukuoka 804-8550, Japan, Pedrycz Witold , Hyakudome Takahiro , Maeda Hiroshi ,   Dept. of Electrical & Computer Engineering, University of Alberta, Edmonton, Canada,   Systems Research Institute Polish Academy of Sciences, Warsaw, Poland,   Panasonic Communications Co., Ltd., Japan
Published: April 2016

A new method for adaptation of touch panel user interface is proposed. The method employs a type-based approach effectively using prior information on user population which is represented by mixture model. An advantage of the approach is that we only need to estimate weights of types which is numerically far less expensive than estimating values of all attributes of the user. State space model is formalized to estimate the weights of types by supposing smoothness prior to time evolution of the weights and mixture model with the time-varying weights. Particle filter is used to estimate the weights based on observation series of user operations up to current time. An experiment on touch panel user interface demonstrates the efficiency of the proposed method.
Lattice Structure of D, T, and SR Fuzzy Flip-Flops Under Max-Min Logic
Authors: Yoshida Shinichi ,   Information Science Research Center, Aoyama Gakuin University, N603, 5-10-1 Fuchinobe, Sagamihara, Kanagawa 229-8558, Japan, Hirota Kaoru ,   Interdisciplinary Graduate School of Science and Engineering, Tokyo Institute of Technology, G3-49, 4259 Nagatsuta, Midori-ku, Yokohama-city 226-8502, Japan
Published: April 2016

Lattice structures of fuzzy flip-flops are described. A binary flip-flop (e.g. D, T, set-type SR, or reset-type SR flip-flop) can be extended to a fuzzy flip-flop in various ways. Under max-min fuzzy logic, there are 4 types of D fuzzy flip-flops extended from a binary D flip-flop, 136 types of SR fuzzy flip-flops extended from a binary SR flip-flop, and only one T fuzzy flip-flop. There is a lattice structure among different types of fuzzy flip-flops extended from a same binary flip-flop in terms of the order of ambiguity and the order of fuzzy logical value. These results show that fuzzy flip-flops under max-min fuzzy logic construct distributive lattice structures. Moreover D and T fuzzy flip-flops constructs Boolean lattice. And there exists a order monotone between two lattices of same fuzzy flip-flop under the order of ambiguity and the order of fuzzy logical value. Proposed analysis and results have potential to establish a fuzzy sequential system design method.
Definition of an Offshore Petroleum Production System by Using Fuzzy Sets and Utility Functions
Authors: Fonseca Tiago C. da ,   State University of Campinas - UNICAMP, Department of Petroleum Engineering, Campinas, SP, Brazil, Mendes José R. P. , Morooka Celso K. , Guilherme Ivan R. ,   Sao Paulo State University - UNESP, Department of Statistics, Applied Mathematics and Computer Science, Rio Claro, SP, Brazil
Published: April 2016

Field development is a very important task in the petroleum industry. Decisions in this area may lead either to profitable success or to expensive failures, and usually involve several distinct areas in the scope of Petroleum Engineering and Science, such as Geology, Petreoleum Engineering, Offshore Engineering and Economics. Therefore, these subjects must be well understood by teams supporting the decision-making process. This work proposes a methodology to support managers in one stage of field development: the definition of the field production system. In order to determinate the production system to be installed in an oil field, attributes such as investment, profitability, safety, environmental preservation and technological experience must be considered. A decision-making team or agent must weight these attributes in order to achieve solutions accordingly to the company strategies and objectives. Combining a few mathematical tools to represent the process, the methodology proposed herein is an approach that considers not only the financial variables involved in a field decision process, but might include other aspects, or attributes, also important to guide a decision. To this end, the application of Multi-Attribute Analysis concepts is suggested. Also, to support the decision-making agent, the approach follows Utility Functions concepts in order to numerically represent the agent trend or inclination to each option. Considering that subjectivity and imprecision are naturally involved in the decision-making process, the approach incorporates Fuzzy Sets Theory concepts as a means of formalizing the computation of this uncertainty.
Multiclassification by Double-Negative Aggregation of SVM Membership
Authors: Tanaka Hidetoshi ,   Information Technology R&D Center, Mitsubishi Electric Corporation, 5-1-1 Ofuna, Kamakura, Japan
Published: April 2016

Multiclassification problems are often binarized into pairwise classifications to use basic classification such as support vector machines (SVM). Instead of the widely used aggregation by fuzzy logical product, we propose simple double-negative aggregation, in which the membership functions use margin areas of SVM discrimination functions, and memberships of negative votes of the class are accumulated to produce the negative membership of the class. This provides results consistent with basic pairwise memberships, enumerates candidates when the total membership of multiple classes is nearly equal, and requires low computational cost in class reconfiguration.
Genetic Network Programming with Acquisition Mechanisms of Association Rules
Authors: Shimada Kaoru ,   Graduate School of Information, Production and Systems, Waseda University, 2-7 Hibikino, Wakamatsu-ku, Kitakyushu-shi, Fukuoka 808-0135, Japan, Hirasawa Kotaro , Hu Jinglu
Published: April 2016

A method of association rule mining using Genetic Network Programming (GNP) is proposed to improve the performance of association rule extraction. The proposed mechanisms can calculate measurements of association rules directly using GNP, and measure the significance of the association via the chi-squared test. Users can define the conditions of importance of association rules flexibly, which include the chi-squared value and the number of attributes in a rule. The proposed system evolves itself by an evolutionary method and obtains candidates of association rules by genetic operations. Extracted association rules are stored in a pool all together through generations and reflected in genetic operators as acquired information. Besides, our method can contain negation of attributes in association rules and suit association rule mining from dense databases. In this paper, we describe an extended algorithm capable of finding important association rules using GNP with sophisticated rule acquisition mechanisms and present some experimental results.
Dedicated Floorplanning Engine Architecture Based on Genetic Algorithm and Evaluation
Authors: Yoshikawa Masaya ,   Department of VLSI System Design, Ritsumeikan University, 1-1-1 Nojihigashi, Kusatsu, Shiga 525-8577, Japan, Terai Hidekazu
Published: April 2016

The floorplanning problem, a basic design step in layout design of very large-scale integrated circuit (VLSI), deals with placing rectangular modules at maximum density. Many studies have dealt with conducted this problem using sequence pairs based on genetic algorithms (GAs), but this generally requires much calculation time. We propose an architecture for high-speed floorplanning using a sequence pair based on GA. The proposed architecture, implemented on the field-programmable gate array (FPGA), achieves high-speed processing. Measurement evaluating the proposed architecture demonstrated speeds 37.1 times greater than software processing.