bluuebunny/crossref_metadata_2025 huggingface dataset:
Computational Intelligence in Circuit Synthesis
Authors: Reis Cecília ,   Institute of Engineering of Porto / GECAD, Rua Dr. António Bernardino de Almeida, Porto, Portugal, Machado J. A. Tenreiro
Published: April 2016

This paper is devoted to the synthesis of combinational logic circuits through computational intelligence or, more precisely, using evolutionary computation techniques. Are studied two evolutionary algorithms, the Genetic and the Memetic Algorithm (GAs, MAs) and one swarm intelligence algorithm, the Particle Swarm Optimization (PSO). GAs are optimization and search techniques based on the principles of genetics and natural selection. MAs are evolutionary algorithms that include a stage of individual optimization as part of its search strategy, being the individual optimization in the form of a local search. The PSO is a population-based search algorithm that starts with a population of random solutions called particles. This paper presents the results for digital circuits design using the three above algorithms. The results show the statistical characteristics of this algorithms with respect to the number of generations required to achieve the solutions. The article analyzes also a new fitness function that includes an error discontinuity measure, which demonstrated to improve significantly the performance of the algorithm.
Estimation of Protein Function Using Optimized Finite State Automaton Based on Accumulated Amino Acid Residue Scores
Authors: Chiba Shinji ,   Department of Information Engineering, Sendai National College of Technology, 4-16-1 Ayashi-chuou, Aoba-ku, Sendai 989-3128, Japan, Sugawara Ken ,   Department of Information Science, Tohoku Gakuin University, 2-1-1 Tenjinzawa, Izumi-ku, Sendai 981-3193, Japan
Published: April 2016

The function of unknown proteins is currently most effective determined by retrieving similar known sequences. Some effective techniques involve sequence retrieval. We propose retrieval using a finite state automaton (FSA). The FSA is created with accumulated amino acid residue scores that express a property of a protein family. We calculate the similarity of known and unknown protein sequences using the FSA and used it to determine protein functions. To improve accuracy, we optimized the FSA using a genetic algorithm. Results from determining protein functions indicated that our proposal was superior to general motif analysis.
Semi-Supervised Pattern Classification Utilizing Fuzzy Clustering and Nonlinear Mapping of Data
Authors: Du Weiwei ,   Department of Visual Communication Design, Kyushu University, 4-9-1 Shiobaru, Fukuoka 815-8540, Japan, Urahama Kiichi
Published: April 2016

We present a semi-supervised algorithm for classification of arbitrarily distributed patterns. We project data into a classification space through two stages, first is a nonlinear mapping with radial basis functions and second is a linear projection with a semi-supervised locality preserving projection. Radial basis functions are arranged by fuzzy clustering of training data. This fuzzy clustering is also exploited for selection of data to be labeled for semi-supervised learning. We devise a simple semi-supervised algorithm in which data similarity is multiplicatively modulated on the basis of label information. We examine performance of the proposed classifier with experiments for synthetic and some real data and show that our method outperforms similar graph spectral algorithms and kernel semi-supervised methods.
A Robotic Auditory System that Interacts with Musical Sounds and Human Voices
Authors: Sawada Hideyuki ,   Department of Intelligent Mechanical Systems Engineering, Faculty of Engineering, Kagawa University, 2217-20 Hayashi-cho, Takamatsu-city, Kagawa 761-0396, Japan, Takechi Toshiya
Published: April 2016

Voice and sounds are the primary media employed for human communication. Humans are able to exchange information smoothly using voice under different situations, such as a noisy environment and in the presence of multiple speakers. We are surrounded by various sounds, and yet are able to detect the location of a sound source in 3D space, extract a particular sound from a mixture of sounds, and recognize the source of a specific sound. Also, music is composed of various sounds generated by musical instruments, and directly affects our emotions and feelings. This paper introduces real-time detection and identification of a particular sound among plural sound sources using a microphone array based on the location of a speaker and the tonal characteristics. The technique will also be applied to an adaptive auditory system of a robotic arm, which interacts with humans.
Space Invariant Independent Component Analysis and ENose for Detection of Selective Chemicals in an Unknown Environment
Authors: Duong Tuan A. ,   Jet Propulsion Laboratory/California Institute of Technology, 4800 Oak Grove Dr. Pasadena, CA 91109, Ryan Margaret A. , Duong Vu A.
Published: April 2016

In this paper, we present a space invariant architecture to enable the Independent Component Analysis (ICA) to solve chemical detection from two unknown mixing chemical sources. The two sets of unknown paired mixture sources are collected via JPL 16-ENose sensor array in the unknown environment with, at most, 12 samples data collected. Our space invariant architecture along with the maximum entropy information technique by Bell and Sejnowski and natural gradient descent by Amari has demonstrated that it is effective to separate the two mixing unknown chemical sources with unknown mixing levels to the array of two original sources under insufficient sampled data. From separated sources, they can be identified by projecting them on the 11 known chemical sources to find the best match for detection. We also present the results of our simulations. These simulations have shown that 100% correct detection could be achieved under the two cases: a) under-completed case where the number of input (mixtures) is larger than number of original chemical sources; and b) regular case where the number of input is as the same as the number of sources while the time invariant architecture approach may face the obstacles: overcomplete case, insufficient data and cumbersome architecture.
A Qualitative Model for Service Oriented Computing
Authors: Zhang Jian Ying ,   Faculty of Information & Communication Technologies, Swinburne University Of Technology, Victoria 3122, Australia, Deng Hepu , Kowalczyk Ryszard ,   School of Business Information Technology, RMIT University, Victoria 3010, Australia
Published: April 2016

In this paper we introduce fuzzy causal network as a qualitative model for service oriented computing. Firstly, we give a brief description of fuzzy causal network. Secondly, we explain how fuzzy causal network can be used as a qualitative model for service oriented computing. Thirdly, We use fuzzy causal network to model a real world problem in the area of service oriented computing to demonstrate the correctness and effectiveness of this model. Finally, we outline some open research problems for further study. The proposed model has potential to become a good framework for causal discovery and decision support in service oriented computing, especially in the cases where intensive interaction among various services are involved.
A Model of Hierarchical Knowledge Representation – Toward Knowware for Intelligent Systems
Authors: Ding Liya ,   Faculty of Information Technology, Macau University of Science and Technology, Avenida Wai Long, Taipa, Macau SAR, China
Published: April 2016

We propose a model for multiresolutionary knowledge representation; define concepts of domain, application, and working hierarchies; and discuss inference mechanisms in the knowledge hierarchy. We also introduce an automatic construction of the knowledge hierarchy for the development of intelligent systems.
Distance Field Model Concept for Space Representation
Authors: Murai Yasuyuki ,   School of Pharmaceutical Sciences, Nihon Pharmaceutical University, 10281 Komuro, Inamachi, Kita-adachi-gun, Saitama 362-0806, Japan, Asaoka Suguru , Tsuji Hiroyuki , Tatsumi Hisayuki , Tokumasu Shinji ,   Dept. of Information and Computer Sciences, Kanagawa Institute of Technology, 1030 Shimo-ogino, Atsugi, Kanagawa 243-0292, Japan,   Dept. of Computer Science, Tsukuba University of Technology, 4-12-7 Kasuga, Tsukuba, Ibaraki 305-8521, Japan
Published: April 2016

The distance field model (DFM), a new approach to space reasoning, i.e., the recognition and understanding of space, is realized by introducing the concept that a spatial object placed in a space generates a “distance field” represented by a function that maps arbitrary point P in space into a real number: the distance from P to this object. Analyzing the distance field, it is easy to judge, for example, what the surroundings are or how close obstacles are. To enable readers to understand the DFM concept, we introduce an object-oriented scheme to represent functions, and, by constructing a simplified object-oriented version of 2-dimensional (2D) DFM, we have compactly and comprehensively implemented the model. We also show that DFM is not limited to 2-dimensional space, but can be extended from the original to 3-dimensional (3D) or 2D-3D mixed systems.
Tracking of Multiple Moving Objects in Dynamic Image of Omni-Directional Camera Using PHD Filter
Authors: Ikoma Norikazu ,   Faculty of Engineering, Kyushu Institute of Technology, 1-1 Sensui-cho, Tobata-ku, Kita-Kyushu, Fukuoka 804-8550, Japan, Yamaguchi Ryuichi , Kawano Hideaki , Maeda Hiroshi ,   NTT DATA Corporation, Toyosu Center Building, 3-3-3 Toyosu, Koto-ku, Tokyo 135-6033, Japan
Published: April 2016

A method of multiple moving objects tracking in dynamic image of omni-directional camera has been proposed. Finite random set (FRS) based state space model is employed in the method due to its inherent nature capable to represent the scene having occlusion and appearance of object as well as missing and false detection in observation. Sequential Monte Carlo (SMC) implementation of Probability hypothesis density (PHD) filter has been used for estimating state of the state space model. The state is a finite random set of single object states, where each element of the set consists of position and velocity of the object in panoramic image coordinate of omni-directional camera image. We propose a new method to display tracking result from weighted particles obtained from the estimation process by SMC implementation of PHD filter. Key idea of the method is to put an integer label on each particle, where the label indicates specific object among multiple objects in the image scene tracked by the particle. Numerical simulation and real image experiments illustrate tracking performance of the proposed method.
Printing Pressure State Inspection System Based on Fuzzy Inference
Authors: Jing Jianping ,   Department of Computational Intelligence and System Science, Interdisciplinary Graduate School of Science and Engineering, Tokyo Institute of Technology, G3-49, 4259 Nagatsuta, Midori-ku, Yokohama 226-8502, Japan, Dong Fangyan , Hatakeyama Yutaka , Takama Yasufumi , Yamaguchi Toru , Hirota Kaoru ,   Faculty of System Design, Tokyo Metropolitan University, 6-6 Asahigaoka, Hino, Tokyo 191-0065, Japan
Published: April 2016

A Printing Pressure expectation system based on fuzzy inference has been installed in a liquid crystal display panel (LCD) production plant to solve problems in determining the printer printing pressure in real-world LCD production, in which the recognition of printing pressure conditions and control are very important and difficult, influencing product quality. This is usually done conducted by skilled engineers, whose performance depends on tacit knowledge. We propose using fuzzy inference to solve this problem. Printing area images are observed with cameras and abstract features extracted using image processing. System output is the status of printing pressure, divided into excessive pressure (EP), good pressure (GP), and low pressure (LP). Based on abstract features, the state is calculated using fuzzy membership functions. Shapes of membership functions are determined based on sampled glass obtained in actual LCD production line. Experiments are conducted using 2000 samples of glass printed using actual printers, or which results are compared to those of skilled engineers. We found that the proposed system yields quality higher than that of skilled engineers. We installed our system on an actual production line, where it is expected to increase product quality and production speed while and cutting production costs.
Effects of Initialization on Rule Extraction in Structural Learning
Authors: Shiratsuchi Hiroshi ,   School of Humanity-Oriented Science and Engineering, Kinki University, 11-6 Kayanomori, Iizuka, Fukuoka 820-8555, Japan, Gotanda Hiromu , Inoue Katsuhiro , Kumamaru Kousuke ,   Department of Systems Innovation and Informatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502, Japan
Published: April 2016

This paper studies how our previously proposed initialization effects the rule extraction of neural networks by structural learning with forgetting. The proposed initialization consists of two steps: (1) initializing weights of hidden units so that their separation hyperplanes should pass through the center of an input pattern set and (2) initializing those of output units to zero. From simulation results on Boolean function discovery problems with 5 and 7 inputs, it has been confirmed that the proposed initialization yields a simpler network structure and higher rule extraction ability than the conventional initialization giving uniform random number to all the initial weights of the network.
A Genetic Network Programming Based Method to Mine Generalized Association Rules with Ontology
Authors: Yang Guangfei ,   Graduate School of Information, Production and Systems, Waseda University, 2-7 Hibikino, Wakamatsu-ku, Kitakyushu, Fukuoka 808-0135, Japan, Shimada Kaoru , Mabu Shingo , Hirasawa Kotaro , Hu Jinglu
Published: April 2016

In this paper, we propose a Genetic Network Programming based method to mine equalized association rules in multi concept layers of ontology. We first introduce ontology to facilitate building the multi concept layers and propose Dynamic Threshold Approach (DTA) to equalize the different layers. We make use of an evolutionary computation method called Genetic Network Programming (GNP) to mine the rules and develop a new genetic operator to speed up searching the rule space.
Verifying the Reliability of Web Services Interactions for the Robot Communication Platform
Authors: Narita Masahiko ,   Strategy and Technology Division, Software Unit, Fujitsu Limited, 2-15-16 Shinyokohama, Kouhoku-ku, Yokohama, Kanagawa 222-0033, Japan, Shimamura Makiko , Yashiro Sadao , Iwasa Kazunori , Yamaguchi Toru ,   Graduate School of System Design, Tokyo Metropolitan University, 6-6 Asahigaoka, Hino, Tokyo 191-0065, Japan
Published: April 2016

As Loosely Coupled Robot Communication and the common robot service platform using Web Services gain acceptance, the multi-vendor/multi-implementation environment is becoming increasingly disseminated and interoperability increasingly critical. In contrast Reliable messaging is a key to such Web Services-based systems, but no openly available verification suite currently exists for this technology. We discuss requirements of conformance and interoperability tests for Web Services reliability, and report results of coverage tests and application-driven tests using the verification suite we developed for reliable messaging. This suite verified implementations conforming to WS-Reliability, a reliable message specification. It also proves that application-driven tests requiring neither test program installation nor probe, effectively ensure interoperability in actual application together with common robot service platform implementation.
Support Vector Machine Classifier with WHM Offset for Unbalanced Data
Authors: Li Boyang ,   Graduate School of Information, Production and Systems, Waseda University, 2-7 Hibikino, Wakamatsu-ku, Kitakyushu-shi, Fukuoka 808-0135, Japan, Hu Jinglu , Hirasawa Kotaro
Published: April 2016

We propose an improved support vector machine (SVM) classifier by introducing a new offset, for solving the real-world unbalanced classification problem. The new offset is calculated based on the unbalanced support vectors resulting from the unbalanced training data. We developed a weighted harmonic mean (WHM) algorithm to further reduce the effects of noise on offset calculation. We apply the proposed approach to classify real-world data. Results of simulation demonstrate the effectiveness of our proposed approach.
Generation of Character Motion by Using Reactive Motion Capture System with Force Feedback
Authors: Choi Woong ,   Ritsumeikan University, 1-1-1 Nojihigashi, Kusatsu, Japan, Hashimoto Naoki , Walker Ross , Hachimura Kozaburo , Sato Makoto ,   Tokyo Institute of Technology, 4259 Nagatsuta, Midori-Ku, Yokohama,  Japan
Published: April 2016

Creating reactive motions with conventional motion capture systems is difficult because of the different task environment required. To overcome this drawback, we developed a reactive motion capture system that combines conventional motion capture system with force feedback and a human-scale virtual environment. Our objective is to make animation with reactive motion data generated from the interaction with force feedback and the virtual environment, using the fact that a person’s motions in the real world can be represented by the reactions of the person to real objects. In this paper we present the results of some animations made under various scenarios using animating reactive motion generation with our reactive motion capture system. Our results demonstrate that the reactive motion generated by this system was useful for producing the animation including scenes of reactive motion.
Intelligent Synchronization for Mirrored Game Servers: A Real Case Study
Authors: Ferretti Stefano ,   Department of Computer Science, University of Bologna, Via Mura Anteo Zamboni, 7, 40127 Bologna, Italy, Roccetti Marco , Palazzi Claudio E.
Published: April 2016

Multiplayer Online Games (MOGs) embody intensive applications that require smart solutions able to cope with the high network traffic generated by players, variable latencies, and system failures. To this aim, the anatomy of the game architecture should reflect the possibly wide geographical dispersion of players interacting in a game session. Whereas the use of mirrored game servers has been recognized as a scalable solution to support MOGs, yet, a critical aspect remains that of identifying an efficient synchronization scheme able to responsively guarantee the consistency of the redundant game state. To address this issue, we added intelligence to an optimistic synchronization scheme for mirrored game server architectures: our scheme is able to classify events and, based on their semantics, relax ordering and reliability constraints to gain responsiveness without sacrificing consistency. In this work, we describe the devised scheme and report on an experimental assessment that is based on a real implementation of a mirrored game server architecture, deployed over the Internet. Results definitively show the efficacy of our approach.
Haar Wavelets for Online-Game Player Classification with Dynamic Time Warping
Authors: Thawonmas Ruck ,   Intelligent Computer Entertainment Laboratory, Graduate School of Science and Engineering, Ritsumeikan University, 1-1-1 Nojihigashi, Kusatsu, Shiga 525-8577, Japan, Iizuka Keita
Published: April 2016

Online game players’ action sequences, while important to understand their behavior, usually contain noise and/or redundancy, making them unnecessarily long. To acquire briefer sequences representative of players’ features, we apply the Haar wavelet transform to action sequences and reconstruct them from selected wavelet coefficients. Results indicate that this approach is effective in classification when thek-nearest neighbor classifier is used to classify players based on dynamic time warping distances between reconstructed sequences.
Selected Papers from TAAI 2006
Authors: Yang Hsin-Chang , Lin Wen-Yang , Chang Chun-Yang , Yang Cheng-Hong , Chen Shyi-Ming
Published: April 2016

The 11th Conference on Artificial Intelligence and Applications (TAAI 2006), which was held during Dec. 15-16, 2006 at Kaohsiung, Taiwan, is the annual conference of Taiwanese Association for Artificial Intelligence. The conference is intended to provide a forum for researchers and scholars in the related fields of artificial intelligence. Past conferences have proven them successful attempts to become the most important meeting of artificial intelligence researchers in Taiwan. This is also true for TAAI 2006, which focuses on various aspects on theory and practice of artificial intelligence. In this special issue, 11 papers presented in the conference are selected and extended for their outstanding performance on the conference. These papers cover wide spreading aspects, which include versatile motion planning, particle swarm optimization, data mining, image retrieval, music retrieval, natural language processing, navigation, fuzzy logic, gaming, and bioinformatics. This issue thus concisely summarizes recent advances in artificial intelligence and its applications. The readers should find them valuable and inspiring. We hope that this issue should provide a valuable resource for their researches. As guest editors of this special issue, we like to express our greatest gratitude to those that help this issue come true. Thanks to all contributors and referees for their elaborate works and careful reviews that assure the high quality of this issue. Special thanks should go to Mr. Makoto Shimada of Fuji Technology Press for his efforts and kind assistance in publishing this issue. Finally, we also like to thank the Editors in Chief of JACIII, Prof. Toshio Fukuda and Prof. Kaoru Hirota, for their generous hospitality in supporting this special issue.
A Combination of Shuffled Frog-Leaping Algorithm and Genetic Algorithm for Gene Selection
Authors: Yang Cheng-San ,   Institute of biomedical engineering, National Cheng Kung University, Tainan, Taiwan 70101, Chuang Li-Yeh , Ke Chao-Hsuan , Yang Cheng-Hong ,   Department of Chemical Engineering, I-Shou University, Kaohsiung, Taiwan 84001,   Department of Electronic Engineering, National Kaohsiung University of Applied Sciences, Kaohsiung, Taiwan 80778
Published: April 2016

Microarray data referencing to gene expression profiles provides valuable answers to a variety of problems, and contributes to advances in clinical medicine. The application of microarray data to the classification of cancer types has recently assumed increasing importance. The classification of microarray data samples involves feature selection, whose goal is to identify subsets of differentially expressed gene potentially relevant for distinguishing sample classes and classifier design. We propose an efficient evolutionary approach for selecting gene subsets from gene expression data that effectively achieves higher accuracy for classification problems. Our proposal combines a shuffled frog-leaping algorithm (SFLA) and a genetic algorithm (GA), and chooses genes (features) related to classification. The K-nearest neighbor (KNN) with leave-one-out cross validation (LOOCV) is used to evaluate classification accuracy. We apply a novel hybrid approach based on SFLA-GA and KNN classification and compare 11 classification problems from the literature. Experimental results show that classification accuracy obtained using selected features was higher than the accuracy of datasets without feature selection.
An Approach to Improve Estimation Performance of GM(1,1) Model
Authors: Hiseh Cheng-Hsiung ,   Department of Computer Science and Information Engineering, Chaoyang University of Technology, 168 Jifong E. Rd., Wufong Township Taichung County, Taiwan 41349, Huang Ren-Hsien , Feng Ting-Yu
Published: April 2016

In this paper, we present an approach to improve estimation performance of GM(1,1) model. It consists of three stages in the GM(1,1) modeling: the preprocessing 1-AGO (first-order accumulated generating operation), first-order difference equation, and 1-IAGO (first-order inverse accumulated generating operation). Note that the solution of first-order difference equation is of exponential form. Therefore, it is assumed that the data after 1-AGO is of exponential-like form. However, in many cases the 1-AGO preprocessed data may not have an exponential-like form. Consequently, an inaccurate modeling results and thus poor performance for GM(1,1) model. Based on the observation, we replace the first-order difference equation in the GM(1,1) modeling with a polynomial to relieve the requirement of exponential-like form in the 1-AGO preprocessed data. By this doing, the estimation performance of GM(1,1) model is improved. Through examples, the proposed approach is justified. As expected, the simulation results indicate that the proposed approach outperforms the conventional GM(1,1) model in the given fitting and forecasting examples.
An Efficient DCT-Based Image Retrieval Approach Using Distance Threshold Pruning
Authors: Tsai Tienwei ,   Department of Information Management, Chihlee Institute of Technology, Chiang Te-Wei , Huang Yo-Ping ,   Department of Accounting Information Systems, Chihlee Institute of Technology, No.313, Sec.1, Wunhua Rd., Banciao City, Taipei County 220, Taiwan,   Department of Electrical Engineering, National Taipei University of Technology, 1, Sec. 3, Chung-Hsiao E. Rd., Taipei 106, Taiwan
Published: April 2016

Content-based image retrieval (CBIR) techniques would allow indexing and retrieving images based on their low-level contents, which involves a large number of image pixels and thus becomes an inherently and essentially computational intensive task. This paper proposes a distance threshold pruning (DTP) method to alleviate computational burden of CBIR without sacrificing its accuracy. In our approach, the images are converted into the YUV color space, and then transformed into discrete cosine transform (DCT) coefficients. Benefited from the energy compacting property of DCT, Only the low-frequency DCT coefficients of Y, U, and V components are stored. On querying an image, at the first stage, the DTP serves as a filter to remove those candidates with widely distinct features. At the second stage, the detailed similarity comparison (DSC) is performed on those remaining candidates passing through the first stage. The experimental results show that both high efficacy and high data reduction rate can be achieved simultaneously by using the proposed approach.
Evolving Particle Swarm Optimization Implemented by a Genetic Algorithm
Authors: Liu Jenn-Long ,   Department of Information Management, I-Shou University, 1, Section 1, Hsueh-Cheng Rd., Ta-Hsu Hsiang, Kaohsiung County, Taiwan 840, Taiwan
Published: April 2016

Particle swarm optimization (PSO) is a promising evolutionary approach related to a particle moves over the search space with velocity, which is adjusted according to the flying experiences of the particle and its neighbors, and flies towards the better and better search area over the course of search process. Although the PSO is effective in solving the global optimization problems, there are some crucial user-input parameters, such as cognitive and social learning rates, affect the performance of algorithm since the search process of a PSO algorithm is nonlinear and complex. Consequently, a PSO with well-selected parameter settings may result in good performance. This work develops an evolving PSO based on the Clerc’s PSO to evaluate the fitness of objective function and a genetic algorithm (GA) to evolve the optimal design parameters to provide the usage of PSO. The crucial design parameters studied herein include the cognitive and social learning rates as well as constriction factor for the Clerc’s PSO. Several benchmarking cases are experimented to generalize a set of optimal parameters via the evolving PSO. Furthermore, the better parameters are applied to the engineering optimization of a pressure vessel design.
An Integrated Algorithm for Autonomous Navigation of a Mobile Robot in an Unknown Environment
Authors: Hee Lee Gim ,   DSO National Laboratories, 20 Science Park Drive, Singapore 118230, Marcelo H. Ang Jr.  ,   Department of Mechanical Engineering, National University of Singapore, 9 Engineering Drive 1, Singapore 117576
Published: April 2016

Global path planning algorithms are good in planning an optimal path in a known environment, but would fail in an unknown environment and when reacting to dynamic and unforeseen obstacles. Conversely, local navigation algorithms perform well in reacting to dynamic and unforeseen obstacles but are susceptible to local minima failures. A hybrid integration of both the global path planning and local navigation algorithms would allow a mobile robot to find an optimal path and react to any dynamic and unforeseen obstacles during an operation. However, the hybrid method requires the robot to possess full or partial prior information of the environment for path planning and would fail in a totally unknown environment. The integrated algorithm proposed and implemented in this paper incorporates an autonomous exploration technique into the hybrid method. The algorithm gives a mobile robot the ability to plan an optimal path and does online collision avoidance in a totally unknown environment.
Mobile Robot Navigation Using Open Computer Vision with Fuzzy Controller
Authors: Gonzales Julirose ,   Department of Engineering Design & Manufacture University Malaya, Kuala Lumpur, Malaysia, Taha Zahari
Published: April 2016

This research presents a fuzzy controller technique in navigation with obstacle avoidance for a general purpose mobile robot in a given global environment with image processing technique using Open Source Computer Vision (OpenCV) library on Visual C++. Fuzzy Logic is used to control the navigation of the robot towards the goal while avoiding obstacles along the way by changing its direction of movement. The positions of the mobile robot, obstacle and the destination are taken into consideration and an overhead camera (above the robot’s environment) is used to gather these necessary information. The images captured are processed using different techniques to get the desired positions and is directly integrated with the fuzzy controller making the algorithm more efficient compared to other vision-guided navigation techniques.
Helicopter Motion Control Using Model-Based Sliding Mode Controller
Authors: Nugroho Gesang ,   Department of Engineering Design & Manufacture University of Malaya, Kuala Lumpur, Malaysia, Taha Zahari
Published: April 2016

This paper describes a model-based controller design for helicopter using the sliding mode approach. The controller design assumes that only measured output are available and uses sliding mode observer to estimate all states of the system. The estimated states are then used to construct a model reference sliding mode control law. Simulation shows good performance for lateral velocity, longitudinal velocity, vertical velocity and yaw rate control.
Performance Analysis of Container Unloading Operations at the Port of Suva Using a Simplified Analytical Model (SAM)
Authors: Kumar Shivendra ,   Memcor Aust. Pty Ltd, Siemens Water Technologies, NSW, Australia, Vlacic Ljubo ,   Intelligent Control Systems Laboratory, Griffith University, QLD, Australia
Published: April 2016

Time has fast become one of the most important factors in global trading. While little can be done to reduce the travel time of ships, significant effort can be put into improving the efficiency of container terminal. This paper presents a simple analytical model (SAM) for predicting container dwell times and determining equipment utilization. The paper exemplifies the operations of the Port of Suva and presents an analysis of its operations. To improve the current performance, a cooperative control schema is proposed. It is shown in this paper that the prediction model and performance measures are a useful tool for container terminal planners.
Effect of Genetic Encoding on Evolution of Efficient Neural Controllers
Authors: Capi Genci ,   Graduate School of Science and Engineering for Research, University of Toyama, Gofuku Campus, 3190 Gofuku, Toyama 930-8555, Japan
Published: April 2016

In this paper, we present a new method based on multiobjective evolutionary algorithms to evolve low complexity neural controllers for the robots that have to perform two different tasks, simultaneously. In our method, each task and the structure of neural controller are considered as separated objective functions. We compare the results of two different encoding schemes: (1) Connectionist encoding and (2) Node based encoding. Simulation results show that multiobjective evolution can be successfully applied to generate low complexity neural controllers. In addition, node based encoding outperformed connectionist encoding in terms of robot performance and robustness of the neural controller.
Trading Rules on Stock Markets Using Genetic Network Programming with Sarsa Learning
Authors: Chen Yan ,   Graduate school of Information, Production and Systems, Waseda University, 2-7 Hibikino, Wakamatsu-ku, Kitakyushu, Fukuoka, Mabu Shingo , Shimada Kaoru , Hirasawa Kotaro
Published: April 2016

In this paper, the Genetic Network Programming (GNP) for creating trading rules on stocks is described. GNP is an evolutionary computation, which represents its solutions using graph structures and has some useful features inherently. It has been clarified that GNP works well especially in dynamic environments since GNP can create quite compact programs and has an implicit memory function. In this paper, GNP is applied to creating a stock trading model. There are three important points: The first important point is to combine GNP with Sarsa Learning which is one of the reinforcement learning algorithms. Evolution-based methods evolve their programs after task execution because they must calculate fitness values, while reinforcement learning can change programs during task execution, therefore the programs can be created efficiently. The second important point is that GNP uses candlestick chart and selects appropriate technical indices to judge the timing of the buying and selling stocks. The third important point is that sub-nodes are used in each node to determine appropriate actions (buying/selling) and to select appropriate stock price information depending on the situation. In the simulations, the trading model is trained using the stock prices of 16 brands in 2001, 2002 and 2003. Then the generalization ability is tested using the stock prices in 2004. From the simulation results, it is clarified that the trading rules of the proposed method obtain much higher profits than Buy&Hold method and its effectiveness has been confirmed.
Fuzzyc-Means for Data with Rectangular Maximum Tolerance Range
Authors: Endo Yasunori ,   Department of Risk Engineering, Faculty of Systems and Information Engineering, University of Tsukuba, Hasegawa Yasushi , Hamasuna Yukihiro , Miyamoto Sadaaki ,   Graduate School of Systems and Information Engineering, University of Tsukuba
Published: April 2016

This paper provides new clustering algorithms for data with tolerance. Tolerance is understood in a broad sense, e.g., calculation errors and loss of attribute of data. The concept of tolerance is modified by using new concept of tolerance vector. First, the concept is explained and optimization problems of clustering are formulated using the vectors. Second, the problems are solved using Karush-Kuhn-Tucker conditions. Third, the new clustering algorithms are constructed by using the solutions of the problems. Moreover, the effectiveness of proposed algorithms is verified through some numerical examples.
Time Related Association Rules Mining with Attributes Accumulation Mechanism and its Application to Traffic Prediction
Authors: Zhou Huiyu ,   Graduate School of Information, Production and Systems, Waseda University, Wei Wei , Shimada Kaoru , Mabu Shingo , Hirasawa Kotaro
Published: April 2016

In this paper, we propose a method of association rule mining using Genetic Network Programming (GNP) with time series processing mechanism and attributes accumulation mechanism in order to find time related sequence rules efficiently in association rule extraction systems. GNP, a kind of evolutionary computation, represents solutions using graph structures. Because of the inherent features of GNP, it works well in dynamic environments. In this paper, GNP is applied to generate candidate association rules using the database consisting of a large number of time related attributes. In order to deal with a large number of attributes, GNP individual accumulates fitter attributes gradually during rounds, and the rules of each round are stored in a Small Rule Pool using a hash method, then, the rules are finally stored in a Big Rule Pool after the check of the overlap at the end of each round. The aim of this paper is to better handle association rule extraction of the databases in a variety of time-related applications, especially in the traffic prediction problems. The algorithm which can find the important time related association rules is described and several experimental results are presented considering a traffic prediction problem.
An Evolutionary Hybrid Scheduling Algorithm for Computational Grids
Authors: Benedict Shajulin ,   Software Technologies Lab, TIFAC Core in Network Engineering, S Rejitha R. , Vasudevan V. ,   Department of Computer Engineering, Kalasalingam University
Published: April 2016

Grids promote user collaboration through flexible, coordinated sharing of distributed resources to solve a single large problem. Grid scheduling, similar to resource discovery and monitoring, is inherently more complex in Grid environments. We propose two approaches for solving Grid scheduling problems with the simultaneous objectives of maximizing the number of workflow executions and minimizing the waiting time variance among tasks of each workflow. One is the multiple objective Niched Pareto Genetic Algorithm (NPGA) that involves evolution during a comprehensive search and work on multiple solutions. After the Genetic search, we strengthen the search using Simulated Annealing as a local search meta-heuristic. For comparison, we evaluate other scheduling, such as, Tabu Search (TS), Simulated annealing (SA), and Discrete Particle Swarm Optimization (Discrete PSO). Results show that our proposed evolutionary Hybrid scheduling involving NPGA with an SA search, works better than other scheduling in considering workflow execution time within a deadline and waiting time variance in tasks with minimal iterations.
A Study on the Location Analysis Using Spatial Analysis and Ordered Weighted Averaging Operator Weighting Functions
Authors: Oh Se-Woong ,   Dept. of Marine Safety, MOERI/KORDI, Daejeon, Korea, Park Gyei-Kark , Park Jong-Min , Suh Sang-Hyun ,   Dept. of Marine information system, Mokpo National Maritime Univ., Mokpo, Korea
Published: April 2016

In this thesis, we proposed the method combining spatial analysis, selection method of weighting values, aggregating decision strategy. To select a sites proposed for ship anchorage, we analyzed spatial data. Fuzzy AHP was used as selection method of weighting values to incorporate the fuzzy set theory and the basic nature of subjectivity due to ambiguity to achieve a flexible decision approach suitable for uncertain and fuzzy environments. To obtain the score that corresponds to the best alternative or the ranking of the alternatives, we need to use a total order for the fuzzy numbers involved in the problem. In this paper, we consider a definition of such a total order: the degree of Orness (1, 3/4, 2/3, 1/2, 1/3, 1/4, 0) reflected with the ordered weighted averaging (OWA) operators. A numerical example was given to illustrate the approach.
Genetic Network Programming with Rules
Authors: Ye Fengming ,   2-7 Hibikino, Wakamatsu, Kitakyushu, Fukuoka 808-0135, Japan, Yu Lu , Mabu Shingo , Shimada Kaoru , Hirasawa Kotaro
Published: April 2016

Genetic Network Programming (GNP) is an evolutionary approach which can evolve itself and find the optimal solutions. It is based on the idea of Genetic Algorithm and uses the data structure of directed graphs. Many papers have demonstrated that GNP can deal with complex problems in the dynamic environments very efficiently and effectively. As a result, recently, GNP is getting more and more attentions and is being used in many different areas such as data mining, extracting trading rules of stock markets, elevator systems, etc and GNP has obtained some outstanding results. In order to improve GNP's performance further, this paper proposes a new method called GNP with Rules. The aim of the proposed method is to balance exploitation and exploration of GNP, that is, to strengthen exploitation ability by using the exploited information extensively during the evolution process of GNP. The proposed method consists of 4 steps: rule extraction, rule selection, individual reconstruction and individual replacement. These 4 steps are added to the conventional algorithm of GNP. In order to measure the performance of the proposed method, the tileworld was used as the simulation environment. The simulation results show some advantages of GNP with Rules over conventional GNPs.
Parallel Distributed Compensation Based Stabilization of A 3-DOF RC Helicopter: A Tensor Product Transformation Based Approach
Authors: Baranyi Péter ,   Computer and Automation Research Institute of the Hungarian Academy of Sciences, H-1111 Budapest, Kende u. 13-17, Hungary, Korondi Péter , Tanaka Kazuo ,   Department of Automation and Applied Informatics, Budapest University of Technology and Economics, Hungary,   Department of Mechanical Engineering and Intelligent Systems, The University of Electro-Communications, Tokyo 182-8585, Japan
Published: April 2016

This paper presents a control solution for the stabilization of the 3-DOF RC helicopter via the combination of the TP (Tensor Product) model transformation and the PDC (Parallel Distributed Compensation) control design framework. First we recall the nonlinear model of the RC helicopter and its simplified version, in order, to facilitate control design. Then we execute the TP model transformation on the simplified model to yield its TP model representation, that is a kind of polytopic model with specific characteristics, whereupon the PDC framework can immediately be applied. The control design considers practical control specifications such as: good speed of response and physical constrain on the control effort to avoid actuator saturations. We guarantee these specifications by LMI (Linear Matrix Inequality) conditions developed under the PDC frameworks. Further, we avoid the discrepancies, introduced via the simplification of the model, by applying LMI conditions specialized for robust control. By simultaneously solving these LMI conditions, we render a stabilizing nonlinear controller that achieves good speed of response with small control effort without actuator saturations. Simulation results are included to validate the control design. It will be pointed out that the resulting controller is equivalent with the controller successfully applied in the real control experiment of the helicopter presented in a recent paper.

The main conclusion of this paper is, that the proposed design process is systematic, non-heuristic and straightforward, the stability proof of the resulting controller is tractable via the feasibility test of LMIs and, hence, exact. The whole design procedure is automatically computed via commercialized mathematical tools (MATLAB LMI Toolbox) without analytical interaction. The computational time is about minutes on a regular PC.
Fuzzy-Possibilistic Classification: Resolution of Initialization Problem
Authors: Boudouda Houria ,   Laboratoire d'Automatique et d'Informatique de Guelma (LAIG), Université 8 mai 1945 de Guelma, B.P.401, Guelma 24000, Algeria, Nemissi Mohamed , Seridi Hamid , Akdag Herman ,   CResTIC, Université de Reims Champagne Ardenne, B.P.1035, 51687, Reims Cedex, France,   LIP6 Université P. & M. Curie, 75687 Paris Cedex 05, France
Published: April 2016

The methods of automatic classification resulting from the artificial intelligence are generally the consequences of a formalism based on an artificial reasoning quasi similar to that of the human expert. All the approaches of automatic classification developed so far, whether in an exact or approximate context, are dissociated from each other by the membership concept of an object to a class. In this paper, we present a new approach hybrid of unsupervised automatic classification under the C-Means (means of C classes) family. This new approach, based on the fusion of fuzzy and possibility theory and initialized by a membership matrix, allows on the one hand to solve simultaneously the problem of overlapping and coincidence, to reduce the noise effect and on the other hand to accelerate the classification process. The model validation is carried out by the FCM (Fuzzy C-Means), the PCM (Possibilistic C-Means)and the FPCM (Fuzzy-Possibilistic C-Means) for two cases of initialization by using Iris, Textured image and Tight human data basis.
Determining Extrinsic Parameters for Active Stereovision
Authors: Wang Huawei ,   Key Laboratory of Complex Systems and Intelligence Science, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China, Xu De
Published: April 2016

In the novel method we propose for determining extrinsic parameters for active stereovision, we first map the relationship between rotational and yaw angles based on least squares fitting, then optimize the rotational axis between two cameras using the Levenberg-Marquardt algorithm. Extrinsic parameters are then easily derived for active stereovision based on the mapping model without complex recalibration. The results of experiments confirmed our proposed method's feasibility.
Profile Generation for TV Program Recommendation Based on Utterance Analysis
Authors: Takama Yasufumi ,   Tokyo Metropolitan University, Tokyo, Japan, Muto Yuki
Published: April 2016

This paper proposes a method for generating user profile that is to be used for TV program recommendation. The proposed method does not estimate user's interest in a TV program only based on its watching time as most of existing methods do, but also based on user's utterances by applying sentiment analysis. Three kinds of scores are calculated for each watched program, based on which fuzzy inference is performed to estimate its rating. After the estimation, profile structure is obtained by generating category and subcategory layers. This paper mainly focuses on estimation of users' ratings on TV programs. Experiments are performed with test subjects, and the results show the proposed method can improve the estimation accuracy of ratings compared with existing approach using only watching time. Although the proposed method includes language-dependent processing, it is expected the core of estimation rating can be applied to other languages.