bluuebunny/crossref_metadata_2025 huggingface dataset:
Advanced Genetic Algorithms Based on Adaptive Partitioning Method
Authors: Han Chang-Wook ,   School of Electrical Engineering and Computer Science, Yeungnam University, 214-1 Dae-dong, Gyongsan, Gyongbuk, 712-749, South Korea, Nobuhara Hajime ,   Department of Intelligent Interaction Technologies, Graduate School of Systems and Information Engineering, University of Tsukuba, 1-1-1 Tennoudai, Tsukuba science city, Ibaraki 305-8573, Japan
Published: April 2016

Genetic algorithms (GA) are well known and very popular stochastic optimization algorithm. Although, GA is very powerful method to find the global optimum, it has some drawbacks, for example, premature convergence to local optima, slow convergence speed to global optimum. To enhance the performance of the GA, this paper proposes an adaptive genetic algorithm based on partitioning method. The partitioning method, which enables a genetic algorithm to find a solution very effectively, adaptively divides the search space into promising sub-spaces to reduce the complexity of optimization. This partitioning method is more effective as the complexity of the search space is increasing. The validity of the proposed method is confirmed by applying it to several bench mark test function examples and a traveling salesman problem.
GML Topology Data Storage Schema Design
Authors: Li Yuzhen ,   Graduate School of Science and Technology, Chiba University, 1-33 Yayoi-cho, Inage-ku, Chiba 263-8522, Japan, Lu Jianming , Guan Jihong , Fan Mingying , Haggag Ayman , Yahagi Takashi
Published: April 2016

Geography Markup Language (GML) was developed to standardize the representation of geographical data in extensible markup language (XML), which facilitates geographical information exchange and sharing. Increasing amounts of geographical data are being presented in GML as its use widens, raising the question of how to store GML data efficiently to facilitate its management and retrieval. We analyze topology data in GML and propose storing nonspatial and spatial data from GML documents in spatial databases (e.g, Oracle Spatial, DB2 Spatial, and PostGIS/PostgreSQL.). We then use an example to analyze the topology relation.
Robust Output Feedback Guaranteed Cost Control of Uncertain Fuzzy Systems with Immeasurable Premise Variables
Authors: Yoneyama Jun ,   Department of Electronics and Electrical Engineering, Aoyama Gakuin University, 5-10-1 Fuchinobe, Sagamihara, Kanagawa 229-8558, Japan
Published: April 2016

This paper is concerned with robust output feedback stabilization with guaranteed cost of uncertain Takagi-Sugeno fuzzy systems with immeasurable premise variables. When we consider Takagi-Sugeno fuzzy systems, the selection of premise variables plays an important role. If the premise variable is the state of the system, then a fuzzy system describes a wide class of nonlinear systems. However, the state is not measurable in the output feedback control problem. In this case, a control design based on parallel distributed compensation is impossible because a controller depends on immeasurable premise variables. In this paper, we consider the robust output feedback stabilization problem with guaranteed cost where the premise variable is not measurable. We formulate this problem as a robust stabilization of uncertain linear systems. Numerical examples are given to illustrate our theory.
Applying SIP VoIP to Context-Aware Telephony
Authors: Wang Hsien-Chang ,   Department of Information Management, Chang Jung Christian University, 396 Chang Jung Rd., Sec.1, Kway Jen, Tainan 71101, Taiwan, R.O.C., Chen Gu-Jun , Hsu Jenq-Muh ,   Department of Computer Science and Information Engineering, Chung Cheng University, 168, University Rd., Min-Hsiung Chia-Yi, Taiwan, R.O.C.
Published: April 2016

The simple, efficient Session Initial Protocol (SIP) is widely used to integrate context-awareness computing techniques. We propose a SIP-based research framework that integrates automatic speech generation to make communication between users more convenient. Based on the proposed approach, we built and tested a SIP context server and several SIP agents hosted on PCs. If the called party is absent or busy, a context-aware message is automatically replied to the caller via synthesized natural speech. Experiments conducted by 50 college students using ADSL connected to the SIP server showed encouraging results, especially in user satisfaction in message transfer rates and service waits.
Coincidence-Based Scoring of Mappings in Ontology Alignment
Authors: Seyed H. Haeri (Hossein)  ,   Web Intelligence Laboratory, Computer Engineering Department, Sharif University of Technology and School of Computer Science, Institute for Studies in Theoretical Physics and Mathematics (IPM), Abolhassani Hassan , Qazvinian Vahed , Hariri Babak Bagheri
Published: April 2016

Ontology Matching (OM) which targets finding a set of alignments across two ontologies, is a key enabler for the success of Semantic Web. In this paper, we introduce a new perspective on this problem. By interpreting ontologies as Typed Graphs embedded in a Metric Space,coincidenceof the structures of the two ontologies is formulated. Having such a formulation, we define a mechanism to score mappings. This scoring can then be used to extract a good alignment among a number of candidates. To do this, this paper introduces three approaches: The first one, straightforward and capable of finding the optimum alignment, investigates all possible alignments, but its runtime complexity limits its use to small ontologies only. To overcome this shortcoming, we introduce a second solution as well which employs a Genetic Algorithm (GA) and shows a good effectiveness for some certain test collections. Based on approximative approaches, a third solution is also provided which, for the same purpose, measures random walks in each ontology versus the other.
Knowledge Engineering and Embedded Systems Software: Educational Challenges
Authors: Imamura Kosuke ,   Eastern Washington University, 319 F Computing & Engineering Building, Cheney, WA 99004-2493, USA
Published: April 2016

There is need for engineering tools to build knowledge bases suitable for realtime perception and control. As embedded systems are applied to complex systems, development of software becomes a daunting task and software failures become more likely with possible negative impact on our economy. Realtime, embedded systems are also common in critical systems so that failures in these systems could affect human lives. To counter this situation, application of knowledge engineering is sought. However, undergraduate software engineering curriculum for embedded systems doesn’t satisfy the current needs. We recently redesigned our embedded systems course to focus on issues and problems in embedded software systems to prepare our students for high level embedded applications and possible development of realtime/embedded intelligent systems.
A Joint-Receipt Conjoint Structure and its Additive Representation
Authors: Matsushita Yutaka ,   Department of Psychological Informatics, Kanazawa Institute of Technology, 7-1 Ohgigaoka, Nonoichi, Ishikawa 921-8501, Japan
Published: April 2016

This paper introduces a componentwise joint receipt operation ⊕ on ann-component product set Πni=1gi, and develops an axiom system to justify an additive representation for a binary relation ≿ on Πni=1gi. Basically, our axiom system is similar to then-component (n≥ 3), additive conjoint structure. However, the introduction of the operation ⊕ yields two new axioms – additive solvability and invariance under multiplication – and hence we can weaken the independence axiom of the conjoint structure. The weakened independence axiom requires the independence of the order for each single factor from fixed levels of the other factors, while the conjoint structure involves the independence of the order for two or more factors. Finally, it is shown by a brief experimental test that the weakened independence axiom is sustained.
Evolution and Learning Mediated by Differences in Developmental Timing
Authors: Ohnishi Kei ,   Human Media Creation Center / Kyushu, 3-8-1 Asano, Kokura-Kita-ku, Kitakyushu, Fukuoka 802-0001, JAPAN, Uchida Masato , Oie Yuji ,   Network Design Research Center, Kyushu Institute of Technology, 3-8-1 Asano, Kokura-Kita-ku, Kitakyushu, Fukuoka 802-0001, JAPAN,   Faculty of Computer Science and Systems Engineering, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502, JAPAN
Published: April 2016

The present paper introduces a mutation-based evolutionary algorithm that evolves genes to regulate the developmental timings of phenotypic values. For each generation, an individual in the evolutionary algorithm time-sequentially generates a given number of entire phenotypes before finishing its life. Each gene represents a cycle time of changing probability for determining its corresponding phenotypic value, which is an indicator of developmental timing. In addition, the algorithm has a learning mechanism such that, during the lifetime of an individual, genes representing a long cycle time can change the probability of adaptation more easily than genes representing a short cycle time. Therefore, if the diversity of the genes is maintained, it can be expected that the algorithm provides a different evolution speed to each phenotypic value. The present paper also discusses a new approach to depicting an evolutionary optimization process. An evolutionary optimization process involves the identification of linkage between variables, and therefore, network structures formed using the identified linkage information determine how the evolutionary algorithm solves a given optimization problem. The proposed approach regards an evolutionary optimization process as a change in the network topology that emerges in the process of linkage identification. The simulation results indicate that evolution and learning mediated by the difference in developmental timing helps to sequentially solve hard uniformly-scaled bit optimization problems with linkage between variables.
Fuzzy Activity Network Method for Project Scheduling Under Resource Constraints
Authors: Long Luong Duc ,   Nagaoka University of Technology, 1603-1 Kamitomioka Machi, Nagaoka, Niigata 940-2188, Japan, Ohsato Ario
Published: April 2016

In this article, a fuzzy activity network method is developed for project scheduling under resource constraints. Trapezoidal fuzzy numbers are used for estimating uncertain durations of activities, and then these fuzzy numbers are replaced by suitable crisp durations for project scheduling under resource constraints. In the next step, the critical chain is identified for determining the project duration, and uncertainties associated with activities are addressed by using feeding/project buffers to protect the project schedule from disturbances. For minimizing project duration, the proposed method considers both the suitable crisp durations and the start times of activities as decision variables. Hence, a new procedure based on genetic algorithm and priority heuristics is also developed for efficiently determining these decision variables. Furthermore, the method also considers selecting the best possible relationships between activities to minimize project duration. The proposed method using buffers makes it possible to improve project scheduling under resource constraints.
Acquisition of Behavioral Patterns Depends on Self-Embodiment Based on Robot Learning Under Multiple Instructors
Authors: Kotake Masato ,   Interdisciplinary Graduate School of Science and Engineering, Tokyo Institute of Technology, 4259 Nagatsuta-cho, Midori-ku, Yokohama, 226-8503, Japan, Katagami Daisuke , Nitta Katsumi ,   Daikin Industries, Ltd., 1304 Kanaokacho, Kitaku, Sakaishi, Osaka
Published: April 2016

We focus on robotic learning under multiple instructors. Even when their goal is the same, different instructors inevitably was different approaches. We propose incorporating DP matching and clustering, classifying the teaching demonstrations of instructors into groups of similar ones. Experiments in which an AIBO robot was taught to walk forward demonstrated that our proposal acquired appropriate teaching approaches based on AIBO’s different embodiments and maximizing task accomplishment.
Towards Building Secure Smart Spaces for Information Security in the Physical World
Authors: Hattori Shun ,   Department of Social Informatics, Graduate School of Informatics, Kyoto University, Yoshida-Honmachi, Sakyo, Kyoto 606-8501, Japan, Tanaka Katsumi
Published: April 2016

This paper introduces the concept of Secure Spaces, one step ahead of Smart Spaces, on information security. We define Secure Spaces as physically isolated environments in which any resource is completely protected from its unauthorized objects with respect to information security. In other words, only if inside them, any information resource is completely protected from being accessed by its unauthorized visitors, and any visitor is completely protected from being exposed to her unwanted information resources. To build such a secure Smart Space, we propose a formalized model and architecture for space entry control based on its dynamically changing contents such as its visitors, physical information resources, and virtual information resources via its embedded output devices.
Use of Fuzzy Neural Network in Diagnosing Postmenopausal Women with Osteoporosis Based on Dental Panoramic Radiographs
Authors: Arifin Agus Zainal ,   Department of Informatics, Faculty of Information Technology, Institut Teknologi Sepuluh Nopember (ITS), Jurusan Teknik Informatika, Kampus ITS Sukolilo, Surabaya, 60113, Indonesia, Asano Akira , Taguchi Akira , Nakamoto Takashi , Ohtsuka Masahiko , Tsuda Mikio , Kudo Yoshiki , Tanimoto Keiji ,   Division of Information Engineering, Graduate School of Engineering, Hiroshima University, 1-7-1 Kagamiyama, Higashi-Hiroshima 739-8521, Japan,   Department of Oral and Maxillofacial Radiology, Hiroshima University Hospital, Hiroshima, Japan,   Department of Oral and Maxillofacial Radiology, Division of Medical Intelligence and Informatics, Graduate School of Biomedical Sciences, Hiroshima University, Hiroshima, Japan,   Department of Obstetrics and Gynecology, Mazda Hospital, Hiroshima, Japan,   Department of Obstetrics and Gynecology, Division of Clinical Medical Science, Graduate School of Biomedical Sciences, Hiroshima University, Hiroshima, Japan
Published: April 2016

A thin or eroded cortex of the mandible detected on dental panoramic radiographs is independently associated with low skeletal bone mineral density (BMD) or osteoporosis in postmenopausal women. The purposes of this study were to develop new computer-aided diagnosis system that combines these two panoramic measures by using fuzzy neural networks (FNN) for identifying postmenopausal women with osteoporosis. Dental panoramic radiographs of 100 postmenopausal women who visited our clinic and had BMD assessments at the lumbar spine and the femoral neck were used in this study. Mandibular cortical width and shape were measured by computer-aided systems and used as the inputs. This system partitioned the input space into a set of subspaces using a novel fuzzy thresholding and constructed the fuzzy inference system incorporated with multi layer perceptron neural network. Our results show that the combination of cortical width and shape by using FNN can be used for the identification of postmenopausal women with osteoporosis in dental clinic. Dentists may identify postmenopausal women accurately by using the new FNN based system and refer them to medical professional for BMD testing.
Double-Deck Elevator Group Supervisory Control System Using Genetic Network Programming with Ant Colony Optimization with Evaporation
Authors: Yu Lu ,   Graduate School of Information, Production and Systems, Waseda University, 2-7 Hibikino, Wakamatsu-ku, Kitakyushu, Fukuoka 803-0135, Japan, Zhou Jin , Mabu Shingo , Hirasawa Kotaro , Hu Jinglu , Markon Sandor ,   Fujitec Co. Ltd. Prod. Dev. HQ, Big Wing, Hikone, Shiga 522-8588, Japan
Published: April 2016

Recently, Artificial Intelligence (AI) technology has been applied to many applications. As an extension of Genetic Algorithm (GA) and Genetic Programming (GP), Genetic Network Programming (GNP) has been proposed, whose gene is constructed by directed graphs. GNP can perform a global searching, but its evolving speed is not so high and its optimal solution is hard to obtain in some cases because of the lack of the exploitation ability of it. To alleviate this difficulty, we developed a hybrid algorithm that combines Genetic Network Programming (GNP) with Ant Colony Optimization (ACO) with Evaporation. Our goal is to introduce more exploitation mechanism into GNP. In this paper, we applied the proposed hybrid algorithm to a complicated real world problem, that is, Elevator Group Supervisory Control System (EGSCS). The simulation results showed the effectiveness of the proposed algorithm.
An Iterative Approach for Fuzzy Clustering Based on Feature Significance
Authors: Han Jianchao ,   Department of Computer Science, California State University Dominguez Hills, 1000 E. Victoria St. Carson, CA 90747 USA, Beheshti Mohsen
Published: April 2016

Clustering is a technique to group a set of unsupervised data based on the conceptual clustering principle: maximizing the intraclass similarity and minimizing the interclass similarity. Existing clustering approaches concentrate in the different data types and assume that all features play the same role in algorithm validations. However, some features may be more significant than others in forming clusters. In this paper, we consider the feature significance and include it in the clustering algorithms. An iterative approach for fuzzy clustering based on the feature significance is presented and applied in the k-means algorithm for numerical data, the k-modes algorithm for categorical data, and the k-prototypes algorithm for mixed data.
Mining Association Rules from TV Watching Log for TV Program Recommendation
Authors: Takama Yasufumi ,   Tokyo Metropolitan University, 6-6 Asahigaoka, Hino, Tokyo 191-0065, Japan, Hattori Shunichi
Published: April 2016

This paper proposes a method for extracting association rules from users’ TV watching logs, aiming at TV program recommendation. In Japan, a TV is usually located in a living room, where family members communicate each other while watching the same TV programs. Based on the choice of TV channels and conversation while watching TV, we can estimate other person’s interests and preference, which is a basis for establishing friendly relationship between others. Therefore, giving the robot a capability of recommending TV program will contribute to establish friendly communication with human partners. Furthermore, transition to digital terrestrial television broadcasting (DTTB) will bring us difficulty in finding TV program worth watching from a number of TV channels in near future. Therefore, a method for recommending TV programs will be one of the most important technologies for realizing intelligent support of our daily lives, such as a partner robot. The proposed method extracts association rules from user profiles that are generated from their TV watching logs, based on which TV programs are recommend to a target user. In order to have extensibility in terms of information resource for generating user profile, the method employs a user profile of commonly-used bookmark format. When association rules are extracted from a set of the profiles, generalization law is introduced so that variety of users’ behaviors can be reduced. Experiments are performed with actual users’ logs, and the result shows the generalization law contributes to increase the accuracy of TV program recommendation.
Modeling of Wood Aging Caused by Biological Deterioration
Authors: Yin Xin ,   Ritsumeikan University, 1-1-1 Nojihigashi, Kusatsu, Shiga 525-8577, Japan, Fujimoto Tadahiro , Chiba Norishige , Tanaka Hiromi T. ,   Iwate University, 4-3-5 Ueda, Morioka, Iwate 020-8551, Japan
Published: April 2016

We propose visually simulating wood aging by microorganisms using an ant colony optimization algorithm to generate wood aging patterns. Ants deposit pheromone similar to termites and wood deterioration caused by termites is simulated using this algorithm. Patterns generated by this algorithm resemble many pattern in nature, meaning ant paths are representative of worm paths and ant pheromones are representative of microorganism growth. We demonstrate the effectiveness of this technique in experiments.
I3P: A Protocol for Increasing Reliability and Responsiveness in Massively Multiplayer Games
Authors: Kackley Jeremy ,   University Of Southern Mississippi, School of Computing, 118 College Dr., Box 5106, Hattiesburg, MS 39406, Gambrell Matthew , Gourd Jean ,   Amaze Entertainment, 816 Congress Ave SE, Suite 600, Austin, TX 78701
Published: April 2016

Developing broadband and internet technologies offers possibilities for new ways of minimizing the server bottleneck in online gaming as well as an increase in response and reliability. We look at a peer-to-peer (P2P) approach to circumnavigate some of the reliance on the central server and propose a protocol designed to increase responsiveness and reliability – which is also useful in meeting the unique requirements of a P2P approach.
Non Linear Disturbance Accommodation Fuzzy Control
Authors: Abdelbari Slim ,   Signals and Systems Laboratory, ENIT, Tunisia, Ezzine Jelel
Published: April 2016

This paper deals with the problem of chaotic disturbances accommodation when these are generated by known non linear dynamics. In order to accomplish this goal, Takagi-Sugeno fuzzy models are called for as they offer the advantage of having virtually a linear rule consequent to approximate non linear systems. A control law inspired from the known disturbance accommodation control theory (DAC theory) is used to make the effects of disturbances vanish or attenuated while the considered linear plant is stabilized at the same time. An illustrative example is provided.
Algorithm for Web Service Discovery Based on Information Retrieval Using WordNet and Linear Discriminant Functions
Authors: Sotolongo Ricardo ,   Department of Computational Intelligence and Systems Science, Tokyo Institute of Technology, G3-49, 4259 Nagatsuta, Midori-ku, Yokohama 226-8502, Japan, Kobashikawa Carlos , Dong Fangyan , Hirota Kaoru
Published: April 2016

An algorithm based on information retrieval that applies the lexical database WordNet together with a linear discriminant function is proposed. It calculates the degree of similarity between words and their relative importance to support the development of distributed applications based on web services. The algorithm uses the semantic information contained in the Web Service Description Language specifications and ranks web services based on their similarity to the one the developer is searching for. It is applied to a set of 48 real web services in five categories, then compared them to four other algorithms based on information retrieval, showing an averaged improvement over all data between 0.6% and 1.9% in precision and 0.7% and 3.1% in recall for the top 15 ranked web services. The objective was to reduce the burden and time spent searching web services during the development of distributed applications, and it can be used as an alternative to current web service discovery systems such as brokers in the Universal Description, Discovery, and Integration (UDDI) platform.
Further Results on T-S Fuzzy Controller Design Subject to Input Constraint
Authors: Han Hugang ,   Department of Management Information System, Prefectural University of Hiroshima, 1-1-71 Ujina-higashi, Minami-ku, Hiroshima 734-8558, Japan
Published: April 2016

In general, when using the Takagi-Sugeno (T-S) fuzzy model to develop a control system, the state feedback control gain can be obtained by solving some linear matrix inequalities (LMIs). In this paper, we consider a class of nonlinear systems with input constraint (saturation). To obtain the control gain, we require to employ certain extra LMIs besides the general ones. As a result, all the LMIs are more conservative. At the same time, one of the extra LMIs confines the initial state to a region, which is referred to as an ellipsoid, and is relevant to a matrix variable in the LMIs. Therefore, the goals of this paper are: 1) making the ellipsoid as large as possible so that the initial state can be confined to the region easily and; 2) making all the LMIs more feasible to obtain the control gain.
H∞ Filtering Approach for GSM Navigation Systems
Authors: Chen Hsin-Yuan ,   Institute of Automatic Control Engineering, Feng Chia University
Published: April 2016

The GSM navigation system designs using H∞ filtering approach is conducted and the resulting performance is assessed. The H∞ filtering problem is shown to have many solutions for each proper value of the prescribed design parameter γ, which is different from the Kalman filter with only one solution. The Kalman filter, which employed in the GSM receiver as the navigational state estimator, provides optimal solution if the noise statistics for both the measurement and the system process are completely known. Practically, the noises are varying with time, which results in filtering performance degradation. In design, H∞ filter can be employed to ensure that the energy gain from the disturbances to the estimation error will not exceed an upper bound.
Neuro-Fuzzy Approaches for Forecasting Electrical Load Using Additional Moving Average Window Data Filter on Takagi-Sugeno Type MISO Networks
Authors: Pasila Felix ,   Electrical Engineering Department, Petra Christian University, Siwalankerto 121-131, Surabaya, Indonesia, Palit Ajoy K. , Thiele Georg ,   Department of Electrical Engineering, ITEM, University of Bremen, D-28359 Bremen, Germany,   Department of Electrical Engineering, IAT, University of Bremen, D-28359 Bremen, Germany
Published: April 2016

The paper describes a neuro-fuzzy approach with additional moving average window data filter and fuzzy clustering algorithm that can be used to forecast electrical load using the Takagi-Sugeno (TS) type multi-input single-output (MISO) neuro-fuzzy network efficiently. The training algorithm with additional moving average filter is efficient in the sense that it can bring the performance index of the network, such as the sum squared error (SSE), down to the desired error goal much faster than the simple Levenberg-Marquardt algorithm (LMA). The fuzzy clustering algorithm allows the selection of initial parameters of fuzzy membership functions, e.g. mean and variance parameters of Gaussian membership functions of neuro-fuzzy networks, which are otherwise selected randomly. The initial parameters of fuzzy membership functions, which result in low SSE value with given training data of neuro-fuzzy network, are further fine tuned during the network training. Finally, the above training algorithm is tested on TS type MISO neuro-fuzzy structure for long-term forecasting application of electrical load time series.
Rough Sets Based Rule Generation from Data with Categorical and Numerical Values
Authors: Sakai Hiroshi ,   Department of Mathematics and Computer Aided Science, Faculty of Engineering, Kyushu Institute of Technology, Koba Kazuhiro , Nakata Michinori ,   Faculty of Management and Information Science, Josai International University
Published: April 2016

Rough set theory has been mainly applied to data with categorical values. In order to handle data with numerical values in this theory, a familiar concept of ‘wildcards’ was employed, and a new framework of rough sets based rule generation has been proposed. Two characters @ and # were introduced into this framework, and numerical patterns were also defined for numerical values. The concepts of ‘coarse’ and ‘fine’ for rules were explicitly defined according to numerical patterns. This paper enhances the previous framework, and describes the implementation of an utility program. This utility program is applied to the data in UCI Machine Learning Repository, and some useful rules are obtained.
A Behavioral Decision Model Based on Fuzzy Targets in Decision Making Using Weather Information
Authors: Hiramatsu Akio ,   School of Knowledge Science, Japan Advanced Institute of Science and Technology, Huynh Van-Nam , Nakamori Yoshiteru
Published: April 2016

Due to inevitable uncertainty in weather forecasts, many decision problems influenced by weather information have been formulated for decision making in uncertain situations. The fuzzy target-based decision making model we propose assumes that the decision maker assesses a fuzzy target expressing an aspiration, then selects the decision maximizing the possibility of attaining this target aspiration before making a decision. We then show that the decision maker's different behavior about the aspiration leads to different decisions depending on the decision maker's personal philosophy or experience. This behavioral analysis provides an interpretation for influencing psychological features of the decision maker in decision making and introduces an interesting link to attitudes towards risk by means of utility function.
Fuzzyc-Means Algorithms Using Kullback-Leibler Divergence and Helliger Distance Based on Multinomial Manifold
Authors: Inokuchi Ryo ,   Doctoral Program in Risk Engineering, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki 305-8573, Japan, Miyamoto Sadaaki ,   Department of Risk Engineering, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki 305-8573, Japan
Published: April 2016

In this paper, we discuss fuzzy clustering algorithms for discrete data. Data space is represented as a statistical manifold of the multinomial distribution, and then the Euclidean distance are not adequate in this setting. The geodesic distance on the multinomial manifold can be derived analytically, but it is difficult to use it as a metric directly. We propose fuzzyc-means algorithms using other metrics: the Kullback-Leibler divergence and the Hellinger distance, instead of the Euclidean distance. These two metrics are regarded as approximations of the geodesic distance.
Formulation of Fuzzyc-Means Clustering Using Calculus of Variations and Twofold Membership Clusters
Authors: Miyamoto Sadaaki ,   Department of Risk Engineering, School of Systems and Information Engineering, University of Tsukuba
Published: April 2016

A membership matrix of fuzzyc-means clustering is associated with corresponding fuzzy classification rules as membership functions defined on the whole data space. We directly derive such functions in fuzzyc-means and possibilistic clustering using the calculus of variations, generalizing ordinary fuzzyc-means and deriving new twofold membership clustering using this framework.
A Data Mining Approach to Rainfall Intensity Classification Using TRMM/TMI Data
Authors: Chen Shan-Tai ,   Dept. of Computer Science, Chung Cheng Institute of Technology, National Defense University, Dou Shung-Lin , Chen Wann-Jin ,   St., Tashi, Taoyuan, Taiwan, R.O.C.,   Dept. of Environment Information Engineering, Chung Cheng Institute of Technology, National Defense University
Published: April 2016

The systematic approach we propose for classifying oceanic rainfall intensity during the typhoon season consists of two major steps – 1) identifying the rain areas and 2) classifying rainfall intensity intonormalandheavyfor these areas. The heterogeneous hierarchical classifier (HHC), an ensemble model we developed for accurately identifying heavy rainfall events, consists of a set of base classifiers. The base classifiers are independently constructed through heterogeneous data mining approaches such as artificial neural networks, decision trees, and self-organizing maps. The meteorological satellite Tropical Rainfall Measuring Mission (TRMM) microwave imager (TMI) data from 2000 to 2005 are used to create the classification models. TRMM precipitation radar (PR) data and rain gauge data from Automatic Rainfall and Meteorological Telemetry System (ARMTS) measurement are used as ground truth data to evaluate models. Two thirds of the dataset is used for model training and one third for testing. Experimental results show that the proposed model classifies rainfall intensity highly accurately and outperforms previously published methods.
Prototype Design of Decision Supports for GRID Clearinghouse Service Framework
Authors: Chiang Johannes K. ,   Dept. of MIS, National Cheng-Chi University, Taipei 116, Taiwan, Chao Kiekang
Published: April 2016

Grid Computing, as an emerging technology, facilitates computer resource sharing, distribution over the Internet. Large-scale collaboration and engineering development for GRID Computing has been engaged world wide. The vigorous momentum of the technology has captured great business attention. The resources management in such large-scale distributed environment becomes a great challenge, and will be the critical issue before GRID deployed into commercial operation while resource allocation and use need to be properly managed in realistic and economic justification. This study aims to elaborate a new business model regarding service trading and billing. The Grid Architecture for Computational Economy (GRACE) framework can be the fundamental framework which reveals resource trading behavior. This paper explored the resource trading models and the way to integrate them into a uniform computing environment. Thereby the concepts and Architecture is to be investigated and extended with Economic Service Architecture (ESA). Last but not least, this paper carries out necessary Service Interfaces and Service Data Entities.
Cognitive Informatics Model for Non-Overlapped Image Filtering Based on the Optical Aberrations of the Eye
Authors: Reskó Barna ,   Budapest Tech, Regional Education and Innovation Center, Antal Ákos , Baranyi Péter ,   Budapest University of Technology and Economics, Dept. of Mechatronics, Optics and Instr. Tech.,   Computer and Automation Research Institute, Hungarian Academy of Sciences
Published: April 2016

Recent results in retinal research have shown that ganglion cell receptive fields cover the mammalian retina in a mosaic arrangement, with insignificant amounts of overlap in the central fovea. This finding implies that from the informatics point of view there is a major conceptual gap between traditional and widely accepted, convolution based image filtering algorithms, and the way visual information is processed by the retina in the eye. The use of traditional filters with non-overlapping operator architectures leads to considerable information losses between centers of filter kernels. This paper introduces a novel model of the eye-retina system that fills the conceptual gap of information processing between the retina and the overlapping (convolution based) architectures used by today's widely adapted algorithms. The proposed computational model takes into consideration data convergence, as well as the dynamic and optical properties of the eye lens. Based on the evaluation of the model, three hypotheses are formulated on the role of the optical precision of the eye-lens and involuntary eye accommodation dynamics.
HOSVD Based Canonical Form for Polytopic Models of Dynamic Systems
Authors: Szeidl László ,   Budapest Tech Polytechnic Institute, J. von Neumann Faculty of Informatics, H-1034 Budapest, Bécsi út 96/B, Hungary, Várlaki Péter ,   Budapest University of Technology and Economics, Department of Chassis and Lightweight Structures, H-1111 Budapest, Bertalan L. u. 2, Hungary
Published: April 2016

The higher order singular-value-based canonical form of linear parameter varying models we define, extracts a models's most important invariant characteristics. We studied the numerical reconstructibility of the canonical form using a routinely executable tractable uniform method, and present convergency theorems for given numerical reconstruction constraint.
Micro Robot Control by Use of Electroencephalograms from Right Frontal Area
Authors: Yamanoi Takahiro ,   Division of Electronics and Information Engineering, Hokkai-Gakuen University, W11-1-1, S26, Chuo-ku, Sapporo 064-0926 Japan, Toyoshima Hisashi , Yamazaki Toshimasa , Ohnishi Shin-ichi , Sugeno Michio , Sanchez Elie ,   Japan Technical Software, West 3-1-14, North 21, North ward, Sapporo 001-0021, Japan,   Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502, Japan,   Faculty of Information Science, Doshisha University, Miyako-dani, Tatara, Kyotanabe, Kyoto 610-0394 Japan,   LIF, Faculty of Medicine, Aix-Marseille University, Faculte de Medecine, 27 Bd Jean Moulin 13385 Marseille Cedex5, France
Published: April 2016

In order to develop a brain machine interface, the authors have investigated the brain activity during human recognition of characters and symbols representing directional meaning. They have recorded electroencephalograms (EEGs) from subjects viewing four types of Kanji (Chinese characters being used currently in the Japanese language) and arrows that were presented on a CRT. Each of the four characters or symbols denoted direction for upward, downward, leftward and rightward, respectively. Subjects were asked to read the characters or symbols, silently. EEGs were averaged for each stimulus type and direction, and event related potentials (ERPs) were obtained. The equivalent current dipole source localization (ECDL) method has been applied to these ERPs. In both cases, equivalent current dipoles (ECDs) were localized to areas related to the working memory for spatial perception, such as the right upper or the right middle frontal areas. Taking into account these facts, the authors have investigated a single trial EEGs of the subject precisely after the latency at 400 ms, and it was determined effective sampling latencies for the discriminant analysis to four types of arrow: ↑, ↓, ←, and →. EEG data have been sampled at latency from 400 ms to 900 ms at 25 ms interval by the three channels in the right upper and the right middle frontal gyri. Results of the discriminant analysis for four type objective variates, presented discriminant rates were above 80%. By four type code of infrared rays according to the discrimination results from a PC, the authors have controlled a micro robot, the e-puck, with four orders: forward, rotate clockwise, rotate counterclockwise and stop.
Network Administrator Assistance System Based on Fuzzy C-means Analysis
Authors: Chen Benhui ,   Graduate School of Information, Production and Systems, Waseda University, Hu Jinglu , Duan Lihua , Gu Yinglong ,   Dali University Gucheng Xuefu Road of Dali City, Yunnan, 671007, China
Published: April 2016

In this research we design a network administrator assistance system based on traffic measurement and fuzzy c-means (FCM) clustering analysis method. Network traffic measurement is an essential tool for monitoring and controlling communication network. It can provide valuable information about network traffic-load patterns and performances. The proposed system utilizes the FCM method to analyze users' network behaviors and traffic-load patterns based on traffic measurement data of IP network. Analysis results can be used as assistance for administrator to determine efficient controlling and configuring parameters of network management systems. The system is applied in Dali University campus network, and it is effective in practice.
Experimental Study on Pair Swap Strategy in Quantum-Inspired Evolutionary Algorithm
Authors: Imabeppu Takahiro ,   Department of Information and Computer Science, Faculty of Engineering, Kagoshima University, 1-21-40 Korimoto, Kagoshima 890-0065, Japan, Nakayama Shigeru , Ono Satoshi
Published: April 2016

Quantum-Inspired Evolutionary Algorithm (QEA), a type of stochastic algorithm for solving combinatorial optimization problems, is evolutionary computation using quantum bits and superposition states in quantum computation. Although coarse-grained parallel, QEA has many parameters that must be adjusted manually. The simpler algorithm, Quantum-inspired Evolutionary Computation with Pair Swap operator (QEAPS), the authors propose involves just one population and a simple genetic operation exchanging best solution information between two individuals chosen randomly, instead of the migration operation used in QEA, and thereby fewer parameters to be adjusted. The authors found in experiments that QEAPS finds highly qualified solutions, is more robust against constraint handling, and has a higher search performance of thanks to diversified best solution information.
Selected Papers from SCIS & ISIS 2008 – No.1
Authors: Nakamura Tsuyoshi ,   Associate Professor,
Published: April 2016

Welcome to this special issue of the Journal of Advanced Computational Intelligence and Intelligent Informatics (JACIII). I am pleased to introduce 41 selected papers presented at the 3rd International Conference on Soft Computing and Intelligent Systems (SCIS) and the 7th International Symposium on Advanced Intelligent Systems (ISIS) held on September 17-21, 2008, at Nagoya University in Nagoya, Japan. This conference featured 401 original papers in presentations attended by some 500 participants. SCIS & ISIS is a biennial international joint conference in the field of soft computing and intelligent systems, including branches of research ranging from fuzzy systems, neural networks, and evolutionary computation to multiagent systems, artificial intelligence, and robotics. This current issue presents 20 papers covering most of the conference topics including fuzzy theory, self-organizing maps, robotics, computer vision, and optimization algorithms. I would like to thank the authors and reviewers and SCIS & ISIS 2008 for making this special issue possible. I am also grateful to Prof. Toshio Fukuda, Nagoya University, and Prof. Kaoru Hirota, Tokyo Institute of Technology, the editors-in-chief, and the SCIS & ISIS 2008 conference staff for inviting me to guest-edit this Journal.
Animated Two-Dimensional Barcode Generation Using Optimization Algorithms – Redesign of Formulation, Operator, and Quality Evaluation
Authors: Ono Satoshi ,   Department of Information and Computer Science, Faculty of Engineering, Kagoshima University 1-21-40, Korimoto, Kagoshima, 890-0065, Japan, Morinaga Kensuke , Nakayama Shigeru
Published: April 2016

To improve on our previously proposed but problem-plagued innovation for generating animated and illustrated Quick Response (QR) codes, this paper proposes a method which formulates the animated QR code generation problem as an optimization problem rather than as a set of still QR code decoration problems. The proposed method also uses optimization operators designed for this problem and quality evaluation to maintain natural, smooth movement. Experiments demonstrate that the proposed method can generate animated QR codes involve a maximum of eight illustrations moving inside the code which maintaining decoding feasibility and smooth illustration movement.

Erratum
 Due to a wrong manipulation during the correction of the proofs of the above paper, the running head title (short title) was incorrect. The correct running head title should have read as "Animated Two–Dimensional Barcode Generation."
Proposal of a Method to Extract Arbitrary FiguresUsing One-Dimensional Histograms
Authors: Nakashima Shota ,   Department of Electrical Engineering, Kyushu Institute of Technology, Kitakyushu, Japan, Miyauchi Makoto , Serikawa Seiichi ,   Department of Integrated Arts and Sciences, Kitakyushu National College of Technology, Japan
Published: April 2016

Arbitrary figure extraction, a basic image processing problem, is done typically using the generalized Hough transform (GHT). GHT and its successors tend, however, to consume humongous amounts of processing time and memory space. The arbitrary figure extraction we propose using one-dimensional histograms takes advantage of the Polytope method, which features: (1) The histogram distribution changes if parameters representing figures change. (2) Optimum parameters are obtained, if the value of the highest-frequency histogram becomes maximum. This approach makes memory space very small, processing time very short, effective by extracts arbitrary curves with different aspect ratios, and the algorithm is simple.
Clustering Algorithm Based on Probabilistic Dissimilarity
Authors: Yamashiro Makito ,   Graduate School of Systems and Information Engineering, University of Tsukuba, Endo Yasunori , Hamasuna Yukihiro ,   Department of Risk Engineering, Faculty of Systems and Information Engineering, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki 305-8573, Japan
Published: April 2016

The clustering algorithm we propose is based on probabilistic dissimilarity, which is formed by introducing the concept of probability into conventional dissimilarity. After defining probabilistic dissimilarity, we present examples of probabilistic dissimilarity functions. After considering an objective function with probabilistic dissimilarity. Furthermore, we construct a clustering algorithm probabilistic dissimilarity based using optimal solutions maximizing the objective function. Numerical examples verify the effectiveness of our algorithm.
A Granular Framework for Recognition of Arabic Handwriting
Authors: Beldjehem Mokhtar ,   Sainte Anne University, 1589 Walnut Street Halifax, Nova Scotia, B3H 3S1, Canada
Published: April 2016

We propose a novel cognitively motivated unifying framework for Arabic handwriting recognition that takes into account the nature of the human reading process of Arabic handwriting. This Modular Granular Architecture tackles the problem by observing Arabic handwriting from both perceptual and linguistic points of view and hence analyzes the underlying input signal from different granularity levels. It is based on three levels of abstraction: a low granularity level that uses perceptual features called global visual indices, a medium granularity level that is the conventional recognition stage and a high granularity level that consists on morphological analysis dedicated to segmentation/recognition. The original idea is the effective use of Arabic word's morphology in the recognition not only in post-processing. This architecture carries well around the Arabic word's morphology, as typically in Arabic, the Arabic word's morphology is by excellence the logical structure (even semantic) of a given Arabic word, whereas the visual data constitute the physical geometric (topological) structure of a given word. We need to integrate both of them for an effective cooperative recognition of Arabic Handwriting. This framework subsumes the lexicon-driven approaches; in that it can recognize a word that does not exist within the lexicon.
Eligibility Propagation to Speed up Time Hopping for Reinforcement Learning
Authors: Kormushev Petar S. ,   Department of Computational Intelligence and Systems Science, Tokyo Institute of Technology, Yokohama, 226-8502, Japan, Nomoto Kohei , Dong Fangyan , Hirota Kaoru ,   Graduate School of Science and Engineering, Yamagata University, Yamagata, Japan
Published: April 2016

A mechanism called Eligibility Propagation is proposed to speed up the Time Hopping technique used for faster Reinforcement Learning in simulations. Eligibility Propagation provides for Time Hopping similar abilities to what eligibility traces provide for conventional Reinforcement Learning. It propagates values from one state to all of its temporal predecessors using a state transitions graph. Experiments on a simulated biped crawling robot confirm that Eligibility Propagation accelerates the learning process more than 3 times.