bluuebunny/crossref_metadata_2025 huggingface dataset:
Rain-Area Identification Using TRMM/TMI Data by Data Mining Approach
Authors: Chen Shan-Tai ,   Dept. of Computer Science, Chung Cheng Institute of Technology,  National Defense University, No.190, Sanyuan 1, Wu Chien-Chen , Chen Wann-Jin , Hu Jen-Chi ,   St., Tashi, Taoyuan, Taiwan,   School of Defense Science, Chung Cheng Institute of Technology,  National Defense University
Published: April 2016

Rain-area identification distinguishes between rainy and non-rainy areas, which is the first step in some critical real-world problems, such as rain intensity identification and rain-rate estimation. We develop a data mining approach for oceanic rain-area identification during typhoon season, using microwave data from the Tropical Rainfall Measuring Mission (TRMM) satellite. Three schemes tailored for the problem are developed, namely (1) association rule analysis for uncovering the set of potential attributes relevant to the problem, (2) three-phase outlier removal for cleaning data and (3) the neural committee classifier (NCC) for achieving more accurate results. We created classification models from 1998-2004 TRMM Microwave Imager (TRMM-TMI) satellite data and used Automatic Rainfall and Meteorological Telemetry System (ARMTS) rain gauge data measurements to evaluate the model. Experimental results show that our approach achieves high accuracy for the rain-area identification problem. The classification accuracy of our approach, 96%, outperforms the 78.6%, 77.3%, 83.3% obtained by the scattering index, threshold check, and rain flag methods, respectively.
Hybrid GPS/GSM Positioning Systems Design with Fuzzy Logic
Authors: Chen Hsin-Yuan ,   Department of Automatic Control Engineering, Feng Chia University, Taichung, Taiwan, Liu Chen-Hung
Published: April 2016

Global Positioning System (GPS) is based on a satellite system. Much work has been carried out on a non-satellite positioning systems using the existing Global System of Mobile Communications (GSM) infrastructure. This leads to a hybrid GPS/GSM positioning systems in the paper. We purpose to adopt GSM for positioning via an integrated switch design with fuzzy logic, if GPS lose efficacy. This induces four main research features in this paper. (1) Designs with optimal-based Fourier analyzer to achieve error of GPS decrease is presented. (2) New applications of fuzzy set theory are presented to solve the problem of GPS accuracy. (3) An integrated switch of fuzzy logic is designed for selecting GPS or GSM positioning systems. (4) Discussions on various GSM positioning methods are adopted with different error and location parameters of environment.
Design and Implementation of Combinatorial Game Calculator
Authors: Kao Kuo-Yuan ,   Department of Information Management, National Penghu University, Penghu, Taiwan
Published: April 2016

The universal game calculator we developed based on a combinatorial game model features a general-purpose game analysis engine, unlike other game-specific programs. Users input game rules by defining game functions and input different game locations by describing game expressions, enabling the system to analyze optimal game strategies.
A Unified Approach to Planning Versatile Motions for an Autonomous Digital Actor
Authors: Li Yueh-Tse ,   Computer Science Department, National Chengchi University, 64, Sec. 2, Zhi-Nan Rd. Taipei, Taiwan 116, Li Tsai-Yen
Published: April 2016

Enabling a digital actor to move autonomously in a virtual environment is a challenging problem that has attracted much attention in recent years. The systems proposed in several researches have been able to plan the walking motions of a humanoid on an uneven terrain. In this paper, we aim to design a planning system that can generate various types of motions for a humanoid with a unified planning approach. Based on our previous work, we add two additional motion abilities: leaping and moving obstacles into the system. In previous work, the order of moving obstacles is determined first, and then the corresponding paths for the pushing/pulling motions are generated. In this work, we take a unified approach that accounts for all types of motions at the same time. We have implemented a planning system with this unified approach for a humanoid moving in a layered virtual environment. Several simulation examples are demonstrated in this paper to illustrate the effectiveness of the system.
Fuzzy Inference based Mentality Expression for Eye Robot in Affinity Pleasure-Arousal Space
Authors: Yamazaki Yoichi ,   Tokyo Institute of Technology, Hatakeyama Yutaka , Dong Fangyan , Nomoto Kohei , Hirota Kaoru
Published: April 2016

A Mentality expression system based on the affinity pleasure-arousal space of eye robots is proposed for the communication between human beings and households robots. Mentality status in the proposed space is calculated by a fuzzy inference method that uses language category as the output of a speech understanding module. The constructed eye robot system expresses mentality in the affinity-pleasure-arousal space by predefined eyelid and ocular movements. Mentality expression experiments with two scenarios are performed. Since the results of an evaluation questionnaire show an average evaluation value of 3.6 out of 5.0, the proposed system is suitable for a communication architecture between interlocutor and robot. The system provides a basic interface for an information terminal in home environments.
Reduction Models in Competitive Learning Founded on Distortion Standards
Authors: Maeda Michiharu ,   Department of Computer Science and Engineering, Faculty of Information Engineering, Fukuoka Institute of Technology, 3-30-1 Wajiro-higashi, Higashi-ku, Fukuoka 811-0295, Japan, Shigei Noritaka , Miyajima Hiromi , Suzaki Kenichi ,   Department of Electrical and Electronic Engineering, Faculty of Engineering, Kagoshima University, 1-21-40 Korimoto, Kagoshima 890-0065, Japan
Published: April 2016

Two reductions in competitive learning founded on distortion standards are discussed from the viewpoint of generating necessary and appropriate reference vectors under the condition of their predetermined number. The first approach is termed the segmental reduction and competitive learning algorithm. The algorithm is presented as follows: First, numerous reference vectors are prepared and the algorithm is processed under competitive learning. Next, reference vectors are sequentially eliminated to reach their prespecified number based on the partition error criterion. The second approach is termed the general reduction and competitive learning algorithm. The algorithm is presented as follows: First, numerous reference vectors are prepared and the algorithm is processed under competitive learning. Next, reference vectors are sequentially erased based on the average distortion criterion. Experimental results demonstrate the effectiveness of our approaches compared to conventional techniques in average distortion. The two approaches are applied to image coding to determine their feasibility in quality and computation time.
Implementation of a Real-Time FPGA-Based Intelligent Parallel Parking System
Authors: Masmoudi Mohamed Slim ,   Electrical Engineering Department, Ecole Nationale D’Ingenieurs de Sfax, B.P. W, 3038 Sfax, Tunisia, Tsui Willie , Song Insop , Karray Fakhreddine , Masmoudi Mohamed , Derbel Nabil ,   Electrical and Computer Engineering Department, University of Waterloo, 200 University Avenue West, Waterloo, Ontario, Canada, N2L 3G1,   Ericsson Inc., 5000 Ericsson Dr, Warrendale, PA 15086, USA
Published: April 2016

Parallel parking is a challenging maneuver for many drivers, especially in crowded cities with heavy traffic congestion and limited parking spaces. This research focuses on the development of an intelligent parallel parking system. Conventional vehicular control techniques generally require the use of analytical models. However, complexities due to nonlinear car dynamics create multiple challenges when modeling car motion. As fuzzy logic control is appropriate for nonlinear and complex systems where human expert knowledge is available, it is well-suited for the parking application. This paper presents the design and implementation of a real-time fuzzy logic based parallel parking system. Two control units consisting of a main controller and a secondary fuzzy logic controller are utilized. The latter controller is employed for the realization of the wall following task, which has an important role in the parking system. Based on performance and flexibility considerations, the control units are implemented onto a reconfigurable hardware platform, namely a Field Programmable Gate Array (FPGA). A prototype vehicle is developed to ensure the proposed algorithm provides vehicular systems with the parallel parking ability.
An Optimized Multi-Output Fuzzy Logic Controller for Real-Time Control
Authors: Gunay Noel S. ,   Department of Manufacturing Engineering and Management,  De La Salle University, 2401 Taft Avenue, Manila, 1004 Philippines, Dadios Elmer P.
Published: April 2016

Any real-time control application run by a digital computer (or any sequential machine) demands a very fast processor in order to make the time-lag from data sensing to issuance of a control action closest to zero. In some instances, the algorithm used requires a relatively large primary memory which is crucial especially when implemented in a microcontroller. This paper presents a novel implementation of a multi-output fuzzy controller (which is known in this paper as MultiOFuz), which utilizes lesser memory and executes faster than a type of an existing multiple single-output fuzzy logic controllers. The design and implementation of the developed controller employed the object-oriented approach with program level code optimizations. MultiOFuz is a reusable software component and the simplicity of how to interface this to control applications is presented. Comparative analyses of algorithms, memory usage and simulations are presented to support our claim of increased efficiency in both execution time and storage use. Future directions of MultiOFuz are also discussed.
Modeling Decisions for Artificial Intelligence
Authors: Torra Vicenç ,   Institut d'Investigaci'o en, Narukawa Yasuo ,   Toho Gakuen
Published: April 2016

In August 2007, the 4th International Conference on Modeling Decisions for Artificial Intelligence (MDAI1) was held in Kitakyushu, Japan. This special issue has its origins in the conference. We present nine papers related to soft computing tool applications. The first paper, by Honda and Okazaki, presents an axiomatization of a generalized Shaply value. The second paper, by García-Lapresta, also related to decision-making, introduces a multistage decision procedure in which decision-makers opinions are weighted by their contribution to an agreement. The third paper, by Torra and Miyamoto, concerns the problem of loading a container, outlining a system for loading nonorthogonal objects. The fourth paper, by Sakai, Koba, and Nakata, is devoted to rule generation based on rough sets. The fifth paper by Hiramatsu, Huynh, and Nakamori, deals with a fuzzy-based model applied to weather information. The sixth paper, by Inokuchi and Miyamoto, discuss fuzzy clustering algorithms for discrete data. The seventh paper, by Miyamoto, Kuroda, and Arai, studies an algorithm for the sequential extraction of clusters compared to mountain clustering. In the eighth paper, Miyamoto formulates fuzzy clustering using the calculus of variations. The ningth and final paper treats fuzzy clustering, in which Endo et al. discuss fuzzy c-means for data with tolerance. In closing, we thank the referees for their work on reviews and Prof. Hirota for editing this special issue. We also thank the Fuji Technology Press Ltd. staff for its advice.

1Work partially funded by Spanish MEC (projects ARES – CONSOLIDER INGENIO 2010 CSD2007-00004 – and eAEGIS – TSI2007-65406-C03-02)
Favoring Consensus and Penalizing Disagreement in Group Decision Making
Authors: García-Lapresta José Luis ,   PRESAD Research Group, Dep. of Applied Economics, University of Valladolid
Published: April 2016

In this paper we introduce a multi-stage decision making procedure where decision makers' opinions are weighted by their contribution to the agreement after they sort alternatives into a fixed finite scale given by linguistic categories, each one having an associated numerical score. We add scores obtained for each alternative using an aggregation operator. Based on distances among vectors of individual and collective scores, we assign an index to decision makers showing their contributions to the agreement. Opinions of negative contributors are excluded and the process is reinitiated until all decision makers contribute positively to the agreement. To obtain the final collective weak order on the set of alternatives, we weigh the scores that decision makers assign to alternatives by indices corresponding to their contribution to the agreement.
Selected Papers from ISIS 2007
Authors: Park G. K. ,   Professor,
Published: April 2016

The 8th International Symposium on Advanced Intelligent Systems (ISIS 2007), a biennial joint conference between Korea and Japan, focuses on artificial intelligence, intelligent systems, information technology, and their applications. ISIS 2007, held on September 5-8 at Soraksan National Park under the auspices of the Korea Fuzzy Logic and Intelligent Systems Society (KFIS), was attended by 265 researchers, engineers, and other professionals and featured 206 presentations. Of more than 20 papers preliminarily selected and reviewed by the ISIS 2007 International Program Committees, 7 were chosen for the special issue of the Journal of Advanced Computational Intelligence and Intelligent Informatics, centering on advanced intelligent systems including robotics, pattern recognition, data mining, and decision-making systems. The content and conclusions presented in these fine papers should prove both interesting and informative to specialists and generalists alike. I thank the authors and reviewers for their painstaking contributions to this special issue and Prof. Kaoru Hirota of the Tokyo Institute of Technology for inviting me to guest-edit this work.
Concept of Humatronics and its Application to Human-Robot Communication Support Under TV Watching Environment
Authors: Takama Yasufumi ,   Graduate School of System Design, Tokyo Metropolitan University, Namba Hiroki , Iwase Yoshihiro , Muto Yuki , Hattori Shunichi
Published: April 2016

In recent years, various systems based on electronics, such as cars, computers and robots have been getting into our daily life. Unfortunately, asymmetrical relationship exists between human and such systems, imposing burdens especially on elderly or handicapped persons. In order to solve various problems for balancing such asymmetrical relationship, humatronics is proposed. Main purpose of humatronics is to establish the symmetric interaction between human and electronic systems by giving the systems the capability of understanding humans. Another important factor for humatronics is computer networks, over which the systems can share knowledge, information, and experiences. This paper introduces the concept of humatronics and its future direction. A prototype of partner robot system, called HuRoC, is also proposed as an application of humatronics concept. It is expected that the concept of humatronics will progress the related research fields by bringing a new viewpoint.
Implementation of Humanoid Robot Arm Based on SERCOS Network
Authors: Moon Yongseon ,   School of Information Communication, Sunchon Nat'l Univ., Suncheon 540-742, Korea, Ko Nak Yong , Seo Dong Jin , Kim Eunju , Bae Youngchul ,   College of Electronics and Information Eng., Chosun Univ., Gwangju 501-759, Korea,   Redone Technologies Ssangchon-Dong Seo-Gu Gwangju, Korea,   Korea Institute Science of Technology Information 335 Gwahangno, Yuseong-gu, Daejeon, Korea,   School of Electrical Electronic Communication and Computer Eng.
Published: April 2016

Designing, implementing and evaluating the performance of a humanoid robot arm, we verified its applicability and effectiveness based on the Serial Real-Time Communication System (SERCOS), which follows the concept of modularization corresponding to projected research. We also proposed single joint design modular for humanoid robot arm systems.
Switching Fuzzy Model Construction and Controller Design for Dynamical Systems with Input Nonlinearity
Authors: Ohtake Hiroshi ,   Department of Mechanical Engineering and Intelligent Systems,, Tanaka Kazuo
Published: April 2016

Fuzzy model-based control mainly deals with dynamical systems which affinely depend on control inputs. In this paper, dynamical systems which is permitted to have nonlinearity not only in the states, but also in the inputs is considered. Input nonlinearity makes a nonlinear system complicated and makes the number of fuzzy model rules increase. Thus, switching fuzzy control approach is employed. Firstly, the switching fuzzy model construction with arbitrary linear dividing planes, which is an extension of the ordinary switching fuzzy model construction method with dividing planes corresponding to quadrants, is introduced. Secondly, by applying the switching fuzzy model construction method to the dynamical system with input nonlinearity, the switching fuzzy model with membership functions which depend on control inputs is constructed. Finally, by utilizing the dynamic state feedback control approach, we show that membership functions which depend on control inputs can be calculated. Moreover, by employing augmented system approach, the switching fuzzy dynamic state feedback controller design conditions based on the switching Lyapunov function are derived in terms of linear matrix inequalities. A design example illustrates the utility of this approach.
A Study of Double-Deck Elevator Systems Using Genetic Network Programming with Reinforcement Learning
Authors: Zhou Jin ,   Graduate School of Information, Production and Systems, Waseda University, Hibikino 2-7, Wakamatsu-ku, Kitakyushu, Fukuoka 803-0135, Japan, Yu Lu , Mabu Shingo , Shimada Kaoru , Hirasawa Kotaro , Markon Sandor ,   FUJITEC Co.Ltd. Prod.Dev.HQ, Big Wing, Hikone, Shiga, Japan
Published: April 2016

In order to increase the transportation capability of elevator group systems in high-rise buildings without adding elevator installation space, double-deck elevator systems (DDES) is developed as one of the next generation elevator group control systems. Artificial intelligence (AI) technologies have been employed to find some efficient solutions in the elevator group control systems during the late 20th century. Genetic Network Programming (GNP), a new evolutionary computation method, has been employed as the elevator group control system controller in some studies of recent years. Moreover, reinforcement learning (RL) has been also found to be useful for more improvements of elevator group control performances when it is combined with GNP. In this paper, we proposed a new approach of DDES using GNP with RL, and did some experiments on a simulated elevator group control system of a typical office building to evaluate its applicability and efficiency. Simulation results show that the DDES using GNP with RL performs better than the one without RL in regular and down-peak time, while both of them outperforms a conventional approach and a heuristic approach in all three traffic patterns.
Selected Papers from the 3rd International Symposium on Computational Intelligence and Industrial Applications (ISCIIA2008)
Authors: Yi Jianqiang ,   Professor, Institute of Automation, Chinese Academy of Sciences, China, Dong Fangyan ,   Assistant Professor, Tokyo Institute of Technology, Japan
Published: April 2016

The biennial International Symposium on Computational Intelligence and Industrial Applications (ISCIIA2008) focuses on advanced technologies for soft computing, computational intelligence, and real-world applications. The first ISCIIA was held in 2004 in Haikou, China, the second in Guangzhou, China, and the third, hosted by Dali University, held Nov. 21-Nov. 26 in Dali, Yunnan Province, China. From the 58 papers published in ISCIIA2008 proceedings, 5 have been selected and reviewed for publication in JACIII. This special issue covers learning systems, signal and image processing, human-computer interfacing, intelligent control, and robotics. These selected papers should prove to be interesting and informative for many researchers. ISCIIA2008 was cochaired by Professor Yaping Dai of Beijing Institute of Technology and professor Lihua Duan of Dali University. The local organizing committee member included Prof. Yilong Gu and Dr. Chen, who worked hard from CFP preparation to local arrangement. The founding chairs – Prof. Kaoru Hirota of Tokyo Institute of Technology, Prof. Shibin Zhao of Guangdong Polytechnical Normal University, and Mr. Raymond Tay of Mycom Inc., Singapore – contributed much to the success of ISCIIA2008. The fourth ISCIIA will be held in August in Harbin, China, and we look forward to the advances in computational intelligence technologies and industrial applications the occasion will bring.
Effective Image Mining by Representing Color Histograms as Time Series
Authors: Aghbari Zaher Al ,   Department of Computer Science, University of Sharjah, UAE
Published: April 2016

Due to the wide spread of digital libraries, digital cameras, and the increase access to WWW by individuals, the number of digital images that exist pose a great challenge. Easy access to such collections requires an index structure to facilitate random access to individual images and ease navigation of these images. As these images are not annotated or associated with descriptions, existing systems represent the images by their extracted low level features.

In this paper, we demonstrate two image mining tasks, namely image classification and image clustering, which are preliminary steps in facilitating indexing and navigation. These tasks are based on the extraction of color distributions of images. Then, these color distributions are represented as time series. To make the representation more effective and efficient for the data mining tasks, we have chosen to represent the time series by a new representation called SAX (Symbolic Aggregate approXimation) [14]. SAX based representation is very effective because it reduces the dimensionality and lower bounds the distance measure. We demonstrate by our experiment the feasibility of our approach.
Automatic Acquisition of Image Filtering and Object Extraction Procedures from Ground-Truth Samples
Authors: Rahnamayan Shahryar ,   Faculty of Engineering and Applied Science, University of Ontario Institute of Technology (UOIT), 2000 Simcoe Street North, Oshawa, Ontario, L1H 7K4, Canada, Tizhoosh Hamid R. , Salama Magdy M.A. ,   Faculty of Engineering, University of Waterloo, 200 University Avenue West, Waterloo, Ontario, N2L 3G1, Canada
Published: April 2016

Knowledge- and sample-based learning approaches play a pivotal role in image processing. However, the acquisition and integration of expert knowledge (for the former) and providing a sufficiently large number of training samples (for the latter) are generally hard to perform and time-consuming tasks. Hence, learning image processing tasks from a few gold/ground-truth samples, prepared by the user, is highly desirable. This paper demonstrates how the combination of an optimizer (e.g., genetic algorithm) and image processing tools (e.g., parameterized morphology operations) can be used to generate image processing procedures for image filtering and object extraction. For this purpose, the approach receives the original and the user-prepared image (filtered image or image with extracted target object) as a gold sample which reflects the user's expectations. After carrying out the training or optimization phase, the optimal procedure is generated and ready to be applied to new images. The feasibility of our approach is investigated for two individual image processing categories, namely filtering and object extraction, by well-prepared synthetic images. The proposed architecture and the employed methodologies are explained in detail. Experimental results are provided as well.

The subject matter in this work is covered by a US provisional patent application.
Optimal Location of Wireless LAN Access Points Using Fuzzy ID3
Authors: Hayashi Isao ,   Faculty of Informatics, Kansai University, 2-1-1, Ryozenji, Takatsuki, Osaka 569-1095, Japan, Kobayashi Takashi , Arai Yoshinori , Maeda Toshiyuki , Inoue Atsushi ,   Faculty of Engineering, Tokyo Polytechnic University, 1583, Iiyama, Atsugi, Kanagawa 243-0297, Japan,   Faculty of Management Information, Hannan University, 5-4-33, Amami-higashi, Matsubara, Osaka 580-8502, Japan,   Department of Computer Science, Eastern Washington University, CEB319F EWU, Cheney, WA 99004-2493, USA
Published: April 2016

Although wireless LAN is useful in its small size and mobility, the connection region of transmitted radio wave is strongly affected by other electric devices, consumer products, and differences in size and type of the room. Besides, wireless LAN points (APs) must exclude a personal computers without permitting to connect to the Internet. Therefore, how optimally APs are located is important. In this paper, we propose the APs' optimal location method. The proposed algorithm integrates fuzzy rules acquired by fuzzy ID3 with knowledge of security experts, and estimates the connection region for AP. We discuss how to formulate the method for setting AP optimal location and show the effectiveness of this method by illustrating the examples of AP optimal locations.
Visualization of Huge Climate Data with High-Speed Spherical Self-Organizing Map
Authors: Tachibana Kanta ,   Nagoya University, Sugimoto Norihiko , Shiogama Hideo , Nozawa Toru ,   Keio University,   National Institute for Environmental Studies
Published: April 2016

We propose the use of a high-speed spherical self-organizing map (HSS-SOM) to visualize climate variability as a complementary alternative to empirical orthogonal function (EOF) analysis. EOF analysis, which is the same as principal component analysis, is often used in the fields of meteorology and climatology to extract leading climate variability patterns, its production of linear mapping with only a low contribution rate may preclude producing any meaningful results. Due to computational limitations, however, conventional self-organizing maps are difficult to apply to huge climate datasets. The development of HSS-SOMs with dynamically growing neurons has helped reduce computational time. After demonstrating validity of our HSS-SOM using observational climate data and HSS-SOM effectiveness as a complementary alternative to the EOF, we extract dominant atmospheric circulation patterns from huge amounts of climate data in the general circulation model, in which both present climatology and future climate are simulated. These patterns correspond to those obtained in previous studies, indicating the HSS-SOM's usefulness in climate research.
Virtual Scissors in a Thin Haptic and Force Feedback Environment
Authors: Funahashi Kenji ,   Nagoya Institute of Technology, Gokiso-cho, Showa-ku, Nagoya, Japan, Kubotani Daisuke , Iwahori Yuji , Tanida Koji ,   Rinnai Corporation, 2-26 Fukuzumi-cho, Nakagawa-ku, Nagoya, Japan,   Chubu University, 1200 Matsumoto-cho, Kasugai, Aichi, Japan,   Honda R&D Co., Ltd., 4630 Shimotakanezawa, Haga-machi, Tochigi, Japan
Published: April 2016

The virtual scissors using virtual hands we propose enable users can cut virtual paper with their own hands. One purpose of our proposal is to facilitate implementing different tools simply by changing software parameters. Another is to make a general-purpose system with small-scale input and output devices for general applications, e.g., only using thin haptic information and force feedback. With such virtual reality (VR) scissors, we introduced feedback to cover any impressions such as interface interference during use. We evaluated whether an interaction occurred between vibration feedback and sound effects. Using this system, we found that users could manipulate virtual scissors through a data-glove similar to the use of real ones.
A Modal Characterization of Visibility and Focus in Granular Reasoning
Authors: Kudo Yasuo ,   Department of Computer Science and Systems Engineering,  Muroran Institute of Technology, 27-1 Mizumoto, Muroran 050-8585, Japan, Murai Tetsuya ,   Graduate School of Information Science and Technology,  Hokkaido University, Kita 14, Nishi 9, Kita-Ku, Sapporo 060-0814, Japan
Published: April 2016

We have proposed two key concepts – focus and visibility – as modalities of modal logic. Scott-Montague models that we have proposed represent properties of visibility and focus and the concept that p is visible as modal sentence vis p and p is clearly visible – or is in focus – as modal sentence cv p.
Emergence of Cross-Generational Migration Behavior in Multiagent Simulation
Authors: Hashizume Hideki ,   Dept. of Computer Science and Engineering, Graduate School of Engineering, Nagoya Institute of Technology, Gokiso-cho, Showa-ku, Nagoya, Aichi 466-8555, Japan, Mutoh Atsuko , Kato Shohei , Kunitachi Tsutomu , Itoh Hidenori ,   Daido Institute of Technology, 10-3 Takiharu-cho, Minami-ku, Nagoya, Aichi 457-8530, Japan
Published: April 2016

We describe an artificial ecosystem consisting of five areas and evolving artificial creatures (called agents). The ecosystem is for an analysis of cross-generational migrations of the monarch butterfly. The monarch butterfly is famous for its migration. We report simulations on the emergence of migration behavior pertaining to the monarch butterfly. The area has two kinds of environmental changes: long-term and short-term changes. We focus on temperature as an environmental parameter. Under long-term change, temperature is gradually rising, and under short-term change temperature changes periodically as same as seasonal change. We put agents on the areas. The agent has two genetic components: an environmental adaptation scale and an action decision table. These components represent the physical features of the agent and select an action on the basis of sensory information, respectively. The agent also has a temperature sensor that functions with its environmental adaptation scale. It enables the agent to adapt dynamic temperature changes and to evolve to obtain optimal behaviors. With the ecosystem, we conduct one experiment. The result was that we observed that the range of migration expanded as the temperature rose. Also, we report the result of migration patterns obtained by the agents. These results show that the biology of the monarch butterfly is well modeled by the ecosystem and our evolutionary method.
Inference with Governing Schemes for Propagation of Fuzzy Convex Constraints Based on α-Cuts
Authors: Uehara Kiyohiko ,   Ibaraki University, Hitachi 316-8511, Japan, Koyama Takumi , Hirota Kaoru ,   Tokyo Institute of Technology, Yokohama 226-8502, Japan
Published: April 2016

A governing scheme is proposed for fuzzy constraint propagation from given facts to consequences in convex forms, which is applied to the inference method based on α-cuts and generalized mean. The governing is performed by self-tuning which can reflect the distribution forms of fuzzy sets in consequent parts to the forms of deduced consequences. Thereby, the proposed scheme can solve the problems in the conventional inference based on the compositional rule of inference that deduces fuzzy sets with excessive fuzziness increase and specificity decrease. In simulations, it is confirmed that the proposed scheme can effectively perform the constraint propagation from given facts to consequences in convex forms while reflecting fuzzy-set distributions in consequent parts. It is also demonstrated that consequences are deduced without excessively large fuzziness and small specificity.
An Application of Rough Set Analysis toa Psycho-Physiological Study - Assessing the RelationBetween Psychological Scale and Immunological Biomarker
Authors: Nomura Shusaku ,   Top Runner Incubation Center for Academia-Industry Fusion, Nagaoka University of Technology, 1603-1 Kamitomioka, Nagaoka, 940-2188, Japan, Kudo Yasuo ,   Department of Computer Science and Systems Engineering, Muroran Institute of Technology, 27-1 Mizumoto, Muroran, 050-8585, Japan
Published: April 2016

This study aims at an application of rough set theory to illustrate the relationship between human psychological and physiological states. Recent behavioral medicine studies have revealed that various human secretory substances change according to mental states. These substances, the hormones and immune substances, show temporal increase against mental stress. Thus, it is frequently introduced as biomarkers of mental stress. The relationship between these biomarkers and human chronic stresses or daily mental states was also suggested in the previous studies. However the results of these studies were sometimes inconsistent with each other. Some technical reasons were indicated for this discrepancy. Among that, we focused on the analysis technique investigating the relationship between human psychological state, i.e., scores of a psychological scale, and physiological state, i.e., level of the secretory biomarkers. In this paper, we introduced Rough Set analysis method instead of using a conventional linear correlation analysis method. In the experiment, the salivary secretory immunoglobulin A (IgA), which is a major stress biomarker, of 20 male students was assessed before and after a short-term stressful mental workload. Also, 65 items of psychological mood scale was assessed as a psychological index. The result showed that some items strongly related with the change in the IgA, while no significant linear correlation was obtained among them.
Effect on EEGs When Listening to Harmony
Authors: Yamanishi Ryosuke ,   Department of Computer Science and Engineering,  Graduate School of Engineering, Nagoya Institute of Technology, Gokiso-cho, Showa-ku, Nagoya 466-8555, Japan, Kato Shohei , Kunitachi Tsutomu , Itoh Hidenori ,   Daido Institute of Technology, 10-3 Takiharu-cho, Minami-ku, Nagoya, Aichi 457-8530, Japan
Published: April 2016

In studies of the relationship between relaxation and music, that between brain waves and chords has often been reported despite the fact that “harmony” rather than single chords are listened for in most cases. This raises the problem of whether brain waves measured in subjects differ when harmony is listened to from when single chords are listened to. As a first step to ward solving this problem, we propose original experiments in presentation sound, analytical object, and time zone, clarifying that brain waves were influenced by both chords and of chord correlation in harmony.
A 3D Pseudo-Reconstruction from Single ImageBased on Vanishing Point
Authors: Wang Jingjing ,   Dept. of Computational Intelligence and Systems Science, Tokyo Institute of Technology, G3-49, 4259 Nagatsuta, Midori-ku, Yokohama-city 226-8502, Japan, Dong Fangyan , Takegami Takashi , Go Eiroku , Hirota Kaoru ,   Customer Solutions Development Co., Ltd., 6F, West Wing, KSP BLDG, 3-2-1 Sakado, Takatsu-ku, Kawasaki-city, 213-0012, Japan
Published: April 2016

A 3D (three-dimensional) pseudo-reconstruction method from a single image is presented as a novel approach reconstructing a 3D model with no prior internal knowledge of outdoors image. In the proposed method, an image is represented as a collection of sky layer, ground layer, and object layer. A visual radical coordinate system with vanishing point is established to accommodate the extracted 3D data from images. Learning method is done via the layers database. The experiment results show that the visually acceptable 3D model can be extracted less than one minute. That means a higher resolution in much shorter time, compared to conventional methods. This method can be applied to computer games, industrial measurement, archeology, architecture and visual realities.
Averaging Forest for Online Vision
Authors: Osman Hassab Elgawi ,   Imaging Science and Engineering Lab, Tokyo Institute of Technology, 4259 Nagatsuta, Midori-ku, Yokohama 226-8503, Japan
Published: April 2016

In this study we consider vision as a binary classification problem, where an ensemble of decision-tree-based classifiers is trained on-line, new images are continuously added and the recognition decision is made without delay. Ensemble of decision trees is combined into aforestclassifier usingaveraging, generate an on-lineRandom Forest(RF) classifier. First we employ object descriptor model based on a bag of covariance matrices, to represent an object features, then run our on-line RF learner to select object descriptors and to learn object classifiers. Validation of our proposal with empirical studies in the GRAZ02 dataset domain demonstrates its superior performance over histogram-based counterparts, yielding object recognition performance comparable to state-of-the-art standard RF, AdaBoost, and SVM classifiers, even when only 10% of the training examples are used.
Kernel Canonical Discriminant Analysis Based on Variable Selection
Authors: Ikeda Seiichi ,   Graduate School of Information Science and Technology, Hokkaido University, Sato Yoshiharu
Published: April 2016

We have shown that models support vector regression and classification are essentially linear in reproducing kernel Hilbert space (RKHS). To overcome the over fitting problem, a regularization term is added to the optimization process, deciding the coefficient of regularization term involves difficulties. We introduce the variable selection concept to the linear model in RKHS, where the kernel functions is treated as variable transformation when its value is given by observation. We show that kernel canonical discriminant functions for multiclass problems can be discussed under variable selection, which enables us to reduce the number of kernel functions in the discriminant function, i.e., the discriminant function is obtained as linear combinations of sufficiently small numbers of kernel functions, so, we can expect to get reasonable prediction. We discuss variable selection performance in canonical discriminant functions compared to support vector machines.
Rough Set Approach for Overall Performance Improvement of an Unsupervised ANN-Based Pattern Classifier
Authors: Kothari Ashwin ,   Department of Electronics & C.S. Engineering, Visvesvaraya National Institute of Technology Nagpur, India, 440010, Keskar Avinash
Published: April 2016

Most conventional approaches to pattern classification using unsupervised ANN use clusterification with the entire feature set. The redundancy (dependence) of some features in such cases makes feature space dimensionality too complex to handle. Early convergence is another factor desired for the training phase in networks trying different neural architectures or learning algorithms. As approaches evolve and are applied, the hybridization of neural concepts with other tools has yielded useful results. A rough set is one such approximation tool that works well when in environments heavy with inconsistency and ambiguity in data or involving missing data. Approaches using rough sets may be used at the preprocessing, learning and neuron architectural levels. Preprocessing and architectural approaches are discussed here using Rough sets to improve overall performance of pattern classifiers used in character recognitions.
Image Morphing and Warping: Application to Speech Simulation Using a Single Image
Authors: Karungaru Stephen ,   Department of Information Science and Intelligent Systems, University of Tokushima,, Akashi Takuya , Fukumi Minoru , Akamatsu Norio
Published: April 2016

In this paper, we propose a fully automatic real time one image face gesture simulation using image morphing. Given a single image of a subject, we create several facial expressions of the face by morphing the image based on prior information stored in a data bank. The process involves the automatic detection of the control points both on the target image and the source data. The source data is a string of frames containing the desired facial expressions. A face detection neural network and a lips contour detector using edges and the SNAKES algorithm are employed to detect the face position and features. Five control points and the lips contour, for both the source and target images, are then extracted based on the facial features. Triangulation method is then used to match and warp the source image to the target image using the control points. In this experiment, using one expressionless face portrait, we create an animation to make it appear like the subject is pronouncing the five Japanese vowels. The final results shows the effectiveness of our method.
Online Signature Verification Based on User-Generic Fusion Model with Markov Chain Monte Carlo, Taking into Account User Individuality
Authors: Koishi Kyosuke ,   Department of Electrical Engineering and Bioscience, Waseda University, 3-4-1 Ohkubo, Shinjuku-ku, Tokyo 169-8555, Japan, Kinoshita Shintaro , Muramatsu Daigo , Matsumoto Takashi ,   Department of Electrical and Mechanical Engineering, Seikei University, 3-3-1 Kichijoji-kitamachi, Musashino-shi, Tokyo 180-8633, Japan,   Graduate School of Advanced Science and Engineering, Waseda University. 3-4-1 Ohkubo, Shinjuku-ku, Tokyo 169-8555, Japan
Published: April 2016

Personal authentication is becoming an increasingly important problem. Online signature verification is one promising form of biometric authentication. However, the verification accuracy of online signature verification is not high enough and still needs to be improved. To do so, we previously proposed a user-generic fusion model. Although the verification accuracy was reasonably good, the proposed method cannot adequately take into account users’ individuality. In this paper, to further improve the verification accuracy, we propose a method that can take such individuality into account. First, in a training phase, we divide a training dataset into several groups. Then, several fusion models are generated using a parameterized family of nonlinear functions by a Markov chain Monte Carlo method. In an enrollment phase, in order to take into account users’ individuality, we introduce model reliability. The model reliability for each user and each model is different, enabling us to take users’ individuality into account. In a verification phase, a marginal likelihood is calculated for each group. Then, a verification score is calculated by a weighted sum of the marginal likelihoods from the groups, using the model reliability. To evaluate the performance of the proposed algorithm, we conducted experiments using the SVC2004 database. The verification accuracy was improved over the previous algorithm.
Vehicle Appearance Model for Recognition System Considering the Change of Imaging Condition
Authors: Kuwabara Keiji ,   Department of Electrical Engineering, Nagoya University, Furo-cho, Chikusa-ku, Nagoya, Aichi 464-8603, Japan, Yano Yoshikazu , Okuma Shigeru ,   Department of Electrical and Electronics Engineering, Aichi Institute of Technology, 1247 Yachigusa, Yakusa-cho, Toyota, Aichi 470-0392, Japan
Published: April 2016

We have proposed a technique to recognize a vehicle. In this technique, Gaussian Mixture Model (GMM) is adopted as a classifier. Vehicle appearance changed by imaging conditions such as time, weather and so on, and GMM parameters are also changed by imaging conditions. To recognize vehicle accurately, we have prepared some GMM tuned with the imaging conditions. On the other hand, it is impossible to prepare GMM because imaging condition changes successively. In this paper, we propose a method for estimating GMM and for training GMM parameters which reflect the successive change of imaging condition. Experimental results show that GMM parameters are estimated accurately and training of GMM are speeded up by proposed method.
Analysis of NSGA-II and NSGA-II with CDAS, and Proposal of an Enhanced CDAS Mechanism
Authors: Tsuchida Kyoko ,   Faculty of Engineering, Shinshu University,, Sato Hiroyuki , Aguirre Hernan , Tanaka Kiyoshi ,   Fiber Nanotech Young Researcher Empowerment Program, Shinshu University,
Published: April 2016

In this work, we analyze the functionality transition in the evolution process of NSGA-II and an enhanced NSGA-II with the method of controlling dominance area of solutions (CDAS) from the viewpoint of front distribution. We examine the relationship between the population of the first front consisting of non-dominated solutions and the values of two metrics, NORM and ANGLE, which measure convergence and diversity of Pareto-optimal solutions (POS), respectively. We also suggest potentials to further improve the search performance of the enhanced NSGA-II with CDAS by emphasizing the parameterS, which controls the degree of dominance by contracting or expanding the dominance area of solutions, before and after the boundary generation of functionality transition. Furthermore, we analyze the behavior of the evolution of the enhanced NSGA-II with CDAS using the best parameters combination and compare its performance with two other algorithms that enhance selection of NSGA-II.
Gender and Age Classification Based on Pattern of Human Motion Using Choquet Integral Agent Networks
Authors: Handri Santoso ,   Top Runner Incubation Center for Academia-Industry Fusion, Nakamura Kazuo , Nomura Shusaku ,   Department of Management and Information Systems Science Nagaoka University of Technology, 1603-1 Kamitomiokamachi, Nagaoka, Niigata 940-2188, Japan,   Multimedia Nusantara University, Tangerang — Indonesia
Published: April 2016

Automated human identification from their walking behavior is a challenge attracting much interest among machine vision researchers. However, the systems which are able to detect pedestrian attributes based on their walking behavior remain to be developed. Here, a soft computing approach to determine walking behavior based on motion imagery is studied as the basis for developing pedestrian safety information systems. Gender and age are classified based on motion pattern derived in experiments. At the front end, image and video processing was performed to separate foreground from background images. The widths of silhouette were analyzed using two-dimensional (2D) Fourier transformation to extract human motion features. Feature sub-sets were then selected to find salient, effective classification features. Finally, Choquet integral agent networks (CHIAN) with a competitive learning algorithm were employed to classify gender and age into its classes. The experimental results demonstrated capability of the proposed system to classify gender and age in highly accurately.