bluuebunny/crossref_metadata_2025 huggingface dataset:
Graph/Knot Theoretical Analysis and Generation for Impossible Figures
Authors: Tarui Kento ,   Hirota Laboratory, Department of Computational Intelligence and Systems Science, Tokyo Institute of Technology, 4259 Nagatsuta, Midori-ku, Yokohama 226-8502, Japan, Dong Fangyan , Hatakeyama Yutaka , Hirota Kaoru ,   Center of Medical Information Science, Medical School, Kochi University, Kohasu Oko-cho Nankoku-city Kochi
Published: April 2016

An algorithm to represent impossible multibar figures and their subclass of torus figures is proposed based on graph and knot theory. A multibar type graph, which is an abstract concept of multibar figures, is defined by the junction graph that represents the connections of the lines. It is shown that the junction graph is able to characterize multibar figures where this characterization is realized according to the type of the multibar type graph. An automatic drawing system of torus figures is also presented by analyzing junction graphs that construct the shapes of corners of torus figures. The proposed method aims a basic tool for experiments in visual psychology and possible/impossible figures generation.
Detection Algorithm for Real Surveillance Cameras Using Geographic Information
Authors: Hatakeyama Yutaka ,   Center of Medical Information Science, Medical School, Kochi University, Kohasu, Oko-cho, Nankoku city, Kochi 783-8505, Japan, Mitsuta Akimichi , Hirota Kaoru ,   Customer Solutions Development Co., Ltd., Japan, 3-2-1 Sakado, Takatsu-ku, Kawasaki-city, Kanagawa 213-0012, Japan,   Department of Computational Intelligence and Systems Science, Interdisciplinary Graduate School of Science and Engineering, Tokyo Institute of Technology, 4259 Nagatsuta, Midori-ku, Yokohama 226-8502, Japan
Published: April 2016

Detection algorithm for pedestrians is proposed for the real surveillance system based on color similarity for dynamic color images under low illumination, where the proposed color similarity is defined by color change vectors in the L*a*b* color metric space and the time taken by pedestrians to pass between surveillance camera. It provides continuous detection results through surveillance cameras under lower luminance conditions in real surveillance system. Experimental results for dynamic image taken under low illumination in streets show that detected frames with the proposed algorithm increased by 20% compared to detection results without geographic information. The proposed algorithm is being considered for use in poor security areas in downtown Japan.
Intelligent Information Presentation Corresponding to User Request Based on Collaboration Between Text and 2D Charts
Authors: Watanabe Chiaki ,   Graduate School of Humanities and Sciences, Advanced Sciences, Ochanomizu University, 2-1-1 Ootsuka, Bunkyo-ku, Tokyo 112-8610, Japan, Kobayashi Ichiro
Published: April 2016

We discuss intelligent information provision involving different modal information collaboratively presented, with an example of news articles about stock prices summarized based on 2D chart representation on stock prices. We use the MuST corpus, an annotated corpus for easily extracting trends in information, e.g., statistical values, etc., as the news article corpus to be summarized. We associate the MuST corpus with numerical data on the stock prices, and propose a way to provide people with a summarized text about news articles on prices corresponding to 2D chart representation.
A Global Optimization Method RasID-GA for Neural Network Training
Authors: Sohn Dongkyu ,   Graduate School of Information, Production and Systems, Waseda University, 2-7 Hibikino, Wakamatus-ku, Kitakyushu-shi, Fukuoka, 808-0135, JAPAN, Mabu Shingo , Shimada Kaoru , Hirasawa Kotaro , Hu Jinglu
Published: April 2016

This paper applies an Adaptive Random search with Intensification and Diversification combined with Genetic Algorithm (RasID-GA) to neural network training. In the previous work, we proposed RasID-GA which combines the best properties of RasID and Genetic Algorithm for optimization. Neural networks are widely used in pattern recognition, system modeling, prediction and other areas. Although most neural network training uses gradient based schemes such as well-known back-propagation (BP), but sometimes BP is easily dropped into local minima. In this paper, we train newly developed multi-branch neural networks using RasID-GA with constraint coefficientCby which the feasible solution space is controlled. In addition, we use Mackey-Glass time prediction to test a generalization ability of the proposed method.
Special Issue on Intelligence Techniques in Computer Games and Simulations
Authors: Thawonmas Ruck ,   Professor,
Published: April 2016

This issue presents eight research papers focusing on different aspects of intelligence techniques and their applications in computer games and simulations. They are extended papers from those accepted and presented at the International Symposium on Intelligence Techniques in Computer Games and Simulations that was held in Shiga, Japan on March 1-2, 2007. The issue starts with a paper entitled gUsing Automatic Calibration with Microscopic Traffic Simulation,h which focuses on the use of a genetic algorithm for searching parameter spaces of a microscopic traffic simulation. The second paper, gOn-Line Fault Detection and Compensation of Hydraulic Driven Machines Using Modelling Techniques,h presents the use of modelling information for the fault detection of hydraulic driven machines as well as for the compensation of incipient faults. The third paper, gGeneration of Character Motion by Using Reactive Motion Capture System with Force Feedback,h focuses on animation making with reactive motion data generated from the interaction with force feedback and the virtual environment. The fourth paper, gModeling of Wood Aging Caused by Biological Deterioration,h looks into an application of an ant colony optimization algorithm to generation of wood aging patterns caused by biological deterioration. The next three papers are related to network games. The fifth paper, gIntelligent Synchronization for Mirrored Game Servers: A Real Case Study,h examines the effectiveness of an intelligent optimistic synchronization scheme for mirrored game server architectures in real multiplayer-online-game environment. The sixth paper, gI3P: A Protocol for Increasing Reliability and Responsiveness in Massively Multiplayer Games,h presents a peer-to-peer (P2P) approach for minimizing the server bottleneck in online gaming and for increasing response and reliability. The seventh paper, gHaar Wavelets for Online-Game Player Classification with Dynamic Time Warping,h presents an application of Haar wavelet for reduction of online game players' action sequences used in player classification. Finally, the eighth paper, gDeveloping Natural Language Enabled Games in SCXML,h looks into the potential of SCXML for the game design and implementation. As the guest editor of this special issue, I would like to express my sincere gratitude to the authors in this issue for their hard job. Special thanks go to the anonymous referees for their assistance in the review process. Finally, I wish to thank JACIII staffs for their helps in coordinating the publication of the issue.
Using Automatic Calibration with Microscopic Traffic Simulation
Authors: Kosonen Iisakki ,   Helsinki University of Technology, P.O.box 2100, 02015 TKK, Finland
Published: April 2016

The microscopic simulation is getting increasingly common in traffic planning and research because of the detailed analysis it can provide. The drawback of this development is that the calibration and validation of such a detailed simulation model can be very tedious. This paper summarizes the research on automatic calibration of a high-fidelity micro-simulation (HUTSIM) at the Helsinki University of Technology (TKK). In this research we used ramp operation as the case study. The automatic calibration of a detailed model requires a systematic approach. A key issue is the error-function, which provides a numeric value to the distance between simulated and measured results. Here we define the distance as combination of three distributions namely the speed distribution, gap distribution and lane distribution. We developed an automated environment that handles all the necessary operations. The system organizes the files, executes the simulations, evaluates the error and generates new parameter combinations. For searching of the parameter space we used a genetic algorithm (GA). The overall results of the research were good demonstrating the potential of using automatic processes in both calibration and validation of simulation models.
State Dependant Anytime Control Methodology for Non-Linear Systems
Authors: Várkonyi-Kóczy Annamária R. ,   Dept. of Measurement and Information Systems, Budapest University of Technology and Economics, Magyar tudósok körútja 2. H-1521 Budapest, Hungary
Published: April 2016

Nowadays in solving control problems the processing is performed typically by model-based computer systems, which contain a representation of our knowledge about the nature and the actual circumstances of the problem in hand. If the nature and/or the actual circumstances change, the corresponding model should also be changed. Anytime techniques are very flexible in this respect and can advantageously be used when the operation should be performed under changing circumstances. In this paper, a nonlinear state dependant control methodology is proposed for anytime use and as an example is applied to globally stabilize a given prototypical aeroelastic wing section via one control surface.
A Universal Autonomous Robot Navigation Method
Authors: Várkonyi-Kóczy Annamária R. ,   Dept. of Measurement and Information Systems,  Budapest University of Technology, and Economics Integrated Intelligent Systems Japanese-Hungarian Laboratory, Magyar tudósok körútja 2., H-1117 Budapest, Hungary
Published: April 2016

Recently, autonomous navigation has become an important research topic. There are a lot of applications where the need for autonomous robots is obvious, either because the real or virtual human presence is impossible, dangerous, or expensive, or the tasks to be solved are against the human nature. In most of the applications where robots are to be used, the conditions/environment change along the time that results in an ever-increasing need for universal methods, which are general enough to be used at a wide range of problems. In this paper, a universal, hybrid navigation method is proposed, which is able to work in cases of known, partially known, dynamically changing, or unknown environments. The model consists of two parts which are able to co-operate or to work alone. The modules combine two techniques that deal with a priori information and sensory data separately, thus blends the intelligence and optimality of global navigation methods with the reactivity and low complexity of local ones. The first, global navigation module, based on a priori information, chooses intermediary goals for the local navigation module, for which the so called A* algorithm is used. The second part, carrying out the (local) navigation relying on sensory data, applies a fuzzy-neural representation of an improved potential field based guiding navigation tool. Vision based obstacle detection is implemented by difference detection based on a combination of RGB and HSV representations of the pixels.
RBF Networks Ensemble Construction based on Evolutionary Multi-objective Optimization
Authors: Kondo Nobuhiko ,   Department of Information and Physical Sciences, Osaka University,  Osaka, Japan, Hatanaka Toshiharu , Uosaki Katsuji ,   Department of Management and Information Sciences,  Fukui University of Technology, Fukui, Japan
Published: April 2016

The ensemble learning has attracted much attention over the last decade. While constructing RBF network ensemble the generally encountered problems are how to construct diverse RBF networks and how to combine outputs. The construction of RBF network can be considered as a multi-objective optimization problem regarding model complexity. A set of RBF networks which is multi-objectively optimized can be obtained by solving the above-mentioned problem. In this paper the construction of RBF networks by evolutionary multi-objective optimization method and its ensemble are considered, and it is applied to the pattern classification problem. Also some ensemble member selection methods and output combination methods are considered. Experimental study on the benchmark problem of pattern classification is carried out; then it is illustrated that the RBF network ensemble has a performance, which is comparable to that of other ensemble methods.
Selected Papers from HNICEM 2007
Authors: Dadios Elmer P. ,   Professor, Ph.D.
Published: April 2016

The Third International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM) was held in Century Park Hotel, Manila, Philippines from March 15 to 18, 2007. The theme on this conference was Technology Creativity and Innovations for Economic Development. As has been done from the previous HNICEM conferences, cutting edge papers presented from this conference are reviewed and selected for JACIII special issue publication. In this special issue, 10 articles are selected that will provide valuable references for researchers and practitioners. The first article presents an integrated algorithm that provides a mobile robot the ability to plan an optimal path and does online collision avoidance in a totally unknown environment. The second article presents a fuzzy controller technique in navigation with obstacle avoidance for a general purpose mobile robot in a given global environment with image processing technique using Open Source Computer Vision. The third article presents a model-based controller for helicopter using the sliding mode approach. The controller assumes that only measured outputs are available and it uses sliding mode observer to estimate the state of the system. The fourth article presents a real-time fuzzy logic based parallel parking system in an FPGA platform. The fifth article presents performance analysis of container unloading operations using simplified analytical model (SAM). The sixth article presents a neuro-fuzzy approach with additional moving average window data filter and fuzzy clustering algorithm use to forecast electrical load. The seventh article presents a new design and implementation of a multi-output fuzzy controller for real time control which utilizes lesser memory and executes faster than an existing type of multiple single-output fuzzy logic controllers. The eight article presents a new method based on multi-objective evolutionary algorithms to evolve low complexity neural controllers that allows an agent to perform two different tasks simultaneously. The ninth and tenth articles present genetic networks programming for stock market trading rules and for traffic systems applications, respectively. We extend our warmest thanks and deepest gratitude to the distinguished authors and reviewers who have contributed to this special issue for their outstanding contributions and cooperation. We are also grateful to Prof. Toshio Fukuda and Prof. Kaoru Hirota, chief editors of JACIII, for their continued support to all the HINICEM International Conferences. Come March 12 to 15, 2009, the 4th HNICEM International Conference will be held in Manila, Philippines. We thank the IEEE Philippines for its continuing sponsorship. Also to JACIII journal, as outstanding papers presented in this conference will be selected for publication in a special issue. We invite you to submit your research papers and to participate in HNICEM 2009. For further information, please visit “http://www.dlsu.edu.ph/conferences/hnicem/”.
Comparative Association Rules Mining Using Genetic Network Programming (GNP) with Attributes Accumulation Mechanism and its Application to Traffic Systems
Authors: Wei Wei ,   Graduate School of Information, Production and Systems, Waseda University, Hibikino 2-7, Wakamatsu-ku, Kitakyushu, Fukuoka 808-0135, Japan, Zhou Huiyu , Shimada Kaoru , Mabu Shingo , Hirasawa Kotaro
Published: April 2016

Among several methods of extracting association rules that have been reported, a new evolutionary method named Genetic Network Programming (GNP) has also shown its effectiveness for dense databases. However, the conventional GNP data mining method can not find comparative relations and hidden patterns among a large amount of data. In this paper, we present a method of comparative association rules mining using Genetic Network Programming (GNP) with attributes accumulation mechanism in order to uncover comparative association rules between different datasets. GNP is an evolutionary approach recently developed, which can evolve itself and find the optimal solutions. The objective of the comparative association rules mining is to check two or more databases instead of one, so as to find the hidden relations among them. The proposed method measures the importance of association rules by using the absolute values of the confidence differences of the rules obtained from different databases and can get a number of interesting rules. Association rules obtained by comparison can help us to find and analyze the explicit and implicit patterns among a large amount of data. On the other hand, the calculation is very time-consuming, when the conventional GNP based data mining is used for the large attributes case. So, we have proposed an attributes accumulation mechanism to improve the performances. Then, the comparative association rules mining using GNP has been applied to a complicated traffic system. By mining and analyzing the rules under different traffic situations, it was found that we can get interesting information of the traffic system.
Axiomatization of Shapley Values of Fagle and Kern Type on Set Systems
Authors: Honda Aoi ,   Department of Systems Design and Informatics, Kyushu Institute of Technology,, Okazaki Yoshiaki
Published: April 2016

We propose axiomatizing a generalized Shapley value of games for potential application to games on set systems satisfying the condition of normality. This encompasses both the original Shapley value and Faigle and Kern's Shapley value, which is generalized for a cooperative game defined on a subcoalition.
Container Loading for Nonorthogonal Objects: Detecting Collisions
Authors: Torra Vicenç ,   IIIA, Institut d'Investigaci'o en Intelligència Artificial - CSIC, Miyamoto Sadaaki ,   Department of Risk Engineering, School of Systems and Information Engineering, University of Tsukuba,
Published: April 2016

This paper considers the problem of container loading. That is, the problem of locating a predefined set of objects into containers so that the number of containers is minimized. In this paper we outline our system, which includes as a distinguished aspect the possibility of considering objects that are nonorthogonal. That is, non rectangular objects are permitted to be located in the container.
Algorithms for Sequential Extraction of Clusters by Possibilistic Method and Comparison with Mountain Clustering
Authors: Miyamoto Sadaaki ,   Department of Risk Engineering, School of Systems  and Information Engineering,  University of Tsukuba, Kuroda Youhei , Arai Kenta
Published: April 2016

In addition to fuzzy c-means, possibilistic clustering is useful because it is robust against noise in data. The generated clusters are, however, strongly dependent on an initial value. We propose a family of algorithms for sequentially generating clusters “one cluster at a time,” which includes possibilistic medoid clustering. These algorithms automatically determine the number of clusters. Due to possibilistic clustering's similarity to the mountain clustering by Yager and Filev, we compare their formulation and performance in numerical examples.
Position Uncertainty Reduction of Mobile Robot Based on DINDs in Intelligent Space
Authors: Jin TaeSeok ,   Dept. of Mechatronics eng, DongSeo University, Churye 2dong, Sasang-Gu, Korea, Hashimoto Hideki ,   IIS, The University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo 153-8505, Japan
Published: April 2016

This paper proposes a localization of mobile robot using the images by distributed intelligent networked devices (DINDs) in intelligent space (ISpace). This scheme combines data from the observed position using dead-reckoning sensors and the estimated position using images of moving object, such as those of a walking human, used to determine the moving location of a mobile robot. The moving object is assumed to be a point-object and projected onto an image plane to form a geometrical constraint equation that provides position data of the object based on the kinematics of the intelligent space. Using the a-priori known path of a moving object and a perspective camera model, the geometric constraint equations that represent the relation between image frame coordinates of a moving object and the estimated position of the robot are derived. The proposed approach is applied for a mobile robot in ISpace to show the reduction of uncertainty in the determining of the location of the mobile robot.
Recognition of Traffic Signs and Korean Texts on Traffic Signs Using Japanese Puzzle
Authors: Sohn Youngsun ,   Dept. of Information & Communications Engineering, Tongmyong University, Shin Ilsik ,   Research Institute of Medium & Small Shipbuilding
Published: April 2016

This paper embodies a recognition system that recognizes the traffic signs and the Korean characters on the traffic signs through reverse application of a Japanese puzzle. The Japanese puzzle used in this system reveals the shape of the intended object when marked onto the mesh grids according to the (x,y) coordinate information provided by the puzzle creator. When the puzzle described above is applied to the color and the shape of the traffic sign after the separating the traffic sign image from the inputted image, the system outputs the traffic sign and its contents as text if the image is recognized as a traffic sign. With the black-and-white image processing and unneeded penciling procedure, the proposed system outperformed the existing systems at a faster processing speed and higher recognition rate.
Humanoid Knowledge-Based Decision Support Systems (KBDSS) – Subjective or Objective Data Query
Authors: Langanke Ulrich H. ,   Budapest Tech, Institute of Intercultural Communication, H-1087 Budapest, Hungary
Published: April 2016

“Decision” as a cognitive phenomenon can be interpreted as the result of neural behavior. Various scientific models have ever since dealt with this essential aspect of human interaction, involving different philosophic backgrounds such as holistic vs. analytic approaches, Aristotle vs. Confucius, Confucius vs. Buddha with their respective impact on the definition of “subjectivity” and “objecitiviy.” This paper fosters three appoaches for subjective and/or objective decision making: 1) Explained neurobiologically as an internal representation or 2) “autopoietically” according to Radical Constructivism and 3) mathematically as a Fuzzy algorithm. In order to choose the appropriate computational decision support techniques the structural complexity of the phenomenon “decision” has to be determined. Globalization with its manyfold cross-cultural contacts may serve as an example for the relevance of the implementation of KBDSS in real-life settings. When it comes to cross-cultural exchange, business or otherwise, skills in decision making are a cue value and a key to success. The main issue of this paper is to develop a multidisciplinary humanoid approach to the configuration of KBDSS that enable us to follow the traces and identify the origins – neural, sensual, linguistc or other – that are implicitely or explicitely involved in human decision making.
Hierarchical Bayesian Modeling for Estimating Shared Hidden States with Application to Tracking
Authors: Kawamoto Kazuhiko ,   Kyushu Institute of Technology, 1-1 Sensui-cho, Tobata-ku, Kitakyushu 804-8550, Japan
Published: April 2016

We consider a problem of estimating shared hidden states of stochastic time–series models from individual observations. To solve the problem, we formulate the problem with hierarchical Bayesian Models and propose a particle filter, which yields a numerical solution to sequential Bayesian estimation, for the hierarchical models. The proposed method can be applied to the problem of tracking an extended object or a group of objects moving in formation. We decompose the state space into the shared and unshared substates, assuming that the individual unshared substate independently evolves with time. This assumption enables us to treat multiple targets individually. Experiments with numerical and real image sequences show the effectiveness of the proposed method.
Random-TD Function Approximator
Authors: Osman Hassab Elgawi ,   Image Science and Engineering Lab, Tokyo Institute of Technology, 4259 Nagatsuta, Midori-ku, Yokohama 226-8503, Japan
Published: April 2016

In this paper, adaptive controller architecture based on a combination of temporal-difference (TD) learning and an on-line variant of Random Forest (RF) classifier is proposed. We call this implementation Random-TD. The approach iteratively improves its control strategies by exploiting only relevant parts of action and is able to learn completely in on-line mode. Such capability of on-line adaptation would take us closer to the goal of more robust and adaptable control. To illustrate this and to demonstrate the applicability of the approach, it has been applied to a non-linear, non-stationary control task, Cart-Pole balancing and on high-dimensional control problems –Ailerons, Elevator, Kinematics, and Friedman–. The results demonstrate that our hybrid approach is adaptable and can significantly improves the performance of TD methods while speeding up the learning process.
Shape Classification in Continuous Rotation Manipulation by Universal Robot Hand
Authors: Nakamoto Hiroyuki ,   Hyogo Prefectural Institute of Technology, 3-1-12 Yukihira-cho, Suma-ku, Kobe 654-0037, Japan, Kobayashi Futoshi , Imamura Nobuaki , Shirasawa Hidenori , Kojima Fumio ,   Kobe University, 1-1 Rokkodai-cho, Nada-ku, Kobe 657-8501, Japan,   Hiroshima International University, 5-1-1 Hirokoshingai, Kure 737-0112, Japan,   The Advanced Materials Processing Institute Kinki Japan,  7-1-8 Doi-cho, Amagasaki 660-0807, Japan
Published: April 2016

We propose a shape classification method in continuous rotation manipulation by a multi-fingered robot hand. Our robot hand has five fingers equipped with tactile sensors. Each tactile sensor can measure a pressure distribution once every 10 ms while the robot hand rotates an object continuously. Our proposal classification method consists of the following processes: A kurtosis is calculated from each pressure distribution, and it quantifies the shape of the current contact surface. By rotating an object and measuring a time-series pressure distribution, the hand obtains a time-series kurtosis. An evaluated value is calculated from the time-series kurtosis and reference patterns using continuous dynamic programming (CDP) matching scheme. The contact shape is classified whether the evaluated value is lower than a given threshold. Experiments demonstrate the effectiveness of our proposal shape classification.
Operation Planning of District Heating and Cooling Plants Considering Contract Violation Penalties
Authors: Kato Kosuke ,   Graduate School of Engineering, Hiroshima University 1-4-1 Kagamiyama, Higashi-Hiroshima, Hiroshima 739-8527, Japan, Sakawa Masatoshi , Ishimaru Keiichi , Ushiro Satoshi ,   Urban Facilities Division, Shinryo Corporation 3-7-1 Nishishinjuku, Shinjuku-ku, Tokyo 163-1021, Japan
Published: April 2016

Urban district heating and cooling (DHC) systems operate large freezers, heat exchangers, and boilers to stably and economically supply hot and cold water, steam, etc., based on customer demand. We formulate an operation-planning problem as a nonlinear integer programming problem for an actual DHC plant. To reflect actual decision making appropriately, we incorporate contract-violation penalties into the running cost consisting of fuel and arrangements expenses. We, then, solve operation-planning problems with and without penalties, demonstrating the effectiveness of taking penalties into consideration.
Analysis of Components of Waterfront Parks by Using Conjoint Analysis Including an Incomplete Answer
Authors: Shingu Kiyoshi ,   Department of Oceanic Architecture and Engineering and  Department of Computer Science, Nihon University, Funabashi-shi, Chiba 274-8501, Japan, Hiratsuka Kiyotoshi ,   Department of Computer Science, Nihon University, Funabashi-shi, Chiba 274-8501, Japan
Published: April 2016

The Tokyo Metropolitan Government established “Seaside park plan” and “Tokyo municipal seaside park ordinance.” The Tokyo Metropolitan Government considers that the conservation of seaside, riverside and urban parks make the creation of places where residents of Tokyo can come in contact with nature. However, it seems that the planning and construction of those parks have been carried out by the administration from one-side view. The waterfront parks are public and have been used by many people. As there have been no data what components of parks are important for users of parks, eight seaside parks called waterfront parks were watched, degrees of satisfactory of eighty two residents who live in metropolitan area about components of parks were surveyed, and degrees of concern about components of the parks have been obtained by conjoint analysis. Those waterfront parks are located at Odaiba near Tokyo Bay. Here, the components of parks are as follows; 1) Hydrophile, 2) Rest area, 3) Public transport and location, 4) Recreational facilities, 5) Scenery and appearance, 6) Maintenance, 7) Monuments, and 8) Openness. The following main results have been obtained from the research. : a) Both female and male consider scenery and appearance, and hydrophile are important. b) Rest area is the least important than other components to those in their 20's, but important to those in their 30's, 40's and 60's. c) Public transport and location are least important for teens, but important to all age groups. d) Those in their 50's found 1) Hydrophile, 3) Public transport and location, and 5) Scenery and appearance are important. If there is an incomplete answer in response, we can utilize the data effectively by using the proposed method.
A New Method for Simplifying Algebraic Expressions in Genetic Programming Called Equivalent Decision Simplification
Authors: Mori Naoki ,   Osaka Prefecture University, Osaka, Japan, McKay Bob , Hoai Nguyen Xuan , Essam Daryl , Takeuchi Saori ,   Seoul National University, Seoul, Korea,   Seoul National University, Seoul, Korea,   University of New South Wales ADFA, Canberra, Australia,   Mitsubishi Electric Corporation, Hyogo, Japan
Published: April 2016

Symbolic Regression is one of the most important applications of Genetic Programming, but suffers from one of the key issues in Genetic Programming, bloat. For a variety of reasons, reliable techniques to remove bloat are highly desirable. This paper introduces a novel approach of removing bloat, Equivalent Decision Simplification, in which subtrees are evaluated over the set of regression points. The effectiveness of the proposed method is confirmed by computer simulation taking simple Symbolic Regression problems as examples.
A Normative Approach to Fuzzy Logic Reasoning Using Residual Implications
Authors: Tsukamoto Yahachiro ,   Department of Information Engineering, Faculty of Science and Technology, Meijo University, Shiogamaguchi, Tempaku-ku, Nagoya 468-8502, Japan
Published: April 2016

Logical problems with fuzzy implications have been investigated minutely (Baczynski and Jayaram [1]). Considering some of the normative criteria to be met bygeneralized modus ponens, we have formulated a method of fuzzy reasoning based on residual implication. Among these criteria, the specificity possessed by the conclusion deduced bygeneralized modus ponensshould not be stronger than that of the consequent in the fuzzy implication.
Adapting Real Mobile Robots to Complex Environments Using a Pattern Association Network Controller (PAN-C)
Authors: Zin Indra Bin Mohd ,   Industrial Computing Research Group, Graduate School of Information Science and Technology, University Kebangsaan Malaysia, Malaysia, Alnajjar Fady , Murase Kazuyuki ,   Department of Human and Artificial Intelligence System, Graduate School of Engineering, University of Fukui, 3-9-1 Bunkyo, Fukui 910-8507, Japan,   Research and Education Program for Life Science, University of Fukui, Bunkyo, Fukui, Japan
Published: April 2016

Adapting real mobile robots to complex or dynamic environments is just one of the many challenges robotics researchers face. The difficulty in such environments is in developing a simple, quick adaptive controller that adapts robots to patterns in these environments, especially when individual patterns require unique behavior from the robot. Although most standard evolutionary algorithms attempt to obtain optimal networks for such environments, this is difficult to attain due to network confusion in adapting and readapting patterns. We propose a simple adaptive controller able to learn and remember. It simplifies environments into simple groups of patterns, each of which the robot can independently learn and memorize. The memory introduced in the controller enhances the robot's ability to track its own experience and to cope with upcoming events. Experimental results show that the controller handles general complexity and gives the robot more adaptability, stability, and autonomy.
Variable Ranking for Online Ensemble Learning
Authors: Osman Hassab Elgawi ,   Image Science and Engineering Lab, Tokyo Institute of Technology, 4259 Nagatsuta, Midori-ku, Yokohama, Kanagawa 226-8503, Japan
Published: April 2016

In proposing, incremental feature selection based on correlation ranking (CR) for classification problems, we develop on-line training using the random forests (RF) algorithm, then evaluate the performance of the combination based on an NIPS 2003 Feature Selection Challenge dataset. Results show that our approach achieves performance comparable to others batch learning algorithms, including RF.
Special Issue on Selected Papers from SCIS & ISIS 2008 No.2
Authors: Kanoh Masayoshi ,   Assistant Professor
Published: April 2016

Welcome to the second special issue on selected papers from SCIS & ISIS 2008, a joint conference combining the 4th Soft Computing and Intelligent Systems (SCIS) and the 9th International Symposium on advances Intelligent Systems (ISIS) held at Nagoya University, Japan, in September 2008. smallskip Three earlier conferences were held in: the National Institute of Advanced Industrial Science and Technology (AIST), Japan (2002); Keio University, Japan (2004); and Tokyo Institute of Technology, Japan (2006). smallskip Conference topics include fuzzy logic, clustering, evolutionary computation, machine learning, rough sets, man-machine interaction/interfaces, neural networks, computer vision, image processing, cognitive modeling, computational intelligence, etc. smallskip Papers of interest containing novel algorithms and ideas in these fields have been selected, so I hope you will enjoy this issue.smallskip I would like to thank the many people who have produced these special issues for SCIS & ISIS2008, and all of the authors and reviewers. Without your help, this issue would not have been possible.
Clustering Based on Multiple Criteria for LVQ and K-Means Algorithm
Authors: Morii Fujiki ,   Dept. of Information and Computer Sciences, Nara Women's University, Nara 630-8506, Japan, Kurahashi Kazuko ,   FUJIFILM Corporation, Medical Systems Business DIV., Kanagawa 238-8538, Japan
Published: April 2016

When classifying linearly separable data by learning vector quantization (LVQ) or K-Means algorithm (KMA), we cannot necessarily obtain satisfactory classification results for bad selections of initial cluster centers and differences among the distributions of class data. In this paper, to realize reliable classification, clustering based on multiple criteria for LVQ and KMA is proposed, and its performance is provided. To obtain suitable cluster centers, KMA with the split and merge procedure proposed by Kaukoranta et al. is introduced to minimize the squared-error distortion. LVQ using those cluster centers as initial ones is applied to the data, andΚclusters are produced. Introducing a criterion of whether each cluster reveals unimodality, subclusters split by KMA for clusters having no unimodality are merged into appropriate neighboring clusters except one subcluster, and the validity of the classification result is checked.
Social Effects of Robot Therapy in a Care House
– Change of Social Network of the Residents for One Year –
Authors: Wada Kazuyoshi ,   Tokyo Metropolitan University, 6-6 Asahigaoka, Hino, Tokyo 191-0065, Japan, Shibata Takanori ,   National Institute of Advanced Industrial Science and Technology, 1-1-1 Umezono, Tsukuba, Ibaraki 305-8568, Japan
Published: April 2016

Robot therapy in the form of two seal robots was conducted for a year at a care house in Japan. Residents and robots interacted over 9 hours a day. To determine psychological and social robot influence, individual residents were interviewed using the “free pile sort” method and their social interaction was analyzed. After robot use was discontinued, follow-up showed that robots encouraged residents to communicate with each other and strengthen their individual ties during the year of robot use.
Extraction of Evaluation Keywords for Analyzing Product Evaluation in User-Reviews Using Hierarchical Keyword Graph
Authors: Yoshikawa Tomohiro ,   Nagoya University, Furo-cho, Chikusa-ku, Nagoya 464-8603, Japan, Uchida Yuki , Furuhashi Takeshi , Hirao Eiji , Iguchi Hiroto ,   NEC corporation, 11-5 Shibaura 2-chome, Minato-Ku, Tokyo 108-8557, Japan
Published: April 2016

Recently, the number of sites on the Internet which give users the opportunity to write their ideas and opinions for the public to read have been increasing. In addition, the number of people who want to know the opinions of others about interesting products has also been increasing. However, it is very difficult for people to read complete reviews on the Internet. This study tries to develop a new system for the analysis of reviews, a system which shows evaluation information about products using graphs of evaluation keywords. This paper focuses on the extraction of evaluation keywords from reviews on the Internet. This paper proposes a method for extracting evaluation keywords and displays its results as graphs. It employs HK Graph (Hierarchical Keyword Graph), which can visualize the relationship among words in a hierarchical network structure based on the co-occurrence information for the keyword graph.
Genetic Network Programming with Rule Accumulation and its Application to Tile-World Problem
Authors: Wang Lutao ,   Graduate School of Information, Production and Systems, Waseda University, Japan, Mabu Shingo , Ye Fengming , Eto Shinji , Fan Xuefeng , Hirasawa Kotaro ,   School of Electronics and Information Engineering, Tongji University,
Published: April 2016

Genetic Network Programming (GNP) is an evolutionary algorithm derived from GA and GP. Directed graph structure, reusability of nodes, and implicit memory function enable GNP to deal with complex problems in dynamic environments efficiently and effectively, as many paper demonstrated. This paper proposed a new method to optimize GNP by extracting and using rules. The basic idea of GNP with Rule Accumulation (GNP with RA) is to extract rules with higher fitness values from the elite individuals and store them in the pool every generation. A rule is defined as a sequence of successive judgment results and a processing node, which represent the good experiences of the past behavior. As a result, the rule pool serves as an experience set of GNP obtained in the evolutionary process. By extracting the rules during the evolutionary period and then matching them with the situations of the environment, we could, for example, guide agents' behavior properly and get better performance of the agents. In this paper, we apply GNP with RA to the problem of determining agents' behaviors in the Tile-world environment in order to evaluate its effectiveness. The simulation results demonstrate that GNP with RA could have better performances than the conventional GNP both in the average fitness value and its stability.
About Profit Sharing Considering Infatuate Actions
Authors: Uemura Wataru ,   Ryukoku University
Published: April 2016

In reinforcement learning systems based on trial-and error, the agent, that is the subject or the system that perceives its environment and takes actions which maximize its chances of success, is rewarded when it attains the target level of learning of the learning exercise. In Profit Sharing, the reinforcement learning process is pursued for the accumulation of such rewards. In order to continue the process of reward accumulation, the agent insists upon the repetition of the particular actions that are being learned and avoids selecting other actions, making the agent less adaptable to changes in the environment. In view of the above, this paper attempts to propose the introduction of the concept of infatuation to eliminate the reluctance of the agent to adapt to new environments. If the agent is a living being, when a single particular reinforcement learning process is repeated, the stimulus the agent perceives in each of the processes gradually loses its intensity due to familiarization. However, if the agent encounters a set of rules that are different from those of the particular repeated learning process, then the agent reverts to the previous particular learning process, and the stimulus the agent receives after the said reversion recovers its intensity. The intention here is to apply the concept of assimilation infatuation to Profit Sharing, and to confirm its effects through experiments.
Multiple-Timescale PIA for Model-Based Reinforcement Learning
Authors: Yamaguchi Tomohiro ,   Nara National Collage of Technology, 22 Yata-cho, Yamatokoriyama, Nara 639-1080, Japan, Imatani Eri ,   Nara Institute of Science and Technology, 8916-5 Takayama, Ikoma, Nara 630-0192, Japan
Published: April 2016

This paper discusses dynamic-programming-based multiagent reinforcement learning in the MDP model. To learn cooperative actions among agents, a major difficulty in multiagent reinforcement learning is the problem of simultaneous learning. To solve this problem, each agent should learn in different time. We propose multiple-timescale reinforcement learning improving their learning results exclusively. We conducted comparative experiments between multiple-timescale and exclusive policy improvement, reducing optimal common-payoff Nash solution search cost.
A Commanding and Visualization Software Suite for Controlling the Mars Rovers and Other Planetary Robots
Authors: Hartman Frank R. ,   Jet Propulsion Laboratory, California Institute of Technology Pasadena, CA, USA, Cooper Brian , Maxwell Scott , Wright John , Yen Jeng
Published: April 2016

The Rover Sequencing and Visualization Program is a suite of tools for the commanding of planetary rovers and other robots. The overall program architecture is based on components synchronizing and messaging over an inter-process communication bus. This paper will provide a description of these components and discuss how they work together to provide several modes of information display and visualization for robotic commanding. Results from over 3000 sols of combined operations of the Mars Rovers Spirit and Opportunity will be used to illustrate features of the RSVP suite. Support for the Phoenix Mars Lander, Mars Science Lander and other lunar and planetary missions will also be discussed.
A Method for Accelerating the HITS Algorithm
Authors: Mirzal Andri ,   Graduate School of Information Science and Technology, Hokkaido University, Kita 14 Nishi 9, Kita-Ku, Sapporo 060-0814, Japan, Furukawa Masashi
Published: April 2016

We present a new method to accelerate the HITS algorithm by exploiting hyperlink structure of the web graph. The proposed algorithm extends the idea of authority and hub scores from HITS by introducing two diagonal matrices which contain constants that act as weights to make authority pages more authoritative and hub pages more hubby. This method works because in the web graph good authorities are pointed to by good hubs and good hubs point to good authorities. Consequently, these pages will collect their scores faster under the proposed algorithm than under the standard HITS. We show that the authority and hub vectors of the proposed algorithm exist but are not necessarily be unique, and then give a treatment to ensure the uniqueness property of the vectors. The experimental results show that the proposed algorithm can improve HITS computations, especially for back button datasets.
Management System for Tagged Image Databases Using Lattice Structures
Authors: Sawase Kazuhito ,   Department of Intelligent Interaction Technologies, University of Tsukuba, 1-1-1 Tenoudai, Tsukuba science city, Ibaraki 305-8573, Japan, Nobuhara Hajime
Published: April 2016

Tagged Image Management System based on Lattice Structures (TaILaS), the next-generation management we propose for a large number of images, constructs a concept lattice from image tags using formal concept analysis, then visualizes the Hasse diagram of this lattice for any numbers of images. TaILaS enables users to avoid having to classify images which are difficult to divide into folders. TaILaS runs on a conventional computer (CPU = 2.13 GHz,MM= 2 GB) and is implemented in Processing as a Java-based programming language. Experiments involving 11 subjects using 100 images selected from the Corel Image Gallery confirmed that TaILaS manages huge image databases effectively.
Image Enhancement Based on Nonlinear Smoothing and Sharpening for Noisy Images
Authors: Tanaka Go ,   Graduate School of Science and Engineering, Yamaguchi University, 1677-1 Yoshida, Yamaguchi 753-8512, Japan, Suetake Noriaki , Uchino Eiji
Published: April 2016

The novel image enhancement approach we propose is based on blending the ε-filtering and a conventional unsharp masking. The proposed selective unsharp masking is especially effective in enhancing noisy images corrupted by Gaussian noise, as confirmed in image enhancement experiments.
Replacement of Lyapunov Function by Locally Convergent Robust Fixed Point Transformations in Model-Based Control a Brief Summary
Authors: Tar József K. ,   Transportation Informatics and Telematics Knowledge Centre, Óbuda University H-1034 Budapest, Bécsi út 96/B, Hungary
Published: April 2016

Lyapunov’s 2ndMethod is a popular approach in the model-based control of nonlinear systems since normally it can guaranteeglobal asymptotic stability. However, the cost of the application of Lyapunov function frequently can be inefficient and complicated parameter tuning process containing unnecessary number of almost arbitrary control parameters, and vulnerability of the tuning process against not modeled, unknown external disturbances. Improved versions of the original, model-based tuning are especially sensitive to the external perturbations. All these disadvantages can simply be avoided by the application of robust fixed point transformations at the cost of giving up the guarantee of global asymptotic stability. Instead of that simple,stable iterative controlof local basin of attraction can be constructed on the basis of an approximate system model that can well compensate the effects of unknown external disturbances, too. Since the basin of convergence of the method in principle can be left, setting the three adaptive parameters of this controller needs preliminary simulation investigations. These statements are illustrated and substantiated via simulation results obtained for the adaptive control of various physical systems.