bluuebunny/crossref_metadata_2025 huggingface dataset:
Fuzzy Inference with Schemes for Guaranteeing Convexity and Symmetricity in Consequences 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 fuzzy inference method is proposed on the basis of α-cuts, which can mathematically prove to deduce consequences in both convex and symmetric forms under the required conditions, studied here, when fuzzy sets in the consequent parts of fuzzy rules are all convex and symmetric. The inference method can reflect the distribution forms of fuzzy sets in consequent parts of fuzzy rules, guaranteeing the convexity in deduced consequences. It also has a control scheme for the fuzziness and specificity in deduced consequences. The controllability provides a way to suppress excessive fuzziness increase and specificity decrease in deduced consequences. Simulation studies show that the proposed method can deduce consequences in convex and symmetric forms under the required conditions. It is also demonstrated that the distribution forms of consequent parts are reflected to deduced consequences. Moreover, it is found that the fuzziness and specificity of deduced consequences can be effectively controlled in the simulations.
Robust Guaranteed Cost Control of Uncertain Fuzzy Systems Under Sampled-Data Inputs
Authors: Yoneyama Jun ,   Department of Electrical Engineering and Electronics, Aoyama Gakuin University, 5-10-1 Fuchinobe, Sagamihara, Kanagawa 229-8558 Japan
Published: April 2016

A dynamical system is usually modeled as a continuous-time system, while the control input is applied at discrete instants. This is called a sampled-data control system. This paper is concerned with robust sampled-data control with guaranteed cost for uncertain fuzzy systems. The sampled-data control input is usually the zero-order hold and hence has a piecewise-continuous delay. Thus, an input delay system approach to robust sampled-data control is introduced. Sufficient robust guaranteed cost performance conditions for the closed-loop system with a sampled-data state feedback controller are given in terms of linear matrix inequalities(LMIs). Such robust conditions are derived via descriptor approach to fuzzy time-delay systems under the assumption that a sampling interval may vary but is not greater than some prescribed number. A design method of robust sampled-data guaranteed cost controller for uncertain fuzzy systems. Numerical examples are given to illustrate our sampled-data state feedback control.
Posture Estimation of a Human Body from Thermal Images of 2D Appearance Models of 3D Ellipsoidal Model
Authors: Hayase Mitsuhiro ,   Graduate School of Computer and Cognitive Science,  Chukyo University, Shimada Susumu ,   School of Information Science and Technology, Chukyo University 101 Tokodachi, Kaizu-cho, Toyota, 470-0393 Japan
Published: April 2016

We propose a new model-based recognition method that involves the use of three-dimensional (3D) ellipsoidal models in various sizes and proportions as well as their two-dimensional (2D) appearance models. Most model-based vision is intended to recognize specified objects, and the model is specific to the object. However, our method can recognize various proportions of objects and was applied in posture estimation of the human body from thermal images.
Soccer Player’s Pose Recognition by Creative Search for Generating Free Viewpoint Images
Authors: Kawanaka Haruki ,   School of Information Science & Technology, Aichi Prefectural University 1522-3 Ibaragabasama, Kumabari, Nagakute-cho, Aichi-gun, Aichi 480-1198, Japan, Matsubara Fuminori , Iwahori Yuji ,   Department of Computer Science, Chubu University 1200 Matsumoto-cho, Kasugai, Aichi 487-8501, Japan
Published: April 2016

The low-cost generation we have proposed for a free viewpoint image of soccer games uses a multiple viewpoint image database designed with computer graphics for recognizing player poses in the real image and for generating virtual scenes. A pose is recognized from a player’s silhouette and applying parametric eigenspace method. Because only silhouette information is used, however, limb positioning may be backward or the body misdirected. Our new proposal for excluding misplaced left and right limb poses assumes that changes in a pose, especially limb positioning, between sequential frames are continuous, so the limb positioning in 3D space and 2D images can be determined and the search range restricted in eigenspace. We also propose generating continuous frames for cases in which a correct pose exists outside of the restricted range by setting an initial state and handling error.
Comparison and Evaluation of Different Cluster Validity Measures Including Their Kernelization
Authors: Hashimoto Wataru ,   Department of Risk Engineering, School of Systems and Information Engineering, University of Tsukuba, Ibaraki 305-8573, Japan, Nakamura Tetsuya , Miyamoto Sadaaki
Published: April 2016

Many different measures proposed for cluster validity remain to be compared using sufficient numbers of numerical examples. We compare the performance of five measures of the sum of determinants and the sum of traces of fuzzy covariances of clusters, the Xie-Beni index, the Davies-Bouldin index, and the Fukuyama-Sugeno index together with their kernelized versions, focusing on algorithms for calculating kernelized measures. We compared the effectiveness of these indices using thousands of automatically generated clusters. We found that no single measure outperforms the others, and that, contrary to the common understanding that determinants are better than traces, the sum of traces performs as well as the sum of determinants and, kernelized measures perform as well as nonkernelized ones.
An Adaptive Muscular Force Generation Mechanism Based on Prior Information of Handling Object
Authors: Sekiyama Kosuke ,   Dep. of Micro-Nano Systems Engineering, Nagoya University, Furo-cho, Chikusa-ku, Nagoya-city, Aichi 464-8603, Japan, Ito Masahiro , Fukuda Toshio , Suzuki Takashi , Yamashita Koshiro ,   TOKAI RIKA CO., LTD.,  3-260, Toyota, Oguchi-cho, Niwa-gun, Aichi 480-0195, Japan
Published: April 2016

Evaluating the influences of human-machine interface (HMI) visual information is vital to developing the user-oriented and human-friendly equipment, robots, etc. We define HMI visual information as prior information, such as size, color, shape, etc. The relationship between prior information and the Kansei feeling is evaluated by surface electromyogram (sEMG). This study deals with object-grasping motion and measures sEMG signals during the motion. Prediction on object-grasping motion is predicted from sEMG signals and defined as Force Prediction (FP). Differences between prediction of HMI operation and actual results are assumed to influence on Kansei feeling concerning the operation. Subjects given different prior information calculate FP about plastic bottles when grasping them. Experimental results show that the FP differs even though the plastic bottles have the same weight. The influence of prior information on FP is visually plotted in a three-dimensional map which is called Size-Color-iEMG map, and its application is to HMI design.
Adaptive Kernel Quantile Regression for Anomaly Detection
Authors: Moriguchi Hiroyuki ,   Mie University, 1577 Kurimamachiya-cho, Tsu, Mie 514-8507, Japan, Takeuchi Ichiro , Karasuyama Masayuki , Horikawa Shin-ichi , Ohta Yoshikatsu , Kodama Tetsuji , Naruse Hiroshi ,   Nagoya Institute of Technology, Gokiso-cho, Showa-ku, Nagoya 466-8555, Japan
Published: April 2016

In this paper, we study a problem of anomaly detection from time series-data. We use kernel quantile regression (KQR) to predict the extreme (such as 0.01 or 0.99) quantiles of the future time-series data distribution. It enables us to tell whether the probability of observing a certain time-series sequence is larger than, say, 1 percent or not. In this paper, we develop an efficient update algorithm of KQR in order to adapt the KQR in on-line manner. We propose a new algorithm that allows us to compute the optimal solution of the KQR when a new training pattern is inserted or deleted. We demonstrate the effectiveness of our methodology through numerical experiment using real-world time-series data.
Human-Assisted Fuzzy Image Similarity Analysis Based on Information Compression
Authors: Vachkov Gancho ,   Department of Reliability-based Information Systems Engineering, Faculty of Engineering, Kagawa University, Hayashi-cho 2217-20, Takamatsu, Kagawa 761-0396, Japan
Published: April 2016

The fuzzy similarity analysis we propose in this paper is used for unsupervised image classification. We introduce a special growing unsupervised learning algorithm for information compression (granulation) of the original “raw data” (the RGB pixels) of an image with a smaller number of neurons (information granules). Two important parameters are extracted from each image, namely the center of gravity (COG) and the model volume of the image, taken as the number of neurons obtained from information compression. These two features are used as inputs for special fuzzy inference for numerically calculating the degree of similarity between a pair of images. The fuzzy inference procedure can be tuned based on a predefined human preference (list of similar images), thus performing human-assisted similarity analysis. The choice of the optimization algorithm and the selection of the optimization criterion are questions open to the user to answer. The proposed computation scheme for similarity analysis is illustrated on a test example of 16 flower images and results are discussed.
The Method to Analyze Freely Described Data from Questionnaires
Authors: Kimura Masaomi ,   Shibaura Institute of Technology, 3-7-5 Toyosu, Koto City, Tokyo, Japan
Published: April 2016

Text mining has been growing; mainly due to the need to extract useful information from vast amounts of textual data. Our target here is text data, a collection of freely described data from questionnaires. Unlike research papers, newspaper articles, call-center logs and web pages, which are usually the targets of text mining analysis, the freely described data contained in the questionnaire responses have specific characteristics, including a small number of short sentences forming individual pieces of data, while the wide variety of content precludes the applications of clustering algorithms used to classify the same. In this paper, we suggest the way to extract the opinions which are delivered by multiple respondents, based on the modification relationships included in each sentence in the freely described data. Certain applications of our method are also presented after the introduction of our approach.
Pseudo Online Independent Component Analysis for Dynamical Mixing Using Gradient Optimization
Authors: Haneda Takahiro ,   School of Computer Science and Engineering The University of Aizu, Tsuruga, Ikki-Machi, Aisu-Wakamatsu City, Fukushima 965-8580, Japan, Ding Shuxue
Published: April 2016

Many of the algorithms developed for independent component analysis (ICA) operate efficiently only in batch processing because they include ensemble expectation operations. The online ICA algorithm that we propose in this paper operates recursively on sequential finite lengthened inputblocksfor learning mixing parameters and for separating sources based on maximized non-Gaussianity of outputs. To enable the algorithm to work more efficiently in separating nonstationary sources and/or in dynamical mixing environments, we proposed an online estimation for the covariance matrix of input signals and an online optimization for the step size. The covariance matrix is used to calculate the whitening matrix and must be estimated appropriately for ICA processing to be efficient. Determining the step-size parameter in an online algorithm is critical to higher convergence and stability after convergence but is notoriously tiresome, especially if sources or/and the mixing environment are nonstationary. The online optimized step size we proposed is automatically optimized for suiting the powers in each block. Numerical experiments confirm that the online algorithm converges efficiently and separates sources appropriately.
A Genetic-Algorithm-Based Temporal Subtraction for Chest Radiographs
Authors: Inaba Takeshi ,   Graduate School of Information Science and Technology Aichi Prefectural University, Nagakute-cho, Aichi 480-1198, Japan, He Lifeng , Suzuki Kenji , Murakami Kazuhito , Chao Yuyan ,   Department of Radiology, Division of Biological Sciences, The University of Chicago, 5841 South Maryland Avenue, Chicago, IL 60637, USA,   Graduate School of Environmental Management,  Nagoya Sangyo University, Owariasahi-city, Aichi 488-8711, Japan
Published: April 2016

To assess pathological chest change, radiologists compare the same patient's chest radiographs taken at different times. Supporting radiologists' diagnostics, temporal-subtraction images constructed from the previous and current radiographs have enhanced the visualization of pathological change. This paper presents a genetic-algorithm-based temporal subtraction for chest radiographs. First, we extract ribs from previous and current images and use them for global matching of the two images. Then, we divide the lung area in the current image into many subareas. For individual subarea, we use the genetic algorithm for local matching to find its corresponding area in the previous image efficiently. Results demonstrated that pathological change were accurately enhanced in temporal-subtraction images without major misregistration artifacts, accurately visualizing of pathological change and proving useful in improving radiologists, diagnostic performance.
HACO2 Method for Evolving Hyperbox Classifiers with Ant Colony Optimization
Authors: Ramos Guilherme N. ,   Department of Computational Intelligence and Systems Science, Tokyo Institute of Technology, G3-49, 4259 Nagatsuta, Midori-ku, Yokohama-city 226-8502, Japan, Dong Fangyan , Hirota Kaoru
Published: April 2016

A method, called HACO2 (Hyperbox classifier with Ant Colony Optimization - type 2), is proposed for evolving a hyperbox classifier using the ant colony meta-heuristic. It reshapes the hyperboxes in a near-optimal way to better fit the data, improving the accuracy and possibly indicating its most discriminative features. HACO2 is validated using artificial 2D data showing over 90% accuracy. It is also applied to the benchmark iris data set (4 features), providing results with over 93% accuracy, and to the MIS data set (11 features), with almost 85% accuracy. For these sets, the two most discriminative features obtained from the method are used in simplified classifiers which result in accuracies of 100% for the iris and 83% for the MIS data sets. Further modifications (automatic parameter setting), extensions (initialization short comings) and applications are discussed.
Multiobjective Random Fuzzy Linear Programming Problems Based on the Possibility Maximization Model
Authors: Hasuike Takashi ,   Graduate School of Information Science and Technology, Osaka University, 2-1 Yamadaoka, Suita, Osaka 565-0871, Japan, Katagiri Hideki , Ishii Hiroaki ,   Graduate School of Engineering, Hiroshima University, 1-4-1, Kagamiyama Higashi Hiroshima 739-8527, Japan
Published: April 2016

Two multiobjective random fuzzy programming problems considered based on the possibility maximization model using possibilistic and stochastic programming are not initially well defined due to the random variables and fuzzy numbers included. To solve them analytically, probability criteria are set for objective functions and chance constraints are introduced. Taking into account the decision maker’s subjectivity and the original plan’s flexibility, a fuzzy goal is introduced for each objective function. The original problems are then changed into deterministic equivalent problems to make the possibility fractile optimization problem equivalent to a linear programming problem. The possibility maximization problem for probability is changed into a nonlinear programming problem, and an analytical solution is constructed extending previous solution approaches.
Human Resource Selection Based on Performance ClassificationUsingWeighted Support Vector Machine
Authors: Wang Qiangwei ,   Graduate School of Information, Production and Systems, Waseda University,, Li Boyang , Hu Jinglu
Published: April 2016

Recruitment and selection have the first priority in human resource management. Poor selection decisions can be enormously costly to organization. So a valid decision method is needed. Traditional selection method is based on linear model. However, it is not proper for this intricate nonlinear relationship between applicants and job performance. In this paper, we introduce a new human resource selection system using weighted support vector machine (WSVM) which is fit for the nonlinear problem. It gives the selection process a feedback and improves classification results. Besides, we also proposed a new weight generating method to keep the characteristic of human resource. It reduces the effect of outliers and noise for classification, and distinguishes different importance of selection criterions. Furthermore, different weights are compared for WSVM. Questionnaire survey was issued to acquire dataset. Simulation results show that our proposed selection system is valid for human resource selection; it performs higher classification accuracy than traditional linear method. It can be used to support the decision making in human resource selection. Besides, different weight generating methods are compared for WSVM, our proposed weight generating method obtains better efficiency than standard SVM and class center based weight generating method.
On Tolerant Fuzzyc-Means Clustering
Authors: Hamasuna Yukihiro ,   Doctoral Program in Risk Engineering, University of Tsukuba, Endo Yasunori , Miyamoto Sadaaki ,   Department of Risk Engineering, Faculty of Systems and Information Engineering, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki 305-8573, Japan
Published: April 2016

This paper presents a new type of clustering algorithms by using a tolerance vector called tolerant fuzzyc-means clustering (TFCM). In the proposed algorithms, the new concept of tolerance vector plays very important role. In the original concept of tolerance, a tolerance vector attributes to each data. This concept is developed to handle data flexibly, that is, a tolerance vector attributes not only to each data but also each cluster. Using the new concept, we can consider the influence of clusters to each data by the tolerance. First, the new concept of tolerance is introduced into optimization problems based on conventional fuzzyc-means clustering (FCM). Second, the optimization problems with tolerance are solved by using Karush-Kuhn-Tucker conditions. Third, new clustering algorithms are constructed based on the explicit optimal solutions of the optimization problems. Finally, the effectiveness of the proposed algorithms is verified through numerical examples by fuzzy classification function.
A Granular Unified Min-Max Fuzzy-Neuro Framework for Learning Fuzzy Systems
Authors: Beldjehem Mokhtar ,   Sainte Anne University, 1589 Walnut Street Halifax, Nova Scotia, B3H 3S1, Canada
Published: April 2016

We propose a novel computational granular unified framework that is cognitively motivated for learning if-then fuzzy weighted rules by using a hybrid neuro-fuzzy or fuzzy-neuro possibilistic model appropriately crafted as a means to automatically extract or learn fuzzy rules from only input-output examples by integrating some useful concepts from the human cognitive processes and adding some interesting granular functionalities. This learning scheme uses an exhaustive search over the fuzzy partitions of involved variables, automatic fuzzy hypotheses generation, formulation and testing, and approximation procedure of Min-Max relational equations. The main idea is to start learning from coarse fuzzy partitions of the involved variables (both input and output) and proceed progressively toward fine-grained partitions until finding the appropriate partitions that fit the data. According to the complexity of the problem at hand, it learns the whole structure of the fuzzy system, i.e. conjointly appropriate fuzzy partitions, appropriate fuzzy rules, their number and their associated membership functions.
Combination of Two Evolutionary Methods for Mining Association Rules in Large and Dense Databases
Authors: Gonzales Eloy ,   Graduate School of Information, Production and Systems, Waseda University, Taboada Karla , Mabu Shingo , Shimada Kaoru , Hirasawa Kotaro ,   Information, Production and Systems Research Center, Waseda University
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 small databases in the sense that they have a relatively small number of attributes. However, this conventional GNP method is not be able to deal with large databases with a huge number of attributes, because its search space becomes very large, causing bad performance at running time. The aim of this paper is to propose a new method to extract association rules from large and dense databases with a huge amount of attributes through the combination of conventional GNP based mining method and a specially designed genetic algorithm (GA). Each of these evolutionary methods works in its own processing level and they are highly synchronized to act as one system.

Our strategy consists in the division of a large and dense database into many small databases. These small databases are considered as individuals and form a population. Then the conventional GNP based mining method is applied to extract association rules for each of these individuals. Finally, the population is evolved through several generations using GA with special genetic operators considering the acquired information. Two complementary processing levels are defined: Global Level and Local Level, each with its own independent tasks and processes. In the Global Level mainly GA process is carried out, whereas in the Local Level, conventional GNP based mining method is carried out in parallel and they generate their own local pools of association rules. Several special genetic operations for GA in the Global Level are proposed and the performance of each of them and their combination is shown and compared.

In our simulations, the conventional GNP based mining method and our proposed method are compared using a real world large and dense database with a huge amount of attributes. The results show that extending the conventional GNP based mining method using GA allows to extract association rules from large and dense databases directly and more efficiently than the conventional GNP method.
Object Categorization Using Biologically Inspired Nodemaps and the HITEC Categorization System
Authors: Csapo Adam ,   Budapest Univ. of Technology and Economics, H-1111, Budapest, Muegyetem Rkp. 3-9, Hungary, Resko Barna , Tikk Domonkos , Baranyi Peter
Published: April 2016

The computerized modeling of cognitive visual information has been a research field of great interest in the past several decades. The research field is interesting not only from a biological perspective, but also from an engineering point of view when systems are developed that aim to achieve similar goals as biological cognitive systems. This paper briefly describes a general framework for the extraction and systematic storage of low-level visual features, and demonstrates its applicability in image categorization using a linear categorization algorithm originally developed for the characterization of text documents. The performance of the algorithm together with the newly developed feature array was evaluated using the Caltech 101 database. Extremely high (95% and higher) success rates were achieved when distinguishing between pairs of categories using independent test images. Efforts were made to scale up the number of categories using a hierarchical, branch-and-bound decision tree, with limited success.
Analyzing Strength-Based Classifier System from Reinforcement Learning Perspective
Authors: Wada Atsushi ,   National Institute of Information and Communications Technology, 2-2-2 Hikaridai, Seikacho, Sorakugun, Kyoto, Japan, Takadama Keiki ,   The University of Electro-Communications, 1-5-1 Chofugaoka, Chofu, Tokyo, Japan,   PRESTO, Japan Science and Technology Agency (JST), 4-1-8 Honcho Kawaguchi, Saitama 332-0012, Japan
Published: April 2016

Learning Classifier Systems (LCSs) are rule-based adaptive systems that have both Reinforcement Learning (RL) and rule-discovery mechanisms for effective and practical on-line learning. With the aim of establishing a common theoretical basis between LCSs and RL algorithms to share each field's findings, a detailed analysis was performed to compare the learning processes of these two approaches. Based on our previous work on deriving an equivalence between the Zeroth-level Classifier System (ZCS) and Q-learning with Function Approximation (FA), this paper extends the analysis to the influence of actually applying the conditions for this equivalence. Comparative experiments have revealed interesting implications: (1) ZCS's original parameter, the deduction rate, plays a role in stabilizing the action selection, but (2) from the Reinforcement Learning perspective, such a process inhibits the ability to accurately estimate values for the entire state-action space, thus limiting the performance of ZCS in problems requiring accurate value estimation.
Is Gradient Descent Update Consistent with Accuracy-Based Learning Classifier System?
Authors: Wada Atsushi ,   National Institute of Information and Communications Technology, 2-2-2 Hikaridai, Seikacho, Sorakugun, Kyoto, Japan, Takadama Keiki ,   The University of Electro-Communications, 1-5-1 Chofugaoka, Chofu, Tokyo, Japan,   PRESTO, Japan Science and Technology Agency (JST), 4-1-8 Honcho Kawaguchi, Saitama 332-0012, Japan
Published: April 2016

Learning Classifier Systems (LCSs) are rule-based adaptive systems that have both Reinforcement Learning (RL) and rule-discovery mechanisms for effective and practical online learning. An analysis of the reinforcement process of XCS, one of the currently mainstream LCSs, is performed from the aspect of RL. Upon comparing XCS's update method with gradient-descent-based parameter update in RL, differences are found in the following elements: (1) residual term, (2) gradient term, and (3) payoff definition. All possible combinations of the variants in each element are implemented and tested on multi-step benchmark problems. This revealed that few specific combinations work effectively with XCS's accuracy-based rule-discovery process, while pure gradient-descent-based update showed the worst performance.
Information Theoretic Approach for Measuring Interaction in Multiagent Domain
Authors: Arai Sachiyo ,   Graduate School of Engineering, Chiba University, 1-33 Yayoi-cho, Inage-ku, Chiba, Ishigaki Yoshihisa
Published: April 2016

Although a large number of reinforcement learning algorithms have been proposed for the generation of cooperative behaviors, the question of how to evaluate mutual benefit or loss among them is still open. As far as we know, an emerged behavior is regarded as a cooperative behavior when embedded agents have finally achieved their global goal, regardless of whether or not mutual interference has had any effect during the course of the learning process of each agent. Thus, we cannot detect any harmful interaction on the way to achieving a fully-converged policy. In this paper, we propose a measure based on information theory for evaluating the degree of interaction during the learning process from the viewpoint of information sharing. In order to discuss the bad effects of concurrent learning, we apply our proposed measure to a situation in which there exist conflicts among the agents, and we show the availability of our measure.
Time Horizon Generalization in Reinforcement Learning: Generalizing Multiple Q-Tables in Q-Learning Agents
Authors: Hatcho Yasuyo ,   The University of Electro-Communications, 1-5-1, Chofugaoka, Chofu, Tokyo 182-8585, Japan, Hattori Kiyohiko , Takadama Keiki ,   PRESTO, Japan Science and Technology Agency (JST), 4-1-8 Honcho Kawaguchi, Saitama 332-0012, Japan
Published: April 2016

This paper focuses on generalization in reinforcement learning from the time horizon viewpoint, exploring the method that generalizes multiple Q-tables in the multiagent reinforcement learning domain. For this purpose, we propose time horizon generalization for reinforcement learning, which consists of (1) Q-table selection method and (2) Q-table merge timing method, enabling agents to (1) select which Q-tables can be generalized from among many Q-tables and (2) determine when the selected Q-tables should be generalized. Intensive simulation on the bargaining game as sequential interaction game have revealed the following implications: (1) both Q-table selection and merging timing methods help replicate the subject experimental results without ad-hoc parameter setting; and (2) such replication succeeds by agents using the proposed methods with smaller numbers of Q-tables.
Capacity Expansion Problem by Monte Carlo Sampling Method
Authors: Shiina Takayuki ,   Chiba Institute of Technology, 2-17-1 Tsudanuma, Narashino, Chiba 275-0016, Japan
Published: April 2016

We consider the stochastic programming problem with recourse in which the expectation of the recourse function requires a large number of function evaluations, and its application to the capacity expansion problem. We propose an algorithm which combines an L-shaped method and a Monte Carlo method. The importance sampling technique is applied to obtain variance reduction. In the previous approach, the recourse function is approximated as an additive form in which the function is separable in the components of the stochastic vector. In our approach, the approximate additive form of the recourse function is perturbed to define the new density function. Numerical results for the capacity expansion problem are presented.
Global Optimal Routing Algorithm for Traffic Systems with Multiple ODs
Authors: Wang Yu ,   Graduate School of Information, Production and Systems, Waseda University, Hibikino 2-7, Wakamatsu-ku, Kitakyushu, Fukuoka 808-0135, Japan, Mabu Shingo , Eto Shinji , Hirasawa Kotaro
Published: April 2016

Global optimal routing for multiple Origin-Destinations (ODs) in traffic systems becomes extremely complicated when considering the traffic volumes on the road sections. This paper proposes a Combinational Algorithm which is combined by Conventional Method, U Algorithm, SU Algorithm, SRU Algorithm, SAU Algorithm and SRAU Algorithm to solve this problem. Among the above 6 algorithms, SRAU Algorithm contributes to the Combinational Algorithm the most, where firstly, all original ODs are sorted by their traffic volumes, and then the order is randomized to generate some routing candidates. For each candidate, before finding the optimal route of the current OD, the traveling times on the optimal routes calculated by ODs with high priority are adjusted and then Q Value-based dynamic programming is utilized to find the optimal route. Next, an updating process is needed to update the traveling time on the route using the current OD. Finally the best solution can be selected out of all solutions. Sufficient simulations show that the proposed routing algorithm is efficient enough to obtain the near optimal solution even in very large scale traffic systems. Also the consideration of the traffic volumes on the road sections enables our proposal to apply to real traffic systems.
Learning and Technical Market -Effects of In-Sample Data Selection-
Authors: Kurokawa Tomio ,   Department of Information Science, Aichi Institute of Technology, Yagusa-cho, Toyota 470-0392, Japan
Published: April 2016

For a learning system, nothing is more influential than data to be learned. Any data, whether in-sample or out-of-sample, is intrinsically particular. This is typically true for complex market data. Data for the last year on a single stock name, for instance, is only a small segment of data patterns if compared to unseen out-of-sample data having potentially numerous patterns. Any technical system trained with limited in-sample data is more or less particular, thus likely becomes to be overfitted. If in-sample data is particular, its performance for out-of-sample data is expected to be poor, no matter how well the technical system is trained. In other words, a better trained technical system frequently has lower performance for out-of sample data. If not well controlled, training is generally a particularity-seeking process of in-sample data, requiring some mechanism outside of the learning process. Possible solutions may exist in in-sample data selection, learning sophistication, or technical indicator effectiveness. This study is intended to provide a better understanding of technical markets and learning, and suggestions on in-sample data selection. Experiments examine how the selection of in-sample data affects the performance of a target system for out-of-sample data and show technical market and learning phenomena using genetic algorithms (GA).
Selected Papers from HNICEM 2009
Authors: Dadios Elmer P.
Published: April 2016

The Fourth International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM) was held in Traders-Shangrila Hotel, Manila, Philippines from March 12 to 15, 2009. The theme of this conference was Convergence of Cutting Edge Technologies for Global Enterprise. As has been done from the previous HNICEM conferences, cutting edge papers presented in this conference were reviewed and selected for JACIII special issue publication. In this special issue, 8 articles were selected that will provide valuable references for researchers and practitioners. The first article presents a commanding and visualization software suite for Mars rovers and other planetary robots. This paper focuses on the support for the Phoenix Mars Lander, Mars Science Lander and other lunar and planetary missions conducted by NASA. The second article discusses an auction-based consensus mechanism for cooperative targets tracking using minimum numbers of mobile sensors in order to reduce energy consumption due to sensor mobilization. The third article is on concept of neighborhood degree and its application to switching plural optimization methods in scheduling. The fourth article covers human behavior classification using thinning algorithm and support vector machine. The fifth article is about fuzzy relevant feedback in image retrieval for color feature using query vector modification method. An experimental identification of manipulator dynamics through the minimization of its natural oscillations is presented in the sixth article. This work utilizes the actual force of gravity for the manipulator link to achieve natural oscillation. The oscillatory motion allows the system to be converted into an optimization problem through the minimization of the frequency of oscillation. On the other hand, the seventh article is an integrated eco-design tool for Malaysian automobile industry. The eighth and final article presents Omni-directional Vision for Navigation of a Mobile Robot. We extend our warmest thanks and deepest gratitude to the distinguished authors who have contributed to this special issue for their outstanding contributions and to the reviewers for their assistance and cooperation. We are also grateful to Prof. Toshio Fukuda and Prof. Kaoru Hirota, Editors-in-Chief of JACIII, and to the HNICEM International Advisory Board for their continued support to all HNICEM International Conferences. Come March 2011, the 5th HNICEM International Conference will be held in Manila, Philippines. We express our deepest gratitude to the IEEE Philippines Section for its continuing sponsorship. To JACIII, thank you for the support. Outstanding papers presented in this conference will be selected for publication in a special issue of JACIII. We invite you to submit your research papers and to participate in HNICEM 2011. For further information, please visit http://www.dlsu.edu.ph/conferences/hnicem/.
Experimental Identification of Manipulator Dynamics Through the Minimization of its Natural Oscillations
Authors: Jamisola Rodrigo S. ,   De La Salle University, 2401 Taft Ave, 1004 Manila, Philippines, Jr.  , Dadios Elmer P.
Published: April 2016

This work presents amethod of identifying the dynamics parameters of rigid-body manipulators through the minimization of its natural oscillations. It is assumed that each link has an actuated joint that is different from its center of mass, such that its driving torque is influenced by gravitational force. In this earlier results of our study, it is assumed that the inertias can be expressed in terms of the mass and center of mass. This work utilizes the actual force of gravity for the manipulator link to achieve natural oscillation. The oscillatory motion allows the system to be converted into an optimization problem through the minimization of the frequency of oscillation. The correct dynamics parameters are found when the minimum frequency of oscillation is achieved. The proposed method is analyzed and a theorem is presented that supports the claims presented in this work together with implementation results.
Integrated Eco-Design Tool for Malaysian Automobile Industry
Authors: Gonzales Julirose ,   Center for Product Design and Manufacturing, Department of Engineering Design and Manufacture Faculty of Engineering, University Malaya 50603 Kuala Lumpur, Malaysia, Sakundarini Novita , Ariffin Raja , Taha Zahari bin
Published: April 2016

Today’s manufacturing companies are burdened with the pressures of managing their products throughout its life cycles and ensure that their products have minimum impact in the environment through Eco-design methods. This paper looks into the different Product Eco-evaluation methods in terms of Material Recyclability, Manufacturing Process and Disassemblability used for Eco-design, especially in the automobile industry. From these methods, the authors propose an Eco-Design framework suited for Malaysian local automobile SMEs given their limited role in Automobile design.
Neural Network Ensemble-Based Solar Power Generation Short-Term Forecasting
Authors: Chaouachi Aymen ,   Department of Electronic and Information Engineering, Tokyo University of Agriculture and Technology, Nagasaka Laboratory, 2-24-16, Nakamachi, Koganei, Tokyo 184-8588, Japan, Kamel Rashad M. , Nagasaka Ken
Published: April 2016

This paper presents the applicability of artificial neural networks for 24 hour ahead solar power generation forecasting of a 20 kW photovoltaic system, the developed forecasting is suitable for a reliable Microgrid energy management. In total four neural networks were proposed, namely: multi-layred perceptron, radial basis function, recurrent and a neural network ensemble consisting in ensemble of bagged networks. Forecasting reliability of the proposed neural networks was carried out in terms forecasting error performance basing on statistical and graphical methods. The experimental results showed that all the proposed networks achieved an acceptable forecasting accuracy. In term of comparison the neural network ensemble gives the highest precision forecasting comparing to the conventional networks. In fact, each network of the ensemble over-fits to some extent and leads to a diversity which enhances the noise tolerance and the forecasting generalization performance comparing to the conventional networks.
Inference Based on α-Cut and Generalized Mean with Fuzzy Tautological Rules
Authors: Uehara Kiyohiko ,   Ibaraki University, Hitachi 316-8511, Japan, Koyama Takumi , Hirota Kaoru ,   Tokyo Institute of Technology, Yokohama 226-8502, Japan
Published: April 2016

Theoretical aspects are provided for inference based on α-cuts and generalized mean (α-GEMII). In order to clarify the basic properties of the inference, fuzzy tautological rules (FTRs) are focused on, which are composed by setting fuzzy sets in consequent parts identical to those in antecedent parts of initially given fuzzy rules. It is mathematically proved that the consequences deduced with FTRs are closer to given facts as the number of FTRs increases. The aspects provided in this paper are appropriate from axiomatic viewpoints and can contribute to interpretability in fuzzy systems constructed with α-GEMII. They are not obtained in conventional methods based on the compositional rule of inference. Simulations are performed by evaluating difference (mean square errors) between given facts and deduced consequences under the condition that convex and symmetric fuzzy sets are given as facts. Their results show that the difference becomes smaller as the number of FTRs increases. Thereby, it is confirmed that α-GEMII has an advantage in the interpretability with respect to FTRs over the conventional methods.
Fuzzy Visual Hull Algorithm for Three-Dimensional Shape Reconstruction of TKA Implants from X-Ray Cone-Beam Images
Authors: Kobashi Syoji ,   Graduate School of Engineering, University of Hyogo, 2167 Shosha, Himeji, Hyogo 671-2280, Japan, Shibanuma Nao , Hata Yutaka ,   WPI Immunology Frontier Research Center, Osaka University,   Kobe Kaisei Hospital, Japan,   Graduate School of Medicine, Kobe University, Japan
Published: April 2016

Three-Dimensional (3-D) shape reconstruction of total knee arthroplasty (TKA) implantsin vivoplays a key role to investigate implanted knee kinematics. TKA implants typically consist of metal femoral and tibial components and a polyethylene tibial insert. X-ray computed tomography (CT) causes severe metal artifacts, making the 3-D shape in reconstructed images extremely difficult to understand. This article proposes a new method of 3-D reconstruction from X-ray cone-beam images. Called a fuzzy visual hull, it introduces fuzzy logic in recognizing X-ray images. X-ray cone-beam images are fuzzified and back-projected into a fuzzy voxel space. Defuzzifying the fuzzy voxel space enables the 3-D TKA implant shape to be reconstructed. The results of evaluation using TKA implantsin vitroand computer-synthesized images demonstrated that the fuzzy visual hull provides high robustness against noise added to X-ray cone-beam images. The new approach also reconstructed the 3-D polyethylene insert despite the difficulty of recognizing the region in conventional X-ray CT.
Fuzzy-Clustering-Based Discriminant Method of Multiple Quadric Surfaces for Noisy and Sparse Range Data
Authors: Kawano Hideaki ,   Faculty of Engineering, Kyushu Institute of Technology, 1-1 Sensui-cho, Tobata-ku, Kitakyushu 804-8550, Japan, Maeda Hiroshi , Ikoma Norikazu
Published: April 2016

In this paper, a fuzzy-clustering-based discriminant method of multiple quadric surfaces in a scene is proposed. This method is intended for scenes involving multiple objects, where each object is approximated by a primitive model. The proposed method is composed of three steps. In the first step, 3D data is reconstructed using a stereo matching technique from a stereo image whose scene involves multiple objects. Next, the 3D data is divided into a single object by employing Fuzzy c-Means accompanied by Principal Component Analysis (PCA) and a criterion with respect to the number of clusters. Finally, the shape of each object is extracted by Fuzzy c-Varieties with noise clustering. The proposed method was evaluated with respect to some pilot scenes whose ground truth data is known, and it was shown to specify each location and each shape for multiple objects very well.
Color Transfer Based on Normalized Cumulative Hue Histograms
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

Color transfer involves recoloring reflecting a reference image color in an input image, for example, by a color distribution or chroma distribution. The color transfer algorithm we propose transferring hue information by modifying the input image hue histogram so that its shape approaches that of the reference image as closely as possible. Color transfer effectively reflecting images considers image chroma information and relative hue histogram occurrence frequency as demonstrated in results of experiments.
Image Completion Considering Local Orientations of Rotated Patterns
Authors: Orii Hideaki ,   Faculty of Engineering, Kyushu Institute of Technology, 1-1 Sensui-cho, Tobata-ku, Kitakyushu 804-8550, Japan, Kawano Hideaki , Maeda Hiroshi , Ikoma Norikazu
Published: April 2016

Image completion yields whole images by producing plausible parts missing due to the removal of foreground or background elements. Conventionally, missing parts are produced by optimizing the objective function, defined based on pattern similarity between the missing region and the remaining image (data region). The resulting image may be compromised, however, by data region pattern variations. Augmenting data region pattern variations positively produced good results, but tends to cause processing search time to mushroom proportionately. To avoid this, we propose pattern extension based on rotating data region pattern variations and minimizing calculation time using the local orientation of rotated patterns. The effectiveness of this approach was demonstrated by comparing conventional and proposed methods.
Various Defuzzification Methods on DNA Similarity Matching Using Fuzzy Inference System
Authors: Widyanto M. Rahmat ,   Faculty of Computer Science, University of Indonesia, Depok Campus, Depok 16424, West Java, Indonesia, Soedarsono Nurtami , Katayama Norihiro , Nakao Mitsuyuki ,   Faculty of Dentistry, University of Indonesia Salemba Campus, Jakarta, Indonesia,   Biomodeling Laboratory, Department of Applied Information Sciences, Graduate School of Information Sciences, Tohoku University, 6-3-09 Aoba, Aramaki-aza, Aoba-ku, Sendai 980-8579, Japan
Published: April 2016

A DNA similarity matching using fuzzy inference system is proposed to measure a similarity between human STR (Short Tandem Repeat) based DNA (Deoxyribonucleic Acid) profiles. Moreover, various defuzzification methods are also tested to observe their behavior on different DNA data characteristics. Experiment on real human STR based DNA profile data shows that the proposed DNA similarity matching produces more realistic similarity values compared to those of the conventional one. Experiment on various defuzzification methods on DNA similarity matching shows that Sugeno defuzzification method is more suitable than those of other defuzzification methods.
Suppression Effect of α-Cut Based Inference on Consequence Deviations
Authors: Uehara Kiyohiko ,   Ibaraki University, 4-12-1 Nakanarusawa-cho, Hitachi 316-8511, Japan, Koyama Takumi , Hirota Kaoru ,   Tokyo Institute of Technology, G3-49 4259 Nagatsuta, Midori-ku, Yokohama 226-8502, Japan
Published: April 2016

This paper clarifies that inference based on α-cut and generalized mean (α-GEMII) is effective in suppressing consequence deviations. The suppression effect of α-GEMII is numerically evaluated in comparison to conventional inference based on the Compositional Rule of Inference (CRI). CRI-based parallel inference causes discontinuous deviations in the least upper and greatest lower bounds of deduced fuzzy sets even when it models the continuous input-output relation of a system and given facts change continuously. In contrast, α-GEMII can suppress the deviations because of its schemes originally developed for constraint propagation control. In simulations, indices are defined for numerically evaluating the degree to which deduced consequences follow the change in fuzzy outputs of given systems. Simulation results show that α-GEMII is effective in suppressing the deviations, compared to CRI-based parallel inference. In effective use of the schemes for suppressing the consequence deviations, α-GEMII can be applied to nonlinear prediction filters for complex time series, especially with fluctuations that do not always originate from a correlation between time series data.