bluuebunny/crossref_metadata_2025 huggingface dataset:
Reinforcement Learning Scheme for Flocking Behavior Emergence
Authors: Morihiro Koichiro ,   Hyogo University of Teacher Education, 942-1 Shimokume, Kato-shi, Hyogo 673-1494, Japan, Isokawa Teijiro , Nishimura Haruhiko , Tomimasu Masahito , Kamiura Naotake , Matsui Nobuyuki ,   Division of Computer Engineering, Graduate School of Engineering, University of Hyogo, 2167 Shosha, Himeji, Hyogo 671-2201, Japan,   Graduate School of Applied Informatics, University of Hyogo, 1-3-3 Chuo-ku, Kobe, Hyogo 650-0044, Japan
Published: April 2016

Collective behavior such as bird flocking, land animal herding, and fish schooling is well known in nature. Many observations have shown that there are no leaders to control the behavior of a group. Several models have been proposed for describing the grouping behavior, which we regard as a distinctive example of aggregate motions. In these models, a fixed rule is provided for each of the individuals a priori for their interactions in a reductive and rigid manner. In contrast, we propose a new framework for the self-organized grouping of agents by reinforcement learning. It is important to introduce a learning scheme for causing collective behavior in artificial autonomous distributed systems. The behavior of agents is demonstrated and evaluated through computer simulations and it is shown that their grouping behavior emerges as a result of learning.
FCM-Type Fuzzy Clustering of Mixed Databases Considering Nominal Variable Quantification
Authors: Honda Katsuhiro ,   Graduate School of Engineering, Osaka Prefecture University, 1-1 Gakuen-cho, Nakaku, Sakai, Osaka 599-8531, Japan, Uesugi Ryo , Ichihashi Hidetomo
Published: April 2016

This paper proposes a clustering algorithm that performs FCM-type clustering of datasets including categorical data. The proposed algorithm iterates categorical data quantification in FCE clustering so that quantified scores suit the current fuzzy partition. The objective function is the linear combination of two cost functions, i.e., the objective function of FCE clustering and the clustering criterion of quantified category scores. Because quantified category scores are assigned considering the relationship among categories, they are useful for interpreting the cluster structure.
Hierarchical Parallel Placement Using a Genetic Algorithm for Realizing Low Power Consumption
Authors: Yoshikawa Masaya ,   Department of VLSI System Design, Ritsumeikan University, 1-1-1 Nojihigashi, Kusatsu, Shiga 525-8577, Japan, Terai Hidekazu
Published: April 2016

With portable information devices now widely disseminated, low power consumption LSIs are increasingly needed in battery, implementation, and heat generation applications. We propose parallel placement to realize low power consumption and confirm its effectiveness in experiments with a commercial electronic design automation (EDA) tool for designing LSIs. Our proposal hierarchically combines outline and detail placement based on genetic algorithm. In selection operators, new evaluation functions are introduced for realizing the reduction of power consumption focusing on the signal transition probability. Considering a parallel processing, in which a processing speed has a scalable relation with the number of processors, and by implementing it in a parallel computer, its effect is demonstrated.
Infinite Computation in the Equivalent Transformation Model
Authors: Mabuchi Hiroshi ,   Faculty of Software and Information Science, Iwate Prefectural University, Iwate, Japan, Akama Kiyoshi , Koike Hidekatsu , Miura Katsunori ,   Information Initiative Center, Hokkaido University, Sapporo, Japan,   Faculty of Social Information, Sapporo Gakuin University, Ebetsu, Hokkaido, Japan
Published: April 2016

There are many logic programs that do not terminate but perform useful computation in some sense. The usual theory of logic programming adopts least fixpoints to define the meaning of programs, which fails to capture the intended meaning of infinite computation. To give an appropriate sense to useful infinite computation, the theory of logic programming has adopted greatest fixpoints in place of least fixpoints. However, this solution developed in logic paradigm can not explain finite and infinite computation in a unified manner. This paper proposes a new approach to infinite computation based on the equivalent transformation paradigm, where infinite computation is regarded as repeated equivalent transformations and is given appropriate sense in the same way as the usual finite computation.
Biological Rhythm Based Wearable Sleep State Observer
Authors: Wakuda Yuki ,   Dept. of Micro-Nano System Engineering, Nagoya University, Furo-cho, Chikusa-ku, Nagoya, Aichi 464-8673, Japan, Noda Akiko , Hasegawa Yasuhisa , Arai Fumihito , Fukuda Toshio , Kawaguchi Mitsuo ,   School of Health Sciences, Nagoya University, 1-1-20 Daiko Minami, Higashi-ku, Nagoya 461-8603, Japan,   Dept. of Intelligent Interaction Technologies, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki 305-8573, Japan,   Dept. of Bioengineering and Robotics, Tohoku University, 6-6-01 Aoba, Aramaki, Aoba-ku, Sendai 980-8579, Japan,   Daito ME Co., LTD., Tashiro Hon-dori 2-1, Chikusa-ku, Nagoya 464-8652, Japan
Published: April 2016

This research aimed to observe human biological rhythm and adjust the human sleep wake pattern based on controlling wakeup timing using low stress system. An ordinary alarm clock operates according to preset time. Biological rhythm determines the human sleep cycle, which affects sleep depth and wakeup timing, which in turn resets the daily rhythm that affects human’s behavior, life-cycle pattern, life-style related disease. We developed a wearable biological rhythm based awakening controller (BRAC) that determines the biological rhythm in the sleep state (sleep cycle) and stimulates the user at a suitable time to enable the person to wakeup refreshed. The proposed system BRAC gauges human’s sleep quality and rhythms from peak to peak interval time of fingertip-pulse waves, that are measured more easily than polysomnography (PSG). In this paper, we detail the method of sleep cycle estimation using a wearable sensor device as the first feature of BRAC, then, in experiments, evaluate the performance of sleep cycle estimation based on a comparison of the BRAC-sleep cycle and the PSG-determined sleep stage.
Emulating Qubits with Fuzzy Logic
Authors: Hannachi M. Skander ,   Department of Computational Intelligence and Systems Science,  Interdisciplinary Graduate School of Science and Engineering, Tokyo Institute of Technology, G3-49, 4259 Nagatsuta, Midori-ku, Yokohama 226-8502, Japan, Hatakeyama Yutaka , Hirota Kaoru
Published: April 2016

An approach for emulating quantum circuits using conventional analog hardware is presented based on the intuitive similarity between fuzzy logic and quantum superposition, as well as some geometrical analogies. This approach has the advantage of being easy to implement on dedicated hardware for parallel processing of membership functions and fuzzy inference, compared to conventional quantum computing, which requires quantum mechanical systems which are extremely sensitive to noise and difficult to extend to large scale systems. Using geometrical analogies and a suitable transformation, qubits are modeled as pairs of fuzzy membership functions evolving on the unit square and basic one qubit gates are modeled as transformations on this unit square. A fuzzy implementation of the one bit and two bit Deutsch-Jozsa algorithm is proposed. Physical implementation and advantages, such as the possibility of implementing nonlinear or non-unitary gates, as well as drawbacks of the proposed model compared to conventional quantum computing are shown.
Embodied Conversational Agents for H5N1 Pandemic Crisis
Authors: Goh Ong Sing ,   School of Information Technology, Division of Arts, Murdoch University, Murdoch, Western Australia 6150, Fung Chun Che , Wong Kok Wai , Depickere Arnold
Published: April 2016

This paper presents a novel framework for modeling embodied conversational agent for crisis communication focusing on the H5N1 pandemic crisis. Our system aims to cope with the most challenging issue on the maintenance of an engaging while convincing conversation. What primarily distinguishes our system from other conversational agent systems is that the human-computer conversation takes place within the context of H5N1 pandemic crisis. A Crisis Communication Network, called CCNet, is established based on a novel algorithm incorporating natural language query and embodied conversation agent simultaneously. Another significant contribution of our work is the development of a Automated Knowledge Extraction Agent (AKEA) to capitalize on the tremendous amount of data that is now available online to support our experiments. What makes our system differs from typical conversational agents is the attempt to move away from strictly task-oriented dialogue.
A Feature Vector Approach for Inter-Query Learning for Content-Based Image Retrieval
Authors: Chung Kien-Ping ,   School of Information Technology, Murdoch University, South Street, Murdoch, Perth, Australia, 6150, Fung Chun Che
Published: April 2016

Use of relevance feedback (RF) in the feature vector model has been one of the most widely used approaches to fine tuning queries for content-based image retrieval (CBIR). We propose a framework that extends RF to capturing the inter-query relationship between current and previous queries. Using the feature vector model, this avoids the need to “memorize” actual retrieval relationships between actual image indexes and the previous queries. This approach is suited to image database applications in which images are frequently added and removed. In the previous work [1], we developed a feature vector framework for inter-query learning using statistical discriminant analysis. One weakness of the previous framework is that the criteria for exploring and merging with an existing visual group are based on two constant thresholds, which are selected through trial and error. Another weakness is that it is not suited to mutually interrelated data clusters. Instead of using constant values, we have further extended the framework using positive feedback sample size as a factor for determining thresholds. Experiments demonstrated that our proposed framework outperforms the previous framework.
Bayesian Spatial Autoregressive for Reducing Blurring Effect in Image
Authors: Halim Siana ,   Industrial Engineering Department, Petra Christian University, Indonesia
Published: April 2016

We apply the Bayesian Spatial Autoregressive, which is developed by Geweke and LeSage, for reducing the blurring effect in the image. This blurring effect, particularly comes from the synthesizing semi regular texture via, e.g., two dimensional block bootstrap. We model the error, i.e., the difference between the true image and the synthesis one, as the Bayesian Spatial Autoregressive (SAR). Moreover, the weight matrix is defined in a specific manner, such that the problem in the computational for a very large matrix can be avoided. Finally, we use the error estimate, as the result of Bayesian SAR modelling, for reducing the blurring effect in the synthesis image.
Parallel Learning Model and Topological Measurement for Self-Organizing Maps
Authors: Maeda Michiharu ,   Department of Control and Information Systems Engineering, Kurume National College of Technology, 1-1-1 Komorino, Kurume 830-8555, Japan, Miyajima Hiromi , Shigei Noritaka ,   Department of Electrical and Electronic Engineering, Faculty of Engineering, Kagoshima University, 1-21-40 Korimoto, Kagoshima 890-0065, Japan
Published: April 2016

The parallel learning model we propose for self-organizing maps (SOMs) uses reference vectors for simultaneously given multiple input and introduces a topological measurement of ordering for reference vectors in a multidimensional array. We term the parallel SOM algorithm parallel learning and the criterion of ordering the twist index. Parallel learning simultaneously updates reference vectors corresponding to individual input when multiple input is prepared. The twist index is the criterion for evaluating multidimensional ordering of the topological array for reference vectors. When the parallel degree changes for a SOM, the topology preserving map (TPM) for post-learning is evaluated using the twist index. Although adaptation generally yields different results for single and multiple input given at each step in neural networks, parallel learning in SOMs produces results almost the same as sequential learning. Discussing the formation rate of TPMs and average distortion, we examine the effectiveness of our approach through numerical experiments.
Classification of Liver Disease from CT Images Using a Support Vector Machine
Authors: Lee Chien-Cheng ,   Department of Communications Engineering, Yuan Ze University, Chungli, Taoyuan 320, Taiwan, Chen Sz-Han , Chiang Yu-Chun ,   Department of Mechanical Engineering, Yuan Ze University, Chungli, Taoyuan 320, Taiwan
Published: April 2016

We propose a classifier based on the support vector machine (SVM) for automatic classification in liver disease. The SVM, stemming from statistical learning theory, involves state-of-the-art machine learning. The classifier is a part of computer-aided diagnosis (CADx), which assists radiologists in accurately diagnosing liver disease. We formulate discriminating between cysts, hepatoma, cavernous hemangioma, and normal tissue as a supervised learning problem, and apply SVM to classifying the diseases using gray level and co-occurrence matrix features and region-based shape descriptors, calculated from regions of interest (ROIs), as input. Significant features of ROI enable us to simplify SVM input space and to feed the SVM representative information. By simplifying and clarifying the diagnosis process, we separate the classification of liver disease into hierarchical multiclass classification. We use the receiver operating characteristic (ROC) curve to evaluate diagnosis performance, demonstrating the classifier’s good performance.
Asymptotic Behavior of Linear Approximations of Pseudo-Boolean Functions
Authors: Ding Guoli ,   Department of Mathematics, Louisiana State University, Baton Rouge, LA 70803, USA, Lax Robert F. , Chen Peter , Chen Jianhua ,   Department of Computer Science, Louisiana State University, Baton Rouge, LA 70803, USA
Published: April 2016

We study the problem of approximating pseudo-Boolean functions by linear pseudo-Boolean functions. Pseudo-Boolean functions generalize ordinary Boolean functions by allowing the function values to be real numbers instead of just the 0-1 values. Pseudo-Boolean functions have been used by AI and theorem proving researchers for efficient constraint satisfaction solving. They can also be applied for modeling uncertainty. We investigate the possibility of efficiently computing a linear approximation of a pseudo-Boolean function of arbitrary degree. We show some example cases in which a simple (efficiently computable) linear approximating function works just as well as the best linear approximating function, which may require an exponential amount of computation to obtain. We conjecture that for any pseudo-Boolean function of fixed degree k >1 where k is independent of the number of Boolean variables, the best linear approximating function works better than simply using the linear part of the target function. We also study the behavior of the expected best linear approximating function when the target pseudo-Boolean function to be approximated is random.
Key Characteristics of Hymns
Authors: Kamitani Tatsuo ,   School of Management and Business Administration, Kyoto Sosei University, Fukuchiyama, Kyoto 620-0886, Japan, Matsuda Minoru ,   Graduate School of Engineering, Osaka Electro-Communication University, Neyagawa, Osaka 572-8530, Japan
Published: April 2016

The authors propose a way of patterning the bars of monophonic melodies in hymns and finding keys from such patterns. The authors represent monophonic melody bars on a vector showing the duration of pitch chroma contained in each bar. In experiments, the authors patterned bars of 352 four/four beat hymns, defined the distance between the bar key and melody, and found they key from this, correctly 83.1% of the time. The authors also propose a way to determine the maximum likelihood of a contaminated normal distribution of distance between patterns using an EM algorithm. Key characteristics of hymns were then studied using this method.
Optimization Method RasID-GA for Numerical Constrained Optimization Problems
Authors: Sohn Dongkyu ,   Graduate School of Information, Production and Systems, Waseda University, 2-7 Hibikino, Wakamatsu-ku, Kitakyushu-shi, Fukuoka 808-0135, Japan, Mabu Shingo , Hirasawa Kotaro , Hu Jinglu
Published: April 2016

This paper proposes Adaptive Random search with Intensification and Diversification combined with Genetic Algorithm (RasID-GA) for constrained optimization. In the previous work, we proposed RasID-GA which combines the best properties of RasID and Genetic Algorithm for unconstrained optimization problems. In general, it is very difficult to find an optimal solution for constrained optimization problems because their feasible solution space is very limited and they should consider the objective functions and constraint conditions. The conventional constrained optimization methods usually use penalty functions to solve given problems. But, it is generally recognized that the penalty function is hard to handle in terms of the balance between penalty functions and objective functions. In this paper, we propose a constrained optimization method using RasID-GA, which solves given problems without using penalty functions. The proposed method is tested and compared with Evolution Strategy with Stochastic Ranking using well-known 11 benchmark problems with constraints. From the Simulation results, RasID-GA can find an optimal solution or approximate solutions without using penalty functions.
Discrimination of Sidewalk Surface Condition Based on Image Textures and Meteorological Information
Authors: Santoso Handri ,   Graduate School of Information Science and Control Engineering, Nagaoka University of Technology, Nagaoka, Niigata 940-2188, Japan, Nakamura Kazuo ,   Department of Management and Information Systems Science, Nagaoka University of Technology, Nagaoka, Niigata 940-2188, Japan
Published: April 2016

Slippery roads, especially during and after a heavy snow fall, may lead to accidents causing injuries and fatalities to vulnerable person such as the aged. In this context, it is important to keep pedestrian aware of sidewalk condition. This paper aims at proposing detection of several sidewalk conditions under different environment circumstance. At the front end, image and video processing is perrformed to separate background and foreground images. Background image features are extracted using several texture feature generators. In this study, factor analysis methods are employed to examine the pattern of correlations among variables, and to reduce data dimensionality. Finally, Artificial Neural Network is employed to discriminate sidewalk surface condition, i.e., dry, wet, or snow.
A Generalized Inference Method for Fuzzy Quantified and Truth-Qualified Natural Language Propositions
Authors: Okamoto Wataru ,   Yokohama, Kanagawa 240-0042, Japan, Tano Shun’ichi , Inoue Atsushi , Fujioka Ryosuke ,   University of Electro-Communications, Chofu, Tokyo 182-8585, Japan,   Eastern Washington University, Cheney, WA 99004-2412, USA,   Kobe Sogo Sokki Co., Ltd, Kobe, Hyogo 650-0012, Japan
Published: April 2016

We propose a generalized inference method for constructing natural language communication. The method is used to obtain fuzzy quantifierQ’  when “QAareFis τ →Q’(m’A) aremFism’’τ” is inferred (Q, Q’: fuzzy quantifiers,A: fuzzy subject,m, m’, m’’: modifiers,F: fuzzy predicate, τ: truth qualifier). We show thatQ’  is resolved step by step for a non-increasing type (few, ...) and a non-decreasing type (most, ...).
Fuzzy Concept Lattices Constrained by Hedges
Authors: Belohlavek Radim ,   Dept. Systems Science and Industrial Engineering, T. J. Watson School of Engineering and Applied Science Binghamton University - SUNY, Binghamton, NY 13902-6000, USA, Vychodil Vilem ,   Dept. Computer Science, Palacky University, Olomouc, Tomkova 40, CZ-779 00 Olomouc, Czech Republic
Published: April 2016

We study concept lattices constrained by hedges. The principal aim is to control, in a parameterical way, the size of concept lattices, i.e. the number of conceptual clusters extracted from data. The paper presents theoretical insight, comments, and examples. We introduce new, parameterized, concept-forming operators and study their properties. We obtain an axiomatic characterization of the concept-forming operators. Then, we show that a concept lattice with hedges is indeed a complete lattice which is isomorphic to an ordinary concept lattice. We describe the isomorphism and its inverse. These mappings serve as translation procedures. As a consequence, we obtain a theorem characterizing the structure of concept lattices with hedges which generalizes the well-known main theorem of ordinary concept lattices. Furthermore, the isomorphism and its inverse enable us to compute a concept lattice with hedges using algorithms for ordinary concept lattices. Further insight is provided for boundary choices of hedges. We demonstrate by experiments that the size reduction using hedges as parameters is smooth.
Unsupervised and Semi-Supervised Graph-Spectral Algorithms for Robust Extraction of Arbitrarily Shaped Fuzzy Clusters
Authors: Du Weiwei ,   Department of Visual Communication Design, Kyushu University, 4-9-1 Shiobaru, Fukuoka 815-8540, Japan, Urahama Kiichi
Published: April 2016

We present unsupervised and semi-supervised algorithms for extracting fuzzy clusters in weighted undirected regular, undirected bipartite, and directed graphs. We derive the semi-supervised algorithms from the Lagrangian function in unsupervised methods for extracting dominant clusters in a graph. These algorithms are robust against noisy data and extract arbitrarily shaped clusters. We demonstrate applications for similarity searches of data such as image retrieval in face images represented by undirected graphs, quantized color images represented by undirected bipartite graphs, and Web page links represented by directed graphs.
An Optimal Design Method for Artificial Neural Networks by Using the Design of Experiments
Authors: Inohira Eiichi ,   Department of Biological Functions and Engineering, Graduate School of Life Science and Systems Engineering, Kyushu Institute of Technology, 2-4 Hibikino, Kitakyushu, Fukuoka 808-0196, Japan, Yokoi Hirokazu
Published: April 2016

This paper presents a method to optimally design artificial neural networks with many design parameters using the Design of Experiment (DOE), whose features are efficient experiments using an orthogonal array and quantitative analysis by analysis of variance. Neural networks can approximate arbitrary nonlinear functions. The accuracy of a trained neural network at a certain number of learning cycles depends on both weights and biases and its structure and learning rate. Design methods such as trial-and-error, brute-force approaches, network construction, and pruning, cannot deal with many design parameters such as the number of elements in a layer and a learning rate. Our design method realizes efficient optimization using DOE, and obtains confidence of optimal design through statistical analysis even though trained neural networks very due to randomness in initial weights. We apply our design method three-layer and five-layer feedforward neural networks in a preliminary study and show that approximation accuracy of multilayer neural networks is increased by picking up many more parameters.
Improved Estimation of Embedding Parameters of Nonlinear Time Series by Structural Learning of Neural Network with Fuzzy Regularizer
Authors: Manabe Yusuke ,   Graduate School of Software and Information Science, Iwate Prefectural University, 152-52 Sugo, Takizawa-mura, Iwate 020-0193, Japan, Chakraborty Basabi
Published: April 2016

This work proposes an improved refinement scheme of estimation of optimal embedding parameters of a nonlinear time series by a feed-forward neural network trained by structural learning with a fuzzy regularizer (FR). The newly proposed fuzzy rules for tuning regularization parameter enables automatic selection of optimal model with lesser computational load than the basic refinement scheme with RNS proposed by authors earlier. From the simulation results, it has been found that the proposed scheme is very efficient in estimation of optimal embedding parameters in lesser computational time. The short term prediction results also show that the estimated embedding parameters produce better and stable one step prediction.
Context Dependent Automatic Textile Image Annotation Using Networked Knowledge
Authors: Furukawa Yosuke ,   Human-Interface Laboratory, Computer Science Course, Meiji University, 1-1-1 Higashi-mita, Tama-ku, Kawasaki-shi, Kanagawa 214-8571, Japan, Kamoi Yusuke , Sato Tatsuya , Takagi Tomohiro
Published: April 2016

This paper presents a new method of an automatic image annotation system that estimates keywords from an image. Typical automatic image annotation systems extract features from an image and recognize keywords. However this method has two problems. One is that it treats features statically. Features should change depending on what keywords are attached so keywords should not be treated equally. Another is that it does not consider the level of keywords. Visual keywords, such as color or texture, can be recognized easily from image features, while high-level semantics such as context are hard to recognize from the features. To solve these problems, our approach is to recognize context by using networked specialist knowledge and to recognize keywords by changing feature values dynamically depending on the context. To evaluate our system, we conducted two experiments of applying it to textile images. As a result, we obtained improved accuracy and confirmed the effectiveness of using networked knowledge.
Fuzzy Sensor Fusion for Humanitarian Demining
Authors: Zyada Zakarya ,   Mechanical Eng. Dept., Tanta Univ., Seberpay, Tanta, Egypt, Kawai Yasuhiro , Matsuno Takayuki , Fukuda Toshio ,   Micro-Nano System Eng. Dept., Nagoya Univ., 1 Furo-cho, Chikusa-ku, Nagoya 464-8603, Japan,   Dept. of Intelligent Systems Design Eng., Toyama Prefectural University, 5180 Kurokawa, Imizu-City, Toyama, Japan
Published: April 2016

In this paper, an automatic sensor-fusion based detection algorithm of an anti-personnel land mine is presented. A “feature in-decision out” fuzzy sensor fusion algorithm for a ground penetrating radar (GPR), and a metal detector (MD), for anti-personnel landmine detection is introduced. The inputs to the fuzzy fusion system are features extracted from both GPR and MD measurements. The output from the fuzzy fusion system is a decision if there is a land mine and at what depth it would be. Fuzzy fusion rules are extracted from training data through a fuzzy learning algorithm. Experimental test results are presented to demonstrate the validity of the proposed fuzzy fusion algorithm and hence its influence in minimizing the false alarm rate for humanitarian demining.
A Pseudo Data Generation Method and a Two-Stage Quantitation Method for Simultaneous Determination Sensor of Nucleotide Derivatives
Authors: Fukuda Akito ,   Department of Information and Computer Science, Keio University, 3-14-1 Hiyoshi, Kohoku-ku, Yokohama 223-8522, Japan, Kondo Sayaka , Maruyama Kenichi , Suzuki Koji , Hagiwara Masafumi ,   Department of Applied Chemistry, Keio University, 3-14-1 Hiyoshi, Kohoku-ku, Yokohama 223-8522, Japan
Published: April 2016

In this paper, we propose a pseudo data generation method and a two-stage quantitation method for simultaneous determination of nucleotide derivatives sensor that determines concentration of nucleotide derivatives based on sensor response. Conventional sensors are difficult to determine concentration of nucleotide derivatives simultaneously because they have similar structures and they influence each other, so the precision is low. In order to archive high precision and simultaneous determination sensor, this paper proposes a pseudo data generation method and a two-stage quantitation method. To analyze sensor response, we use GRNN (General Regression Neural Network). With this sensor, concentration of nucleotide derivatives is determined simultaneously, easily and fast. It was confirmed by the experiments that proposed methods are effective for determining concentration of nucleotide derivatives.
Genetic Algorithm for the Optimization of Collaborative Systems
Authors: Gonsalves Tad ,   Laboratory of Information & Systems Engineering, Faculty of Science & Technology, Sophia University, 7-1 Kioicho, Chiyoda-ku, Tokyo 102-8554, Japan, Baba Shinichiro , Itoh Kiyoshi ,   Department of Computational Intelligence and Systems Science, Interdisciplinary Graduate School of Science and Engineering, Tokyo Institute of Technology, 1776-15-402 Maginu, Miyamae, Kawasaki, Kanagawa 216-0035, Japan
Published: April 2016

The “survival of the fittest” strategy of the Genetic Algorithm has been found to be robust and is widely used in solving combinatorial optimization problems like job scheduling, circuit design, antenna array design, etc. In this paper, we discuss the application of the Genetic Algorithm to the operational optimization of collaborative systems, illustrating our strategy with a practical example of a clinic system. Collaborative systems (also known as co-operative systems) are modeled as server-client systems in which a group of collaborators come together to provide service to end-users. The cost function to be optimized is the sum of the service cost and the waiting cost. Service cost is due to hiring professionals and/or renting equipment that provide service to customers in the collaborative system. Waiting cost is incurred when customers who are made to wait in long queues balk, renege or do not come to the system for service a second time. The number of servers operating at each of the collaborative places, and the average service time of each of the servers are the decision variables, while server utilization is a constraint. The Genetic Algorithm tailored to collaborative systems finds the minimum value of the cost function under these operational constraints.
Automatic Generation of VHDL for Control Logic of Air Conditioning Using Evolutionary Computation
Authors: Kojima Kazuyuki ,   Graduate School of Science and Engineering, Saitama University, 255 Shimo-okubo, Sakura-ku, Saitama-shi, Saitama 338-8570, Japan, Watanuki Keiichi
Published: April 2016

With dramatic advances in electronics, electronic control has been increasingly applied to control based on sensors and actuators and improved energy efficiency and performance. With increasing system complexity, however, time required to develop the system controller has increased. To automate electronic controller design, we apply evolutionary hardware, starting with the targeted air-conditioning system and task definition, followed by the framework of applying a genetic algorithm to controller design automation, focusing on chromosome coding. We then present the fitness function we use to develop the air-conditioner controller automatically. Evolutionary simulation verified the feasibility of our framework.
In What Sense is “the Prisoner’s Dilemma Game” a Dilemma for a Human or a Programmed Agent?
Authors: Wada Shihomi ,   Future University-Hakodate, 116-2 Kamedanakano town, Hakodate, Hokkaido 041-8655, Japan, Suzuki Keiji
Published: April 2016

In this paper, we compare the experimental results of human agents with that of programmed agents using the double-bind prisoner’s dilemma game, in which an ordinary prisoner’s dilemma game is nested into another dilemma; that is, a player has to decide firstly if s/he will play prisoner’s dilemma or not. Comparing human-agent experimental results with programmed agent experimental results, we found remarkable similarities and differences as follows: (1) when cooperation is chosen in the second stage, both human and programmed agents show similar distributions of the first choices, (2) in the case when agents choose defection in the second stage, human agents have a tendency to play an ordinary prisoner’s dilemma game, (3) in the case when agents choose defection in the second stage, programmed agents show almost equal distribution. These results suggest that programmed agents may be cooperators and get close to the Pareto optimal equilibrium in an appropriate setting.
Controlling Bus Suspension Using an Optimal Fuzzy Controller
Authors: Armenta Tadeo ,   Departamento de Procesos y Sistemas, Universidad Simón Bolívar, Aptdo. 1081A, Caracas Venezuela, Strefezza Miguel
Published: April 2016

The optimal fuzzy controller we proposed for automating bus suspension uses a simple algorithm. After observing the fuzzy controller’s response, we determined which parameters influence its operation.
Maintaining Individual Diversity by Fuzzy c -Means Selection
Authors: Sakakura Yoshiaki ,   College of Information Science and Engineering, Ritsumeikan University,1-1-1 Noji-higashi, Kusatsu, Shiga 525-8577, Japan, Taniguchi Noriyuki , Hoshino Yukinobu , Kamei Katsuari ,   Graduate School of Science and Engineering, Ritsumeikan University, 1-1-1 Noji-higashi, Kusatsu, Shiga 525-8577, Japan,   Department of Electronic and Photonic Systems Engineering, Kochi University of Technology, 185 Miyanokuchi, Tosayamada-cho, Kami, Kochi 782-8502, Japan
Published: April 2016

In a GA search, maintaining diversity of individuals is an effective approach for preventing premature convergence and finding multiple optima. Our research aims to maintain the diversity. In this paper, a new selection for maintaining the diversity is proposed, and the selection is applied to simple GA (sGA). In the selection, the individuals are classified by Fuzzy c -means (FCM). Accordingly, several clusters are identified and each of the individuals gets a membership value for each of the clusters. The proposed selection selects individuals based on both the fitness values and the membership values. We discuss the behavior of maintaining individual diversity and search capabilities of the GA with the proposed selection via comparative experiments with a crisp cluster-based selection. Based on the results of the experiments, we were able to determine that the GA with the proposed selection makes the individuals wider distributed in a solution space compared to the crisp clustering based selection. The GA were also able to find more applicable optima compared to sGA and GA with a crisp clustering selection.
Fuzzy Clustering Based on Total Uncertainty Degree
Authors: Esaki Tomohito ,   The University of Electro-Communications, 1-5-1 Chofugaoka, Chofu 182-8585, Japan, Hashiyama Tomonori , Tsukamoto Yahachiro ,   Grad. School of Information Systems, The University of Electro-Communications, 1-5-1 Chofugaoka, Chofu 182-8585, Japan,   Dept. of Information Engineering, Faculty of Science and Technology, Meijo University, 1-501 Shiogamaguchi, Tempaku-ku, Nagoya 468-8502, Japan
Published: April 2016

Traditional Fuzzy c-Means (FCM) methods have probabilistic and additive restrictions that ∑ μ (x) = 1; the sum of membership values on the identified membership function is one. Possibilistic clustering methods identify membership functions without such constraints, but some parameters used in objective functions are difficult to understand and membership function shapes are independent of clusters estimated through possibilistic methods. We propose novel fuzzy clustering using a total uncertainty degree based on evidential theory with which we obtain nonadditive membership functions whose their shapes depend on data distribution, i.e., they mutually differ. Cluster meanings thus become easier to understand than in possibilistic methods and our proposal requires only one parameter “fuzzifier.” Numerical experiments demonstrated the feasibility of our proposal conducted.
Soft-Target-Based Predictive Fuzzy Control for a Cart-Pendulum System with Dynamic Constraints
Authors: Chen Yougen ,   Department of Intelligent Interaction Technologies, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki 305-8573, Japan, Yasunobu Seiji
Published: April 2016

Human decisions to act are based on broad targets and respond flexibly in different situations. Such, self-adaptation to dynamic constraints is difficult but important for autonomous control. Conventional control usually uses a single target that results in inflexibility in responding to dynamic environments such as changes in constraints. We propose a predictive fuzzy intelligent controller based on soft targets defined as a series of target sets that include many target elements with different satisfaction grades and are converted to target setting knowledge by fuzzy logic. This controller was applied to upswing and stabilization control of a cart-pendulum system with dynamic changing limit positions as constraints to realize situational self-adaptation and target self-regulation. Simulation and experiments demonstrated the feasibility of our soft-target-based intelligent controller.
The Design of Central Pattern Generators Based on the Matsuoka Oscillator to Generate Rhythmic Human-Like Movement for Biped Robots
Authors: Liu Guang Lei ,   Department of Advanced Systems Control Engineering, Graduate School of Science and Engineering, Saga University, 1 Honjomachi, Saga 840-8502, Japan, Habib Maki K. , Watanabe Keigo , Izumi Kiyotaka
Published: April 2016

We propose a controller based on a central pattern generator (CPG) network of mutually coupled Matsuoka nonlinear neural oscillators to generate rhythmic human-like movement for biped robots. The parameters of mutually inhibited and coupled Matsuoka oscillators and the necessary interconnection coupling coefficients within the CPG network directly influence the generation of the required rhythmic signals related to targeted motion. Our objective is to analyze the mutually coupled neuron models of Matsuoka oscillators to realize an efficient CPG design that leads to have dynamic, stable, sustained rhythmic movement with robust gaits for bipedal robots. We discuss the design of a CPG model with new interconnection coupling links and its inhibitation coefficients for a CPG-based controller. The new design was studied through interaction between simulated interconnection coupling dynamics with six links and a musculoskeletal model with the 6 degrees of freedom (DOFs) of a biped robot. We used the weighted outputs of mutually inhibited oscillators as torques to actuate joints. We verified the effectiveness of our proposal through simulation and compared the results to those of Taga’s CPG model, confirming better, more efficient generation of stable rhythmic walking at different speeds and robustness in response to disturbances.
Motion Overlap for a Mobile Robot to Express its Mind
Authors: Kobayashi Kazuki ,   Research Center for Human Media, Kwansei Gakuin University, 2-1 Gakuen, Sanda, Hyogo 669-1337, Japan, Yamada Seiji ,   National Institute of Informatics, 2-1-2 Hitotsubashi, Chiyoda, Tokyo 101-8430, Japan
Published: April 2016

This paper discusses how a mobile robot may express itself to get help from users in a cooperative task. We focus on a situation in which a robot expresses its state of mind to get a user to lend it help. The design we propose, calledmotion overlap(MO), enables a robot to express human-like behavior in communicating with others. We reasoned that human-like behavior in a robot could help the user to understand its state of mind. We designed a small sweeping robot based on MO that conductsback and forth movement, and compared its MO expression in experiments with other nonverbal communication, i.e., buzzers and blinking LEDs. We found that the MO expression encouraged most users to help the robot. Differences among results obtained for the three types of expression were statistically significant, and results demonstrate that MO has potential in the design of robots for the home.
Training of Agent Positioning Using Human’s Instruction
Authors: Akiyama Hidehisa ,   National Institute of Advanced Industrial Science and Technology, Katagami Daisuke , Nitta Katsumi ,   Tokyo Institute of Technology
Published: April 2016

In the real-world multiagent/multirobot problems, a position of each agent is an important factor to affect agents’ performance. In the real-world problem such as soccer, the agent(player)’s position should be changed based on the current environmental state. Because the real-world problem is generally dynamic and continuous, assigning the most desirable position for any state is not possible. We formalize this issue as a map from a focal point like a ball position in a soccer field to a desirable position of each agent. We conducted experiments showing that agent positioning are acquired efficiently through intuitive human operation and function approximation models using supervised learning, and conducted performance evaluation experiments to determine the most suitable model. In performance evaluation experiments, we evaluated the generalization capability for each model with datasets which several data are randomly removed from original dataset and dataset which several specific data are intentionally removed from original dataset. Experiment results showed that our proposed function approximation model combining Delaunay triangulation and linear interpolation produced the highest performance.
A Proposal of a Language-Based Context-Sensitive Programming System
Authors: Sugimoto Toru ,   Faculty of Engineering, Shibaura Institute of Technology, 3-7-5 Toyosu, Koto-ku, Tokyo 135-8548, Japan, Ito Noriko , Iwashita Shino ,   Faculty of Culture and Information Science, Doshisha University, 1-3 Tatara Miyakodani, Kyotanabe, Kyoto 610-0394, Japan,   School of Computer Science, Tokyo University of Technology, 1404 Katakura, Hachioji, Tokyo 192-0982, Japan
Published: April 2016

This paper describes a programming system with which even novice users can easily specify complex requests as natural language texts, in other words, write programs in everyday language, to operate computing systems. Based on a study of task specification texts written by non-programmers, the processing model is designed so that implicit and ambiguous information in an input text is detected and transformed into an explicit executable program structure. Context dependency in programming is also considered. Context-dependent factors in the situation of program development and the situation of program execution are considered in text understanding and program execution phases, bridging the gap between these two contexts. We have developed a prototype system that deals with personal email management tasks. In this paper, we explain our processing model, give evaluation results, and discuss our proposal’s effectiveness and projected work.
Forest and Field Fire Search System Using MODIS Data
Authors: Nunohiro Eiji ,   Tokyo University of Information Sciences, 1200-2 Yatoh-cho, Wakaba-ku, Chiba 265-8501, Japan, Katayama Kei , Mackin Kenneth J. , Park Jong Geol ,   Graduate School of Tokyo University of Information Sciences, 1200-2 Yatoh-cho, Wakaba-ku, Chiba 265-8501, Japan
Published: April 2016

Tokyo University of Information Sciences receives MODIS (Moderate Resolution Imaging Spectroradiometer) data from NASA’s Terra and Aqua satellites, and provides the processed data to universities and research institutes as part of the academic frontier project. This paper considers the utilization of MODIS data for a system to search for fire regions in forests and fields. For the search system to be effective, the system must be able to extract the location, range and distribution of fires in forests and fields from a large scale image database quickly with high accuracy. In order to achieve high search response time and to improve the accuracy of the analysis, we propose a forest and field fire search system which implements a) a parallel distributed system configuration using multiple PC clusters, and b) MOD02, MOD03 and MOD09 process levels of MODIS data for input data which provide higher resolution and more accurate readings than the standard MOD14 process level data.
Comparison of Fractional Robust- and Fixed Point Transformations- Based Adaptive Compensation of Dynamic Friction
Authors: Tar József K. ,   Institute of Intelligent Engineering Systems, John von Neumann Faculty of Informatics, Budapest Tech, H-1034 Budapest, Bécsi út 96/B, Hungary, Rudas Imre J. , Pátkai Béla ,   Distributed Information and Automation Lab, Department of Engineering, University of Cambridge, Mill Lane, Cambridge CB2 1RX, UK
Published: April 2016

The features of fractional order robust and fixed-point transformation based adaptive controllers of a “Ball-Beam System” are compared to each other. The speciality of this task is that the position of the ball along the beam is indirectly controlled via directly controlling the other axis, the tilting angle of the beam. It is assumed that this tilting axle suffers from considerable dynamic friction mathematically approximated by the LuGre model. By neglecting the internal physics of the tilting drive this system can be modeled as a 4thorder one because only the 4thtime-derivative of the ball’s position can directly be influenced by the tilting torque. The system also has saturation since the available acceleration of the ball is limited by the gravitation. It is shown that little reduction of the order of the differential equation controlling the decay of the error metrics in a Sliding Mode / Variable Structure controller considerably improves the robust controller. However, really precise solution can be obtained by the adaptive controller. These statements are illustrated and substantiated via simulation.
Feedback Control of an Inverted Pendulum by Use of Artificial Intelligence
Authors: Rekdalsbakken Webjørn ,   Institute of Technology and Nautical Science, Aalesund University College, N-6025 Aalesund, Norway
Published: April 2016

An inverted pendulum represents an unstable system which is excellent for demonstrating the use of feedback control with different kinds of control strategies. In this work feedback control of the inverted pendulum is examined from the point of view of a state space model of the system. First a pole placement algorithm is thoroughly explored. After this artificial intelligence (AI) methods are investigated to better cope with the nonlinearities of the physical model. The technique used is based on a hybrid system combining a Neural Network (NN) with a Genetic Algorithm (GA). The NN controller is trained by the GA against the behaviour of the physical model. The results of the training process show that the chromosome population tends to stabilize at a suboptimal level, and that changes in the environmental parameters have to take place to reach a new optimal level. The results, however, show that the NN controller gradually adapts to the behaviour of the pendulum.