bluuebunny/crossref_metadata_2025 huggingface dataset:
Implementation of Passive Telemetry RF Sensor System Using Unscented Kalman Filter Algorithm
Authors: Lee John-Tark ,   Department of Electrical Engineering, Dong-A University, 840, Hadan 2 dong, Shaku, Busan 604-714, Korea, Kim Kyung-Yeop , Lee Su-Ho ,   Senior Researcher, R&D Center, KJ Radio Corporation, 2F, Dukchang Bldg., 8-1, 2-Ga, DongKwang-Dong, Jung-Gu, Busan 600-022, Korea
Published: April 2016

This article describes a newly designed and implemented capacitive sensor system. An Unscented Kalman Filter (UKF) algorithm based passive telemetry RF sensor system was modeled with parasitic parameters over a range of high frequencies of approximately 200 KHz. And the system was also successfully implemented on the DSP. Under the constraints that it should be “wireless,” “implantable,” and “batteryless,” the system was simply built to consisted of passive components R, L and C by the inductive coupling principle, and it focused on the accurate estimation of a capacitive parameter in the secondary part. The UKF algorithm, which can coped with the drawbacks of the extended Kalman filter in noisy nonlinear systems, was applied to estimate the capacitive parameter with nonlinearity. The input/output learning data for the UKF algorithm were acquired from the specially designed phase difference detector and amplitude detector. The newly suggested parameter estimation technique can be easily applied to the precise measurement system, which should coped with sensitive environmental changes, such as changes in pressure or humidity.
Guaranteed Cost Output Feedback Control of Fuzzy Systems via LMI Approach
Authors: Nishikawa Shusaku ,   Department of Electrical Engineering and Electronics, Aoyama Gakuin University, 5-10-1 Fuchinobe, Chuo-ku, Sagamihara, Kanagawa 252-5258, Japan, Yoneyama Jun
Published: April 2016

This paper is concerned with the stability with guaranteed cost for a fuzzy system with immeasurable premise variables via output feedback. It is well known that Takagi-Sugeno fuzzy model describes a wide class of nonlinear systems especially when its premise variables include immeasurable functions. However, when it comes to output feedback control design of such a fuzzy system, a conventional Parallel Distributed Compensator (PDC) is not feasible because the PDC shares the same immeasurable premise variables as those of a fuzzy system. In this paper, we introduce an output feedback controller with the estimate of the premise variables of an original fuzzy system. We then formulate the stabilization problem with guaranteed cost for a fuzzy system with immeasurable premise variables. Our control design method is based on a set of strict LMI conditions. No tuning parameter is necessary a priori to solve them. The stability with guaranteed cost takes care of not only stabilization but also control performance. Our proposed method attempts to minimize the upper bound of the performance index, which results in the satisfactory trajectories of the system. Finally, numerical examples are given to illustrate our control design method.
An Online Incremental Semi-Supervised Learning Method
Authors: Shen Furao ,   The State Key Laboratory for Novel Software Technology, and Jiangyin Information Technology Research Institute, Nanjing University, Nanjing 210093, P.R. China, Yu Hui , Kamiya Youki , Hasegawa Osamu ,   The Imaging Science and Engineering Lab., Tokyo Institute of Technology, R2-52, 4259 Nagatsuda, Midori-ku, Yokohama 226-8503, Japan
Published: April 2016

Using labeled data and large amounts of unlabeled data, our proposed online incremental semisupervised learning automatically learns the topology of input data distribution without prior knowledge of numbers of nodes or network structure. Using labeled data, it labels generated nodes and divides a learned topology into substructures corresponding to classes. Node weights used as prototype vectors enable classification. New labeled or unlabeled data is added incrementally to the system during learning. Experimental results for artificial and real-world data show that this learning efficiently learns online incremental tasks even in noisy and non-stationary environments.
Basic Study on Assembling of Objective Functions in Many-Objective Optimization Problems
Authors: Otake Shun ,   Nagoya University, Furo-cho, Chikusa-ku, Nagoya 464-8603, Japan, Yoshikawa Tomohiro , Furuhashi Takeshi
Published: April 2016

Genetic Algorithms (GAs) have been widely applied to Multiobjective Optimization Problems (MOPs), called MOGA. A set of Pareto solutions in MOPs having plural fitness functions are searched, then GA is applied in a multipoint search. MOGA performance decreases with the increasing number of objective functions because solution space spreads exponentially. An effective MOGA search is an important issue in many objective optimization problems. One effective approach is assembling objective functions and reducing their number, but appropriate assembly and the number of objective functions to be assembled has not been studied sufficiently. Our purpose here is to determine the effects of assembling objective functions by studying assembly effects when MOGA is applied to a simplified Nurse Scheduling Problem (sNSP) in two types of assembly based on objective function meaning and correlation coefficients.
Recommendation System Using Weighted TF-IDF and Naive Bayes Classifiers on RSS Contents
Authors: Paik Incheon ,   School of Computer Science, University of Aizu, Tsuruga, Ikki-machi, Aizu-Wakamatsu City, Fukushima 965-8580, Japan, Mizugai Hiroshi ,   Development Division, Rakuten Co. Ltd., Shinagawa Seaside Rakuten Tower, 4-12-3 Higashishinagawa, Shinagawa-ku, Tokyo 140-0002, Japan
Published: April 2016

A recent increase in RDF Site Summary (RSS) feeds, used for news updates and blogs, has been caused by the widespread use of blogs. This means that much effort is now needed to search the contents of RSS feeds because of this enormous quantity of material. To solve this problem, recommendation systems enable users to obtain relevant RSS contents easily and quickly. In previous research, an RSS recommendation system was proposed that used the similarity between the Term Frequency (TF) of the RSS contents and the TF derived from the contents of the user’s browsing history for RSS feeds. In this paper, we use Term Frequency-Inverse Document Frequency (TF-IDF) calculations to propose a Weighted TF-IDF method, which focuses on the terms folded by the title tags in RSS contents as characteristic terms. In addition, we propose a new recommendation method, which uses a Naive Bayes classifier in a Machine Learning-based approach. Via experiments, we compare the proposed methods and the existing method in a prototype recommendation system, and we show that the proposed methods outperform the existing method with respect to several evaluation measurements.
High-Speed Maximum Power Point Tracker for Photovoltaic Systems Using Online Learning Neural Networks
Authors: Kohata Yasushi ,   Graduate School of Information Science and Technology, Hokkaido University, Sapporo, Hokkaido 060-0814, Japan, Yamauchi Koichiro , Kurihara Masahito ,   Chubu University, Department of Information Science, 1200 Matsumoto-cho, Kasugai-shi, Aichi 487-8501, Japan
Published: April 2016

Photo Voltaic (PV) devices have a Maximum Power Point (MPP) at which they generate maximum power. Because the MPP depends on solar radiation and PV panel temperature, it is not constant over time. A Maximum Power Point Tracker (MPPT) is widely used to continuously obtain maximum power, but if the solar radiation changes rapidly, the efficiency of most classic MPPT (e.g., the Perturbation and Observation (P&O) method) reduces. MPPT controllers using neural network respond quickly to rapidly changing solar radiation but must usually undergo prelearning using PV-specific data, so we propose MPPT that handles both online learning of PV properties and feed-forward control of the DC-DC converter with a neural network. Both simulation results and actual device performance using our proposed MPPT showed great efficiency even under rapidly changing solar radiation. Our proposal is implemented using a small microcomputer using low computational power.
Linguistic Geometry: The Age of Maturity
Authors: Stilman Boris ,   STILMAN Advanced Strategies, 1623 Blake Street, Suite 200, Denver, CO 80202, USA, Yakhnis Vladimir , Umanskiy Oleg ,   Department of Computer Science, University of Colorado Denver, Campus Box 109, Denver, CO 80217-3364, USA
Published: April 2016

This paper describes the current status of research and applications of Linguistic Geometry (LG), a type of game theory scalable to the level of real life defense systems. LG is compared to conventional gaming approaches with respect to their applicability to real world problems. LG generates winning strategies for all sides in a conflict in real time by constructing them out of a limited set of blocks called zones. Several examples of zones are introduced. The paper describes the process of discovery of new zones essential for various types of military operations, which represent different classes of abstract board games. We also included a brief explanation of scalability of the LG applications. This paper describes a universal tool kit, LG-PACKAGE, which allows a user to build his/her own applications of LG. This tool kit includes GDK (Game Development Kit), GIK (Game Integration Kit), GRT (Game Resource Tool), GST (Game Solving Tool), GNS (Game Network Services) and GMI (Game Mobile Interface). In the end we included a description of the most advanced experiments with the LG applications conducted by DARPA and US Army and assessments of these experiments by military experts.
An Artificial Intelligence Approach to Develop a Time-Series Prediction Model of the Arc Furnace Resistance
Authors: Haruni Abu Mohammad Osman ,   Centre of Renewable Energy and Power Systems (CREPS), University of Tasmania, Tasmania, Australia, Negnevitsky Michael
Published: April 2016

The control scheme of an arc furnace electrode positioning system aims to deliver an optimum stable reaction zone below the electrodes by maintaining a fixpoint resistance. However, because of random movement of melted materials during melting period, the resistance of the arc furnace changes randomly. As a result, the electrodes have to move accordingly to obtain a fix-point resistance. Moreover, it is often found that the arc furnace resistance changes very fast and it is impossible for the electrode to track the random change of resistance. Consequently, the furnace becomes unstable and it is often impossible to achieve required production per unit power. Hence, the control system often relies on prediction tools. However, it is difficult to predict the arc furnace resistance using conventional mathematical models. As a result, in this paper, Adaptive Neuro-Fuzzy Inference System (ANFIS) is used to capture the random and time-varying nature of arc furnace resistance. The performance of the proposed model is evaluated by presenting a case study where the outputs of the proposed model are compared with the data recorded from an actual metallurgical plant.
Implementation of Neural Network Models for Parameter Estimation of a PEM-Electrolyzer
Authors: Becker Steffen ,   University of Tasmania, GPO Box 252-65, Hobart 7001, Tasmania, Australia, Karri Vishy ,   Australian College of Kuwait, P.O.Box 1411, Safat-13015, Kuwait
Published: April 2016

Predictive models were built using neural networks for hydrogen flow rate, electrolyzer system-efficiency and stack-efficiency respectively. A comprehensive experimental database forms the foundation for the predictive models. It is argued that, due to the high costs associated with the hydrogen measuring equipment; these reliable predictive models can be implemented as virtual sensors. These models can also be used online for monitoring and safety of hydrogen equipment. The quantitative accuracy of the predictive models is appraised using statistical techniques. These mathematical models are found to be reliable predictive tools with an excellent accuracy of ±3% compared with experimental values. The predictive nature of these models did not show any significant bias to either over prediction or under prediction. These predictive models, built on a sound mathematical and quantitative basis, can be seen as a step towards establishing hydrogen performance prediction models as generic virtual sensors for wider safety and monitoring applications.
Attentive Deskwork Support System
Authors: Tamura Yusuke ,   The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656 Japan, Sugi Masao , Arai Tamio , Ota Jun
Published: April 2016

We propose an attentive deskwork support system that quickly delivers required objects to people who work at desks. To meet this goal, we propose methods to understand a user’s request for support. To know the existence of the user request, the system uses the characteristics of the user’s hand and eye movements and detects hand-reaching movements. The system understands the content of the user’s request by integrating sensory and contextual information using a probabilistic model. Finally, the system determines a point of delivery by predicting a user’s hand movement and delivers required objects by using self-moving trays. The experiments are conducted to evaluate the usefulness of the system proposed here.
Personal Preference Analysis for Emotional Behavior Response of Autonomous Robot in Interactive Emotion Communication
Authors: Taki Ryohei ,   Dept. of Human and Artificial Intelligent Systems, Graduate School of Engineering, University of Fukui, 3-9-1 Bunkyo, Fukui 910-8507, Japan, Maeda Yoichiro , Takahashi Yasutake
Published: April 2016

Robots must understand human intention flexibly before the two can live together, for example. Interaction Emotion Communication (IEC), bidirectional communication based on emotional behavior between human beings and robots, raises the personal affinity a robot has for human beings. IEC consists of three processes – (1) recognizing human emotion, (2) generating robot emotion, and (3) expressing robot emotion. We focus here on generating robot emotion. Emotional behavior patterns desirable in a robot vary with the person, so we also conducted individual preference analysis of emotional behavior.
Fuzzyc-Means Clustering for Uncertain Data Using Quadratic Penalty-Vector Regularization
Authors: Endo Yasunori ,   Department of Risk Engineering, Faculty of Systems and Information Engineering, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki 305-8573, Japan, Hasegawa Yasushi , Hamasuna Yukihiro , Kanzawa Yuchi ,   DENSO Co., Ltd., 1-1 Showa-cho, Kariya, Aichi 448-8661, Japan,   Faculty of Engineering, Shibaura Institute of Technology, 3-7-5 Toyosu, Koto-ku, Tokyo 135-8548, Japan
Published: April 2016

Clustering - defined as an unsupervised data-analysis classification transforming real-space information into data in pattern space and analyzing it - may require that data be represented by a set, rather than points, due to data uncertainty, e.g., measurement error margin, data regarded as one point, or missing values. These data uncertainties have been represented as interval ranges for which many clustering algorithms are constructed, but the lack of guidelines in selecting available distances in individual cases has made selection difficult and raised the need for ways to calculate dissimilarity between uncertain data without introducing a nearest-neighbor or other distance. The tolerance concept we propose represents uncertain data as a point with a tolerance vector, not as an interval, while this is convenient for handling uncertain data, tolerance-vector constraints make mathematical development difficult. We attempt to remove the tolerance-vector constraints using quadratic penaltyvector regularization similar to the tolerance vector. We also propose clustering algorithms for uncertain data considering optimization and obtaining an optimal solution to handle uncertainty appropriately.
Variable Weighting in PCA-Guided k-Means and its Connection with Information Summarization
Authors: Honda Katsuhiro ,   Graduate School of Engineering, Osaka Prefecture University, 1-1 Gakuen-cho, Nakaku, Sakai, Osaka 599-8531, Japan, Notsu Akira , Ichihashi Hidetomo
Published: April 2016

In the present paper, a variable selection model in k-Means is proposed, in which a variable weighting mechanism is introduced to PCA-guided k-Means. Variable weights are estimated in a manner similar to FCM clustering, while the membership indicator is derived using a PCA-guided method, in which the principal component scores are calculated by considering the variable weights. The variable weights emphasize the variables that have meaningful cluster information in the calculation of the membership indicators, and the absolute responsibility of each variable is revealed by soft transition to possibilistic values. It is also shown that the variable weights are derived in a manner similar to variable selection for PCA, with the goal being information summarization. The characteristics of the proposed method are demonstrated in an application to document clustering.
A Similarity Rough Set Model for Document Representation and Document Clustering
Authors: Thanh Nguyen Chi ,   Department of Management and Information System Science, Nagaoka University of Technology, 1603-1 Kamitomioka, Nagaoka, Niigata 940-2188, Japan, Yamada Koichi , Unehara Muneyuki
Published: April 2016

Document clustering is a textmining technique for unsupervised document organization. It helps the users browse and navigate large sets of documents. Ho et al. proposed a Tolerance Rough Set Model (TRSM) [1] for improving the vector space model that represents documents by vectors of terms and applied it to document clustering. In this paper we analyze their model to propose a new model for efficient clustering of documents. We introduce Similarity Rough Set Model (SRSM) as another model for presenting documents in document clustering. The model is evaluated by experiments on test collections. The experiment results show that the SRSM document clusteringmethod outperforms the one with TRSM and the results of SRSM are less affected by the value of parameter than TRSM.
Competition is Bad for Consumers: Analysis of an Artificial Payment Card Market
Authors: Alexandrova-Kabadjova Biliana ,   General Directorate of Central Banking Operations, Bank of Mexico, Avenida 5 de Mayo No.6, Colonia Centro, Delegacion Cuauhtemoc, 06059 Distrito Federal, Mexico City, Mexico, Tsang Edward , Krause Andreas ,   Department of Computer Science, University of Essex, Wivenhoe Park, Colchester CO4 3SQ, Great Britain,,   School of Management, University of Bath, Bath BA2 7AY, Great Britain
Published: April 2016

This paper investigates the competition between payment card network platforms in an artificial payment card market. In the market, we model the interactions between consumers, merchants, and competing card schemes and obtain their optimal pricing structure. We allow platform operators to charge consumers and merchants with fixed fees, provide net benefits from card usage/acceptance, and engage in marketing activities. We assume that the consumer side exhibits lower demand elasticity. With these settings, we establish that consumers benefit from a reduction of the numbers of competing payment cards through lower fees and higher net benefits, while merchants remain largely unaffected. The two-sided nature of the market leads to the result that having more competitors actually reduces prosperity for customers.
Virtual Social Experiment of Tram Railway Extension Using Multi-Agent-Based Traffic Simulator
Authors: Fujii Hideki ,   School of Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyoku, Tokyo 113-8656, Japan, Sakurai Takafumi , Yoshimura Shinobu
Published: April 2016

The promotion of the use of public transportation is a means of solving traffic and environmental problems. In European countries, trams are a common means of public transportation. There are also some areas in Japan where new installations of trams or the extension of existing tram lines has been considered, but these plans cannot be implemented because their effects cannot be predicted. This study uses the traffic simulator MATES, which employs a multiagent model, to do a virtual social experiment on an Okayama City tram railway extension issue actually under discussion in Okayama Prefecture. It assesses the effect of a modal shift from cars to trams caused by the increase in convenience that the changes in the road structure and the extension in the railway would bring about.
Inference for Nonlinear Mapping with Sparse Fuzzy Rules Based on Multi-Level Interpolation
Authors: Uehara Kiyohiko ,   Ibaraki University, Hitachi 316-8511, Japan, Sato Shun , Hirota Kaoru ,   Tokyo Institute of Technology, Yokohama 226-8502, Japan
Published: April 2016

An inference method is proposed for sparse fuzzy rules on the basis of interpolations at a number of points determined by α-cuts of given facts. The proposed method can perform nonlinear mapping even with sparse rule bases when each given fact activates a number of fuzzy rules which represent nonlinear relations. The operations for the nonlinear mapping are exactly the same as for the case when given facts activate no fuzzy rules due to the sparseness of rule bases. Such nonlinear mapping cannot be provided by conventional methods for sparse fuzzy rules. In evaluating the proposed method, mean square errors are adopted to indicate difference between deduced consequences and fuzzy sets transformed by nonlinear fuzzy-valued functions to be represented with sparse fuzzy rules. Simulation results show that the proposed method can follow the nonlinear fuzzy-valued functions. The proposed method contributes to both reducing the number of fuzzy rules and providing nonlinear mapping with sparse rule bases.
Intelligent Automated Guided Vehicle Controller with Reverse Strategy
Authors: Kato Shigeru ,   Niihama National College of Technology, 7-1 Yagumo-cho, Niihama-shi, Ehime 792-8580, Japan, Wong Kok Wai ,   Murdoch University, South St., Murdoch, WA 6150, Australia
Published: April 2016

This paper describes the intelligent Automated Guided Vehicle (AGV) control system using Fuzzy Rule Interpolation (FRI) method. The AGV used in this paper is a virtual vehicle simulated using computer. The purpose of the control system is to control the simulated AGV by moving along the given path towards a goal. Some obstacles can be placed on or near the path to increase the difficulties of the control system. The intelligent AGV should follow the path by avoiding these obstacles. This system consists of two fuzzy controllers. One is the original FRI controller that mainly controls the forward movement of the AGV. Another one is the proposed reverse movement controller that deals with the critical situation. When the original FRI controller faces the critical situation, our proposed reverse controller will control the AGV to reverse and move forward towards the goal. Our proposed reverse controller utilizes the advantage of FRI method. In our system, we also develop a novel switching system to switch from original to the developed reverse controller.
Fuzzy Nonlinear Regression Analysis Using Fuzzified Neural Networks for Fault Diagnosis of Chemical Plants
Authors: Kimura Daisaku ,   Electrical Engineering and Computer Sciences, Graduate School of Engineering, University of Hyogo, 2167 Shosha, Himeji, Hyogo 671-2201, Japan, Nii Manabu , Yamaguchi Takafumi , Takahashi Yutaka , Yumoto Takayuki ,   Corporate Technology Administration Department, KANEKA Corporation, 3-2-4 Nakanoshima, Kita-ku, Osaka, Japan
Published: April 2016

In systems such as chemical plants or circulatory systems, failure of piping, sensors or valves causes serious problems. These failures can be avoided by the increase in sensors and operators for condition monitoring. However, since adding sensors and operators leads to an increase in cost, it is difficult to realize. In this paper, a technique of diagnosing target systems based on a fuzzy nonlinear regression is proposed by using a fuzzified neural network that is trained with time-series data with reliability grades. Our proposed technique uses numerical data recorded by the existing monitoring system. Reliability grades are beforehand given to the recorded data by domain experts. The state of a target system is determined based on the fuzzy output from the trained fuzzified neural network. Our proposed technique makes us determine easily the state of the target systems. Our proposed technique is flexibly applicable to various types of systems by considering some parameters for failure determination of target systems.
Spatiotemporal Brain Activity During Hiragana Word Recognition Task
Authors: Toyoshima Hisashi ,   Japan Technical Software Corporation, West 3-1-14, North 21, North Ward, Sapporo 001-0021, Japan, Yamanoi Takahiro , Yamazaki Toshimasa , Ohnishi Shin-ichi ,   Department of Electronics and Information Engineering, Hokkai-Gakuen University, W11-1-1, S26, Chuo-ku, Sapporo 064-0926, Japan,   Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502, Japan
Published: April 2016

The 19-channel Event-Related Potentials (ERPs) we recorded during recognition of hiragana (one type of Japanese phonetic characters) were simultaneously and independently presented as a word and a nonword to opposite eyes using a field-sequential stereoscopic 3D display with a liquid-crystal shutter, a word and a non-word were simultaneously and independently presented to the left (right) and the right (left) eyes, respectively. Each word consists of 3 hiragana characters. Three subjects were instructed to press a button when they understood the meaning of the visual stimuli after 3,000 ms poststimulus. Equivalent Current Dipole source Localization (ECDL) with 3 unconstrained ECDs was applied to the ERPs. In the case of right-handed subjects, the ECDs were localized to the Wernicke’s area at around 600 ms. In the case of left-handed subject, the ECD was localized to the Wernicke’s homologue. After that ECDs were then localized to the prefrontal area, the superior frontal gyrus, and the middle frontal gyrus. At around 800 ms, the ECDs were localized to the Broca’s area, then after that ECDs were relocalized to the the Wernicke’s area and to the Broca’s area.
Cerebral Contour Extraction with Particle Method in Neonatal MR Images
Authors: Kobashi Syoji ,   Graduate School of Engineering, University of Hyogo, 2167 Shosha, Himeji, Hyogo 671-2280, Japan, Yokomichi Daisuke , Wakata Yuki , Ando Kumiko , Ishikura Reiichi , Kuramoto Kei , Hirota Shozo , Hata Yutaka ,   WPI Immunology Research Center, Osaka University, 3-1 Yamadaoka, Suita, Osaka 565-0871, Japan,   Hyogo College of Medicine, 75 Yamauchi-cho, Sasayama, Hyogo 669-2337, Japan
Published: April 2016

Cerebral surface extraction from neonatal MR images is the basic work of quantifying the deformation of the cerebrum. Although there are many conventional methods of segmenting the cerebral region, only the rough area is given by counting the number of surface voxels in the segmented region. This article proposes a new method of extraction that is based on the particle method. The method introduces three kinds of particles that correspond to cerebrospinal fluid, gray matter, and white matter; it converts the brain MR images into the set of particles. The proposed method was applied to neonatal magnetic resonance images, and the experimental results showed that the cerebral contour was extracted with a root-mean-square-error of 0.51 mm compared with the ground truth contour given by a physician.
Next Generation Passive Optical Networking Technology Predictions by Using Hybrid MCDM Methods
Authors: Huang Chi-Yo ,   Department of Industrial Education, National Taiwan Normal University, No.162, Section 1, He-ping East Road, Taipei 106, Taiwan, Chang Shih-Yu , Yang Yu-Hsien , Tzeng Gwo-Hshiung ,   Dept. of Computer Science, National Tsing Hua University, No.101, Section 2, Kuang Fu Road, Hsinchu, Taiwan,   Institute of Project Management, Kainan University, No.1 Kainan Road, Luchu, Taoyuan 338, Taiwan,   Institute of Management of Technology, National Chiao Tung University, 1001 Ta-Hsueh Road, Hsinchu 300, Taiwan
Published: April 2016

The evolution of broadband telecommunication technologies has enabled applications over networks, which, in turn, has enabled the emergence of novel network services and applications such as VoIP and video on demand in triggering demand for higher network bandwidth. Passive Optical Networking (PON) is suitable for eliminating bandwidth insufficiency, so national governments and communities are observing the emergence of Fiber-To-The-Home (FTTH) technologies. Ethernet-based Passive Optical Networks (EPON) and Gigabit-based PON (GPON) are two candidates for next-generation PON in bandwidth efficiency. To predict which is more suitable nationally, we define a foresight framework based on hybrid Multiple Criteria Decision Making (MCDM) methods for selecting the most suitable PON. The development of next-generation PON, including Ethernet PON (EPON) and Gigabit PON (GPON), is introduced and compared. Experts from industry and academia were invited to predict the most suitable next-generation PON for Japan and emerging economies such as Taiwan. Results showed that EPON is most suitable for Japan and GPON most suitable for Taiwan. Results for the technological foresight can serve as the basis for national telecommunication policy and business product definition.
Product Innovation of Shinzaburo Hanpu and Regional Characteristic of Kyoto
Authors: Irisawa Yusuke ,   Graduate School of Commerce, Waseda University, 1-6-1 Nishi-Waseda, Shinjuku-ku, Tokyo 169-8050, Japan, Nagasawa Shin’ya
Published: April 2016

There is a new company “Ichizawa Hanpu Kougyo” in Kyoto, which was born by their family troubles. As a result, a new brand called “Shinzaburo Hanpu” was born. Though this manufacturing company manages only a channel in Kyoto, it has gained national popularity. So, we have thought that it makes for the elaborate fabrication of this company to create the customer value with “a standing for the customer” and “a standing for manufacturing.” This study aims to analyze the essence of “Shinzaburo Hanpu” brand from the perspective of the relation between its product innovation competence and innovation, based on the viewpoint that Mr. Shinzaburo Ichizawa is the essence of manufacturing for former Ichizawa Hanpu Kougyo and Ichizawa Shinzaburo Hanpu. In “Shinzaburo Hanpu” case, we have examined the relation of the product innovation in the region of Kyoto.
Rough Sets Based Prediction Model of Tick-Wise Price Fluctuations
Authors: Matsumoto Yoshiyuki ,   Department of Economics, Shimonoseki City University, 2-1-1 Daigaku-cho, Shimonoseki, Yamaguchi 751-8510, Japan, Watada Junzo ,   Graduate School of Information, Production and Systems, Waseda University, 2-7 Hibikino, Wakamatsu-ku, Kitakyushu-shi, Fukuoka 808-0135, Japan
Published: April 2016

Rough sets theory was proposed by Z. Pawlak in 1982. This theory enables us to mine knowledge granules through a decision rule from a database, a web base, a set and so on. We can apply the decision rule to reason, estimate, evaluate, or forecast unknown objects. In this paper, the rough set model is used to analyze of time series data of tick-wise price fluctuation, where knowledge granules are mined from the data set of tick-wise price fluctuations.
Fuzzy Robust Regression Model by Possibility Maximization
Authors: Yabuuchi Yoshiyuki ,   Faculty of Economics, Shimonoseki City University, 2-1-1 Daigaku-cho, Shimonoseki, Yamaguchi 751-8510, Japan, Watada Junzo ,   Graduate School of Information, Production and Systems Waseda University, 2-4 Hibikino, Wakamatsu, Kitakyushu, Fukuoka 808-0196, Japan
Published: April 2016

Since management and economic systems are complex, it is hard to handle data obtained in management and economic areas. Hitherto, in the fields, much research has focused on the structure and analysis of such data. H. Tanaka et al. proposed a fuzzy regression model to illustrate the potential possibilities inherent in the target system. J. C. Bezdek proposed a switching regression model based on a fuzzy clustering model to separate mixed samples coming from plural latent systems and apply regression models to the groups of samples coming from each system. It is hard to illustrate a rough and moderate possibility of the target system. In this paper, to deal with the possibility of a social system, we propose a new fuzzy robust regression model.
Integrated Rule Mining Based on Fuzzy GNP and Probabilistic Classification for Intrusion Detection
Authors: Lu Nannan ,   Graduate School of Information, Production and Systems, Waseda University, 2-7 Hibikino, Wakamatsu-ku, Kitakyushu, Fukuoka 808-0135, Japan, Mabu Shingo , Hirasawa Kotaro
Published: April 2016

With the increasing popularity of the Internet, network security has become a serious problem recently. How to detect intrusions effectively becomes an important component in network security. Therefore, a variety of algorithms have been devoted to this challenge. Genetic network programming is a newly developed evolutionary algorithm with directed graph gene structures, and it has been applied to data mining for intrusion detection systems providing good performances in intrusion detection. In this paper, an integrated rule mining algorithm based on fuzzy GNP and probabilistic classification is proposed. The integrated rule mining uses fuzzy class association rule mining algorithm to extract rules with different classes. Actually, it can deal with both discrete and continuous attributes in network connection data. Then, the classification is done probabilistically using different class rules. The integrated method showed excellent results by simulation experiments.
Exploring the Natural Reaction of Young and Aged Person with Telenoid in a Real World
Authors: Ogawa Kohei ,   ATR Intelligent Robotics and Communication Laboratory, 2-2-2 Hikaridai, Seikacho, Souraku-gun, Kyoto 619-0288, Japan, Nishio Shuichi , Koda Kensuke , Balistreri Giuseppe , Watanabe Tetsuya , Ishiguro Hiroshi ,   Graduate School of Engineering Science, Osaka University, 1-3 Machikaneyama, Toyonaka, Osaka 560-8531, Japan,   Dipartimento di Ingegneria Informatica, Università degli Studi di Palermo, Viale delle Scienze, Edificio 690128 Palermo, Italia
Published: April 2016

This paper describes two field tests conducted with shopping mall visitors and with aged persons defined as in their 70s to 90s. For both of the field tests, we used an android we developed called Telenoid R1 or just Telenoid. In the following field tests we interviewed participants about their impressions of the Telenoid. The results of the shopping mall showed that almost half of the interviewees felt negative toward Telenoid until they hugged it, after which opinions became positive. Results of the other test showed that the majority of aged persons reported a positive opinion and, interestingly, all aged persons who interacted with Telenoid gave it a hug without any suggestion to do so. This suggests that older persons find Telenoid to be acceptable medium for the elderly. Younger persons may also find Telenoid acceptable, seeing that visitors developed positive feelings toward the robot after giving it a hug. These results should prove valuable in our future work with androids.
Cooperative Behavior Learning Based on Social Interaction of State Conversion and Reward Exchange Among Multi-Agents
Authors: Zhang Kun ,   Dept. of System Design Engineering, Graduate School of Engineering, University of Fukui, Maeda Yoichiro , Takahashi Yasutake ,   Dept. of Human and Artificial Intelligent Systems, Graduate School of Engineering, University of Fukui, 3-9-1 Bunkyo, Fukui 910-8507, Japan
Published: April 2016

In multi-agent systems, it is necessary for autonomous agents to interact with each other in order to have excellent cooperative performance. Therefore, we have studied social interaction between agents to see how they acquire cooperative behavior. We have found that sharing environmental states can improve agent cooperation through reinforcement learning, and that changing environmental states to target-related individual states improves cooperation. To further improve cooperation, we propose reward redistribution based on reward exchanges among agents. In receiving rewards from both the environment and other agents, agents learned how to adjust themselves to the environment and how to explore and strengthen cooperation in tasks that a single agent could not do alone. Agents thus cooperate best through the interaction of state conversion and reward exchange.
Model Reference Control for Collision Avoidance of a Human-Operated Quadrotor Helicopter
Authors: Piriyanont Busara ,   School of Electrical Engineering and Computer Science, The University of Newcastle, University Drive, Callaghan, NSW 2308, Australia, Uchiyama Naoki , Sano Shigenori ,   Toyohashi University of Technology, Japan, 1-1 Hibarigaoka, Tempaku, Toyohashi, Aichi 441-8580, Japan
Published: April 2016

Because quadrotor helicopter has four fixed-pitch rotors, its control system can be much simpler than that of conventional helicopters; thus, it is expected to find wide application in many remote control situations, such as in hazardous environments. This paper proposes a collision avoidance control for a quadrotor helicopter based on the concept of a social force model. The proposed control incorporates commands given from human operators and compensates for operator mistakes in real time to achieve collision avoidance of a quadrotor helicopter. The proposed method uses distance sensors to achieve real-time collision avoidance. Its effectiveness is shown by experimental results, in which the algorithm successfully drives the helicopter along the desired trajectory without a collision.
Optimization of Fuzzy Logic Controller for Trajectory Tracking Using Genetic Algorithm
Authors: Shill Pintu Chandra ,   Department of System Design Engineering, Graduate School of Engineering, University of Fukui, 3-9-1 Bunkyo, Fukui 910-8507, Japan, Akhand M. A. H. , Amin Md. Faijul , Murase Kazuyuki ,   Department of Computer Science and Engineering (CSE), Khulna University of Engineering & Technology (KUET), Khulna 9203, Bangladesh
Published: April 2016

Most Fuzzy Logic Controllers (FLCs) to date are working based on expert knowledge derived from heuristic knowledge of experienced operators. Conventional fuzzy logic controllers have poor adaptability due to invariable Membership Function (MF) parameters and fixed rule set. Conventional manual coded FLCs use only expert knowledge bases and do poorly with complex problems, especially with large numbers of input variables. We have developed FLCs using a Genetic Algorithm (GA) to automatically acquire knowledge that we call a genetic-fuzzy in which the GA is used to adaptively generate fuzzy rules and simultaneously selecting an appropriate MF shape. We also evaluate different membership functions in the fuzzy logic control. FLC sensibility is analysed and compared for different membership functions. We compare our proposed genetic-fuzzy approach to such existing methods, including as a manually coded conventional method, conventional method with complementary membership function, and a neuro-fuzzy method on a widely used test bed; backing up a truck reversal problem. Simulation results have shown our proposal to be superior to existing widely used methods.
Hybrid Ensemble Construction with Selected Neural Networks
Authors: Akhand M. A. H. ,   Dept. of Computer Science and Engineering, Khulna University of Engineering & Technology (KUET), Khulna 9203, Bangladesh, Shill Pintu Chandra , Murase Kazuyuki ,   Dept. of Human and Artificial Intelligence Systems, Graduate School of Engineering, University of Fukui
Published: April 2016

A Neural Network Ensemble (NNE) is convenient for improving classification task performance. Among the remarkable number of methods based on different techniques for constructing NNEs, Negative Correlation Learning (NCL), bagging, and boosting are the most popular. None of them, however, could show better performance for all problems. To improve performance combining the complementary strengths of the individual methods, we propose two different ways to construct hybrid ensembles combining NCL with bagging and boosting. One produces a pool of predefined numbers of networks using standard NCL and bagging (or boosting) and then uses a genetic algorithm to select an optimal network subset for an NNE from the pool. Results of experiments confirmed that our proposals show consistently better performance with concise ensembles than conventional methods when tested using a suite of 25 benchmark problems.
Feature Extraction with Space Folding Model and its Application to Machine Learning
Authors: Pham Minh Tuan ,   Department of Computational Science and Engineering, Graduate School of Engineering, Nagoya University, Furo-cho, Chikusa-ku, Nagoya 464-8603, Japan, Yoshikawa Tomohiro , Furuhashi Takeshi , Tachibana Kanta ,   Department of Information Design, Faculty of Informatics, Kogakuin University, 1-24-2 Nishi-Shinjuku, Tokyo 163-8677, Japan
Published: April 2016

Feature extraction provides an essential element in most machine learning methods, including supervised learning with neural networks. Linearly inseparable data distributions are often non-linearly transformed in some way to make them more linearly separable in the feature space. In this paper, we propose a method of feature extraction with a space folding model. In the proposed method, each basis vector in the m-dimensional data space is divided in the positive and negative directions to optimize it with 2mm-dimensional vectors as variables. 2mvariable vectors are estimated to minimize the cross entropy of class labels and distances so that instances in the same classes are gathered closer together and those in other classes are separated farther apart. The proposed method, in which linear transformation is applied to each quadrant to collectively realize a nonlinear transformation, is expected to lead to improvements in accuracy of discrimination over conventional methods of feature extraction using single linear transformations. In this paper, we have confirmed the effectiveness of the proposed method of feature extraction with a space folding model on a UCI benchmark problem.
Efficient Color Transformations on Quantum Images
Authors: Le Phuc Q. ,   Department of Computational Intelligence and Systems Science, Tokyo Institute of Technology, G3-49, 4259 Nagatsuta, Midori-ku, Yokohama 226-8502, Japan, Iliyasu Abdullah M. , Dong Fangyan , Hirota Kaoru
Published: April 2016

Efficient transformations on the color content of images using single qubit operations are proposed based on the Flexible Representation of Quantum Images (FRQI). Utilizing the single qubit dedicated for encoding color information in the FRQI representation, the proposed operations offer massive speed-up in terms of changes in the color information in comparison with classical ones. Simulations of the FRQI images and circuits of these transformations using synthetic and Lena images on classical computers demonstrate the feasibility of the proposal. Together with position related operations, the color transformations could provide the foundation to achieve practical applications which are inefficient at present.
Detection of Lung Nodules in Thoracic MDCT Images Based on Temporal Changes from Previous and Current Images
Authors: Maeda Shinya ,   Department of Control Engineering, Kyushu Institute of Technology, 1-1 Sensui, Tobata, Kitakyushu 804-8550, Japan, Tomiyama Yasuyuki , Kim Hyoungseop , Miyake Noriaki , Itai Yoshinori , Tan Joo Kooi , Ishikawa Seiji , Yamamoto Akiyoshi
Published: April 2016

Temporal subtraction enhances temporal change by subtracting images captured at different times. Medical images captured currently (current images) and in previous examination (previous images) are subtracted to enhance new lesions and temporal change in existing lesion shadows. Temporal subtraction using chest MultiDetector-Row Computed Tomography (MDCT) images and currently being developed is to be applied to nodule detection in pulmonary regions. Nodule detection using conventional temporal subtraction, however, yields many false-positive results for those 20 mm or less in diameter, requiring improvement. We discuss improvements in nodule detection accuracy using temporal subtraction, first extracting rough nodules from temporal subtraction images as candidate shadows. Features are then acquired from current, previous, and temporal subtraction images. We use intensity features in previous images and shape features in the current images and in features used in conventional methods. Using acquired features, we build a neural network classifier, then extract final pulmonary candidates in unknown shadows.
Proposal ofWearable Multiremote Controller Using Head-Tracking
Authors: Miyata Kohei ,   Kyushu Institute of Technology, 1-1 Sensui-cho, Tobata-ku, Kitakyushu-city, Fukuoka 804-8550, Japan, Kitazono Yuhki , Yang Shiyuan , Serikawa Seiichi ,   Kitakyushu National College of Technology, 5-20-1 Shii, Kokuraminami-ku, Kitakyushu-city, Fukuoka 802-0985, Japan
Published: April 2016

Recently, various remote controllers are developed. However the operation of remote controller is very difficult for the physically handicapped persons who cannot move their hand. The wearable multiremote controller we are proposing uses head tracking via a camera and a head-mounted display. It is portable and can operate household appliances. It is also operated intuitively in head tracking.
Concept Finding Proofs
Authors: Kamide Norihiro ,   Waseda Institute for Advanced Study, Waseda University, 1-6-1 Nishi Waseda, Shinjuku-ku, Tokyo 169-8050, Japan
Published: April 2016

We propose a proof-theoretical way of obtaining detailed and precise information on conceptual hierarchies. The notion of concept finding proof, which represents a hierarchy of concepts, is introduced based on a substructural logic with mingle and strong negation. Mingle, which is a structural inference rule, is used to represent a process for finding a more general (or specific) concept than some given concepts. Strong negation, which is a negation connective, is used to represent a concept inverse operator. The problem for constructing a concept finding proof is shown to be decidable in PTIME.11. This paper is an extended version of [1].