bluuebunny/crossref_metadata_2025 huggingface dataset:
Special Issue on Advanced Intelligent Systems: Part II
Authors: Chun Myung-Geun , Watanabe Toshihiko
Published: April 2016

Following the Special Issue on Advanced Intelligent Systems Vol.16 No.7, Part II presents 5 progressive papers on advanced intelligent systems. They include 10 selected revised papers from the 12th International Symposium on Advanced Intelligent Systems (ISIS 2011) held at La Vie DfOr Resort, Suwon, Korea, from September 28 to October 1, 2011. A brief review of Part IIfs 5 papers follows. A brief review of Part IIfs 5 papers follows. The first paper proposes a new visualization technique involving non-Euclidean relational data using robust linear fuzzy clustering based on an FCMdd framework. Experimental results show that the proposed model achieves multiclass MDS and is useful for revealing complex features. The second paper proposes the detection of lower-extremity arterial regions imaged by non-enhanced MR based on particle filter algorithms. Results applied to fresh blood imaging (FBI) show that the technique is promising. The third paper presents the development of nutritional management system supporting healthy eating habits. The paper reveals new concepts in nutritional management and confirms the effectiveness of the proposed management system through numerical experiments. The fourth paper proposes an emotion generation model with growth functions for robots based on a genetic psychology concept. The technique is promising for realizing evolving emotions in robots. The fifth paper presents the fuzzy control in approaching target objects and object grabbing by a fourwheeled vision-based mobile robot. The paper presents significant developments in mobile robots from the viewpoint of industrial applications. We thank the authors and reviewers for their great efforts in making this publication possible. We are also grateful to the JACIII editorial office for its invaluable assistance and advice in putting this issue together.
Detection of Artery Regions in Lower Extremity Arteries from Non-Enhanced MR Imaging Based on Particle Filter Algorithms
Authors: Koga Yuiko ,   Kyusyu Institute of Technology, 1-1 Sensui-cho, Tobata-ku, Kitakyushu-shi, Fukuoka 804-8550, Japan, Yamamoto Akiyoshi , Kim Hyoungseop , Tan Joo Kooi , Ishikawa Seiji ,   Kyoaikai Tobata Kyoritu Hospital, 2-5-1 Sawami, Tobata-ku, Kitakyushu-shi, Fukuoka 804-0093, Japan
Published: April 2016

Recently, the arteries sclerosis obliterans (ASO) or called peripheral arterial disease (PAD) typically caused by chronic ischemia of limbs increases remarkably. As one of the diagnosis methods, the image diagnosis methods such as MR image are applied in medical fields. In this paper, we propose a vascular extraction method using fresh blood imaging (FBI) method, as well as apply it to computer aided diagnosis (CAD) system. Especially, to prevent the spread outside of the region and improve the segment accuracy of peripheral artery areas, we introduce particle filter algorithms. We performed our method on automatic artery regions detection using non-enhanced MR images. Furthermore, we compared the extracted results to gold standard data and analyzed accuracy by receiver operating characteristic (ROC). The effectiveness of our proposed method and satisfactory of its detected accuracy were confirmed.
Activeness Improves Cognitive Performance in Human-Machine Interaction
Authors: Tamura Yusuke ,   Faculty of Science and Engineering, Chuo University, 1-13-27 Kasuga, Bunkyo-ku, Tokyo 112-8551, Japan, Egawa Mami , Yano Shiro , Maeda Takaki , Kato Motoichiro , Asama Hajime ,   Recruit Marketing Partners, Co., Ltd., 1-9-2 Marunouchi, Chiyoda-ku, Tokyo 100-6640, Japan,   Research Organization of Science and Technology, Ritsumeikan University, 1-1-1 Nojihigashi, Kusatsu-shi, Shiga 525-8577, Japan,   Department of Neuropsychiatry, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo 160-8582, Japan,   Graduate School of Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan
Published: April 2016

In human-machine interaction, automation brings both advantages and potentially unpredictable disadvantages to human cognitive performance. In this study, we hypothesized that active behavior improves cognitive performance in human-machine interaction, and verified this hypothesis through three experiments. Experiment 1 examined the relationship between activeness and reaction time in a target-search task. Experiments 2 and 3 analyzed the factors that improved cognitive performance. Experimental results demonstrated that activeness positively affects cognitive performance and suggested that predictability associated with activeness plays a key role in improving cognitive performance.
A Neural Network Structure Decomposition Based on Pruning and its Visualization Method
Authors: Shibata Atsushi ,   Department of Computational Intelligence and Systems Science, Tokyo Institute of Technology, G3-49, 4259 Nagatsuta, Midori-ku, Yokohama 226-8502, Japan, Lu Jiajun , Dong Fangyan , Hirota Kaoru
Published: April 2016

To decompose neural network structures for composite tasks, a pruning method and its visualization method are proposed. Visualization by placing the neurons on a 2D plane clarifies the structure related to each composited task. Experiments on a composite task using two tasks from a UCI dataset show that the neural network of the composite task contains more than 80% of neurons. The proposed methods target the transfer learning of robot motion, and results of an adaptation experiments are also referred.
Situation-Oriented Hierarchical Classification for Sightseeing Images Based on Local Color Feature
Authors: Chen Chia-Huang ,   Graduate School of System Design, Tokyo Metropolitan University, 6-6 Asahigaoka, Hino, Tokyo 191-0065, Japan, Takama Yasufumi
Published: April 2016

Nowadays, tourists take lots of photos and share them on album websites, so the meaningful grouping of images becomes important and useful. Specifically, sightseeing scenes vary with different situations such as weather and season. The categorization of different situations is thus expected to be beneficial to tourists planning when to visit different places. This paper proposes a hierarchical classification method based on local color feature extraction from the designed region of interest (ROI) andK-means clustering to categorize sightseeing images into several meaningful situations. Hierarchical organization consists of three stages and four situations. In the first stage, night-time images are discriminated from daytime images, then daytime images are divided into sunrise/sunset and other images in the second stage. Finally, cloudy images are separated from sunshiny images in other images obtained in the second stage. Experimental results show that the extraction of color features within the ROI is effective in obtaining clusters with high precision and recall.
Constructing Generative Topographic Mapping by Variational Bayes with ARD Hierarchical Prior
Authors: Yamaguchi Nobuhiko ,   Graduate School of Science and Engineering, Saga University, 1 Honjo, Saga 840-8502, Japan
Published: April 2016

Generative Topographic Mapping (GTM) is a nonlinear latent variable model introduced as a data visualization technique by Bishop et al. In this paper, we focus on variational Bayesian inference in GTM. Variational Bayesian GTM, first proposed by Olier et al., uses a single regularization term and regularization parameter to avoid overfitting and therefore cannot be used to control the degree of regularization locally. To overcome this problem, we propose variational Bayesian inference with Automatic Relevance Determination (ARD) hierarchical prior for use with GTM. The proposed model uses multiple regularization parameters and therefore can be used to control the degree of regularization in local areas of data space individually. Several experiments show that GTM that we propose provides better visualization than conventional GTM approaches.
Obtaining Admissible Preference Orders Using Hierarchical Bipolar Sugeno and Choquet Integrals
Authors: Fujimoto Katsushige ,   College of Symbiotic Systems Science, Fukushima University, 1 Kanayagawa, Fukushima 960-1296, Japan, Sugeno Michio ,   European Centre for Soft Computing, Gonzalo Gutiérrez Quirós S/N, 33600 Mieres, Spain
Published: April 2016

In this paper, we demonstrate the modeling capabilities of certain types of fuzzy integrals such as hierarchical bipolar/cumulative-prospect-theory-type Sugeno and Choquet integrals. The notion ofadmissible preference structures, introduced by Nakama and Sugeno, is one of the weakest restrictions in rational preference structures. Nakama and Sugeno also proved that, under a certain condition, any admissible preference can be modeled by a hierarchical bipolar Sugeno integral. Here, we extend this result and show that if we use an extra dummy attribute, we can use the hierarchical Choquet integral to generate any admissible preference.
Dynamic Scheduling Approaches to Wafer Test Scheduling with Unpredictable Error
Authors: Matsuo Tsubasa ,   Graduate School of Engineering Science, Osaka University, 1-3 Machikaneyama-cho, Toyonaka, Osaka 560-8531, Japan, Inuiguchi Masahiro , Masunaga Kenichiro ,   Renesas Electronics Co., 2-6-2 Otemachi, Chiyoda-ku, Tokyo 100-0004, Janapn
Published: April 2016

Scheduling of semiconductor wafer testing processes can be seen as a resource constraint project scheduling problem (RCPSP). However, it includes uncertainties caused by human factors, wafer errors and so on. Because some uncertainties are not simply quantitative, range estimation of the parameters would not be very useful. Considering such uncertainties, finding a good situation-dependent dispatching rule would be more suitable than solving the RCPSP under uncertainties. In this paper we apply the Pitts approach, one of the genetic algorithms, to the situation-dependent dispatching rule acquisition. We compare the obtained rule with the simple dispatching rules and examine the effectiveness and usefulness of the obtained rule in the problems with unpredictable wafer errors.
Spectral Classification of Oral and Nasal Snoring Sounds Using a Support Vector Machine
Authors: Mikami Tsuyoshi ,   Tomakomai National College of Technology, 443 Nishikioka, Tomakomai 059-1275, Japan, Kojima Yohichiro , Yonezawa Kazuya , Yamamoto Masahito , Furukawa Masashi ,   Department of Clinical Research, National Hospital Organization Hakodate Hospital, 18-16 Kawaharacho, Hakodate 041-8512, Japan,   Graduate School of Information Science & Technology, Hokkaido University, Kita 14, Nishi 9, Kita-ku, Sapporo 060-0814, Japan
Published: April 2016

Since oral breathing during sleep tends to make the upper airway more collapsible, loud snoring caused by oral breathing is found in many sleep apnea/hypopnea patients and should be detected in the earlier stage. But unfortunately we cannot know our own sleep condition or snoring. Thus, a simple method that can detect oral snoring makes it possible to become a useful technique to develop a home medical device. For such purpose, we adopt a Support Vector Machine (SVM) classifier so as to classify oral and nasal snoring sounds based on the spectral properties. Fifteen subjects are asked to simulate snoring with oral and nasal breath respectively and the sounds are recorded with a linear sound recorder. We adopted seven kernel functions (linear, polynomial, sigmoid, Gaussian, Laplacian, chisquare, and Kullback-Leibler) for SVM-based spectral classification. As a result, over 95% of snoring sounds are successfully classified under the various cross validation test.
Multi-Level Control of Fuzzy-Constraint Propagation in Inference Based on α-Cuts and Generalized Mean
Authors: Uehara Kiyohiko ,   Ibaraki University, 4-12-1 Nakanarusawa-cho, Hitachi 316-8511, Japan, Hirota Kaoru ,   Tokyo Institute of Technology, G3-49, 4259 Nagatsuta, Midori-ku, Yokohama 226-8502, Japan
Published: April 2016

An inference method is proposed, which can perform nonlinear mapping between convex fuzzy sets and present a scheme of various fuzzy-constraint propagation from given facts to deduced consequences. The basis of nonlinear mapping is provided by α-GEMII (α-level-set and generalized-mean-based inference) whereas the control of fuzzy-constraint propagation is based on the compositional rule of inference (CRI). The fuzzy-constraint propagation is controlled at the multi-level of α in its α-cut-based operations. The proposed method is named α-GEMS (α-level-set and generalized-mean-based inference in synergy with composition). Although α-GEMII can perform the nonlinear mapping according to a number of fuzzy rules in parallel, it has limitations in the control of fuzzy-constraint propagation and therefore has difficulty in constructing models of various given systems. In contrast, CRI-based inference can rather easily control fuzzy-constraint propagation with high understandability especially when a single fuzzy rule is used. It is difficult, however, to perform nonlinear mapping between convex fuzzy sets by using a number of fuzzy rules in parallel. α-GEMS can solve both of these problems. Simulation results show that α-GEMS is performed well in the nonlinear mapping and fuzzy-constraint propagation. α-GEMS is expected to be applied to modeling of given systems with various fuzzy input-output relations.
Improvement of Eye Gesture Interface
Authors: Gao Dekun ,   Department of Informatics, Graduate School of Informatics and Engineering, The University of Electro-Communications, 1-5-1 Chofugaoka, Chofu, Tokyo 182-8585, Japan, Itakura Naoaki , Mizuno Tota , Mito Kazuyuki
Published: April 2016

Human-computer interaction is an important field in computer science. One means of such interaction is an eye gaze interface using both optical and electrical methods. Conventional optical devices for measuring eye movement are generally expensive and restrict user head movement. In this paper, we propose a novel eye gesture input interface (EGII) that does not require a head-mounted display. Precise eyemovement in combinations of direction is determined by electrooculographic signals amplified via AC coupling. We also developed and evaluated direction algorithms for determining features of the novel EGII. The best algorithm showed a success rate of 97% in recognizing eye movement in different direction combinations. We also created a small portable measurement device containing a biological amplifier to amplify signals and an A/D board for importing signals. Mean accuracy for the EGII was 95.6% out of 12 possible choices using the selected algorithm and our device. This device can thus be used anywhere and anyone.
Designing Internal Reward of Reinforcement Learning Agents in Multi-Step Dilemma Problem
Authors: Ichikawa Yoshihiro ,   The University of Electro-Communications, 1-5-1 Chofugaoka, Chofu, Tokyo 182-8585, Japan, Takadama Keiki
Published: April 2016

This paper proposes the reinforcement learning agent that estimates internal rewards using external rewards in order to avoid conflict in multi-step dilemma problem. Intensive simulation results have revealed that the agent succeeds in avoiding local convergence and obtains a behavior policy for reaching a higher reward by updating the Q-value using the value that is subtracted the average reward from an external reward.
Platform for Two-Dimensional Cellular Automata Models Implemented by Living Cells of Electrically Controlled Green Paramecia Designed for Transport of Micro-Particles
Authors: Otsuka Kohei ,   The University of Kitakyushu, 1-1 Hibikino, Wakamatsu-ku, Kitakyushu 808-0135, Japan, Kawano Tomonori
Published: April 2016

Microscopic traffic flow models are a class of scientific models of vehicular traffic dynamics. Here, we attempted to establish an experimental platform for mimicking microscopic traffic flow models at microscopic dimensions. We achieved this, by monitoring the flow of micro-sized particles transported by the motile cells of living microorganisms. Some researchers have described the cells of protozoan species as “swimming neurons” or “swimming sensory cells” applicable to biological micro-electro-mechanical systems or micro-biorobotics. Therefore these cells, in a controlled environment, may form a good model system for bio-implementable cellular automata for traffic simulation. The living cells of theParameciumspecies including those of green paramecia (Paramecium bursaria), actively migrate towards a negatively charged electrode when exposed to an electric field. This type of cellular movement is known as galvanotaxis.P. bursariawas chosen as amodel organismsince the ideal micro-vehicles required for micro-particle transport must have a particular particle packing capacity within the cells. The present study establishes that the movement of cells with or without the loading of microspheres (Φ, 9.75 µm) can be controlled on a two-dimensional plane under strict electrical controls. Lastly, implementation of microchips equipped with optimally sized micro-flow channels that allow the single-cell traffic of swimmingP. bursariawas proposed for further studies and mathematical modeling.
A Portable Embedded Web Controller Based on LAMP for E-Experiment
Authors: Guo Qiang ,   School of Automation, Beijing Institute of Technology, 5, South Zhongguancun Street, Haidian District, Beijing, China, Dai Yaping , Zou Qunjie
Published: April 2016

In E-experiments, the network servers often used to manage experimental plants centrally require high power consumption. In order to reduce such power consumption, cost and size, this paper presents a portable embedded web controller (PEWC) combining embedded technology with Linux + Apache + MySQL + PHP (LAMP), which is the solution stack most widely used globally in Web development. Instead of using servers, the PEWC is used to build a distributed E-experiment system that enables users to do remote control through web pages online. Experimental results show that this system successfully controls robot-arm systems remotely and reduces power consumption by 99%, cost by 90% and size by 95%.
A Survey of Recent Progress in the Study of Distributed High-Order Linear Multi-Agent Coordination
Authors: Huang Jie ,   School of Automation, Beijing Institute of Technology, Key Laboratory of Intelligent Control and Decision of Complex Systems, Beijing, 100081, China, Fang Hao , Chen Jie , Dou Lihua , Zeng Jie ,   Fujian Institute of Education, Fuzhou, 350001, China
Published: April 2016

Most previous studies ofmulti-agent systems only considered the first- and second-order dynamics. In this review, we present the major results and progress related to distributed high-order linear multi-agent coordination. We also discuss the current challenges and propose several promising research directions, as well as open problems that demand further investigation.
Stabilization of an Underactuated Ball-and-Beam System Using a Second-Order Sliding Mode Control
Authors: Yang Jie ,   School of Automation, Beijing Institute of Technology, 5 South Zhongguancun Street, Haidian, Beijing 100081, China, Wang Qinglin , Li Yuan , She Jinhua ,   School of Computer Science, Tokyo University of Technology, 1401-1 Katakura, Hachioji, Tokyo 192-0982, Japan
Published: April 2016

This paper presents a stabilization method for an underactuated ball-and-beam system (BABS) based on a second-order sliding mode (SOSM) control. The BABS is an underactuated nonlinear system that is widely used to verify nonlinear control performance. Virtual control is introduced to a second-order BABS subsystem to minimize control performance inaccuracy by using model linearization. An actual virtual controller with variable finite-time tracking is achieved using a second-order sliding mode controller. An adaptive robust method is proposed to solve an uncertainty problem with unknown upper bounds, and then a finite-time convergence theory proof is given. Theory, simulation and experiment results verify the efficiency of the BABS controller.
Gait Motion Planning for a Six Legged Robot Based on the Associatron
Authors: Ishikawa Tomo ,   Graduate School of Bionics, Computer and Media Sciences, Tokyo University of Technology, 1404-1 Katakura, Hachioji, Tokyo 192-0982, Japan, Makino Koji , Imani Junya , Ohyama Yasuhiro ,   Graduate School of Medicine and Engineering, University of Yamanashi, 4-3-11 Takeda, Kofu, Yamanashi 400-8511, Japan
Published: April 2016

This research addresses a gait motion planning problem for a six-legged robot walking on an irregular field. In this proposal, we used a simplified neural network model called an Associatron that recalls total motion patterns sequentially frompartial information. The Associatron is used here because it is more effective and adaptable than conventional methods. Using the proposed method, the robot is expected to walk in unknown fields. After verifying planning using an Open Dynamics Engine (ODE) by using simulations, we found that memorized patterns are recalled from developed patterns. We then conducted experiments using a real developed robot. Experiment results show that, when using the proposed planning method, the robot selects suitable gait motion patterns in the presence of an obstacle.
Relational Fuzzy c-Lines Clustering Derived from Kernelization of Fuzzy c-Lines
Authors: Kanzawa Yuchi ,   Shibaura Institute of Technology, 3-7-5 Toyosu, Koto, Tokyo 135-8548, Japan
Published: April 2016

In this paper, two linear fuzzy clustering algorithms are proposed for relational data based on kernel fuzzy c-means, in which the prototypes of clusters are given by lines spanned in a feature space defined by the kernel which is derived from a given relational data. The proposed algorithms contrast the conventional method in which the prototypes of clusters are given by lines spanned by two representative objects. Through numerical examples, it is shown that the proposed algorithms can capture local sub-structures in relational data.
Multi-Objective Optimal Fuzzy Fractional-Order PID Controller Design
Authors: Hajiloo Amir ,   Department of Mechanical and Industrial Engineering, Concordia University, 1455 de Maisonneuve W., Montreal, QC, H3G 1M8, Canada, Xie Wen-Fang
Published: April 2016

The design of the optimal fuzzy fractional-order PID controller is addressed in this work. A multi-objective genetic algorithm is proposed to design rule base and membership functions of the fuzzy logic systems. Three conflicting objective functions in both time and frequency domains have been used in Pareto design of the fuzzy fractional-order PID controller. The simulation results reveal the effectiveness of the proposed method in comparison with the results produced by the fractional-order PID controllers.
Interactive Decision Making for Fuzzy Random Multiobjective Linear Programming Problems with Variance Covariance Matrices Through Fractile Optimization
Authors: Yano Hitoshi ,   School of Humanities and Social Sciences, Nagoya City University, 1 Yamanohata, Mizuho-cho, Mizuho-ku, Nagoya 467-8501, Japan
Published: April 2016

In this paper, we focus on fuzzy random multiobjective linear programming problems with variance covariance matrices through fractile optimization, and propose an interactive decision making method to obtain a satisfactory solution. In the proposed method, it is assumed that the decision maker has fuzzy goals for not only permissible probability levels but also the corresponding objective functions. Such fuzzy goals are quantified by eliciting the corresponding membership functions. Using the fuzzy decision, such two kinds of membership functions are integrated, andDf-Pareto optimal solution concept is defined in the integrated membership space. By using the bisection method and the convex programming technique, a satisfactory solution is obtained from among aDf-Pareto optimal solution set through the interaction with the decision maker.
Evolution of Three Norms of Distributive Justice in an Extended Nash Demand Game
Authors: Kojima Kazuaki ,   Graduate School of Information Science, Nagoya University, Furo-cho, Chikusa-ku, Nagoya 464-8601, Japan, Arita Takaya
Published: April 2016

The Nash demand game (NDG) has been at the center of attention when explaining moral norms of distributive justice on the basis of the game theory. This paper describes the demand-intensity game (D-I game), which adds an “intensity” dimension to NDG in order to discuss various scenarios for the evolution of norms concerning distributive justice, while keeping such simplicity that it can be analyzed by the concepts and tools of the game theory. We perform an ESS analysis and evolutionary simulations, followed by the analysis of replicator dynamics. It is shown that the three norms emerge: the one claiming an equal distribution (Egalitarianism), the one claiming the full amount (Libertarianism), and, as the special case of Libertarianism, the one claiming the full amount but conceding the resource in conflict (Wimpy libertarianism). The evolution of these norms strongly depends on the conflict cost parameter. Egalitarianism emerges with a larger conflict cost while Libertarianism with a smaller cost. Wimpy libertarianism emerges with a relatively larger conflict cost in libertarianism. The simulation results show that there are three types of evolutionary scenarios in general. We see in most of the trials the population straightforwardly converges to Libertarianism or Egalitarianism. It is also shown that, in some range of the conflict cost, the population nearly converges to Egalitarianism, which is followed by the convergence to Libertarianism. It is shown that this evolutionary transition depends on the quasi stability of Egalitarianism.
Switching Angles Optimization of Single Phase PWM DC-AC Inverter by Particle Swarm Optimizations
Authors: Shindo Takuya ,   Nippon Institute of Technology, 4-1 Gakuendai, Miyashiro-machi, Minamisaitama-gun, Saitama 345-8501, Japan, Jin’no Kenya
Published: April 2016

We consider the design procedure for a single-phase PWM DC-AC inverter using a particle swarm optimization algorithm. The switching operation is the most important component of the single-phase PWM DC-AC inverter. The PSO algorithm optimizes the switching angle effectively. The design procedure of the switching angle evaluates total harmonic distortion and the effective value of output. The proposed evaluation function restricts the scope to evaluating harmonic components. Based on numerical simulation results, we confirmed that the performance of the proposed design procedure was improved compared to the conventional sinusoidal PWM procedure. We develop an implementation circuit for our PWM DCAC inverter. By using the implemented circuit, we confirmed that results for implementation circuits are consistent with results for numerical simulations, indicating that the proposed algorithm exhibits better performance than the conventional sinusoidal PWM DC-AC inverter.
Foot Age Estimation System from Walking Dynamics Based on Fuzzy Logic
Authors: Takeda Takahiro ,   Graduate School of System Design, Tokyo Metropolitan University, 6-6 Asahigaoka, Hino, Tokyo 191-0065, Japan, Sakai Yoshitada , Kobashi Syoji , Kuramoto Kei , Hata Yutaka ,   Division of Rehabilitation Medicine, Graduate School of Medicine, Kobe University, 7-5-1 Kusukino, Kobe, Hyogo 650-0017, Japan,   Graduate School of Engineering, University of Hyogo, 2167 Shosha, Himeji, Hyogo 671-2280, Japan,   WPI Immunology Frontier Research Center, Osaka University, 3-1 Yamadaoka, Suita, Osaka 565-0871, Japan
Published: April 2016

This paper describes a foot-age estimation system based on fuzzy logic. The foot-age is one of age related indexes, and it shows the degree of aging by the gait condition. The system estimates the foot-age from sole pressure distribution change during walking. The sole pressure distribution is acquired by a mat-type load distribution sensor. Our estimation system extracts four gait features from sole pressure data, and calculates fuzzy degrees for young age,middle age and elderly age groups from these gait features. The footage of the walking person on the sensor is calculated by fuzzy MIN-MAX center of gravity method. In our experiment, we employed 93 male and 132 female volunteers, and the system estimated their foot-ages with low mean absolute error for their true ages. Additionally, we developed a diagnosis system based on estimated foot-age.
Decimation-Free Directional Filter Banks for Classification and Numbering on Posterior Dental Radiography Using Mesiodistal Neck Detection
Authors: Arifin Agus Zainal ,   Department of Informatics, Institut Teknologi Sepuluh Nopember (ITS), Kampus ITS, Surabaya, 60111, Indonesia, Yuniarti Anny , Khotimah Wijayanti Nurul , Wijaya Arya Yudhi , Hadi Ahmad Mustofa , Jawas Naser , Astuti Eha Renwi ,   Sekolah Tinggi Manajemen Informatika dan Teknik Komputer (STIKOM), Bali, Denpasar, Indonesia,   Department of Dental Radiography, Airlangga University, Surabaya, Indonesia
Published: April 2016

Dental classification and numbering on posterior dental radiography are important tasks for forensic and biomedical applications. This paper proposed a novel method of classification and numbering on posterior dental radiography using Decimation-Free Directional Filter Bank (DDFB) and mesiodistal neck detection. The method was started by a segmentation method for decomposing dental image into directional images using DDFB. Detection of mesiodistal neck tooth separated the crown and the root of teeth. Finally we used support vector machine for classification and numbering. The experimental results achieved a classification accuracy rate of 91%. It approved the robustness of the proposed method for solving the problem of dental classification and numbering.
Incremental Learning on a Budget and its Application to Quick Maximum Power Point Tracking of Photovoltaic Systems
Authors: Yamauchi Koichiro ,   Department of Computer Science, Chubu University, 1200 Matsumoto, Kasugai, Aichi 487-8501, Japan
Published: April 2016

Recent improvements in embedded systems has enabled learning algorithms to provide realistic solutions for system identification problems. Existing learning algorithms, however, continue to have limitations in learning on embedded systems, where physical memory space is constrained. To overcome this problem, we propose a Limited General Regression Neural Network (LGRNN), which is a variation of general regression neural network proposed by Specht or of simplified fuzzy inference systems. The LGRNN continues incremental learning even if the number of instances exceeds the maximum number of kernels in the LGRNN. We demonstrate LGRNN advantages by comparing it to other kernel-based perceptron learning methods. We also propose a light-weighted LGRNN algorithm, -LGRNNLight- for reducing computational complexity. As an example of its application, we present a Maximum Power Point Tracking (MPPT) microconverter for photovoltaic power generation systems. MPPT is essential for improving the efficiency of renewable energy systems. Although various techniques exist that can realize MPPT, few techniques are able to realize quick control using conventional circuit design. The LGRNN enables the MPPT converter to be constructed at low cost using the conventional combination of a chopper circuit and microcomputer control. The LGRNN learns the Maximum Power Point (MPP) found by Perturb and Observe (P&O), and immediately sets the converter reference voltage after a sudden irradiation change. By using this strategy, the MPPT quickly responds without a predetermination of parameters. The experimental results suggest that, after learning, the proposed converter controls a chopper circuit within 14 ms after a sudden irradiation change. This rapid response property is suitable for efficient power generation, even under shadow flicker conditions that often occur in solar panels located near large wind turbines.
Label Propagation for Text Classification Using Latent Topics
Authors: Eriguchi Akiko ,   Advanced Sciences, Graduate School of Humanities and Sciences, Ochanomizu University, 2-1-1 Otsuka, Bunkyo-ku, Tokyo 112-8610, Japan, Kobayashi Ichiro
Published: April 2016

The objective of this paper is to raise the accuracy of multiclass text classification through Graph-Based Semi-Supervised Learning (GBSSL). In GBSSL, it is essential to construct a proper graph which expresses the relation among nodes. We propose a method to construct a similarity graph by employing both surface information and latent information to express similarity between nodes. Experimenting on a Reuters-21578 corpus, we have confirmed that our proposal works well in raising the accuracy of GBSSL in a multiclass text classification task.
A Three-Dimensional Evaluation of EndoButton Displacement Direction After Anterior Cruciate Ligament Reconstruction in CT Image Using Tunnel Axis
Authors: Uozumi Yosuke ,   Graduate School of Engineering, University of Fukui, 3-9-1 Bunkyo, Fukui 910-8507, Japan, Nagamune Kouki , Nakano Naoki , Nagai Kanto , Nishizawa Yuichiro , Hoshino Yuichi , Matsushita Takehiko , Kuroda Ryosuke , Kurosaka Masahiro ,   Graduate School of Medicine, Kobe University, 7-5-2 Kusunoki-cho, Chuo-ku, Kobe 650-0017, Japan,   Kaisei Hospital, 3-11-15 Shinohara Kitamachi, Nada-Ku, Kobe 657-0068, Japan
Published: April 2016

The goal of this study was to propose a threedimensional evaluation of the EndoButton displacement direction after anterior cruciate ligament reconstruction in the multidetector-row computed tomography (MDCT) image by using the tunnel axis. The proposed method was applied experimentally to six subjects. The result of the simulated experiment revealed that the proposed method could analyze EndoButton displacement direction satisfactorily because the error was less than that of the MDCT image resolution. The clinical experiment results revealed displacement relative to the tunnel between time-zero and the followup point. We conclude that the proposed method can quantitatively evaluate the EndoButton displacement direction from the raw MDCT image after anterior cruciate ligament reconstruction; further, our findings suggest that the EndoButton was displaced relative to the tunnel between time-zero and the follow-up point.
Clinical Nurses’ Awareness Structure of Delirium – An Analysis of Spontaneous Utterances in a Group Interview by DEMATEL Method –
Authors: Kawaura Takayuki ,   Department of Mathematics, Kansai Medical University, 2-5-1 Shinmachi, Hirakata, Osaka 573-1010, Japan, Sugatani Yasuyuki ,   Center for Medical Education, Kansai Medical University, 2-5-1 Shinmachi, Hirakata, Osaka 573-1010, Japan
Published: April 2016

In the 1990s, the Japanese population aged 65 and over increased to more than 14%, and Japan became an “aging society.” Now, one in five people are 65 or over (23.4%, and one in ten people are 75 or over (1.6%, meaning that Japanese society is aging substantially. The serious problems that acute hospitals now face involve complications of diseases that are typified by deliriu, and their prevention. Patients with delirium have a higher risk of falling and dying, and delirium has a negative influence on treatment and nursing as well as on a patient’s vital prognosis. However, delirium is a mental state that is often overlooked. Thus, it is very important to develop the observation skills of staff and establish a nursing care system that does not overlook delirium. In this study, we conducted group interviews involving the clinical nurses who care for patients with delirium on a routine basis at Kansai Medical University Takii Hospital, Japan. Their spontaneous utterances about delirium were analyzed using the DEMATEL method, and these utterances were divided into two groups: “causes of delirium” and “delirious patients’ behavior.” From each group, keywords and phrases were chosen and analyzed. Consequently, this study will demonstrate how these clinical nurses feel about delirium and delirious patients.
Fuzzy Multisets in Granular Hierarchical Structures Generated from Free Monoids
Authors: Murai Tetsuya ,   Graduate School of Information Science and Technologies, Hokkaido University, Kita 14, Nishi 9, Kita-ku, Sapporo, Hokkaido 060-0814, Japan, Miyamoto Sadaaki , Inuiguchi Masahiro , Kudo Yasuo , Akama Seiki ,   Graduate School of Systems and Information Engineering, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki 305-8577, Japan,   Graduate School of Engineering Science, Osaka University, 1-3 Machikaneyama, Toyonaka, Osaka 560-8531, Japan,   Department of Computer Science and Systems Engineering, Muroran Institute of Technology, 27-1 Miumoto, Muroran, Hokkaido 050-8585, Japan,   C-Republic, 1-20-1 Higashi-Yurigaoka, Asoh-ku, Kawasaki, Kanagawa 215-0012, Japan
Published: April 2016

Fuzzy multisets defined by Yager take multisets on interval (0,1] as grades of membership. As Miyamoto later pointed out, the fuzzy multiset operations originally defined by Yager are not compatible with those of fuzzy sets as special cases. Miyamoto proposed different definitions for fuzzy multiset operations. This paper focuses on the two definitions of fuzzy multiset operations, one by Yager and the other by Miyamoto. It examines their differences in the framework of granular hierarchical structures generated from the free monoids as proposed in our previous papers. In order to define basic order between multisets on interval (0,1], Yager uses the natural order on the rangeN, the set of natural numbers, whereas Miyamoto newly introduces an order generated frombothdomain (0,1] and rangeNthrough the notion of cuts.
Semi-Supervised Sequential Kernel Regression Models with Penalty Functions
Authors: Tang Hengjin ,   Department of Risk Engineering, School of Systems and Information Engineering, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki 305-8573, Japan, Miyamoto Sadaaki , Endo Yasunori
Published: April 2016

Switching regression models can output multiple clusters and regression models. However, there is one problem: the results have a strong dependency on the predefined number of clusters. To avoid these drawbacks, we have researched sequential extractions. In sequential extractions process, one cluster is extracted at a time using a method of noise-detection, and the number of clusters are determined automatically. We propose semi-supervised sequential kernel regression models with penalty functions. Additionally, we also find that the sensitivity against the regularization parameter λ can be alleviated by semi-supervisions using penalty functions. We show the effectiveness of the proposed method by using numerical examples.
Classification of Evoked Emotions Using an Artificial Neural Network Based on Single, Short-Term Physiological Signals
Authors: Zhang Shanbin ,   College of Electronic and Information Engineering, Southwest University, No.2 of Tiansheng Road, BeiBei District, Chongqing 400715, China, Liu Guangyuan , Lai Xiangwei ,   Computer and Information Science College, Southwest University, No.2 of Tiansheng Road, BeiBei District, Chongqing 400715, China
Published: April 2016

Most automated analysis methods related to biosignalbased human Emotions collect their data using multiple physiological signals, long-term physiological signals, or both. However, this restricts their ability to identify Emotions in an efficient manner. This study classifies evoked Emotions based on two types of single, short-term physiological signals: electrocardiograms (ECGs) and galvanic skin responses (GSRs) respectively. Estimated recognition times are also recorded and analyzed. First, we perform experiments using film excerpts selected to elicit target Emotions that include anger, grief, fear, happiness, and calmness; ECG and GSR signals are collected during these experiments. Next, a wavelet transform is applied to process the truncated ECG data, and a Butterworth filter is applied to process the truncated GSR signals, in order to extract the required features. Finally, the five different Emotion types are classified by employing an artificial neural network (ANN) based on the two signals. Average classification accuracy rates of 89.14% and 82.29% were achieved in the experiments using ECG data and GSR data, respectively. In addition, the total time required for feature extraction and emotional classification did not exceed 0.15 s for either ECG or GSR signals.
Camera Modeling for 3D Sensing Using Fuzzy Modeling Concept Based on Stereo Vision
Authors: Watanabe Toshihiko ,   Faculty of Engineering, Osaka Electro-Communication University, 18-8 Hatsu-cho, Neyagawa, Osaka 572-8530, Japan, Saito Yuichi ,   System Development Division, DACS Corporation, 1-4-8 Kawaramachi, Chuo-ku, Osaka, Osaka 541-0048, Japan
Published: April 2016

Recently, the three-dimensional (3D) sensing technique that uses multiple cameras has been applied to various areas, such as visualization, motion capturing, and so on. However, improvement of camera model calibration is required for higher precision of measurements. In this study, we propose a practicable fuzzy modeling approach for 3D sensing that utilizes stereo vision configuration. The distance between a sensing target and the camera is used to construct a camera fuzzy model that considers optical projection characteristics. In our approach, the weighted least squares method is successfully applied considering a fuzzy partition to formulate the fuzzy model. Then, iterative calculations for solving the inverse problem of the camera fuzzy model are performed to obtain measured coordinates. Through sensing experiments of stereo vision measurement based on the proposed approach, we show that performance of the model is drastically improved compared with the conventional modeling approach.
A Switched Extend Kalman-Filter for Visual Servoing Applied in Nonholonomic Robot with the FOV Constraint
Authors: Fang Yuan ,   School of Information Science and Engineering, Central South  University, Xiaoyong Zhang , Zhiwu Huang , Yu Wentao , Wang Yabo
Published: April 2016

In this paper, a switched Kalmanfilter (KF) is used to predict the status of feature points leaving the field of view (FOV), which is one of the most common constraints in FOV. By using the prediction of status to compensate for the real state of feature points, nonholonomic robots conduct visual servoing tasks efficiently. Results of simulation and experiments verify the effectiveness of the proposed approach.
A Hierarchical Experimental Simulation Platform of Coking Production
Authors: Yan Hui ,   School of Information Science and Engineering, Central South University, Lei Qi , Wu Min ,   School of Automation, China University of Geosciences
Published: April 2016

The complexity of coking production and the correlations among the three major processes involved make it difficult to study and apply effective methods in practice. We have designed a hierarchical simulation platform for coking production in coke ovens for experiments and the validation of the methods used. To handle problems in processing and obtain the comprehensive production targets, the simulation platform provides reliable, easy-to-use conditions for coking production research, which has the functions of simulating processes, examining methods for experiments, monitoring production status and coordinating optimization. To implement these functions, the platform has a three-layer structure and flexible communication interfaces. Results of experiments have demonstrated the simulation platform’s effectiveness and feasibility.
Location Detection of Informative Bright Region in Tunnel Scenes Using Lighting and Traffic Lane Cues
Authors: Lu Jiajun ,   Department of Computational Intelligence and Systems Science, Tokyo Institute of Technology, Dong Fangyan , Hirota Kaoru ,   Education Academy of Computational Life Sciences, Tokyo Institute of Technology
Published: April 2016

To locate Informative Bright Region (IBR) in which visual information is missing owing to limited dynamic range of image sensor, an algorithm is proposed that combines the geometric properties of visual cues into a confidence map. The location of an IBR in a road tunnel scene is estimated in real-time under the condition in which most of the vision information inside the IBR is lost. The algorithm is evaluated by comparing the estimated location of IBR with that annotated by multiple human observers in a self-built tunnel scene video dataset recorded by a car-mounted camera, and the algorithm achieves a running time of 10 ms for each frame. The algorithm aims to provide control timing of imaging sensor on a low-cost platform such as a vehicle driving recorder to enhance the visual contents captured in over-exposed regions.
Measuring the Polarization Degree Based on Optimal Window Median Filtering Algorithm
Authors: Lv Yange ,   School of Electrical Engineering and Automation, Guilin University of Electronic Technology, Pang Liyan , Zhu Guohun ,   Faculty of Health, Engineering and Sciences, University of Southern Queensland Toowoomba
Published: April 2016

Polarization degree measurement requires that the three input axes be orthogonal. This prevents this method from being used if input angles are nonorthogonal. This paper proposes a method for optimal window median filtering preprocessing to measure the polarization degree of nonorthogonal angles. A 7×7 window is selected to filter out most noise and identify an object from polarization degree images. Compared to conventional algorithms, the proposed method selects three nonorthogonal angles, which is applied to measuring the polarization degree of dynamic objects.
Motion Control of a Wheeled Inverted Pendulum Using Equivalent-Input-Disturbance Approach
Authors: Shi Qi ,   Graduate School of Bionics, Computer and Media Sciences, Tokyo University of Technology, Fang Zhejun , She Jinhua , Imani Junya , Ohyama Yasuhiro ,   School of Computer Science, Tokyo University of Technology
Published: April 2016

This paper presents a new method for controlling the motion of a wheeled inverted pendulum (WIP) based on the equivalent-input-disturbance (EID) approach. Coordinate transformation first transforms the WIP into a simple nonlinear system divided into linear and nonlinear parts. The nonlinear part is then treated as a state-and-input-dependent disturbance, and the EID approach is used to estimate and compensate it. Simulation results of an NXTway-GS demonstrate the validity of the method.
Still Corresponding Points Extraction Using a Moving Monocular Camera with a Motion Sensor
Authors: Akamatsu Toshihiro ,   Department of Computational Intelligence and Systems Science, Tokyo Institute of Technology, Dong Fangyan , Hirota Kaoru
Published: April 2016

The method of extracting still corresponding points proposed in this paper uses a moving monocular camera connected to a 6-axis motion sensor. It classifies corresponding points between two consecutive frames containing still/moving objects and chooses corresponding points appropriate for 3D measurement. Experiments are done extracting still corresponding points with 2 scenes from original computer graphics images. Results for scene 1 show that accuracy is 0.98, precision 0.96, and recall 1.00. Robustness against sensor noise is confirmed. Extraction experiment results with real scenes show that accuracy is 0.86, precision 0.88, and recall 0.94. We plan to include the proposed method in 3D measurement with real images containing still/moving objects and to apply it to obstacles avoidance for vehicles and to mobile robot vision systems.
The Research of the Distributed Resource-AwareK-means Clustering Algorithm
Authors: Wang Xiaoni ,   School of Applied Science, Beijing Information Science and Technology University
Published: April 2016

According to the characteristics of the constrained resource in distributed real-time data mining in the Internet of Things (IOT) environment, a distributed data mining method is researched in such environment. Based on the limits of computing ability, storage ability, battery energy resources, network bandwidth, and the Internet single point failure, the distributed network data mining method is researched, and the adaptive technology and peer-to-peer node method are adopted. The DRA-Kmeans algorithm of data mining based on theK-means algorithm is proposed, and the amount of data communication among the sites to reduce the number of iterations and clustering is reduced. Clustering efficiency is improved, and better clustering results and execution efficiency are achieved.