bluuebunny/crossref_metadata_2025 huggingface dataset:
Ink Diffusion Simulation for 3D Virtual Calligraphy
Authors: Shin Jungpil ,   Graduate School of Computer Science and Engineering, The University of Aizu, Aizu-Wakamatsu City, Fukushima 965-8580, Japan, Marumoto Makoto
Published: April 2016

Calligraphy is one of traditional culture in Japan. In calligraphy, writers express their feeling by using various shapes of strokes and various effects such as scratchiness and ink diffusion phenomenon. Therefore, scratchiness and ink diffusion phenomenon are crucial features in calligraphy. The purpose of this paper is realization of ink diffusion phenomenon which is one of the important features in 3D Virtual Calligraphy Simulation. Ink diffusion phenomenon is influenced by various factors such as the conditions of the ink, paper, and brush. In this paper, the extent of ink diffusion phenomenon and ink color is considered on density of ink and quantity of water. Considering these factors, to express ink diffusion phenomenon in 3D Virtual Calligraphy Simulation in real time, new drop model was used. Ink diffusion phenomenon occurs when ink is diffused circularly. Using this principle, we created a drop model and realized ink diffusion phenomenon. With this method, a feathery, blurred edge that is characteristic of ink diffusion phenomenon is also able to be expressed.
Nonlinear Friction Estimation in Elastic Drive Systems Using a Dynamic Neural Network-Based Observer
Authors: Jafari Amir Hossein ,   School of Electrical and Computer Engineering, Oklahoma State University, Stillwater, OK, USA, Dhaouadi Rached , Jhemi Ali ,   College of Engineering, American University of Sharjah, Sharjah, United Arab Emirates
Published: April 2016

This paper presents a neural-network based observer for nonlinear elastic drive systems. The proposed nonlinear observer uses a Diagonal Recurrent Neural Network (DRNN) combined with the dynamics of a linear Two-Mass-Model (2MM) system to identify nonlinear characteristics of the drive system such as Coulomb and nonlinear viscous friction torques. Theoretical analysis of the proposed neural-network based observer, including the neural network structure and the training algorithm convergence, are presented and discussed. Simulation results are confirmed experimentally using a 2MM system setup.
Aligning Mental Representations
Authors: Glückstad Fumiko Kano ,   Department of International Business Communication, Copenhagen Business School, Dalgas Have 15, DK-2000 Frederiksberg, Denmark
Published: April 2016

This work introduces a framework that implements asymmetric communication theory proposed by Sperber and Wilson [1]. The framework applies a generalization model known as the Bayesian model of generalization (BMG) [2] for aligning knowledge possessed by two communicating parties. The work focuses on the application of the BMG to publicly available datasets, the Leuven natural concept database [3] representing semantic structures of domain knowledge possessed by individual subjects [3]. Results indicate that the BMG is potentially a model applicable to simulating the alignment of domain knowledge from the information receiver’s viewpoint.
Improving the Search Ability of Tabu Search in the Distribution Network Reconfiguration Problem
Authors: Takano Hirotaka ,   Faculty of Information Science and Electrical Engineering, Kyushu University, 744 Motooka, Nishi-ku, Fukuoka-shi, Fukuoka 819-0395, Japan, Murata Junichi , Maki Yukino , Yasuda Makoto ,   Department of Electrical and Information Engineering, Gifu National College of Technology, 2236-2 Kamimakuwa, Motosu-shi, Gifu 501-0495, Japan
Published: April 2016

The distribution network reconfiguration problem is to decide whether each sectionalizing switch is to be open or closed in order to maintain or improve electrical power supply reliability, power quality and network operation efficiency. Obtaining the optimal network configuration is, however, extremely difficult because the network reconfiguration problem is actually a large-size combinatorial optimization problem. Many optimization algorithms have thus been applied to the reconfiguration problem to support power utility’s decision-making. This paper proposes a local search-based solution for the reconfiguration problem in which tabu search – one of the most widely used local search-based metaheuristics – is employed to solve the problem. Tabu search is improved by introducing an effective search strategy that utilizes the properties of this kind of problems. Numerical simulations are performed on a complex actual-scale distribution network model in order to verify the validity of the proposed solution.
An Influence of Player’s Ballot-Weight Changing in Consultation Algorithm
Authors: Touma Keisuke ,   Graduate School of Engineering and Science, University of the Ryukyus, 1 Senbaru, Nishihara, Okinawa 903-0213, Japan, Endo Satoshi , Toma Naruaki , Akamine Yuhei , Yamada andKoji ,   Department of Information Engineering, University of the Ryukyus, 1 Senbaru, Nishihara, Okinawa 903-0213, Japan
Published: April 2016

The validity of the consultation algorithm is shown in computer shogi. The consultation algorithm determines a move by votes of computer shogi players. In this research, we discuss a method that changes player vote weights during a game. To change player vote weights, we use the selection history of the move and the evaluation function value. Experimental results suggests the validity of our proposed method.
Acceleration of Reinforcement Learning with Incomplete Prior Information
Authors: Terashima Kento ,   Department of Electrical and Electronic Engineering, Kyushu University, 744 Motooka, Nishi-ku, Fukuoka 819-0395, Japan, Takano Hirotaka , Murata Junichi
Published: April 2016

Reinforcement learning is applicable to complex or unknown problems because the solution search process is done by trial-and-error. However, the calculation time for the trial-and-error search becomes larger as the scale of the problem increases. Therefore, in order to decrease calculation time, some methods have been proposed using the prior information on the problem. This paper improves a previously proposed method utilizing options as prior information. In order to increase the learning speed even with wrong options, methods for option correction by forgetting the policy and extending initiation sets are proposed.
Design of Compensator for Input Dead Zone of Actuator Nonlinearities
Authors: Chen Weijie ,   School of Information Science and Engineering, Central South University, 932 Lushan South Road, Yuelu District, Changsha 410083, China, Wu Jundong , She Jinhua ,   Hunan Engineering Laboratory for Advanced Control and Intelligent Automation, 932 Lushan South Road, Yuelu District, Changsha 410083, China,   School of Electronics Engineering and Computer Science, Peking University, 45 Jia, Peking University, Haidian District, Beijing 100871, China,   School of Computer Science, Tokyo University of Technology, 1404-1 Katakura, Hachioji, Tokyo 192-0987, Japan
Published: April 2016

This paper considers the problem of tracking a periodic signal for a plant with an input dead zone in the actuator. The nonlinearity greatly degrades control performance. To solve this problem, we incorporate an equivalent-input-disturbance (EID) compensator into a repetitive control system (RCS), resulting in a new system configuration. We combine the linear-matrixinequality technique with singular-value decomposition to analyze the stability of the system and to devise a design method. Unlike other methods, this one does not require any information about the dead zone. Simulation results demonstrate its effectiveness.
An Automatic Three-Dimensional Evaluation of Screw Placement After Anterior Cruciate Ligament Reconstruction Using MDCT Images
Authors: Uozumi Yosuke ,   Department of Advanced Interdisciplinary Science and Techonology, Graduate School of Engineering, University of Fukui, 3-9-1 Bunkyo, Fukui 910-8507, Japan, Nagamune Kouki , Nishizawa Yuichiro , Araki Daisuke , Hoshino Yuichi , Matsushita Takehiko , Kuroda Ryosuke , Kurosaka Masahiro ,   Department of Human and Artificial Intelligence Systems, Graduate School of Engineering, University of Fukui, 3-9-1 Bunkyo, Fukui 910-8507, Japan,   Department of Orthopaedic Surgery, 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

A metallic interference screw, which is a fixation device used in anterior cruciate ligament (ACL) reconstruction, is important for fixing the grafts to the tibia. Loosening of fixation sometimes happens because the screw is displaced time passes, which results in instability of the knee. It is therefore important to analyze screw displacement highly accurately. The purpose of this study is to propose an evaluation of screw displacement in MDCT imaging of the knee after ACL reconstruction. Clinical experiments used 22 MDCT images that are not displaced in the clinical sense. Results of simulation experiments show that error was 0.14 mm, which was less thanMDCT image resolution. This study concluded that the proposed method useful in evaluating two MDCT images at different time points.
Modeling and Simulation of Road Traffic Behavior: Artificial Drivers with Personality and Emotions
Authors: Leu George ,   Department of Computer Science, School of Engineering and Information Technology, University of New South Wales Canberra, the Australian Defence Force Academy, Po BOX 7916, Canberra BC 2610, Australia, Curtis Neville J. , Abbass Hussein ,   Defence Science and Technology Organisation, Edinburgh, SA, Australia
Published: April 2016

This study uses an artificially generated, multi-agent traffic environment to bring to attention the behavioral aspects involved in the performance of road traffic networks. Artificial drivers with behavioral capabilities are used to understand how collective human behavior affects traffic performance. These drivers are also used to find optimal behaviors in relation to performance metrics such as network transit time. This paper demonstrates that differences in the distribution of personality features of drivers can generate significant alterations in overall system performance. This can yield to significantly different estimations of the risk levels when compared to results coming from non-behavioral evaluation tools.
Construction of a Molecular Learning Network
Authors: Shirakawa Tomohiro ,   Department of Computer Science, National Defense Academy of Japan, 1-10-20 Hashirimizu, Yokosuka, Kanagawa 239-8686, Japan, Sato Hiroshi
Published: April 2016

Learning ability in unicellular organisms has been studied since the first half of the 20th century, but there is still no clear evidence of unicellular learning. Based on results from previous associative learning experiments using thePhysarumplasmodium, a gene regulatory network model of unicellular learning was constructed. The model demonstrates that, in principle, unicellular learning can be achieved through the cooperation of several biomolecules.
Fuzzy Inference Based Vehicle to Vehicle Network Connectivity Model to Support Optimization Routing Protocol for Vehicular Ad-Hoc Network (VANET)
Authors: Lin Chehung ,   Department of Computational Intelligence and Systems Science, Tokyo Institute of Technology, G3-49, 4259 Nagatsuta, Midori-ku, Yokohama 226-8502, Japan, Dong Fangyan , Hirota Kaoru
Published: April 2016

A Fuzzy Inference based Vehicle to Vehicle Network Connectivity Model is proposed to support Optimization Routing Protocol for Vehicular Ad-hoc Network (VANET), where the real-time vehicle to vehicle network connectivity situation of road segments is expressed using fuzzy inference according to the vehicle distribution situation, and the optimized routing protocol modifies the transmission path dynamically and optimizes packet forwarding. The proposed model expresses the real-time vehicle to vehicle network connectivity of each road segment that cannot be easily expressed directly by a mathematical model and decreases the end-to-end delay and the overall network control overhead. The computation time of the proposed protocol is analyzed and shown asO(IlgI+R+V) whereI,R, andVrepresent the number of intersections on a map, the number of road segments on a map, and the number of vehicles within communication range of the vehicle that wants to transfer a data packet, respectively. The simulation tools NS2 and TraNS are used to perform experiments that include wireless data packet transmission and vehicle mobility traces. The results show that the proposed method decreases end-to-end delay and decreases the control overhead by 20% compared with other routing protocols, e.g. GyTAR and RTRP. This proposal implements an intelligent transportation system application and a traffic-monitoring system in NS2 using the optimization routing protocol. This protocol will be implemented to develop a real vehicle telematics system using the embedded system to improve the user-driving experience.
Atmosphere Understanding for Humans Robots Interaction Based on SVR and Fuzzy Set
Authors: Ohnishi Kazuhiro ,   Department of Computational Intelligence and Systems Science, Tokyo Institute of Technology, G3-49 4259 Nagatsuta, Midori-ku, Yokohama 226-8502, Japan, Dong Fangyan , Hirota Kaoru
Published: April 2016

A method for understanding the atmosphere is proposed for humans-robots interactions in a multi-agent society, where the individual assessment of the atmosphere is estimated using a Support Vector Regression (SVR) method that represents the emotions of all agents and the atmosphere of the entire society is represented as a fuzzy set in a Fuzzy Atmosfield. This method provides the necessary information that allows each agent (human/robot) in the society to understand the differences between the objective characteristics of the atmosphere and the agent’s individual assessment of the subjective atmosphere and to make appropriate behavioral decisions thereafter. In the experiments, 13 scenarios are tested by four humans. The characteristics of the atmosphere are calculated by applying the proposed method to the emotion data from the four humans. The results are compared with the subjective atmosphere information from the four humans and it is found that the average accuracy reaches 90%. This proposal is planned in order to realize customized services for the humans-robots interactions in a “Multi-Agent Fuzzy Atmosfield,” which is the subject of the authors’ group’s ongoing research project.
Formation Control with Event-Triggered Strategy for Multi-Agent Systems
Authors: Zhou Feng ,   School of Information Science and Engineering, Central South University, Hunan Engineering Laboratory for Advanced Control and Intelligent Automation, Changsha, Hunan, 410075, China, Huang Zhiwu , Liu Weirong , Li Liran
Published: April 2016

This paper considers cooperative formation control on networked multi-agent systems, in which the mobile agents have limited resources. Two event-based strategies are introduced to reduce resource utilization and control actuation in formation control. In one strategy, the event trigger function is designed to use system state, whereas in the other, it is designed to use control input. Theoretical and experimental analyses of the pros and cons of the two strategies are given. In addition, the stabilities of the two event-triggered formation control laws are discussed. The results of simulations conducted confirm the feasibility and effectiveness of the proposed methods.
Recommender System Employing Personal-Value-Based User Model
Authors: Hattori Shunichi ,   Graduate School of System Design, Tokyo Metropolitan University, 6-6 Asahigaoka, Hino, Tokyo 191-0065, Japan, Takama Yasufumi
Published: April 2016

This paper proposes a recommender system based on personal-value-based user model. Conventional methods such as collaborative and content-based approaches tend to be less accurate for new users and items due to the lack of a relation between items and user preferences. While existing recommender systems usually employ user preferences of items for making recommendations, proposed method focuses on users’ personal values, which mean value judgments regarding on which attributes users put a high priority. The proposed recommender system employing personal-value-based user model is thus expected to realize more precise recommendations in cold-start situations. As one of typical cold-start situations, a prototype system is developed for recommendation using external resources. Experimental results show that generated user models reflect each user’s value judgment on attributes. In addition, the results also show that recommendation employing the proposed user model realizes improvements of precision in cold-start situations.
Fuzzy Co-Clustering Algorithms Based on Fuzzy Relational Clustering and TIBA Imputation
Authors: Kanzawa Yuchi ,   Shibaura Institute of Technology, 3-7-5 Toyosu, Koto, Tokyo 135-8548, Japan
Published: April 2016

In this paper, two types of fuzzy co-clustering algorithms are proposed. First, it is shown that the base of the objective function for the conventional fuzzy co-clustering method is very similar to the base for entropy-regularized fuzzy nonmetric model. Next, it is shown that the non-sense clustering problem in the conventional fuzzy co-clustering algorithms is identical to that in fuzzy nonmetric model algorithms, in the case that all dissimilarities among rows and columns are zero. Based on this discussion, a method is proposed applying entropy-regularized fuzzy nonmetric model after all dissimilarities among rows and columns are set to some values using a TIBA imputation technique. Furthermore, since relational fuzzy cmeans is similar to fuzzy nonmetricmodel, in the sense that both methods are designed for homogeneous relational data, a method is proposed applying entropyregularized relational fuzzyc-means after imputing all dissimilarities among rows and columns with TIBA. Some numerical examples are presented for the proposed methods.
Complex Control Models with Parametric Families of Fuzzy Constrains in Evaluation of Resort Management System
Authors: Rakus-Andersson Elisabeth ,   Department of Mathematics and Science, Blekinge Institute of Technology, 37179 Karlskrona, Sweden
Published: April 2016

In this paper we propose a complex system, involving two control algorithms, to provide a final estimation of Resort Management System (RMS). This distinct RMS quality value depends on some individual appreciations, assigned by customers to basic services. In order to improve the qualities of control actions, we intend to add parametric membership functions of fuzzy sets to the fuzzification part. Another modification considers the newly designed technique of determining some essential estimates in the processing part of control to employ all entry data in the result of final decision.
Rough Set Approach with Imperfect Data Based on Dempster-Shafer Theory
Authors: Nguyen Do Van ,   Department of Management and Information Systems Science, Nagaoka University of Technology, 1603-1 Kamitomioka, Nagaoka, Niigata 940-2188, Japan, Yamada Koichi , Unehara Muneyuki
Published: April 2016

Original rough set theory deals with precise and complete data, even though real applications frequently contain imperfect information. Missing values are typical imperfect data studied in rough set research. Many ideas have been proposed in the literature to solve the issue of imperfect data, but hardly a single solution is sufficient for multiple types of imperfect data containing imprecision and uncertainty. The paper models some basic relations between objects with respect to an imperfect attribute value using the Dempster-Shafer theory of evidence, and defines uncertain relations between objects with multiple imperfect attribute values by combining basic relations defined in a single attribute. It also proposes new rough set models based on these basic relations and discusses the properties of these models.
Multi-qExtension of Tsallis Entropy Based Fuzzyc-Means Clustering
Authors: Yasuda Makoto ,   Department of Electrical and Computer Engineering, Gifu National College of Technology, 2236-2 Kamimakuwa, Motosu-shi, Gifu 501-0495, Japan, Orito Yasuyuki
Published: April 2016

Tsallis entropy is aq-parameter extension of Shannon entropy. Based on the Tsallis entropy, we have introduced an entropy maximization method to fuzzyc-means clustering (FCM), and developed a new clustering algorithm using a single-qvalue. In this article, we propose a multi-qextension of the conventional single-qmethod. In this method, theqs are assigned individually to each cluster. Eachqvalue is determined so that the membership function fits the corresponding cluster distribution. This is done to improve the accuracy of clustering over that of the conventional single-qmethod. Experiments are performed on randomly generated numerical data and Fisher’s iris dataset, and it is confirmed that the proposed method improves the accuracy of clustering and is superior to the conventional single-qmethod. If the parameters introduced in the proposed method can be optimized, it is expected that the clusters in data distributions that are composed of clusters of various sizes can be determined more accurately.
Classification of Informative Reviews Based on Personal Values
Authors: Takama Yasufumi ,   Graduate School of System Design, Tokyo Metropolitan University, 6-6 Asahigaoka, Hino, Tokyo 191-0065, Japan, Mao Zhongjie , Hattori Shunichi
Published: April 2016

This paper proposes a method for classifying informative reviews based on personal values. Reviews of an item are useful for a user who is considering purchasing it. However, it is difficult for readers to find informative reviews from vast amount of reviews because of existence of too many uninformative reviews. This paper supposes that the value of a review is affected by reader-dependent and independent factors. Typical uninformative reviews in terms of reader-independent factor are copy-and-paste reviews, which do not provide any readers with useful information for their decision-making. On the other hand, it is supposed different readers regard different reviews as informative, which is affected by their personal values. This paper focuses on such a readerdependent factor, and proposes a methods for classifying informative reviews based on reader’s personal value. Experiments are conducted using actual review data provided by Rakuten Inc., of which the results show about 0.7 of average accuracy is achieved. Furthermore, it is also shown proposed method can model judging criteria common to those who have similar personal values.
Integration of Results from Recognition Algorithms Applied to the Uranium Deposits
Authors: Muhamediyev Ravil I. ,   International IT University, 34A/8A, Manas str./Zhandosov str., Almaty 050040, Kazakhstan, Amirgaliyev Yedilkhan , Iskakov Syrymbet Kh. , Kuchin Yan I. , Muhamediyeva Elena ,   Institute of Problems of Information and Control, Ministry of Education and Science of the Republic of Kazakhstan, 125 Pushkina str., Almaty 050010, Kazakhstan,   LLC Geotechnoservice, Kazatomprom, 156-156a, Bogenbay-batyr str., Almaty 050020, Kazakhstan,   Riga Technical University, 1 Kaļķu str., Riga LV-1658, Latvia
Published: April 2016

Data interpretation of electric logging can be performed using self-learning systems such as artificial neural networks (ANNs). Preliminary research shows that by using ANN we can achieve 52-73% of coincidence of interpretable data and experimental results. Therefore, it is necessary to analyze the possibility of using other classification algorithms, and that of using several classification algorithms simultaneously through a unified system (referred to as anintegrator. These algorithms may improve the quality of recognition of individual species. The problem of developing a recognition system that combines several classification algorithms, also known as theintegrator, is formulated here. A simple algorithm is developed for the learning and recognition of an integrator for the post processing stage; this enhances the recognition accuracy by 1-3%.
Improve Discontinuous Output Change in SpikeProp
Authors: Takase Haruhiko ,   Graduate School of Engineering, Mie University, 1577, Kurima-Machiya, Tsu, Mie 514-8507, Japan, Kawanaka Hiroharu , Tsuruoka Shinji ,   Graduate School of Regional Innovation Studies, Mie University, 1577, Kurima-Machiya, Tsu, Mie 514-8507, Japan
Published: April 2016

We try to improve discontinuous output change in SpikeProp. The problem is that small variants in input cause significant change in output. We first show that peaks in activity cause the problem. We then propose reducing the height of peaks by weight decay. Through experiments, we conclude that the square type of weight decay is suitable for solving the problem, because it reduces to more than half the patterns that cause discontinuous output change around them.
Hierarchical Semi-Supervised Factorization for Learning the Semantics
Authors: Shen Bin ,   Computer Science Department, Purdue University, West Lafayette, IN., 47907, USA, Makhambetov Olzhas ,   Computer Science Laboratory, Nazarbayev University Research and Innovation System, 53, Kabanbay batyr ave., Astana, Kazakhstan
Published: April 2016

Most semi-supervised learning methods are based on extending existing supervised or unsupervised techniques by incorporating additional information from unlabeled or labeled data. Unlabeled instances help in learning statistical models that fully describe the global property of our data, whereas labeled instances make learned knowledge more human-interpretable. In this paper we present a novel way of extending conventional non-negativematrix factorization (NMF) and probabilistic latent semantic analysis (pLSA) to semi-supervised versions by incorporating label information for learning semantics. The proposed algorithm consists of two steps, first acquiring prior bases representing some classes from labeled data and second utilizing them to guide the learning of final bases that are semantically interpretable.
3D Elastic Deformable Object Model for Robot Manipulation Purposes
Authors: Sahari Khairul Salleh Mohamed ,   Centre for Advanced Mechatronics and Robotics, Universiti Tenaga Nasional, Jalan IKRAM-UNITEN, 43000 Kajang, Selangor, Malaysia, Hou Yew Cheong
Published: April 2016

This paper presents a mass-spring model applied to the manipulation of an elastic deformable object for home service robot application. A system is also proposed that is used to fold a piece of rectangular cloth from a specific initial condition using a robot. The cloth is modeled as a three-dimensional object in a two-dimensional quadrangular mesh based on a massspring system, and its state is estimated using an explicit integration scheme that computes the particle position as a function of the internal and external forces acting on the elastic deformable object. The current state of the elastic deformable object under robot manipulation is tracked based on the trajectory of the mass points in the mass-spring system model in a self-developed simulator, which integrates a massspring model and a five-degree-of-freedom articulated robotic arm. To test the reliability of the model, the simulator is used to predict the best possible paths for using the robotic arm to fold a rectangular cloth into two. In the test, the state of the object is derived from the model and then compared with the results of a practical experiment. Based on the test, the error is found to be generally acceptable. Thus, this model can be used as an estimator for the vision-based tracking of the state of an elastic deformable object for manipulation by home service robots.
Extraction of Food-Related Onomatopoeia from Food Reviews and its Application to Restaurant Search
Authors: Kato Ayumi ,   Department of Life Support Technology (Molten), The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan, Fukazawa Yusuke , Sanada Hiromi , Mori Taketoshi ,   Service and Solution Development Department, NTT DoCoMo Inc., NTT DoCoMo R&D Center, 3-5 Hikarinooka, Yokosuka, Kanagawa 239-8536, Japan
Published: April 2016

Onomatopoeia has been widely used recently in food reviews about food or restaurants. In this paper, we propose and evaluate a method to automatically extract onomatopoeia including unknown ones from food reviews sites. From the evaluation result, we found that it is able to extract onomatopoeia for specific foods with more than 46% precision; the method found 18 new unknown food-related onomatopoeias, i.e., not registered in an existing onomatopoeia dictionary, and in 62 extracted onomatopoeias. In addition, we propose a system that can present the user with a list of onomatopoeia specific to a restaurant she/he is interested in. The evaluation results indicate that an intuitive restaurant search can be done via a list of onomatopoeia, and that they are helpful for selecting food or restaurants.
Performance Analysis for First-Order Configuration Prediction for Redundant Manipulators Based on Avoidance Manipulability
Authors: Yanou Akira ,   Graduate School of Natural Science and Technology, Okayama University, 3-1-1 Tsushimanaka, Kitaku, Okayama 700-8530, Japan, Hou Yang , Minami Mamoru , Kobayashi Yosuke
Published: April 2016

This paper explores a performance of first-order configuration prediction for redundant manipulators based on avoidance manipulability in order to achieve an on-line control of trajectory tracking and obstacle avoidance for redundant manipulators. In the trajectory tracking process, manipulator is required to keep a configuration with maximal avoidance manipulability in real time. Predictive control in this paper uses manipulators’ future configurations to control current configuration aiming at completing tasks of trajectory tracking and obstacle avoidance on-line and simultaneously with higher avoidance manipulability. We compare Multi-Preview Control with predictive control using redundant manipulator, and show the results through simulations. The effectiveness of predictive control using first-order configuration prediction is also validated in the case of not only straight target trajectory but also curve target trajectory. In addition, an influence of measurement noise on manipulator’s joint angle is newly considered.
Development of a Robotic Boiler Header Inspection Device with Redundant Localization System
Authors: Basri Nur Maisurah Hassan ,   Centre for Advanced Mechatronics and Robotics, Universiti Tenaga Nasional, Jalan IKRAM-UNITEN, 43000 Kajang, Selangor, Malaysia, Sahari Khairul Salleh Mohamed , Anuar Adzly
Published: April 2016

A pipe inspection robot, called a boiler header inspection robot (BHIR), is presented in this paper. The BHIR was designed specifically to inspect the inner surfaces of horizontal boiler header pipes in a thermal power plant in Malaysia that is owned by Tenaga Nasional Berhad (TNB). The main challenge was the geometry of a boiler header pipe: the entry diameter is significantly smaller than the diameter of the main pipe body. Currently, there are two versions of the BHIR: 1) the first version, BHIR-I, which was developed for use in manual inspections by carrying a borescope camera inside a boiler header pipe, and 2) the second version, BHIR-II, which has an onboard visual inspection system that can inspect pipes and acquire images independently. The robot was designed to be able to navigate through the pipe geometry. A unique redundant localization system that uses an accelerometer and encoder sensor was developed to ensure that the robot knows the location of the images taken and its own position inside the boiler header pipe. This paper discusses the prototype development, the localization system and site testing conducted to validate the prototype. Based on the test results, the BHIR prototype with redundant localization was proven to be successful.
Importance of Computational Intelligent in Proteomics
Authors: Mamun Kabir ,   School of Engineering & Physics, The University of the South Pacific, Fiji, Laucala Campus, Suva, Fiji, Sharma Alok
Published: April 2016

Computational Intelligent (CI) techniques have become an apparent need in many bioinformatics applications. In this article, we make the interested reader aware of the necessity of CI, providing a basic taxonomy of proteomics, and discussing their use, variety and potential in a number of both common as well as upcoming proteomics application.
Protein Structural Class Prediction viak-Separated Bigrams Using Position Specific Scoring Matrix
Authors: Saini Harsh ,   The University of the South Pacific, Fiji, Laucala Bay, Suva, Fiji, Raicar Gaurav , Sharma Alok , Lal Sunil , Dehzangi Abdollah , Ananthanarayanan Rajeshkannan , Lyons James , Biswas Neela , Paliwal Kuldip K. ,   Griffith University, Brisbane, Australia,   Royal Brisbane and Women’s Hospital, Brisbane, Australia
Published: April 2016

Protein structural class prediction (SCP) is as important task in identifying protein tertiary structure and protein functions. In this study, we propose a feature extraction technique to predict secondary structures. The technique utilizes bigram (of adjacent andk-separated amino acids) information derived from Position Specific Scoring Matrix (PSSM). The technique has shown promising results when evaluated on benchmarked Ding and Dubchak dataset.
Visual Attention Region Prediction Based on Eye Tracking Using Fuzzy Inference
Authors: Wang Mao ,   Department of System Design Engineering, Graduate School of Engineering, University of Fukui, 3-9-1 Bunkyo, Fukui 910-8507, Japan, Maeda Yoichiro , Takahashi Yasutake ,   Department of Robotics, Faculty of Engineering, Osaka Institute of Technology, 5-16-1 Omiya, Asahi-ku, Osaka 535-8585, Japan,   Department of Human and Artificial Intelligent Systems, Graduate School of Engineering, University of Fukui, 3-9-1 Bunkyo, Fukui 910-8507, Japan
Published: April 2016

Visual attention region prediction has attracted the attention of intelligent systems researchers because it makes the interaction between human beings and intelligent nonhuman agents to be more intelligent. Visual attention region prediction uses multiple input factors such as gestures, face images and eye gaze position. Physically, disabled persons may find it difficult to move in some way. In this paper, we propose using gaze position estimation as input to a prediction system achieved by extracting image features. Our approach is divided into two parts: user gaze estimation and visual attention region inference. The neural network has been used in user gaze estimation as the decision making unit, following which the user gaze position at the computer screen is then estimated. We proposed that prediction in visual attention region inference of the visual attention region be inferred by using fuzzy inference after image feature maps and saliency maps have been extracted and computed. User experiments conducted to evaluate the prediction accuracy of our proposed method surveyed prediction results. These results indicated that the prediction we proposed performs better at the attention regions position prediction level depending on the image.
Image Labeling by Integration of Local Co-Occurrence Histogram and Global Features
Authors: Omiya Takuto ,   Department of Electronical and Electronic Engineering, Meijo University, 1-501 Shiogamaguchi, Tenpaku-ku, Nagoya, Aichi 468-8502, Japan, Hotta Kazuhiro
Published: April 2016

In this paper, we perform image labeling based on the probabilistic integration of local and global features. Several conventional methods label pixels or regions using features extracted from local regions and local contextual relationships between neighboring regions. However, labeling results tend to depend on local viewpoints. To overcome this problem, we propose an image labeling method that utilizes both local and global features. We compute the posterior probability distributions of the local and global features independently, and they are integrated by the product. To compute the probability of the global region (entire image), Bag-of-Words is used. In contrast, local cooccurrence between color and texture features is used to compute local probability. In the experiments, we use the MSRC21 dataset. The result demonstrates that the use of global viewpoint significantly improves labeling accuracy.
An Ultrasound Technique of Bone Thickness Estimation for Pedicle Screw Insertion
Authors: Hassan Muhamad Khairul Ali ,   Graduate School Engineering, University of Fukui, 3-9-1 Bunkyo, Fukui 910-8507, Japan, Nagamune Kouki , Kakutani Kenichiro , Maeno Koichiro , Nishida Kotaro , Kurosaka Masahiro ,   School of Mechatronic Engineering, Universiti Malaysia Perlis,   Department of Orthopaedic Surgery, Graduate School of Medicine, Kobe University
Published: April 2016

Information on bone thickness is useful to surgeons in fixing pedicle screws in place. The quality of pedicle screw insertion continues to increase with the introduction of such techniques as navigation based on computed tomography and fluoroscopy. These techniques reduce error in pedicle screw placement and injury. However, the information reported on the real time measurement of depths drilled through cancellous bone, also known as trabecular bone or sponge bone, by the pedicle screw is minimal. It currently depends on palpation by the physician for judging the boundary between cortical and cancellous bone – an inaccurate technique that may produce errors in screw placement and the risk of injury during surgical processes. Ultrasound is used to help overcome such problems. Bone thickness is estimated in this study using an ultrasound transducer attached to 20 mm of polymethyl methacrylate, a clear glass-like acrylic. The bone thickness of five specimens was measured using ultrasound echo signals. Error in estimating bone thickness was small, 8.121%, showing the accuracy in bone thickness to be more than 90.00% which is suitable for use in estimating bone thickness in pedicle screw insertion.
Fuzzy Cognitive Maps and Bacterial Evolutionary Algorithm Approach to Integrated Waste Management Systems
Authors: Buruzs Adrienn ,   Department of Environmental Engineering Széchenyi István University, Gyõr, Egyetem tér 1., Hungary, Hatwágner Miklós Ferenc , Kóczy László Tamás ,   Department of Information Technology Széchenyi István University, Gyõr, Egyetem tér 1., Hungary,   Department of Telecommunications and Media Informatics Budapest University of Technology and Economics, Budapest, Magyar tudósok körútja 2., Hungary
Published: April 2016

Sustainable waste management systems necessarily include many interacting factors. Due to the complexity and uncertainties occurring in sustainable waste management systems, we propose the use of Fuzzy Cognitive Maps (FCM) and Bacterial Evolutionary Algorithm (BEA) [1] to support the planning and decision making process of integrated systems, as the combination of methods FCM and BEA seems to be suitable to model such complex mechanisms as Integrated Waste Management Systems (IWMS). This paper is an attempt to assess the sustainability of the IWMS in a holistic approach. While the FCM model represents the IWMS as a whole, the BEA is used for parameter optimization and identification. An interpretation of the results obtained by the FCM for the actual regional IWMS is also presented. We have obtained some surprising results, contradicting the general assumptions in the literature concerning the relative importance of constituting components in waste management systems.
Robust Watermarking Using n-Diagonalization Based on Householder Transform
Authors: Park Jaesung ,   Department of Intelligent Interaction Technologies, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki 305-8573, Japan, Sawase Kazuhito , Nobuhara Hajime
Published: April 2016

Digital image watermarking based on singular value decomposition (SVD) is highly robust against misuse, but lacks the ability to distinguish whether watermarks are correct due to the importance of singular values being lower than two orthogonal matrices. To achieve highly accurate watermark extraction while maintaining high robustness, we propose robust watermarking based on discrete wavelet transform (DWT) and n-diagonalization formalized by Householder transformation. We propose that DWT be used to ensure visibility and that n-diagonalization be used to control information quantity related to watermark extraction accuracy. Experimental results confirm the robustness of our proposed method and that the extraction accuracy of the proposed method is approximately 2 times better than that of SVD.
Plasticity in Network Organizations
Authors: Alqithami Saad ,   Department of Computer Science, Southern Illinois University, Carbondale, IL 62901, USA, Hexmoor Henry
Published: April 2016

Thenetwork organization(NO) is flexible and changes rapidly to address events in volatile environments. These organizations are preferred to traditional organizations that are networked. The property of the NO that enables it to change so rapidly isplasticity. A model is presented for spontaneously formed NO and the quality of plasticity is discussed in the context of this model. We touch on how this model accounts for external change in an environment through internal adjustment. A case study illustrates the main tenets of our conceptualization.
A GPU-Based Programming Framework for Highly-Scalable Multi-Agent Traffic Simulations
Authors: Sano Yoshihito ,   Graduate School of Informatics, Shizuoka University, 3-5-1 Johoku, Hamamatsu, Shizuoka 432-8011, Japan, Fukuta Naoki
Published: April 2016

Highly detailed reproducibility of multi-agent simulations is strongly demanded. To realize such highly reproducible multi-agent simulations, it is important to make each agent respond to its dynamically changing environment as well as scale the simulation to cover important phenomena that could be produced. In this paper, we present a programming framework to realize highly scalable execution of them as well as detailed behaviors of agents. The framework can help simulation developers utilize many GPGPU-based parallel cores in their simulation programs by using the proposed OpenCL-based multi-platform agent code conversion engine. We show our prototype implementation of the framework and how our framework can help simulation developers to code, test, and evaluate their agent codes which select actions and path plants reactively in dynamically changing large-scale simulation environments on various hardware and software settings.
Greedy Network Growth Model of Social Network Service
Authors: Usui Shohei ,   Graduate School of Engineering, The University of Tokyo, 7-3-1 Hongo, bunkyo-ku, Tokyo 113-8656, Japan, Toriumi Fujio , Matsuo Masato , Hirayama Takatsugu , Mase Kenji ,   NTT Network Innovation Laboratories, 3-9-11 Midori-cho, Musashino-shi, Tokyo 180-8585, Japan,   Graduate School of Information Science, Nagoya University, Furo-cho, Chikusa-ku, Nagoya 464-8603, Japan
Published: April 2016

As new network communication tools are developed, social network services (SNS) such as Facebook and Twitter are becoming part of a social phenomenon globally impacting on society. Many researchers are therefore studying the structure of relationship networks among users. We propose a greedy network growth model that appropriately increases nodes and links while automatically reproducing the target network. We handle a wide range of networks with high expressive ability. Results of experiments showed that we accurately reproduced 92.4% of 189 target networks from real services. The model also enabled us to reproduce 30 networks built up by existing network models. We thus show that the proposed model represents the expressiveness of many existing network models.
SIR-Extended Information Diffusion Model of False Rumor and its Prevention Strategy for Twitter
Authors: Okada Yoshiyuki ,   The Institute of Scientific and Industrial Research, Osaka University, 8-1 Mihogaoka, Ibaraki, Osaka 567-0047, Japan, Ikeda Keisuke , Shinoda Kosuke , Toriumi Fujio , Sakaki Takeshi , Kazama Kazuhiro , Numao Masayuki , Noda Itsuki , Kurihara Satoshi ,   Graduate School of Information Systems, The University of Electro-Communications, 1-5-1 Chofugaoka, Chofu, Tokyo 182-8585, Japan,   The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan,   Department of Computer and Communication Sciences, Faculty of Systems Engineering, Wakayama University, 930 Sakaedani Wakayama-shi, Wakayama 640-8510, Japan,   National Institute of Advanced Industrial Science and Technology, 1-1-1 Umezono, Tsukuba, Ibaraki 305-8568, Japan
Published: April 2016

Nowadays, users of Twitter, one of famous social networking service, have rapidly increased in number, and many people have been exchanging information by Twitter. When the Great East Japan Earthquake struck in 2011, people were able to obtain information from social networking services. Though Twitter played an important role, one problem was especially pointed out: false rumor diffusion. In this study, we propose an information diffusion model based on the SIR model and discuss how to prevent false rumor diffusion.
Analysis of a Public Good Game Permitted New Entries: a Role of Defectors to Maintain Cooperation
Authors: Yamamoto Hitoshi ,   Faculty of Business Administration, Rissho University, 3-2-16 Osaki, Shinagawa-ku, Tokyo 141-8602, Japan, Okada Isamu , Ogawa Yuki ,   Faculty of Business Administration, Soka University, 1-236 Tangi, Hachioji City, Tokyo 192-8577, Japan,   Faculty of Geo-Environmental Science, Rissho University, 1700 Magechi, Kumagaya-shi, Saitama 360-0194, Japan
Published: April 2016

We studied a public good game, in which metanorms work, permitted new entries. Although whoever wants to entry the game, either cooperators or noncooperators, can try to participate in the game, a manager of the game can control a policy to permit who are ok. By changing the level of control, we investigated what type of policies is effective to maintain cooperation. Especially, we compared a strict policy of population management that only cooperative participants are permitted to entry it, and a simple policy of population management that non-cooperative participants are permitted. According to our simulation of the model, it is revealed surprisingly that a level of cooperation of the game collapses when a strict policy was adopted. On the other hand, cooperation level maintains high robustly if a tolerant policy that some invaders who are perfect defectors are permitted. We conclude that an existence of a few amount of defection has an effect on cooperation of the society. We call this effect a social vaccine effect.
Estimation of Locations of Densely Distributed Subjects Using NMF with Nonpixel Information
Authors: Kubo Yusuke ,   National Defense Academy of Japan, 1-10-20 Hashirimizu, Yokosuka, Kanagawa 239-8686, Japan, Kubo Masao , Sato Hiroshi , Namatame Akira
Published: April 2016

We propose a method that uses a large number of digital photographs to produce highly accurate estimates of the locations of subjects that have attracted a crowd’s attention. Recently, a very active area of research has been to use humans as sensors in realworld observations that require a large amount of data. Some of these studies have attempted to produce real-time estimates of the subjects that are attracting a crowd’s attention by quickly collecting a large number of photographs. These studies are based on the assumption that, when photographers encounter interesting events, they take pictures. Some of the proposed methods realize high availability by using only photographing information, which includes information about location and azimuth of the camera and it is automatically embedded into photograph. Since this data is very small compared to that of the pixel information, the load on the communication infrastructure is reduced. However, there are problems with the accuracy when there are many attractive subjects in a small region, and they cannot be found with traditional methods that use a sequential search strategy. The proposed method overcomes this problem by applying nonnegative matrix factorization (NMF) to the estimated location of each subject. We verified the effectiveness of this by computational experiments and an experiment under a realistic environment.