bluuebunny/crossref_metadata_2025 huggingface dataset:
Medical Image Diagnosis of Liver Cancer Using a Neural Network and Artificial Intelligence
Authors: Kondo Tadashi ,   Graduate School of Health Sciences, The University of Tokushima, 3-18-15 Kuramoto-cho, Tokushima 770-8509, Japan, Ueno Junji , Takao Shoichiro
Published: April 2016

A revised Group Method of Data Handling (GMDH)-type neural network algorithm using artificial intelligence technology for medical image diagnosis is proposed and is applied to medical image diagnosis of liver cancer. In this algorithm, the knowledge base for medical image diagnosis is used in organizing the neural network architecture for medical image diagnosis. Furthermore, the revisedGMDH-type neural network algorithm has a feedback loop and can identify the characteristics of the medical images accurately using feedback loop calculations. The neural network architecture that optimally fit the complexity of the medical images, is automatically organized so as to minimize the prediction error criterion defined as Prediction Sum of Squares (PSS). It is shown that the revised GMDH-type neural network is accurate and a useful method for the medical image diagnosis of the liver cancer.
An Intelligent Human Support System Using Motion Patterns and Environment Information
Authors: Ho Yihsin ,   Faculty of System Design, Tokyo Metropolitan University, 6-6 Asahigaoka, Hino, Tokyo 191-0065, Japan, Murakami Kazumasa , Shibano Tomomi , Sato-Shimokawara Eri , Yamaguchi Toru
Published: April 2016

Developments in modern technology bring people increased convenience, but at the same time, problems associated with the usage of technology can irritate people. The authors have designed a system that supports users and does not require users to learn how to use it. The system concept was applied to the construction of a health care system. In this system, the first step is to recognize a user’s motion. The system detects human motion and applies the data to determine human motion patterns, thereby learning the user’s action by the motion patterns and environment information. After recognizing the user’s action, the action data is used to calculate the user’s health situation. This enables the system to provide support or give suggestions to the user. In this study, the authors used action data collected by human individuals and calculated the user’s health situation to determine the performance and usability of the health care system.
The Omnipresent Computing Menace to Information Society
Authors: Schuster Alfons ,   Laboratory for Dynamics of Emergent Intelligence, RIKEN Brain Science Institute, Wako-shi, Saitama 351-0198, Japan, Berrar Daniel ,   Interdisciplinary Graduate School of Science and Engineering, Tokyo Institute of Technology, G3-45, 4259 Nagatsuta, Midori-ku, Yokohama 226-8502, Japan
Published: April 2016

Computers have evolved from mere number crunchers to systems demonstrating an astonishing degree of sophistication, decision-making ability, and autonomy. Silicon is no longer the only substrate facilitating information processing. Despite these progresses, machine intelligence is still far from rivaling human intelligence. Nonetheless, we might be all too ready to rely on inferior agents for decision making, to give away sensitive information without fully understanding the consequences involved, or to tinker with genetic code to program carbon-based machines without fully appreciating the risks. This article explores the potentials and risks that information societies may face in the wake of current and emerging intelligent computing paradigms.
Adaptive Nutrient Water Supply Control of Plant Factory System by Reinforcement Learning
Authors: Wakahara Takumi ,   Graduate School, Future University Hakodate, 116-2 Kamedanakano-cho Hakodate, Hokkaido 041-8655, Japan, Mikami Sadayoshi ,   Department of Complex and Intelligent Systems, Future University Hakodate, 116-2 Kamedanakano-cho Hakodate, Hokkaido 041-8655, Japan
Published: April 2016

An adaptive nutrient control method for a plant factory is proposed. The method is based on a Reinforcement Learning (RL) modified for a target in which the same state never comes back during a single episode and a reward is given after a very long delay. In application such as plant growth control, one episode takes a very long time period, and a rapid convergence to a prospective control solution is essential while an extensive exploration is needed since there is usually no precise model available. A method like reinforcement learning is useful for a problem having no reference model. But a necessity of exploration does not match the need for rapid convergence, and a new balancing method is needed. In this research, an average reward distribution method is proposed, which is similar to the profit sharing method but effects more extensively on finding much prospective early solutions, while guaranteeing to converge into a rational solution in a long run. An experiment is conducted in a simple plant factory system, which shows that at least standard reinforcement learning is insufficient for this type of problem. Computer simulations show that the method has good effects on acquiring prospective control policy at early stage comparing to a standard reinforcement learning and a profit sharing method.
“Behavior Composed” for Artificial Flying Creature
Authors: Furukawa Masashi ,   Graduate School of Information and Science, Hokkaido University, Nishi 9, Kita 14, Kita-ku, Sapporo, Hokkaido 060-8014, Japan, Morinaga Makoto , Ooe Ryosuke , Watanabe Michiko , Suzuki Ikuo , Yamamoto Masahito ,   Kitami Institute of Technology, 165 Koencho, Kitami, Hokkaido 090-8507, Japan
Published: April 2016

This study proposes the concepts of “behavior simple” and “behavior composed.” Behavior simple means primitive behavior, and behavior composed is a combination of behaviors simple. An artificial creature first learns some behaviors simple. Then, it learns behavior composed as a combination of behaviors simple, responding to the change of environment. This concept is applied to an Artificial Flying Creature (AFC). The AFC learns two types of flight independently: flapping and gliding. After that, it learns a sophisticated behavior by alternatively selecting these behaviors. Simulation results prove that adequate flight occurs by alternatively using flapping and gliding.
Induction of Cooperative Behavior Through Exchange of Nonverbal Information
Authors: Takahashi Yuzo ,   Ergonomics Laboratory, Graduate School of Information Sciences, Hiroshima City University, 3-4-1 Ozuka-higashi, Asaminami-ku, Hiroshima 731-3194, Japan
Published: April 2016

In this study, the process of inducing mutual cooperation among workers in a remote work environment (telework) was experimentally examined. The gambling task developed by Payne was used, and subjects were not given information regarding their partners’ reputations. In addition, subjects and their partners completed the task in different rooms to avoid the effects of verbal and nonverbal communication. Subjects knew only which buttons their partners pushed, i.e., their behavior in the remote work environment. The number of experimental trials was 100. We found that cooperative behavior was induced by the 33rd trial. Although subjects did not know their partners’ reputations, cooperative behavior arose as subjects saw which buttons their partners pushed. In a control experiment, the subjects competed with a computer, and the results suggested that cooperative behavior was not induced when a human subject competed with a computer. Overall, the results suggest that an exchange of nonverbal behavioral information was necessary for inducing cooperative behavior.
Spatiotemporal Analysis of Brain Activity During Understanding Honorific Expressions
Authors: Sugeno Michio ,   European Centre for Soft Computing, c/ Gonzalo Gutierrez Quiros, s/n, 33600 Mieres-Asturias, Spain, Yamanoi Takahiro ,   Faculty of Engineering, Hokkai-Gakuen University, W 11, S 26, Central ward, Sapporo 064-0926, Japan
Published: April 2016

This paper discusses brain activity during the understanding of sentences from the perspective of Systemic Functional Linguistics. We focus on ideational meaning (propositional meaning in an ordinary sense) and interpersonal meaning (as is typically seen in honorific expressions). The present study is an experimental exploration of the spatiotemporal pathways of neuronal activation. Japanese sentences containing and not containing honorific expressions are compared in electroencephalography experiments. In these experiments, the sentences without honorific expressions have ideationalmeaning, but those with honorific expressions have both ideational and interpersonal meanings. Through the use of the equivalent current dipole source localization method, the spatiotemporal processes of activation of the brain are analyzed. There is a single pathway during the understanding of the sentences without honorific expressions; this pathway is mainly observed in the left hemisphere. On the other hand, there are three pathways in the case of the sentences with honorific expressions, two of which are observed in the right hemisphere. The remaining pathway is the same as the aforementioned single pathway. This fact strongly suggests that the common pathway is concerned with processing ideational meaning. The other two pathways observed during understanding of the sentences with honorific expressions are considered to be related to processing interpersonal meaning.
Evaluating Functional Connectivity in Alcoholics Based on Maximal Weight Matching
Authors: Zhu Guohun ,   Department of Mathematics and Computing, University of Southern Queensland, Li Yan , Wen Peng (Paul) ,   Faculty of Engineering and Surveying, University of Southern Queensland,   Centre for Systems Biology, University of Southern Queensland, USQ Toowoomba Campus, West Street, Toowoomba, QLD 4350, Australia
Published: April 2016

EEG-based applications have faced the challenge of multi-modal integrated analysis problems. In this paper, a greedy maximal weight matching approach is used to measure the functional connectivity in alcoholics datasets with EEG and EOG signals. The major discovery is that the processing of the repeated and unrepeated stimuli in the γ band in control drinkers is significantly more different than that in alcoholic subjects. However, the EOGs are always stable in the case of visual tasks, except for a weakly wave when subjects make an error response to the stimuli.
Information Enhancement on a Focused Object Using Linked Data
Authors: Onishi Kanako ,   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

Various data has recently been made into Linked Data. Each resource defined in Linked Data is represented in the form of RDF data and is linked to other resources. There are many studies that extract particular information from Linked Data by calculating the similarity between the target resource and other resources. We propose two new methods to extract particular information from Linked Data, not only by calculating the similarity between resources but also by investigating what resources the target resource is linked to and how the target resource is linked to the other resources. One of the two methods is based on the resource analysis of Linked Data. We can extract information that has the same property information between a target resource and other resources. The other is a method based on the linked analysis of Linked Data. We can extract information with a particular trend through three scores that we have defined. We have verified that our proposed methods are useful by conducting a subject experiment.
Special Issue on Information Medical Science
Authors: Hatakeyama Yutaka
Published: April 2016

One important and interesting application in computational intelligence technology is medical application. Medical engineering research covers such areas as medical image processing. With most health information now described electronically, hospitals are accumulating large amounts of medical data, including imaging, text, and structured materials. This has made ginformation medical scienceh vastly more important in improving healthcare technology. This special issue introduces an application based on hospital data, medical care support systems, and medical image processing. The application deals with analytical approaches, navigation systems, medical data management, and text mining for summary data. The support system shows diagnosis and robotassisted therapy. Medical imaging targets X-ray, blood cell, and ultrasonography images. As guest editors, we can assure the readers that the papers in this special issue have great social impact in this research area and encourage good relations with engineering and medical practice approaches. We thank the contributors and reviewers for introduce these latest achievements in this exciting field.
Auto-Selection of DPC Codes from Discharge Summaries by Text Mining in Several Hospitals and Analysis of Differences in Discharge Summaries
Authors: Doi Shunsuke ,   Graduate School of Engineering, Chiba University, 1-8-1 Inohana, Chuo-ku, Chiba 260-8677, Japan, Suzuki Takahiro , Shimada Gen , Takasaki Mitsuhiro , Fujita Shinsuke , Tamura Toshiyo , Takabayashi Katsuhiko ,   Department of Medical Informatics and Management, Chiba University Hospital, Japan,   Medical Information Center, St. Luke’s International Hospital, Japan,   Division of Medical Informatics, Saga University Hospital, Japan,   Department of Welfare and Medical Intelligence, Chiba University Hospital, Japan
Published: April 2016

Recently, Electronic Medical Record (EMR) systems have become popular in Japan, and numerous discharge summaries are being stored electronically, although they have not yet been reutilized. We performed text mining by using the term frequencyinverse document frequency method along with a morphological analysis of the discharge summaries from 3 hospitals (the Chiba University Hospital, St. Luke’s International Hospital, and the Saga University Hospital). We found differences in the styles of the summaries between hospitals, while the rates of properly classified Diagnosis Procedure Combination (DPC) codes were almost the same. Beyond the different styles for the discharge summaries, the text mining method was able to obtain appropriate extracts of the proper DPC codes. An improvement was observed by using the integrated model data between the hospitals. It appeared that a large database containing data from many hospitals could improve the precision of text mining.
Special Issue on New Approach to Intelligent Machines
Authors: Takahashi Hiroshi
Published: April 2016

Six papers on Human Machine Interfaces have been published from a new point of view. As one of the important technologies for developing optimal human machine interaction, communication between humans and machines has attracted much interest. In this special issue, the editor focuses on new methods for communicating mutual information on the characteristics of both human operators and machines, including computers and vehicles. In this communication, readers can operate machines easily and comfortably. The first article, by Hiroshi Takahashi and Hirohiko Honda, studies the possibility of applying subliminal visual marks for informing drivers of hazardous factors without causing a nuisance in the driver-assistance systems. The second paper, by Hiroshi Igarashi, proposes subliminal control parameter calibration in interactive human-machine operational environments. Subliminal calibration will enable users to master handling in a short time. The third paper, by Hiroshi Takahashi and Hirohiko Honda, focuses on computer game operation as influenced by ultrasound exposure. The relationship between ultrasound signals and operator behavior is discussed. The fourth paper, written by Hirohiko Honda and Kazuko Tobita, concerns the improvement of personal computer operation using auditory signals for visually handicapped persons, who can then identify operational processes through simple sounds. The fifth contribution, by Hirohiko Honda, involves improving mouse operation with personal computers for those physically challenged who suffer progressive deterioration. The proposed system helps such users maintain quality of life because they are able to continue using a personal computer even as their state of mobility deteriorates. The closing contribution, by Hiroshi Igarashi, Yoshinobu Adachi, and Kazunari Takahashi, deals with multiagent systems based on human social behavior. Adaptability and the total performance of the authorfs proposed technique are confirmed through a pursuit problem in a multiagent system. We thank the referees for their reviews and the Fuji Technology Press Ltd. staff for its encouragement and advice.
Weighted Quasi-Arithmetic Means and the Domain Translations
Authors: Yoshida Yuji ,   Faculty of Economics and Business Administration, University of Kitakyushu, 4-2-1 Kitagata, Kokuraminami, Kitakyushu 802-8577, Japan
Published: April 2016

The monotonicity of the weighted quasi-arithmetic means is discussed from the viewpoint of utility functions and weighting functions. Observing the indices given by utility functions and weighting functions, we find that these indices give similarmonotonicity for the weighted quasi-arithmetic means, and this paper investigates the reason using the direct domain translation adapted to the weighted quasi-arithmetic means. Further, general domain translations are introduced and they are discussed in relation to these indices. Some examples are given for the domain translations and as a special case the translation invariance is presented, and a lot of examples with various utility functions and weighting functions are shown, and their indices and the translated forms are derived.
A Trust Evaluation of Networks Utilizing Matrix Operations Based ont-norms andt-conorms
Authors: Nohmi Masaya ,   Kyushu Institute of Technology, Iizuka, Fukuoka, 820-8502, Japan, Honda Aoi , Okazaki Yoshiaki
Published: April 2016

A new scheme for numerical trust evaluation of networks is proposed. Matrix operations based ont-norms andt-conorms are used for the evaluation. The algebraic properties of the matrix operations are studied. Fuzzy graphs, in which nodes are linked with some membership value, are proposed, using the matrices as adjacent matrices. Furthermore, the fuzzinesses of the trustability distribution are calculated.
Fuzzy Three-Dimensional Voronoi Diagram and its Application to Geographical Data Analysis
Authors: Dai Mian ,   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 concept of fuzzy three-dimensional Voronoi Diagram is presented for spatial relations analysis of real world three-dimensional geographical data, where it is an extension of well known two-dimensional Voronoi Diagram to three-dimensional representation with uncertain spatial relation information in terms of fuzzy set. It makes possible to analyze quantitatively complex boundaries of geographically intricate areas, to give human friendly fuzzy explanation of determining three-dimensional directions, and to express uncertain spatial relations by precise unified fuzzy description. It is applied to decide spatial direction relations of artificial geographicalmountain data, which includes 8 spatial directions with at most 60 relative direction relations, and it leads to detect threedimensional directions whereas the expression of traditional 4 directions and 12 relative directions indicate two-dimensional directions only. The proposed concept aims to discriminate neighbors’ class relations and spatial-temporal changes of specially appointed objects, and also aims to be a tool to achieve the intellective extraction and analysis of geographical data of a mountainous area located in northeast China.
An Efficient Solution of Real-Time Fuzzy Regression Analysis to Information Granules Problem
Authors: Ramli Azizul Azhar ,   Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, Parit Raja, Batu Pahat 86400, Johor Darul Takzim, Malaysia, Watada Junzo , Pedrycz Witold ,   Graduate School of Information, Production and Systems, Waseda University, 2-7 Hibikino, Wakamatsu-ku, Kita Kyushu-shi 808-0135, Japan,   Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Alberta, T6G 2V4, Canada,   Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland
Published: April 2016

Regression models are well known and widely used as one of the important categories of models in system modeling. In this paper, we extend the concept of fuzzy regression in order to handle real-time implementation of data analysis of information granules. An ultimate objective of this study is to develop a hybrid of a genetically-guided clustering algorithm called genetic algorithm-based Fuzzy C-Means (GAFCM) and a convex hull-based regression approach being regarded as a potential solution to the formation of information granules. It is shown that a setting of Granular Computing helps us reduce the computing time, especially in case of real-time data analysis, as well as an overall computational complexity. We propose an efficient real-time information granules regression analysis based on the convex hull approach in which a Beneath-Beyond algorithm is employed to design sub-convex hulls as well as a main convex hull structure. In the proposed design setting, we emphasize a pivotal role of the convex hull approach or more specifically the Beneath-Beyond algorithm, which becomes crucial in alleviating limitations of linear programming manifesting in system modeling.
Dynamic Shadow Generation System Based on Shape Recognition
Authors: Fushio Yuki ,   Graduate School of Informatics, Kansai University, 2-1-1 Ryozenji, Takatsuki 569-1095, Japan, Matsushita Mitsunori ,   Faculty of Informatics, Kansai University, 2-1-1 Ryozenji, Takatsuki 569-1095, Japan
Published: April 2016

This paper proposes a dynamic shadow generation system that combines the shadow of an object with the artificial shadows of virtual characters. In our proposed system, infrared light is used to capture an object’s shadow in order to discriminate between a shadow generated by an object and a virtual shadow generated by the system. Our proposed system determines areas in which shadows do not appear and then the shadows of virtual characters are added to these areas. This paper presents the basic idea of the system, the shadow generation algorithm that it uses, and an empirical study on the sharpness of a generated shadow, along with the distance between an object and a screen used in this system.
Impacts of Multimodal Feedback on Efficiency of Proactive Information Retrieval from Task-Related HRI
Authors: Gonsior Barbara ,   Institute of Automatic Control Engineering (LSR), Technische Universität München, D-80290 Munich, Germany, Landsiedel Christian , Mirnig Nicole , Sosnowski Stefan , Strasser Ewald , Złotowski Jakub , Buss Martin , KoljaKühnlenz  , Tscheligi Manfred , Weiss Astrid , Wollherr Dirk ,   ICT&S Center, University of Salzburg, Sigmund-Haffner-Gasse 18, 5020 Salzburg, Austria
Published: April 2016

This work is a first step towards an integration ofmultimodality with the aim to make efficient use of both human-like, and non-human-like feedback modalities in order to optimize proactive information retrieval from task-related Human-Robot Interaction (HRI) in human environments. The presented approach combines the human-like modalities speech and emotional facial mimicry with non-human-like modalities. The proposed non-human-like modalities are a screen displaying retrieved knowledge of the robot to the human and a pointer mounted above the robot head for pointing directions and referring to objects in shared visual space as an equivalent for arm and hand gestures. Initially, pre-interaction feedback is explored in an experiment investigating different approach behaviors in order to find socially acceptable trajectories to increase the success of interactions and thus efficiency of information retrieval. Secondly, pre-evaluated humanlike modalities are introduced. First results of a multimodal feedback study are presented in the context of the IURO project,1where a robot asks for its way to a predefined goal location.

1. Interactive Urban Robot, http://www.iuro-project.eu
Parameter Optimization of Local Fuzzy Patterns Based on Fuzzy Contrast Measure for White Blood Cell Texture Feature Extraction
Authors: Fatichah Chastine ,   Department Computational Intelligence & Systems Science, Tokyo Institute of Technology, G3-49, 4259 Nagatsuta, Midori-ku, Yokohama 226-8502, Japan, Tangel Martin Leonard , Widyanto Muhammad Rahmat , Dong Fangyan , Hirota Kaoru ,   Informatics Department, Faculty of Technology Information, Institut Teknologi Sepuluh Nopember, Kampus ITS, Surabaya 60111, Indonesia,   Faculty of Computer Science, University of Indonesia, Kampus UI, Depok, Jawa Barat, Indonesia
Published: April 2016

The parameter optimization of local fuzzy patterns based on the fuzzy contrast measure is proposed for extracting white blood cell texture. The proposed method obtains the optimal parameter values of the nucleus and cytoplasm region of white blood cell image and the best accuracy rate of white blood cell classification can therefore be achieved. To evaluate the performance of the proposed method, 100 microscopic white blood cell images and the supervised learning method are used for white blood cell classification. Results show that the average accuracy rate of white blood cell classification using local fuzzy pattern features with optimal parameter values of a nucleus and a cytoplasm region is 4% more accurate than with uniform parameter values and is 5–18% more accurate than other feature extraction methods. White blood cell feature extraction is part of the white blood cell classification in an automatic cancer diagnosis that is being developed. In addition, the proposed method can be used to obtain the optimal parameter of local fuzzy patterns for other types of datasets.
On Network Structure of Stable Strategies in Local Connection Games
Authors: Iwazaki Hikaru ,   Aoyama Gakuin University, 5-10-1 Fuchinobe, Chuo-ku, Sagamihara-shi, Kanagawa 252-5258, Japan, Ujigawa Takenori , Sung Shao-Chin
Published: April 2016

We are concerned with a model of network formation games, called local connection games, in which networks are formed based on players’ strategies. Each player may decide to build links to other players by paying a certain cost fixed in advance, and a strategy of each player is a selection of links to be built. Each player determines and/or changes her or his strategy depending on cost for building links and cost of contacting all other players on the entire network, which influence the structure of the entire network. One of the main interests on the study of local connection games is to characterize all the stable strategy profiles. In this paper, we analyze the influences on network structure of stable strategy profiles caused by the cost for building links. For the unit cost case, we provide, in terms of network structure, a necessary and sufficient condition for strategy profiles to be stable. Moreover, we investigate the relationship between integral cost cases and non-integral cost cases as well.
An Affective Approach to Developing Marketing Strategies of Mineral Water
Authors: Watada Junzo ,   Graduate School of IPS, Waseda University, 2-7 hibikino, Wakamatsu, Kitakyuusyuu-shi, Fukuoka 808-0135, Japan, Yu Le , Shibata Munenori , Khalid Marzuki ,   Taste & Aroma Strategic Research Institute, Shinkawa Chuou, Bldg. 8F, 1-17-24 Shinkawa, Chuouku, Tokyo 104-0033, Japan,   Malaysia University of Tectnology, Malaysia
Published: April 2016

This study is concerned with the development of marketing strategies for mineral water based on consumers’ taste preferences, by analyzing the taste components of mineral water. In this study, we used a twodimensional analysis to classify taste data. We conducted a correlation analysis to identify the characteristics of taste data. We applied a combination of principal component analysis and self-organizing map to classify mineral water tastes. Based on this evaluation, we identified some marketing strategies in the conclusion. According to this study, the taste of mineral water is not determined by the origin and is not influenced by the hardness of the water.
Building a Type-2 Fuzzy Qualitative Regression Model
Authors: Wei Yicheng ,   Graduate School of Information, Production and Systems, Waseda University, 2-7 Hibikino, Wakamatsu-ku, Kitakyushu, Fukuoka 808-0135, Japan, Watada Junzo
Published: April 2016

Type-1 fuzzy regression model is constructed with type-1 fuzzy coefficients dealing with real value inputs and outputs. From the fuzzy set-theoretical point of view, uncertainty also exists when associated with qualitative data (membership degrees). This paper intends to build a qualitative regression model to measure uncertainty by applying the type-2 fuzzy set as the model’s coefficients. We are thus able to quantitatively describe the relationship between qualitative object variables and qualitative values of multivariate attributes (membership degree or type-1 fuzzy set), which are given by subjective recognition and judgment. We will build a basic qualitative model first and then improve it capable of ranging inputs. We will also give a heuristic solution in the end.
PSO-Particle Filter-Based Biometric Measurement for Human Tracking
Authors: Xu Zhenyuan ,   Graduate School of Information, Production and Systems, Waseda University, 2-7 Hibikino, Wakamatsu-ku, Kitakyushu, Fukuoka 808-0135, Japan, Watada Junzo
Published: April 2016

Today, security and surveillance systems are required not only to track the motions of humans but also, in some situations, to recognize and measure biometric features such as width and length. Few methods have been proposed for biometric height measurement in human tracking. Some studies have shown that an infrared ray technique can survey the height of a human, but the equipment required is complicated. The objective of this paper is to build a mathematical model to measure the biometrics of human tracking. This tracking method can show humans’ and objects’ size in a picture so that, if we put this picture in a frame of axes, we can calculate the height and other biometric lengths. To obtain the most accurate results for biometric length surveillance, we need a tracking method that is more exact than conventional tracking results. Combining tracking and detection methods using a particle swarm optimization-particle filter shows results with great accuracy in human tracking.
The Relationship Between Brand Integrity and Business Continuity – A Case Study of Japanese Long-Standing Company, Shioyoshiken –
Authors: Terasaki Shinichiro ,   Waseda University, Sodai-11 Bldg. 3F, 1-6-1 Nishiwaseda, Shinjuku-ku, Tokyo 169-8050, Japan, Nagasawa Shin’ya
Published: April 2016

The number 22,000 represents how many Japanese companies have lasted more than 100 years. This fact attracts the attention of the many researchers who explore the secrets of such business continuity, mainly from the angles of management philosophy, product innovation, and successful business succession. This paper, however, focuses more on brand management for identifying the relationship between brand building and business continuity, employing of the case of Shioyoshiken. Shioyoshiken is renowned for manufacturing sophisticated Japanese sweets for more than 130 years. The Shioyoshiken brand is well appreciated by private customers who emphasize the value of specialized sweets, particularly for Japanese tea parties and weddings. In this case study, we (1) explore Shioyoshiken “rashisa,” which means “identity” in English and (2) identify the secret of Shioyoshiken’s long-lasting brand strength, referring to an interview with Masaaki Takaya, the owner of Shioyoshiken, and deploying analytical frameworks such as 4M, 4P and Kotler’s 3i.
A Cooperative Coevolutionary Stock Trading Model Using Genetic Network Programming-Sarsa
Authors: Yang Yang ,   The Graduate School of Information, Production and Systems, Waseda University, 2-7 Hibikino, Wakamatsu-ku, Kitakyushu, Fukuoka 808-0135, Japan, He Zhaoping , Mabu Shingo , Hirasawa Kotaro
Published: April 2016

This paper presents a cooperative coevolutionary approach for stock trading model using Genetic Network Programming-Sarsa called CCGNP-Sarsa. Although theoretically, a single algorithm with sufficient size could solve any problem, in practice the stock market problem is too large and too complex to construct the appropriate algorithm to solve it. For such problems, cooperative coevolution which simultaneously evolves several species with the sum of their fitness values has been proposed as a successful alternative and was applied to make the stock trading models an integrated one. Such an approach allows different species of the GNP-Sarsa model to evolve in a parallel and cooperative manner, which makes the generated model more robust, generalized and efficient for generating stock trading strategies. CCGNP-Sarsa places as few restrictions as possible to the structure, allowing the model to obtain a wide variety of architecture during the evolution and to be easily used to solve complicated problems. To confirm the effectiveness of the proposed method, the simulations are carried out and compared with other methods like GNP-Sarsa with subroutines, GNP-Sarsa and Buy&Hold method. The results shows that the stock trading models using CCGNP-Sarsa outperforms all the other methods.
A Person Identification Method Using a Top-View Head Image from an Overhead Camera
Authors: Nakatani Ryota ,   Department of Artificial Intelligence, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502, Japan, Kouno Daichi , Shimada Kazutaka , Endo Tsutomu
Published: April 2016

In this paper, we describe a novel image-based person identification task. Conventional face-based person identification methods have a low tolerance for occluded situations, such as overlapping of people in an image. We focus on an image from an overhead camera. Using the overhead camera reduces restrictions on the installation location of the camera and solves the problem of occluded images. First, our method identifies a person area in a captured image by using background subtraction. Then, it extracts four features from the area: (1) body size, (2) hair color, (3) hairstyle, and (4) hair whorl. We apply the four features to the AdaBoost algorithm. Experimental results show the effectiveness of our method.
Introduction of Fixed Mode States into Online Reinforcement Learning with Penalties and Rewards and its Application to Biped Robot Waist Trajectory Generation
Authors: Kuroda Seiya ,   Panasonic Factory Solutions Co., Ltd., 1375 Kamisukiawara, Showa-cho, Nakakoma-gun, Yamanashi 409-3895, Japan, Miyazaki Kazuteru , Kobayashi Hiroaki ,   Research Department, National Institution for Academic Degrees and University Evaluation, 1-29-1 Gakuennishimachi, Kodaira, Tokyo 187-8587, Japan,   Department of Mechanical Engineering Informatics, Meiji University, 1-1-1 Higashimita Tama-ku, Kawasaki, Kanagawa 214-8571, Japan
Published: April 2016

During a long-term reinforcement learning task, the efficiency of learning is heavily degraded because the probabilistic actions of an agent often cause the task to fail, which makes it difficult to reach the goal and receive a reward. To address this problem, a fixed mode state is proposed in this paper. If the agent acquires an adequate reward, a normal state is switched to a fixed mode state. In this mode, the agent selects an action using a greedy strategy, i.e., it selects the highest weight action deterministically. First, this paper combines Online Profit Sharing reinforcement learning with the Penalty Avoiding Rational Policy Making algorithm, then introduces fixed mode states in it. The target task is then formulated, i.e., learning the modified waist trajectory of dynamically stable walking task based on the static stable walking of a biped robot. Finally, we present our simulation results and discuss the effectiveness of the proposed method.
A Mutual-Information-Based Global Matching Method for Chest-Radiography Temporal Subtraction
Authors: Yu Qian ,   Department of Computer Science and Engineering, Nagoya Institute of Technology, Gokiso-cho, Showa-ku, Nagoya, Aichi 466-8555, Japan, He Lifeng , Nakamura Tsuyoshi , Chao Yuyan , Suzuki Kenji ,   College of Electrical & Information Engineering, Shaanxi University of Science and Technology, China,   Graduate School of Information Science and Technology, Aichi Prefectural University, Japan,   Graduate School of Environmental Management, Nagoya Sangyo University, Japan,   Department of Radiology, Division of the Biological Sciences, The University of Chicago, USA
Published: April 2016

Lung cancer is the most common cancer in the world. Early detection is most important for reducing death due to lung cancer. Chest radiography has been widely and frequently used for the detection and diagnosis of lung cancer. To assess pathological changes in chest radiographs, radiologists often compare the previous chest radiograph and the current one from the same patient at different times. A temporal subtraction image, which is constructed from the previous and current radiographs, is often used to support this comparison work. This paper presents a Mutual-Information (MI)-based global matching method for chest-radiography temporal subtraction. We first make a preliminary transformation on the previous radiograph to make the center line of the lungs in the previous radiograph coincide with that of the current one. Then, we specify areas of the lungs to be used for mutual information registration and extract rib edges in these areas. We transform the rib edge image of the previous radiograph until mutual information between the rib edge image of the previous radiograph and that of the current radiograph becomes maximal. Finally, we use the same transform parameters to transform the previous radiograph, and then use the current radiograph and the transformed previous radiograph to construct the temporal subtraction image. The experimental result demonstrates that our proposed method can enhance pathological changes and reduces misregistration artifacts.
YURAGI Synthesis for Ultrasonic Human Brain Imaging
Authors: Yagi Naomi ,   Graduate School of Engineering, University of Hyogo, 2167 Shosha, Himeji, Hyogo 671-2280, Japan, Oshiro Yoshitetsu , Ishikawa Tomomoto , Hata Yutaka ,   Ishikawa Hospital, 2-150 Bessho, Bessho-cho, Himeji, Hyogo 671-0221, Japan,   Himeji Initiative in Computational Medical and Health Technology, 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 proposes YURAGI synthesis for brain imaging under the skull. The advantage of the proposed method over conventional methods is that, using YURAGI synthesis, it is possible to obtain the effective results without image registration. Image registration is generally needed when more than two images are to be synthesized into one image. YURAGI synthesis does not need image registration; thus, its method is simpler than other methods that need image synthesis. The effectiveness of the proposed method was confirmed by comparing its error rate and accuracy with those of other methods. YURAGI leads the simple and energy-saving system with performing autoregulation. Autoregulation is utilized in many biological systems. In this study, YURAGI was applied to an ultrasound-based diagnostic medical imaging technique. The experimental results using YURAGI were superior to those using othermethods. Thus, YURAGI is useful for visualizing the human brain.1

1. This paper has been reviewed and accepted as a regular paper. The paper was invited and incorporated into the Special Issue on Advances in Fuzzy Inference and its Related Techniques.
Special Issue on Smart Grid
Authors: Nagasaka Ken
Published: April 2016

Recently many utilities, manufacturers, researchers, government leaders around the world are working on a very sophisticated issue, Smart Grid, to modernize both the electric power transmission and distribution grids for the future. As a suitable subject for special issue for the Journal of Advanced Computational Intelligence and Intelligent Informatics (JACIII), I found that Smart Grid with its impacts on many fields is a timely subject though related to the fundamental concept of this Journal on Intelligence and real-world applications and so forth. Smart grids are intelligent and self-healing power systems which integrate intelligent transmission network with IT and collect, distribute, and process information about the behavior of all power suppliers and consumers in order to improve reliability, power quality, and to reduce electricity costs. Using a key issue, smart meter, enables smart grids to have smart real time monitoring on a regional and national scale to control and management the grids to avoid or mitigate the system-wide blackouts. In this special issue, we hope to explore breakthrough and new contributions useful to achieve the goal of smart grid. Three papers were selected for this special issue: The first paper proposes a novel idea though a strategic system in energy and environment required in smart grid. Managing sources combination including solar energy as well as the production trading is a new kind of risk management in smart grid. Important extensions of this study includes emissions management program accommodating uncertain and erratic renewable energy sources such as solar and wind energies. The second paper is related to communication aspect required for smart grid technology when renewable energy in small smart communities is interconnected to the smart grid. The simulation model developed in this paper is believed to be a useful tool in real-time power management system in smart grid. Third paper is selected as another hot subject in smart grids; the authors developed an extended procedure that obtains a unit commitment including a significant wind power penetration and PEVs as additional reserves. The shadow prices obtained by the trade-off analysis may provide a basis of evaluating the equivalent cost of the wind farms and the applying PEVs as the reserve and their contribution toward CO2 reduction. Finally, using this opportunity, I would like to thank the reviewers for spending their valuable time for evaluating the papers and quick response which made this special issue catch the time. I would also like to thank the JACIII editorial office for their great assistance for preparing this special issue.
Multi-Level Interpolation for Inference with Sparse Fuzzy Rules: An Extended Way of Generating Multi-Level Points
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

As an extended way for inference based on multi-level interpolation, the number of multi-level points, generated with the α-cuts of given facts, is increased by making larger the number of levels of α. The conventional way uses the number of the levels adopted to define each given fact by α-cuts. A basic study is performed with triangular membership functions, where it is examined how the accuracy of mapping with fuzzy rules changes in increasing the number of the levels. It is also examined how deduced consequences behave when the number is increased. Moreover, convergent core sets of consequences are theoretically derived in the increase by the effective use of non-adaptive inference operations for core sets. They are used as references in simulation studies. Increasing the number of the levels provides nonlinear mapping with more precise reflection of distribution forms of sparse fuzzy rules to consequences. The basic study here contributes to improving the reflection accuracy. In simulations for the basic study, it is found that the mapping accuracy improves when the number of levels of α is increased. It is also confirmed that deduced core sets converge to those theoretically derived. Support sets are also found to converge in increasing the number of the levels. The core sets and support sets of deduced consequences do not, however, monotonically converge. This property causes difficulty in determining the optimized number of the levels so as to satisfy required mapping accuracy. In order to solve this problem, further discussions may be possible to theoretically derive convergent consequences and to use them in practical fields, in accordance with the theoretically derived core sets mentioned above.
Action Selection for Game Play Agents Using Genetic Algorithms in Platform Game Computational Intelligence Competitions
Authors: Hasegawa Ken ,   Graduate School of Information Systems, The University of Electro-Communications, 1-5-1 Chofugaoka, Chofu, Tokyo 182-8585, Japan, Tanaka Narutoshi , Emoto Ryuji , Sugihara Yusuke , Ngonphachanh Ardta , Ichino Junko , Hashiyama Tomonori
Published: April 2016

The application of computational intelligence (CI) and artificial intelligence (AI) to games has been attempted as a typical implementation of intelligent processing on computers. Intelligence in this sense is understood as the ability to search for the best solution efficiently among multiple options, specifically in AI playing board games such as chess. As the processing ability of computers increases, CI/AI systems are outperforming humans in finding potential solutions from a tremendous number of options within a short timeframe. These days, computer games are widely prevalent. CI/AI applications in computer games are focused on animating non-player characters (NPCs), whereas CI/AI applications in the scientific fields are focused on modeling intelligent human activities. The field of computer games faces many issues, such as dealing with dynamic environments that change quickly and processing images at higher resolutions and complexity. The use of computer games as a benchmark for CI/AI technologies has been attempted, and competitions involving various kinds of games have been held to encourage innovation in the field. In this paper, we describe a learning agent that participated in a platform game CI competition held in conjunction with Fuzzy System Symposium (FSS 2012). The approach adopted in this paper is a basic method based on conventional methods. The authors hope that this presentation of our development processes would encourage many researchers to participate in competitions and that it would contribute to progress in the field.
Similarity-Based Fuzzy Classification of ECG and Capnogram Signals
Authors: Betancourt Janet Pomares ,   Tokyo Institute of Technology, G3-49, 4259 Nagatsuta, Midori-ku, Yokohama 226-8502, Japan, Fatichah Chastine , Tangel Martin Leonard , Yan Fei , Sanchez Jesus Adrian Garcia , Dong Fang-Yan , Hirota Kaoru ,   Central Institute of Digital Research, 202, No.1704, Siboney, Playa, Havana, Cuba
Published: April 2016

A method for ECG and capnogram signals classification is proposed based on fuzzy similarity evaluation, where shape exchange algorithm and fuzzy inference are combined. It aims to be applied to quasi-periodic biomedical signals and has low computational cost. On the experiments for atrial fibrillation (AF) classification using two databases: MIT-BIH AF and MITBIH Normal Sinus Rhythm, values of 100%, 94.4%, and 97.6% for sensitivity, specificity, and accuracy respectively, and execution time of 0.6 s are obtained. The proposal is capable of been extended to classify different diseases, from ECG and capnogram signals, such as: Brugada syndrome, AV block, hypoventilation, and asthma among others to be implemented in low computational resources devices.