bluuebunny/crossref_metadata_2025 huggingface dataset:
Hard and Fuzzy c-Means Clustering with Conditionally Positive Definite Kernel
Authors: Kanzawa Yuchi ,   Shibaura Institute of Technology, 3-7-5 Toyosu, Koto-ku, Tokyo 135-8548, Japan, Endo Yasunori , Miyamoto Sadaaki ,   University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki 305-8573, Japan
Published: April 2016

In this paper, we investigate three types of c-means clustering algorithms with a conditionally positive definite (cpd) kernel. One is based on hard c-means and two are based on standard and entropy-regularized fuzzy c-means. First, based on a cpd kernel describing a squared Euclidean distance between data in feature space, these algorithms are derived from revised optimization problems of the conventional kernel c-means. Next, based on the relationship between the positive definite (pd) kernel and cpd kernel, the revised dissimilarity between a datum and a cluster center in the feature space is shown. Finally, it is shown that a cpd kernel c-means algorithm and a kernel c-means algorithm with a pd kernel derived from the cpd kernel are essentially identical to each other. Explicit mapping for a cpd kernel is also described geometrically.
An Explicit Memory Scheme of Genetic Network Programming
Authors: Mabu Shingo ,   Graduate School of Information, Production and Systems, Waseda University, 2-7 Hibikino, Wakamatsu-ku, Kitakyushu, Fukuoka 808-0135, Japan, Ye Fengming , Hirasawa Kotaro
Published: April 2016

Many classical methods such as Genetic Algorithm (GA), Genetic Programming (GP), Evolutionary Strategies (ES), etc. have made significant contribution to the study of evolutionary computation. And recently, a new approach named Genetic Network Programming (GNP) has been proposed especially for solving complex problems in dynamic environments. It is based on the algorithms of classical evolutionary computation techniques and uses data structures of directed graphs which are the unique feature of GNP. Focusing on GNP’s distinguished expression ability of the graph structure, this paper proposes an enhanced architecture for standard GNP in order to improve the performance of GNP by adopting an explicit memory scheme which records and utilizes the exploited information flexibly and extensively during the evolution process of GNP. In the enhanced architecture, the important gene information of the elite individuals is extracted and accumulated in the memory during evolution. Among the accumulated information, some of them are selected and used to guide the agents. In this paper, the proposed architecture is applied to the tileworld which is an excellent benchmark for evaluating the architecture demonstrating its superiority.
Special Issue on Advanced Intelligent Systems
Authors: Chun Myung-Geun , Watanabe Toshihiko
Published: April 2016

The 12th International Symposium on Advanced Intelligent Systems (ISIS 2011) held at La Vie DfOr Resort, Suwon, Korea, on September 28 to October 1, 2011, featured presentations by researchers, engineers and practitioners on the latest accomplishments, innovations and applications in artificial intelligence, intelligent systems, and information technology. The 152 papers consisted of 110 regular papers, 39 organized session papers and 3 invited papers were contributed to the conference. The Program Committee requested ISIS 2011 reviewers to select papers for a special issue of the Journal of Advanced Computational Intelligence & Intelligent Informatics (JACIII), of which about 10 were accepted for publication in a twopart ISIS 2011 special issue, Vol.16, No.7, containing 5 papers. Part II will feature about 5 papers. A brief review of Part I covers 5 papers: First paper proposes a quantitative model for assessing the collision risk in maritime waterway traffic. The method proposes recent maritime traffic characteristics in time-variant CPA waterway environments and models a dynamic causation factor as a risk indicator. Second paper proposes multiagent query refinement realizing personalized query refinement by three strategies ? knowledge-based query expansion, user-device-based query and weighted query expansion strategy. These approaches determine the domain that the initial query belongs to and expand the query by comprehensively considering user interests. Third paper presents variable-step-size incremental conductance direct maximum power point tracking using fuzzy membership for a standalone photovoltaic system under rapidly changing irradiation. Fourth paper applies a natural actor-critic (NAC) and natural evolution strategies (NES) from natural-gradient-based machine learning to path-tracking control problems for autonomous vehicles. Fifth paper presents features for authenticating painting style, specifically that of Piet Mondrain. It demonstrates meaningful features using two supervised learning algorithms ? decision tree induction algorithm C4.5 and the feature generating machine (FGM) ? selecting important features in the course of learning. We thank the reviewers for their time and effort in making this special issue available so quickly. We are also grateful to the JACIII editorial office for invaluable assistance and advice in putting the issue together.
Risk Assessment Model of Maritime Traffic in Time-Variant CPA Environments in Waterway
Authors: Jeong Jung Sik ,   Division of International Maritime Transportation Science, Mokpo National Maritime University, Mokpo City, Republic of Korea, Park Gyei-Kark , Kim Kwang Il
Published: April 2016

This paper proposes a quantitative model for assessing collision risk for maritime traffic in waterways. The proposed method reflects recent maritime traffic characteristics under in time-variant CPA environments in waterways and models a dynamic causation factor as a risk indicator. To eliminate an uncertainty by human factors causing maritime accidents, the proposed model combines maritime accident statistics and weather records with spatial and temporal distribution determined on the basis of recent and real data for ship movements. Because our method reflects the characteristics of recent ship movements in the water area, it can be complementarily used with the conventional model using Bayesian Belief Network (BBN).
Autonomous Vehicle Path Tracking Based on Natural Gradient Methods
Authors: Kwon Ki-Young ,   Department of Control and Instrumentation Engineering, Korea University, Sejong-ro 2511, Sejong 339-700, Korea, Jung Keun-Woo , Yang Dong-Su , Park Jooyoung ,   LG Electronics, Gasan-dong, Geumcheon-gu, Seoul 153-802, Korea
Published: April 2016

Recently, reinforcement learning and evolution strategy have become major tools in the field of machine learning, and have shown excellent performance in various engineering problems. In particular, the Natural Actor-Critic (NAC) approach and the Natural Evolution Strategies (NES) have led to considerable interests in the area of natural-gradient-based machine learning methods with many successful applications. In this paper, we apply the NAC and the NES to pathtracking control problems for autonomous vehicles. Simulation results show that these methods can yield better performance compared to the conventional PID controllers.
Concept of Fuzzy Atmosfield for Representing Communication Atmosphere and its Application to Humans-Robots Interaction
Authors: Liu Zhen-Tao ,   Department of Computational Intelligence and Systems Science, Tokyo Institute of Technology, G3-49, 4259 Nagatsuta, Midori-ku, Yokohama, Kanagawa 226-8502, Japan, MinWu  , Li Dan-Yun , Chen Lue-Feng , Dong Fang-Yan , Yamazaki Yoichi , Hirota Kaoru ,   School of Information Science and Engineering, Central South University, Yuelu Mountain, Changsha, Hunan 410083, China,   Department of Electrical, Electronic, and Information Engineering, Kanto Gakuin University, 1-50-1 Mutsuura-higashi, Kanazawa-ku, Yokohama, Kanagawa 236-8501, Japan
Published: April 2016

The concept of Fuzzy Atmosfield (FA) is proposed to represent a communication atmosphere in society consisting of multiple individuals such as humans and/or robots, where the FA is characterized by 3D fuzzy cubic space with “Friendly-Hostile” (FH), “Lively-Calm” (LC), and “Casual-Formal” (CF) axes, and each state of the FA is visualized using shape-colorlength graphics. It is targeted to be a tool for identifying an atmosphere using quantitative analysis and graphical representation. By a humans-robots interaction experiment in which the FA is used to represent the real-time atmosphere created by four humans and five eye robots in a home party scenario, it shows that Pearson’s correlation coefficient values of 0.92, 0.86, and 0.72 for the FH, LC, and CF axes, respectively, indicate the correspondence between the proposed FA and results of questionnaires, and that subjective estimation of graphical representation of the FA achieves 84% accuracy for shape, 76% for color, and 58% for length. The FA is being extended to the representation of complex atmosphere generated by humans, robots, and background music, and part of results is also shown.
Landmark FN-DBSCAN: An Efficient Density-Based Clustering Algorithm with Fuzzy Neighborhood
Authors: Liu Hao ,   Division of Synergetic Information Science in Graduate School of Information Science and Technology, Hokkaido University, Kita 14, Nishi 9, Kita-ku, Sapporo 060-0814, Japan, Oyama Satoshi , Kurihara Masahito , Sato Haruhiko
Published: April 2016

Clustering is an important tool for data analysis and many clustering techniques have been proposed over the past years. Among them are density-based clustering methods, which have several benefits such as the number of clusters is not required before carrying out clustering; the detected clusters can be represented in an arbitrary shape and outliers can be detected and removed. Recently, the density-based algorithms were extended with the fuzzy set theory, which has made these algorithm more robust. However, the density-based clustering algorithms usually require a time complexity ofO(n2) wherenis the number of data in the data set, implying that they are not suitable to work with large scale data sets. In this paper, a novel clustering algorithm called landmark fuzzy neighborhood DBSCAN (landmark FN-DBSCAN) is proposed. The concept, landmark, is used to represent a subset of the input data set which makes the algorithm efficient on large scale data sets. We give a theoretical analysis on time complexity and space complexity, which shows both of them are linear to the size of the data set. The experiments show that the landmark FN-DBSCAN is much faster than FN-DBSCAN and provides a very good quality of clustering.
Financial Institution Failure Prediction Using Adaptive Neuro-Fuzzy Inference Systems: Evidence from the East Asian Economic Crisis
Authors: Choensawat Worawat ,   School of Science and Technology, Bangkok University, Rama 4 Road, Klong-Toey, Bangkok 10110, Thailand, Polsiri Piruna ,   Faculty of Business Administration, Dhurakij Pundit University, 110/1-4 Prachachuen Road, Laksi, Bangkok 10210, Thailand
Published: April 2016

This paper introduces the use of Adaptive Neuro-Fuzzy Inference Systems (ANFIS) into the area of finance for Thai firms. This study started with collecting financial data from 82 finance companies and 15 commercial banks operating in the period 1992-1997, before the East Asian economic crisis occurred. Financial data on failed and non-failed firms were then examined to develop fuzzy rules based on CAMEL variables. ANFIS is applied to the area of finance for Thai firms for constructing failure prediction models. These models show that prediction accuracy is greater than 90 percent for one to five years prior to failure, indicating the robustness of models over time. In experiments, models yield more accurate forecasting than a logistic model that has been used in the area of finance for Thai firms. The purpose of this study is to present thatmodels using ANFIS are better suited for financial data sets with high nonlinearity than a logistic model.
Thermal Unit Scheduling for CO2Reduction Including Wind Power and Electric Vehicles
Authors: Yamashita Daiki ,   Department of Environment and Energy Engineering, Waseda University, 55th Building S-Wing 705, 3-4-1 Okubo, Shinjuku-ku, Tokyo 169-8555, Japan, Yokoyama Ryuichi , Niimura Takahide ,   Visiting Researcher, Faculty of Economics, Hosei University, 4342 Aihara, Machida, Tokyo, Japan
Published: April 2016

In this paper, we present a method to determine unit commitment schedules, while considering CO2emissions and costs along with the frequency regulation capability of the units, in order tomitigate fluctuations in wind power. We developed an extended procedure that obtains a trade-off solution of cost versus CO2emissions, including a significant wind power penetration, and developed Plug-in Electric Vehicles (PEVs) as additional reserves. The proposed method was tested on a 10-unit, 24-hour model system using the estimated wind power curve derived from an actual wind farm. The results, such as shadow prices of CO2obtained using the trade-off analysis, may provide a basis of evaluating the equivalent cost of wind farms and PEVs, and their contributions to CO2reduction.
Three Dimensional Creativity: Three Navigations to Extend our Thoughts
Authors: Lee Kwang H. ,   Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehakro, Yuseong-gu, Daejeon 305-701, Korea
Published: April 2016

Creativity is an ability to come up with a new idea. In many cases, getting out of reality can bring forth a new idea. Since asking questions stimulates the brain to release us from reality, repeating such questions forms the habit of asking many questions that increases creativity. A framework consisting of three kinds of questions is provided. The three kinds of questions are on axes of “time,” “space,” and “field” and the framework is called as three dimensional creativity. Traveling along the three axes allows escaping from a fixed idea, and thus helps us to raise new ideas.
Tag Line Generating System Using Information on the Web
Authors: Yamane Hiroaki ,   The Department of Information and Computer Science, Keio University, 3-14-1 Hiyoshi, Kohoku-ku, Yokohama 223-8522, Japan, Hagiwara Masafumi
Published: April 2016

This paper proposes a tag line generating systemusing information extracted from the web. Tag lines sometimes attract attention even when they consist of indirect word group of the target. We use web information to extract hidden data and use several tag line corpora to collect a large number of tag lines. First, knowledge related to the input is obtained from the web. Then, the proposed system selects suitable words according to the theme. Also, model tag lines are selected from the corpora using the knowledge. By inserting nouns, verbs and adjectives into model tag lines’ structure, candidate sentences are generated. These tag line candidates are selected by the suitability as a sentence using a text N-gram corpus. The subjective experiment measures the quality of system-generated tag lines and some of them are quite comparable to human-made ones.
Editing Robot Motion Using Phonemic Feature of Onomatopoeias
Authors: Ito Junki ,   Graduate School of Computer and Cognitive Sciences, Chukyo University, 101 Tokodachi, Kaizu-cho, Toyota, Aichi 470-0393, Japan, Kanoh Masayoshi , Nakamura Tsuyoshi , Komatsu Takanori ,   School of Information Science and Technology, Chukyo University, 101 Tokodachi, Kaizu-cho, Toyota, Aichi 470-0393, Japan,   Graduate School of Engineering, Nagoya Institute of Technology, Gokiso-cho, Showa-ku, Nagoya, Aichi 466-8555, Japan,   Faculty of Textile Science and Technology, Shinshu University, 3-15-1 Tokida, Ueda, Nagano 386-8567, Japan
Published: April 2016

Onomatopoeias are words that represent the sound, appearance, or voice of things, thus making it possible to create expressions that bring a scene to life in a subtle fashion. Onomatopoeias can be used to make the process of robot motion generation more easily and intuitively. In previous studies, subjective quantified values of onomatopoeias have been used as indices of robot motion, but the generality of the motion has not been evaluated. In this study, we propose a method for generating robot motion using the objective quantified values of onomatopoeias. We experimentally verified that the proposed method generatedmore suitable motion than did the previous methods.
Equilibrium Pricing Extending the Mean-Variance Theory Using Weighted Possibilistic Mean of Investor’s Subjectivity
Authors: Hasuike Takashi ,   Graduate School of Information Science and Technology, Osaka University, 2-1 Yamadaoka, Suita, Osaka 565-0871, Japan
Published: April 2016

This paper proposes an extended analytical approach to developing an equilibrium pricing vector with various types of investor’s subjectivity based on extended Mean-Variance (MV) theory. Weighted fuzzy mean and variance are introduced in order to represent investor’s subjectivity numerically. Similar to the traditional MV-based equilibrium approach, the equilibrium pricing vector of the proposed model is analytically obtained in mathematical programming. A macroeconomic index based on risky assets, which provides information with respect to the soundness of the capital market with subjectivity, is also constructed.
An Evolutionary Algorithm for Black-Box Chance-Constrained Function Optimization
Authors: Masutomi Kazuyuki ,   Interdisciplinary Graduate School of Science and Engineering, Tokyo Institute of Technology,, Nagata Yuichi , Ono Isao ,   4259 Nagatsuta-cho, Midori-ku, Yokohama 226-8502, Japan,   4259 Nagatsuta-cho, Midori-ku, Yokohama 226-8501, Japan
Published: April 2016

This paper presents an evolutionary algorithm for Black-Box Chance-Constrained Function Optimization (BBCCFO). BBCCFO is to minimize the expectation of the objective function under the constraints that the feasibility probability is higher than a userdefined constant in uncertain environments not given the mathematical expressions of objective functions and constraints explicitly. In BBCCFO, only objective function values of solutions and their feasibilities are available because the algebra expressions of objective functions and constraints cannot be used. In approaches to BBCCFO, a method based on an evolutionary algorithm proposed by Loughlin and Ranjithan shows relatively good performance in a realworld application, but this conventional method has a problem in that it requires many samples to obtain a good solution because it estimates the expectation of the objective function and the feasibility probability of an individual by sampling the individual plural times. In this paper, we propose a new evolutionary algorithm that estimates the expectation of the objective function and the feasibility probability of an individual by using the other individuals in the neighborhood of the individual. We show the effectiveness of the proposed method through experiments both in benchmark problems and in the problem of a inverted pendulum balancing with a neural network controller.
Emotion Generation Model with Growth Functions for Robots
Authors: Harata Miho ,   Graduate School, Kansai University, 3-3-35 Yamate-cho, Suita-shi, Osaka 564-8680, Japan, Tokumaru Masataka ,   Kansai University, 3-3-35 Yamate-cho, Suita-shi, Osaka 564-8680, Japan
Published: April 2016

In this paper, we propose an emotion model with growth functions for robots. Many emotion models for robots have been developed using Neural Networks (NN), which focus on the functions of emotion recognition, control, and expression. One problem that affects these emotion models for robots is the development of a “simplified” emotion generation algorithm. Users readily lose interest in “simple” systems. Most models have attempted to generate complex emotional expressions, whereas no previous studies have considered the “growth of a robot.” Therefore, we propose a growth model for emotions based on changes in the network structure of a self-organizing map. We also applied a multilayer perceptron NN to generate more sophisticated expressions of emotion using growth functions. This model generated a similar behavior to the concept of affective change described in genetic psychology. Our results showed that this emotion model was more suitable for producing a robot with growth functions based on a psychological model.
Turing Test-Based Evaluation of an Experimental System for Generation of Casual English Sentences from Regular English Input
Authors: Clark Eleanor ,   Graduate School of Information Science and Technology, Hokkaido University, Kita 14, Nishi 8, Kita-ku, Sapporo, Hokkaido 060-0814, Japan, Araki Kenji
Published: April 2016

This paper proposes an experimental system for generating slang-style casual English sentences from regular English input using a phonetic database approach, primarily as an AI task, with real-life applications such as social media marketing. An original database consisting of multiple candidates of casual English phonemes was constructed, and linguistic analysis of Twitter data used to establish the optimum frequency of slang tokens per sentence. The human-likeness and legibility of output sentences of the experimental system were evaluated using an experiment based on the classical definition of the Turing test, in which fifty human evaluators attempted to distinguish sentences produced by the system from genuine humanauthored sentences. The experiment results demonstrated that the gap in human-likeness scores between the “human” and “machine” sentences was small, and that some “machine” sentences actually outperformed several of the “human sentences.” The “machine” sentences’ average score of 3.1 on a 5-point scale, where 3 indicated complete uncertainty of whether the sentences were human-authored or machine-authored, can be considered a pass of the Turing test in the established definition. In this paper, we describe the potential approaches to the task, the construction of the phonetic database and the proposed system, and discuss the evaluation results.
Psychological Effects of a Synchronously Reliant Agent on Human Beings
Authors: Jimenez Felix ,   Chukyo University, 101-2 Yagoto Honmachi, Showa-ku, Nagoya 466-8666, Japan, Ando Teruaki , Kanoh Masayoshi , Nakamura Tsuyoshi ,   Nagoya Institute of Technology, Gokiso-cho, Showa-ku, Nagoya, Aichi 466-8555, Japan
Published: April 2016

The ability of human symbiosis robots to communicate is indispensable for their coexistence with humans, so studies on the interaction between humans and robots are important. In this paper, we propose amodel robot self-sufficiency system that empathizes with human emotions, a model in which we apply the urge system to an autonomous system of emotions. We carry out simulation experiments on this model and verify the psychological interaction between the software robot and its users.
Self-Organized Map Based Learning System for Estimating the Specific Task by Simple Instructions
Authors: Masuta Hiroyuki ,   Department of Mechanical Engineering, Kanagawa University, 3-27-1 Rokkakubashi, Kanagawa-ku, Yokohama-shi, Kanagawa 221-8686, Japan, Tamura Yasuto , Lim Hun-ok
Published: April 2016

This paper discusses a learning system for service robots to estimate specific tasks by using simple instructions from human beings. Intelligent robots are expected to operate in human living areas, so service robots should understand specific tasks from simple instructions given by human beings. It is important to perceive environmental situations and to adapt to human preferences. We propose a learningmethod using the Self-Organized Map (SOM) to estimate specific tasks from both human behavior measurement but also environmental measurement. Through simulation experiments, we verified that the proposed SOMbased method considers environmental situations associated time variations and show that service robots decide table-clearing tasks according to human intent.
Non Metric Model Based on Rough Set Representation
Authors: Endo Yasunori ,   Faculty of Engineering, Information and Systems, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki 305-8573, Japan, Heki Ayako , Hamasuna Yukihiro ,   Graduate School of Systems and Information Engineering, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki 305-8573, Japan,   Department of Informatics, Kinki University, 3-4-1 Kowakae, Higashiosaka, Osaka 577-8502, Japan
Published: April 2016

The non metricmodel is a kind of clustering method in which belongingness or the membership grade of each object in each cluster is calculated directly from dissimilarities between objects and in which cluster centers are not used. The clustering field has recently begun to focus on rough set representation instead of fuzzy set representation. Conventional clustering algorithms classify a set of objects into clusters with clear boundaries, that is, one object must belong to one cluster. Many objects in the real world, however, belong to more than one cluster because cluster boundaries overlap each other. Fuzzy set representation of clusters makes it possible for each object to belong to more than one cluster. The fuzzy degree of membership may, however, be too descriptive for interpreting clustering results. Rough set representation handles such cases. Clustering based on rough sets could provide a solution that is less restrictive than conventional clustering and more descriptive than fuzzy clustering. This paper covers two types of Rough-set-based Non Metric model (RNM). One algorithm is the Roughset-based Hard Non Metric model (RHNM) and the other is the Rough-set-based Fuzzy Non Metric model (RFNM). In both algorithms, clusters are represented by rough sets and each cluster consists of lower and upper approximation. The effectiveness of proposed algorithms is evaluated through numerical examples.
A Study on the Effect of Learning Parameters for Inducing Compact SVM
Authors: Kaneda Yuya ,   The University of Aizu, Tsuruga, Ikki-machi, Aizu-Wakamatsu, Fukushima 965-8580, Japan, Zhao Qiangfu , Liu Yong , Yen Neil Y.
Published: April 2016

In recent years, portable computing devices (PCDs) such as smart phones and tablet terminals have been popularized at a tremendous speed. People around the world are now using PCDs for different purposes. To resolve “digital divide” problem, it is desired to embed awareness agents (A-agents) that can recognize different situations, detect important information, and help human users make decisions efficiently and effectively. To use A-agents in one PCD, it is necessary to implement each agent with a reasonable cost. For this purpose, we can use dimensionality reduction (DR). To reduce the total cost, sophisticated DR methods cannot be used. In this paper, we investigate the performance changes of SVM-based A-agents, before and after centroid based DR. Experimental results show that in most cases the performance can be preserved with the properly chosen learning parameters.
Interlinking Distributed Services for Workflow-as-a-Service Based on Linked Data
Authors: Chen Wuhui ,   School of Computer Science and Engineering, The University of Aizu, Aizu-Wakamatsu, Fukushima 965-8580, Japan, Paik Incheon , Tashiro Tetsuya
Published: April 2016

Services are considered to have had a tremendous impact on the Web as a potential silver bullet for supporting a distributed service-based economy on a global scale. However, despite the outstanding progress, their uptake on a Web scale has been significantly less than initially anticipated. Isolated service islands without links to related services have hampered service discovery and composition. In this paper, we propose a methodology to drive innovation from isolated service islands into a global social service network to connect the service islands for Workflow-as-a-Service. First, we propose Linked social service-specific principles based on Linked data principles for publishing services on the open Web as linked social services, and suggest a new platform for constructing global social service network. We then propose an approach to enable exploiting the global social service network, providing Workflow-as-a-Service. Finally, experimental results show that Linked social service can solve the service composition problem by enabling providing Workflow-as-a-Service based on the global social service network, and has the potential to be the next wave of services.
Spherical Visualization of Image Data with Clustering
Authors: Yaguchi Yuichi ,   The University of Aizu, Tsuruga, Ikkimachi, Aizuwakamatsu, Fukushima 965-8580, Japan, Oka Ryuichi
Published: April 2016

This paper proposes to aid the search for images by visualization of the image data on a spherical surface. Many photographs were lost in the Tohoku tsunami, and those that were eventually found are now being scanned. However, the owners of the lost photographs are finding it difficult to search for their images within a large set of scanned images that contain no additional information. In this paper, we apply a spatial clustering technique called the Associated Keyword Space (ASKS) projected from a threedimensional (3D) sphere to a two-dimensional (2D) spherical surface for 2D visualization. ASKS supports clustering, and therefore, we construct an image search system in which similar images are clustered. In this system, similar images are identified by color inspection and by having similar characteristics. In this way, the system is able to support the search for images from within a huge number of images.
PHOTMOSPHERE: A System for Amplifying Connection Between Memory and Record
Authors: Tanaka Kazuhiro ,   Graduate School of Informatics, Kansai University, 2-1-1 Ryozenji, Takatsukishi, Osaka 569-1095, Japan, Matsushita Mitsunori ,   Faculty of Informatics, Kansai University, 2-1-1 Ryozenji, Takatsukishi, Osaka 569-1095, Japan
Published: April 2016

Photographs capture information on situations or circumstances that give some clue about users’ access to past activities. The photographs remind them of their personal experiences and of social events that took place at a certain time. Our research intends to use such information to help them recollect events and to acquire novel information by connecting memory and social records, for example, news reports. Toward this end, we propose a system called PHOTMOSPHERE that helps users recollect past events by connecting memory (photographs) and records (news). We conducted an experiment to observe the effect of showing news with photographs. Participants were shown either photographs alone that they had taken or both news reports and photographs they had taken. They were then asked to describe their recollections under both conditions. We found that, in many cases, news reports helped participants remember events, but not in some cases, for instance, when the subject of the photograph was a person or an object. On the basis of this result, we reformulate PHOTMOSPHERE design criteria.
Tailor-Made Plate Design and Manufacturing System for Treating Bone Fractures in Small Animals
Authors: Doi Akio ,   Advanced Visualization Laboratory, Iwate Prefectural University, 152-52 Sugo, Takizawa-mura, Iwate-gun, Iwate 020-0173, Japan, Takahashi Hiroki , Syuto Bunei , Katayama Masaaki , Nagashima Hiroyuki , Okumura Masahiro ,   Iwate University, 3-18-3 Ueda, Morioka-shi, Iwate 020-8550, Japan,   Animal Hospital, Iwate University, 3-18-3 Ueda, Morioka-shi, Iwate 020-8550, Japan,   Iwate Industrial Research Institute, 2-4-25 Kitaiioka, Morioka-shi, Iwate 020-0857, Japan,   Department of Veterinary Medicine, Hokkaido University, Kita 18, Nishi 9, Kita-ku, Sapporo, Hokkaido 060-0818, Japan
Published: April 2016

We propose the use of a computer-aided design (CAD) system for treating bone fractures in small animals. During surgical planning, the veterinarian sketches a simple plate by referring to computed tomography images. A CAD operator then uses polygonal approximation (triangulation) of the surface of the bone region to model the plate. After the approximated shape is imported into the CAD system as a triangular mesh, a detailed design of the plate is prepared by referring to the abovementioned sketch. The plate can be designed to match the bone surface since the plate surface follows the curvature of the surface of the exported triangular mesh. The bone shape and the plate are eventually converted into polygons, and a structural model identical to the fractured part of the bone can be reproduced using a 3D printer, which allows for alignment to be performed at full scale. In this study, we examine the applicability of the proposed system by designing the most appropriately shaped plates for bone fracture therapy for small dogs brought to a veterinary clinic for treatment.
Assessment of Electromagnetic Frequency Sounding Problem in a Near Surface Geological Environment
Authors: Vu Quang Hiep ,   Database/Bioinformatics Laboratory, Chungbuk National University, 12 Gaeshindong, Heungduk-gu, Cheongju, Chungbuk 361-763, Korea, Li Meijing , Ryu Keun Ho
Published: April 2016

Understanding what exists and happens beneath the Earth’s surface could help human being make better decision in life and production. This paper introduces depth sounding based on artificial magnetic variation in a near-surface geological environment. We focus on solving the forward problem of electromagnetic frequency sounding. First, work on vertical geoelectric layers based on variation in electromagnetic fields is investigated based on the depth and the skin-effect. The skin-effect in the time domain is examined for when the frequency of electromagnetic field decreases, the eddy current would penetrates deep into the environment. Based on this background, we construct a model for solving the frequency electromagnetic sounding problem. This model allows us to detect changes in conductivity related to the Earth’s shell depth versus electromagnetic measurement at one place on the Earth’s surface. A simulation tool is then implemented using MATLAB. This model is expected to contribute especially to teaching in the field of geophysics and to be applied widely in areas such as geological map drawing, structural study, and engineering geology and hydrogeology.
Resource-Aware Clustering Based AODVjr Routing Protocol in the Internet of Things
Authors: Wang Xiaoni ,   School of Applied Science, Beijing Information Science and Technology University, No.12, Qing He Xiao Ying East Road, Haidian District, Beijing, China,   Dongling School of Economics and Management, University of Science and Technology Beijing, Beijing, China
Published: April 2016

Through ad hoc routing protocol AODVjr and resource-aware data mining algorithms research, a resource-aware clustering based routing protocol in the Internet of Things, RA-AODVjr, is proposed. It solves the short comings of the constrained resources of memory, computing power, and the power energy of the wireless sensor’s terminal node in the Internet of Things. RA-AODVjr protocol is designed combining with the RA-cluster and AODVjr routing protocol. This protocol selects the best neighbor in the terminal node and balances the network traffic when terminal node resource is constrained, using the relevance of the ad hoc network. The simulation results show that the agreement achieves load balancing of energy constrained nodes to a certain extent. Compared with the original AODVjr protocol, due to the best neighbor node delivery technology, the local network traffic gets a better balance and less time delay means better choice of routing.
Gradient-Related Non-Photorealistic Rendering for High Dynamic Range Images
Authors: Lu Jiajun ,   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 non-photorealistic rendering (NPR) method based on elements, usually strokes, is proposed for rendering high dynamic range (HDR) images to mimic the visual perception of human artists and designers. It enables strokes generated in the rendering process to be placed accurately on account of improvements in computing gradient values especially in regions having particularly high or low luminance. Experimental results using a designed pattern show that angles of gradient values obtained from HDR images have a reduction in averaged error of up to 57.5% in comparison to that of conventional digital images. A partial experiment on incorporating HDR images into other NPR styles, such as dithering, shows the wide compatibility of HDR images in providing source information for NPR processes.
Design and Implementation of a Data-Oriented Nonlinear PID Controller
Authors: Wakitani Shin ,   Graduate School of Engineering, Hiroshima University, 1-4-1 Kagamiyama, Higashi-hiroshima, Hiroshima 739-8527, Japan, Nawachi Takuya , Martins Guilherme Rosado , Yamamoto Toru ,   Faculty of Engineering, Hiroshima University, 1-4-1 Kagamiyama, Higashi-hiroshima, Hiroshima 739-8527, Japan
Published: April 2016

PID control schemes have been widely used in most industrial control systems. However, it is difficult to determine a suitable set of PID gains, because most industrial systems have nonlinearity. On the other hands, Cerebellar Model Articulation Controller (CMAC) classified as neural networks has been proposed, and design scheme of an intelligent PID controller by using the CMAC-PID tuner has been proposed. However, the CMAC-PID tuner has two problems. One is that the CMAC must be trained in an online manner to get their optimum weights. Another is that the CMAC requires lots of memories and high computational cost for some microcontrollers. In order to train CMAC in an offline manner, CMACFRIT scheme which is a combination of CMAC and Fictitious Reference Iterative Tuning (FRIT) scheme has been proposed in a previous research. FRIT is a scheme to determine control parameters by using a set of experimental data. According to the CMAC-FRIT scheme, CMAC-PID tuner can be trained in an offline manner by using a set of operating data. In this paper, to address the problem of required memory and computational cost, a method that expresses CMAC-PID tuner as a simple nonlinear function by using Group Method of Data Handling (GMDH) is proposed. According to the proposed method, CMAC-PID tuner (which is trained in advance by using a set of operating data) is replaced by a network of N-Adalines (units expressed by a simple nonlinear function). Thus, the proposed algorithmcan be easily added to a microcontroller even if it is a commodity type. The effectiveness of the proposed method is validated by a simulation example. Moreover, to show the usefulness of the proposed method, the algorithm is added to a commodity type microcontroller, and the controller is applied to a magnetic levitation device.
Generating Cooperative Collective Behavior in Swarm Robotic Systems
Authors: Ohkura Kazuhiro ,   Graduate School of Engineering, Hiroshima University, 1-4-1 Kagamiyama, Higashi-Hiroshima 739-8527, Japan, Yasuda Toshiyuki , Matsumura Yoshiyuki ,   Faculty of Textile Science and Technology, Shinshu University, 3-15-1 Tokida, Ueda, Nagano 386-8567, Japan
Published: April 2016

Swarm robotics research involves multirobot systems that consist of many homogeneous autonomous robots but no global controller. In this paper, an evolutionary robotics approach using an artificial neural network is applied to a swarm robotic system. Conventionally, the neural network evolved using only synaptic weights under the condition of a fixed topology. Our research group has been developing a novel approach to a topology and weight evolving artificial neural network named Mutation-Based Evolving Artificial Neural Network (MBEANN). A series of computer simulations shows that MBEANN yields better results in terms of flexibility than conventional solutions to the cooperative package-pushing problem.
Observation of Synchronization Phenomena in Structured Flocking Behavior
Authors: Yamauchi Sho ,   Graduate School of Information Science and Technology, Hokkaido University, North 14 West 9, Sapporo, Hokkaido, Japan, Kawamura Hidenori , Suzuki Keiji
Published: April 2016

Flocking algorithms for multi-agent systems are distributed algorithms that generate complex formational movement despite having simple rules for each agent. These algorithms, known as swarmintelligence, are flexible and robust. However, to exploit these features to generate flexible behavior in an autonomous system, greater flexibility is needed. To achieve this, these algorithms are extended to enable arbitrary lattice formation. In addition, extended flocking algorithms can be assumed to be the aggregation of oscillators and observed the behavior of synchronization. It is difficult to explain the behavior of extended flocking algorithms as a consensus problem but, by assuming the flock as the set of oscillators, it can be explained as a synchronization phenomenon.
Fingernail Detection System Using Differences of the Distribution of the Nail-Color Pixels
Authors: Fujishima Noriaki ,   Graduate school of Systems and Information Engineering, University of Tsukuba, 1-1-1 Tennodai, Tsukuba-shi, Ibaraki 305-8573, Japan, Hoshino Kiyoshi ,   Faculty of Systems, Information and Engineering, University of Tsukuba, 1-1-1 Tennodai, Tsukuba-shi, Ibaraki 305-8573, Japan
Published: April 2016

As far as we know, previous studies have only used color information for fingernail detection. Images of the palm, however, have a lot of pixels that have the same color values as the fingernail. Other kinds of some information are required to distinguish fingernails from areas of the palm having the same color. We found that there seem to be differences in the distribution of nail-color pixels, which is useful for fingernail detection especially when a camera captures a palm side. We found differences of the areas of the fingernail and skin, and have confirmed these are available for corresponding to the palm. In this paper, therefore, we propose a fingernail detection system using differences in the distribution of the nail-color pixels. The system proposed first fixes provisional nail color indicating nail-likelihood as a calibration process. In main processing, the system has two-step searches. In the first step, it detects all areas which have similar colors to the provisional nail color. Then, in the second step, it distinguishes fingernails from other areas using differences in distribution of nail-color pixels. In the experiments, we investigate the relationship between wrist rotation angles corresponding to the gradient of the nail and percentages of the correct detection. As a result, we confirm that our proposed system can detect only fingernails with at least 85% correct answer or more, except a camera is almost facing to the side of a little finger or thumb.
An Intelligent Security Camera System for Kidnapping Detection
Authors: Miyahara Akira ,   University of the Ryukyus, 1 Senbaru Nishihara, Okinawa 903-0213, Japan, Nagayama Itaru
Published: April 2016

In this paper, we propose an automated video surveillance system for kidnapping detection using featurebased characteristics. The localization of moving objects in a video stream and human behavior estimation are key techniques adopted by the proposed system. Some motion characteristics are determined from video streams, and using metrics such as a feature vector, the system automatically classifies the video streams into criminal and non-criminal scenes. The proposed system is called an intelligent security camera. We consider many types of scenarios for the training data set. After constructing the classifier, we use test sequences that are continuous video streams of human behavior consisting of several actions in succession. The experimental results show that the system can effectively detect criminal scenes, such as a kidnapping, by distinguishing human behavior.
Recognition of Indoor Environment by Robot Partner Using Conversation
Authors: Woo Jinseok ,   Graduate School of System Design, Tokyo Metropolitan University, 6-6 Asahigaoka, Hino, Tokyo 191-0065, Japan, Kubota Naoyuki
Published: April 2016

To support daily life before performing an action, a robot partner must perceive an unknown environment. Much research has been done from various viewpoints on self-localization estimation and environment perception. In our research, the robot partner performs self-localization and environment recognition using Simultaneous Localization and Mapping for self-localization estimation and map building. In this paper, we propose a method for recognizing indoor environments by robot partners based on conversations with human beings. Information acquired from maps is identified in order to share the meaning with human beings after the required interpretation. In this paper, we therefore propose a method for recognizing environmental maps by labeling these maps based on symbolic information developed through conversation with human beings. The proposed method is composed of four parts. First, the robot partner applies a steady-state genetic algorithm for self-localization estimation. Second, we use a map building algorithm for expressing the topological map. Third, conversation with human beings is performed for acquiring symbolic information in order to recognize object and position locations through the map. Fourth, we perform experiments and discuss the effectiveness of the proposed technique.
An Algorithm for Recomputing Concepts in Microarray Data Analysis by Biological Lattice
Authors: Hashikami Hidenobu ,   Department of Intelligent Interaction Technologies, University of Tsukuba, 1-1-1 Tennoudai, Tsukuba Science City, Ibaraki 305-8573, Japan, Tanabata Takanari , Hirose Fumiaki , Hasanah Nur , Sawase Kazuhito , Nobuhara Hajime ,   Gene Discovery Research Group, RIKEN Center for Sustainable Resource Science, 3-1-1 Koyadai, Tsukuba, Ibaraki 305-0074, Japan,   Functional Transgenic Crops Research Unit, National Institute of Agrobiological Sciences (NIAS), 2-1-2 Kannondai, Tsukuba, Ibaraki 305-8602, Japan
Published: April 2016

A data-analytic system is proposed for microarray gene expression data based on Formal Concept Analysis (FCA). The purpose of the system is to systematically organize data and to build a complete lattice that analyzes complex relations among genes and give biological interpretation of microarray data. In the system, formal concept analysis handles complex relations, so the microarray data is binarized by setting up a threshold. When change occurs in a conventional algorithm, formal concepts that are nodes of the lattice were calculated from the beginning, but the calculation is inefficient. This paper proposes a new algorithm that has two phase of matrix detection and updating concepts to efficiently update only altered concepts from previously generated concepts. Experiments on run time show that the algorithm takes an average of 0.94 seconds to process real microarray data containing of 43,734 genes and 6 gene expression values.