bluuebunny/crossref_metadata_2025 huggingface dataset:
Velocity Control Strategy for a Spider-Robot Based on Autonomous Walking Form Transition
Authors: Ohnishi Takeo ,   Graduate school of Engineering, University of Fukui, 3-9-1 Bunkyo, Fukui-shi, Fukui 910-8507, Japan, Asakura Toshiyuki ,   Department of Human and Artificial Intelligence Systems, University of Fukui, 3-9-1 Bunkyo, Fukui-shi, Fukui 910-8507, Japan
Published: April 2016

This paper proposes a walking velocity control strategy based on autonomous walking form transition for a spider-robot. A creatural spider walks with changing velocity while adapting to the surrounding conditions. With reference to neurobiological control, we realize autonomous transitions of walking forms. Further, we realize velocity control which adapts to information input from both internal and external sources using the Neuro-Fuzzy method. Through simulations and experiments, we confirm the usefulness of the proposed technique.
Interactive Reality System (IRiS): Interactive 3D Video Playback in Multimedia Applications
Authors: Oikonomou Andreas ,   Biomedical Computing Research Group (BIOCORE), Faculty of Engineering and Computing, Coventry University, Priory Street, Coventry CV1 5FB, United Kingdom, Amin Saad , Naguib Raouf N. G. , Todman Alison , Al-Omishy Hassanein ,   University Hospitals Coventry and Warwickshire, Clifford Bridge Road, Coventry CV2 2DX, United Kingdom
Published: April 2016

We developed a novel interactive video recording and playback technique for biomedical multimedia training but also applicable to other areas of multimedia. The Interactive Reality System (IRiS) improves on video playback used in most multimedia applications by controlling not only time, as in conventional video playback, but also space. A prototype is being tested and evaluated for multimedia training in breast self-examination (BSE). We discuss the advantages of IRiS and compare it to other similar approaches, such as QuickTime and iPIX. We detail the design of IRiS, its development, refinement, final implementation, evaluation, some projected plans and its uses in other biomedical and multimedia training scenarios.
Building Intelligent Robotics Systems with Distributed Components
Authors: Guedea-Elizalde Federico ,   Center for Innovation and Technology, ITESM, Campus Monterrey, Ave. Eugenio Garza Sada 2501, Col Tecnologico, CP 64849, Monterrey, N.L. Mexico, Soto Rogelio , Karray Fakhreddine , Song Insop ,   Center for Intelligent Systems, ITESM, Campus Monterrey , Ave. Eugenio Garza Sada 2501, Col Tecnologico, CP 64849, Monterrey, N.L. Mexico,   Department of Systems Design Engineering, University of Waterloo, 200 University Avenue West, Waterloo, Ontario, Canada N2L 3G1
Published: April 2016

Building an intelligent robot system has been an extensive research area. There are many advances in components needed to construct the robotic system, such as vision systems, sensory systems, planning systems, among others. Integration of this components represents a big challenge for robot designers, due to they come from different vendors and with different interfaces or operating systems. This is more difficult if the overall system development has to deal with environmental uncertainties or changing conditions. In these cases, new tools and equipment are necessary to adapt the initial configuration to the new changing requirements. Each added component increases the complexity of the system due to the interconnection required with the previous components. In this work, we present an approach to solve this integration problem using concepts of distributed computing areas. We named this concept Wrapper Components. This concept is based on a standard middleware software specification. Wrapper components are object-oriented modules that create an abstract interface for a specific class of hardware or software components. If these components provide “intelligent” functions, the overall system is capable of show some basic smart behavior through specific actions to react under changes in the environment. We tested our approach by solving an experimental classical problem named block-world. The intelligent functions are object recognition, environment recognition, planning, tracking capabilities and robot arm control.
Testing Hypotheses on Simulated Data: Why Traditional Hypotheses-Testing Statistics Are Not Always Adequate for Simulated Data, and How to Modify Them
Authors: Aló Richard ,   Center for Computational Sciences and Advanced Distributed Simulation, University of Houston-Downtown, One Main Street, Houston, TX 77002, USA, Kreinovich Vladik , Starks Scott A. ,   Pan-American Center for Earth and Environmental Studies, University of Texas at El Paso, El Paso, TX 79968, USA
Published: April 2016

To check whether a new algorithm is better, researchers use traditional statistical techniques for hypotheses testing. In particular, when the results are inconclusive, they run more and more simulations (n2>n1, n3>n2, ..., nm>nm-1) until the results become conclusive. In this paper, we point out that these results may be misleading. Indeed, in the traditional approach, we select a statistic and then choose a threshold for which the probability of this statistic “accidentally” exceeding this threshold is smaller than, say, 1%. It is very easy to run additional simulations with ever-larger n. The probability of error is still 1% for each ni, but the probability that we reach an erroneous conclusion for at least one of the values ni increases as m increases. In this paper, we design new statistical techniques oriented towards experiments on simulated data, techniques that would guarantee that the error stays under, say, 1% no matter how many experiments we run.
A Framework for Robust and Resilient Critical Infrastructure Systems
Authors: Chandra Jagdish ,   The Institute for Reliability and Risk Analysis, The George Washington University, Washington, DC 20052, U.S.A
Published: April 2016

Infrastructures such as transportation systems, power grids, communication networks, water resources, health delivery systems, and financial networks/institutions are vital to the safety, security and the well-being of the society. Reliable performance and protection of such systems is of paramount importance. Critical infrastructure systems, when viewed as complex interacting networks, present many interesting technical challenges to the modeling, analysis and simulation community. In this paper, we review the generic structure of such systems from the perspective of robust design and resilient behavior.
Knowledge Extraction from a Mixed Transfer Function Artificial Neural Network
Authors: Khan M. Imad ,   Intelligent Systems Research Group, School of Engineering and Information Technology, Deakin University, Waurn Ponds, Geelong, VIC 3217, Australia, Frayman Yakov , Nahavandi Saeid
Published: April 2016

One of the main problems with Artificial Neural Networks (ANNs) is that their results are not intuitively clear. For example, commonly used hidden neurons with sigmoid activation function can approximate any continuous function, including linear functions, but the coefficients (weights) of this approximation are rather meaningless. To address this problem, current paper presents a novel kind of a neural network that uses transfer functions of various complexities in contrast to mono-transfer functions used in sigmoid and hyperbolic tangent networks. The presence of transfer functions of various complexities in a Mixed Transfer Functions Artificial Neural Network (MTFANN) allow easy conversion of the full model into user-friendly equation format (similar to that of linear regression) without any pruning or simplification of the model. At the same time, MTFANN maintains similar generalization ability to mono-transfer function networks in a global optimization context. The performance and knowledge extraction of MTFANN were evaluated on a realistic simulation of the Puma 560 robot arm and compared to sigmoid, hyperbolic tangent, linear and sinusoidal networks.
Converting Constraint Handling Rules to Equivalent Transformation Rules
Authors: Shigeta Yoshinori ,   SoC Research and Development Center, Toshiba Corporation, 580-1 Horikawa-cho, Saiwai-ku, Kawasaki 212-8520, Japan, Akama Kiyoshi , Mabuchi Hiroshi , Koike Hidekatsu ,   Division of Large-Scale Computational Systems, Information Initiative Center, Hokkaido University, Kita 11 Nishi 5, Kita-ku, Sapporo, Hokkaido 060-0811, Japan,   Faculty of Software and Information Science, Iwate Prefectural University, 152-52 Sugo, Takizawa, Iwate 020-0193, Japan,   Faculty of Social Information, Sapporo Gakuin University, 11-banchi Bunkyodai, Ebetsu, Hokkaido 060-8555, Japan
Published: April 2016

We present a way to convert constraint handling rules (CHRs) to equivalent transformation rules (ETRs) and demonstrate the correctness of the conversion in equivalent transformation (ET) theory. In the ET computation model, computation is regarded as equivalent transformations of a description. A description is transformed successively by ETRs. Extensively used in the domain of first-order terms, the ET computation model has also been applied to knowledge processing in such data domains as RDF, UML, and XML. A CHR is a multiheaded guarded rule that rewrites constraints into simpler ones until they are solved. CHRs and ETRs are similar in syntax but they have completely different theoretical bases for the correctness of their computation. CHRs are based on the logical equivalence of logical formulas, while ETRs are based on the set equivalence of descriptions. We convert CHRs to rules used in the ET model and demonstrate converted rules to be correct ETRs, i.e., they preserve meanings of descriptions. We discuss correspondences and differences between CHRs and ETRs in theories, giving examples of correct ETRs that cannot be represented as CHRs.
Towards a Model of Computer Science Graduate Admissions Decisions
Authors: Ward Nigel ,   Department of Computer Science, University of Texas at El Paso, 500 West University Avenue, El Paso, Texas 79968, USA
Published: April 2016

Potential applicants to graduate school find it difficult to predict, even approximately, which schools will accept them. We have created a predictive model of admissions decision-making, packaged in the form of a web page that allows students to enter their information and see a list of schools where they are likely to be accepted. This paper explains the rationale for the model’s design and parameter values. Interesting issues include the way that evidence is combined, the estimation of parameters, and the modeling of uncertainty.
Extending Fuzzy Constraint Satisfaction Problems
Authors: Sudo Yasuhiro ,   Graduate School of Engineering, Hokkaido University, Kita 13, Nishi 8, Kita-ku, Sapporo 060-8628, Japan, Kurihara Masahito , Mitamura Tamotsu ,   Graduate School of Information Science and Technology, Hokkaido University, Kita 13, Nishi 8, Kita-ku, Sapporo 060-8628, Japan,   Department of Information Design, Hokkaido Institute of Technology, 4-1, 7-15, Teine-ward, Sapporo 006-8585, Japan
Published: April 2016

This paper propose a new type of Fuzzy CSP (Constraint Satisfaction Problem) that have a mixture of discrete and continuous domains, and a Spread-Repair algorithm. In traditional CSP and Fuzzy CSP, values for the variables are chosen from the discrete domains. However, this is often inconvenient when one wants to express real world problems. We show that this model, called HDFCSP (Hybrid Domain Fuzzy CSP), can be solved by Spread-Repair, an extension of the well known iterative improvement algorithms. Experimental results on some test problems show that the algorithm actually has an ability of finding partial approximate solutions with high probability in a computation time much shorter than the traditional, discrete-domain FCSP.
Cognitive Vision Inspired Contour and Vertex Detection
Authors: Reskó Barna ,   Computer and Automation Research Institute, Hungarian Academy of Sciences, H-1111 Budapest, Kende utca 13-17, Hungary, Csapó Ádám B. , Baranyi Péter ,   Budapest University of Technology and Economics, H-1111 Budapest, Egry J. utca 18, Hungary
Published: April 2016

This paper presents a visual cortex inspired cognitive model for contour and vertex detection. The model is strongly based on the receptive field characteristics of cortical neurons of the visual cortex. As a step forward compared to the previous version of the model, a new dimension has been added, which replaces the binary signals and operations by operations on real values. The resulting system yields a better approximation of the biological system, as well as provides stronger and more distinct contour lines and vertices. The contour detection and vertex extraction is performed by a vast network of simple units of computation simultaneously processing the visual data. The computational units are organized in a special structure, the Visual Feature Array (VFA), which allows the structural representation of complex operations. The goal of the model is to extract abstract information from an image, which in turn may be used as input for the recognition process of even more abstract visual objects. In order to achieve constant time execution of the model, the aspects of hardware implementation are also treated in this paper.
g-Calculus-Based Compositional Rule of Inference
Authors: Takács Marta ,   Budapest Tech, H-1034 Budapest, Bécsi út 96/b, Hungary
Published: April 2016

We review a specific case, in which the investigated structure is a real semi-ring with pseudo-operations as a step toward investigating the problem of approximate reasoning in fuzzy systems. We focus on special-type fuzzy sets, i.e. g -generated quasi-triangular fuzzy numbers, and special g -generated t-norms and implication in fuzzy approximate reasoning.
Fuzzy Based Brightness Compensation for High Dynamic Range Images
Authors: Várkonyi-Kóczy Annamária R. ,   Dept. of Measurement and Information Systems, Budapest University of Technology and Economics, Magyar tudósok körútja 2. H-1117 Budapest, Hungary, Rövid András , Várlaki Péter ,   Dept. of Mathematics, Széchenyi István University, Egyetem tér 1. 9026 Győr, Hungary
Published: April 2016

High dynamic range of illumination may cause serious distortions and other problems in viewing and further processing of digital images. In this paper a new fuzzy based tone reproduction pre-processing algorithm is introduced which may help in developing hardly or nonviewable features and content of the images making easier the further processing of it.
Feed-Forward Neural Networks Based on the Eigenstates of the Quantum Harmonic Oscillator
Authors: Rigatos Gerasimos ,   Unit of Industrial Automation, Industrial Systems Institute, 26504, Rion Patras, Greece
Published: April 2016

The paper introduces feed-forward neural networks where the hidden units employ orthogonal Hermite polynomials for their activation functions. The proposed neural networks have some interesting properties: (i) the basis functions are invariant under the Fourier transform, subject only to a change of scale, (ii) the basis functions are the eigenstates of the quantum harmonic oscillator, and stem from the solution of Schrödinger’s diffusion equation. The proposed feed-forward neural networks belong to the general category of nonparametric estimators and can be used for function approximation, system modelling and image processing.
Approximate Reasoning in Supervised Classification Systems
Authors: Seridi Hamid ,   Laboratoire d’Automatique et Informatique de Guelma (LAIG), Université 08 mai 1945 de Guelma, B.P. 401 Guelma 24000, Algeria, Akdag Herman , Mansouri Rachid , Nemissi Mohamed ,   Laboratoire d’Etude et de Recherche en Informatique (LERI), Université de Reims Champagne Ardenne, Rue des Grayères BP1035 51687 Reims Cedex2, France,   Laboratoire de Genie Civil et D’hydraulique (LGCH), Université 08 mai 1945 de Guelma, B.P. 401 Guelma 24000, Algeria
Published: April 2016

In knowledge-based systems, uncertainty in propositions can be represented by various degrees of belief encoded by numerical or symbolic values. The use of symbolic values is necessary in areas where the exact numerical values associated with a fact are unknown by experts. In this paper we present an expert system of supervised automatic classification based on a symbolic approach. This last is composed of two sub-systems. The first sub-system automatically generates the production rules using training set; the generated rules are accompanied by a symbolic degree of belief which characterizes their classes of memberships. The second is the inference system, which receives in entry the base of rules and the object to classify. Using classical reasoning (Modus Ponens), the inference system provides the membership class of this object with a certain symbolic degree of belief. Methods to evaluate the degree of belief are numerous but they are often tarnished with uncertainty. To appreciate the performances of our symbolic approach, tests are made on the Iris data basis.
Rough Set Approximation as Formal Concept
Authors: Ytow Nozomi ,   Graduate School of Life and Environmental Sciences, University of Tsukuba, Morse David R. , Roberts David McL. ,   Faculty of Mathematics and Computing, The Open University,   The Natural History Museum
Published: April 2016

Formal Concept Analysis (FCA) defines a formal concept as a pair of sets: objects and attributes, called extent and intent respectively. A rough set, on the other hand, approximates a concept using sets of objects only (in terms of FCA). We show that 1) a formal concept can be composed using a set of objects and its complement, 2) such object-based formal concepts are isomorphic to formal concepts based on objects and attributes, 3) upper and lower approximations of rough sets give generalization of formal concept, and 4) the pair of positive and negative sets (sensu rough set theory) are isomorphic to complemental formal concepts when the equivalence of the rough set gives positive and negative sets unique to each of the formal concepts. Implications of this are discussed.
A Family of Polymodal Systems and its Application to Generalized Possibility Measures and Multi-Rough Sets
Authors: Miyamoto Sadaaki ,   Department of Risk Engineering, School of Systems and Information Engineering, University of Tsukuba, 1-1-1 Tennodai, Ibaraki 305-8573, Japan, Murai Tetsuya , Kudo Yasuo ,   Graduate School of Information Science and Technology, Hokkaido University, Kita 14, Nishi 9, Kita-Ku, Sapporo 060-0814, Japan,   Department of Computer Science and Systems Engineering, Muroran Institute of Technology, 27-1 Mizumoto, Muroran 050-8585, Japan
Published: April 2016

Polymodal systems generally have large areas of applications to theoretical computer science including the theory of programming, while other applications are not yet fully explored. In this paper we consider a family of polymodal systems with the structure of lattices on the polymodal indices. After investigating theory of the polymodal systems such as the completeness, we study two applications. One is generalized possibility measures in which lattice-valued measures are proposed and relations with the ordinary possibility and necessity measures are uncovered. Second application is consideration of an information system as a table such as the one in the relational database. It is known that rough sets are used to discover regularities from such information tables. Applying polymodal logic concept, we generalize rough sets which are called multi-rough sets here. Our consideration is mainly to establish theoretical frameworks in these two application areas and hence no real examples are shown here.
Pipeline Valve Control Based on EVALPSN Safety Verification
Authors: Nakamatsu Kazumi ,   School of Human Science and Environment, University of Hyogo, 1-1-12 Shinzaike, Himeji 670-0092, Japan
Published: April 2016

We have developed an annotated logic program called an Extended Vector Annotated Logic Program with Strong Negation (abbr. EVALPSN), which can deal with defeasible deontic reasoning and contradiction. We have already applied EVALPSN to safety verification and control such as railway interlocking safety verification. In this paper, we show how to apply EVALPSN to pipeline valve control with an example for brewery pipeline valve control. The safety verification control is based on EVALPSN programming to avoid unexpected mix of different sorts of liquid.
Structure-Based Attribute Reduction in Variable Precision Rough Set Models
Authors: Inuiguchi Masahiro ,   Department of Systems Innovation, Graduate School of Engineering Science, Osaka University, 1-3 Machikaneyama, Toyonaka, Osaka 560-8531, Japan
Published: April 2016

In this paper, structure-enhancing approaches to attribute reduction are proposed. Ten kinds of meaningful reducts are defined. The relations among them are clarified. Moreover their relations to reducts by structure-preserving approaches are also investigated. A few computational approaches to the proposed reducts are briefly described.
Efficient Processing of XML Tree Pattern Queries
Authors: Chen Yangjun ,   Department of Applied Computer Science, University of Winnipeg, 515 Portage Avenue, Winnipeg, Manitoba R3B 2E9, Canada, Che Dunren ,   Department of Computer Science, Southern Illinois University, Carbondale, IL 62901, USA
Published: April 2016

In this paper, we present a polynomial-time algorithm for TPQ (tree pattern queries) minimization without XML constraints involved. The main idea of the algorithm is a dynamic programming strategy to find all the matching subtrees within a TPQ. A matching subtree implies a redundancy and should be removed in such a way that the semantics of the original TPQ is not damaged. Our algorithm consists of two parts: one for subtree recognization and the other for subtree deletion. Both of them needs only O(n2) time, where n is the number of nodes in a TPQ.
Natural Language-Mediated Software Agentification
Authors: Iwazume Michiaki ,   National Institute of Information and Communications Technology (NICT), 3-5 Hikaridai, Seika-cho, Soraku-gun, Kyoto 619-0289, Japan, Kobayashi Ichiro , Sugeno Michio ,   Faculty of Science, Ochanomizu University, Tokyo, Japan,   Faculty of Culture and Information Science, Doshisha University, Kyoto, Japan
Published: April 2016

The digital divide is growing among pros and tyros as information technology (IT) becomes increasingly disseminated. The concept of “everyday-language computing” has been proposed [8] as a solution to this problem. We focus will focus on agentifying and verbalizing software applications and propose a scheme for operating and running them. We have designed the basic language application interface (LAPI) architecture to manipulate and manage conventional computer applications through language, devised a linguistic model of software functions and actions, and implemented a prototype to evaluate our ideas on software verbalization.
Programming in Everyday Language: A Case for Email Management
Authors: Sugimoto Toru ,   RIKEN Brain Science Institute, 2-1 Hirosawa, Wako, Saitama 351-0198, Japan, Ito Noriko , Iwashita Shino , Sugeno Michio ,   Faculty of Culture and Information Science, Doshisha University, 1-3 Tatara Miyakodani, Kyotanabe, Kyoto 610-0394, Japan,   Faculty of Design, Kyushu University, 4-9-1 Shiobaru, Minami-ku, Fukuoka 815-8540, Japan
Published: April 2016

We present a processing model of a natural language interface that accepts task specification texts consisting of more than one sentence. Such an interface enables users to easily specify complex requests as coherent texts, in other words, to write a program in everyday language, to operate computing systems. Corresponding to the characteristics of task specification texts, processing consists of paraphrasing, detection of loop structures, and executable program generation using rhetorical information. Algorithms have been fully implemented in our everyday language programming system that deals with personal email management tasks. In this paper, we explain our processing model using an example from the email management domain, give evaluation results, and discuss its effectiveness and future work.
Toward a Generalization of Rough Sets Based on Active and Passive Relations
Authors: Emoto Masashi ,   Meiji University, 1-1-1 Higashi-mita, Tama-ku, Kawasaki, Japan, Intan Rolly , Mukaidono Masao ,   Department of Informatics Engineering, Petra Christian University, Jl. Siwalankerto 121-131, Surabaya 60236, Indonesia
Published: April 2016

In the generalization of rough sets, many concepts use a relation weaker than the equivalence relation usually used in classical rough sets, e.g., induced by a conditional probability relation. The conditional probability relation is binary and assumes that the relationship between two data (elements or objects) resembles a relationship between two events in conditional probability. We use the asymmetric property of the conditional probability relation to propose active and passive relations, then discuss a generalization and properties of rough sets based on active and passive relations.
A Qualitative Reasoning Model for Tradeoff Analysis in Multiple Objective Decision Making
Authors: Wanyama Tom ,   Department of Electrical and Computer Engineering, University of Calgary, 2500 University Drive, N.W. Calgary, Alberta, Canada, T2N 1N4, Far Behrouz Homayoun
Published: April 2016

In Multi-Criteria Decision Making problems such as choosing a development policy, selecting software products, or searching for commodities to purchase, it is often necessary to evaluate solution options in respect of multiple objectives. The solution alternative that performs best in all the objectives is the dominant solution, and it should be selected to solve the problem. However, usually the selection objectives are incomparable and conflicting, making it impossible to have a dominant solution among the alternatives. In such cases, tradeoff analysis is required to identify the objectives that can be optimized, and those that can be comprised in order to choose a winning solution. In this paper we present a tradeoff analysis model based on the principles of qualitative reasoning that provides visualization support for understanding interaction and tradeoff dependences among solutions evaluation criteria which affect the tradeoff among selection objectives. Moreover, the decision support system based on our tradeoff analysis model facilitates discovery of hidden solution features so as improve the completeness and certainty of the user preference model.
Constraint Solving Specializations for Equality on an Interval-Variable Domain
Authors: Mabuchi Hiroshi ,   Faculty of Software and Information Science, Iwate Prefectural University, Iwate, Japan, Akama Kiyoshi , Miura Katsunori , Ishikawa Takahiko ,   Information Initiative Center, Hokkaido University, Sapporo, Japan,   Information Initiative Center, Hokkaido University, Sapporo, Japan,   Faculty of Health and Welfare Science, Nayoro City University, Nayoro, Japan
Published: April 2016

In the equivalent transformation (ET) model, where computation is regarded as ET using rules that constitute a program, this paper develops a theory for equality-constraint solving which unification does in the theory of logic programming. This paper clarifies the relationship between Constraint Solving Specializations for Equality (CSSEs) and unifiers. The concept of CSSE can be used to solve equality constraint in a clause, and promotes component-based programming. An algorithm for finding CSSEs in the domain including interval variables is constructed by combining simpler ET rules. Termination and correctness of the algorithm is proven.
Genetic and Bacterial Programming for B-Spline Neural Networks Design
Authors: Botzheim János ,   Department of Telecommunications and Media Informatics, Budapest University of Technology and Economics, Magyar Tudósok Krt. 2, Budapest, H-1117, Hungary, Cabrita Cristiano , Kóczy László T. , Ruano Antonio E. ,   Centre for Intelligent Systems, University of Algarve, Campus de Gambelas, 8000 Faro, Portugal,   Institute of Information Technology and Electrical Engineering, Széchenyi István University, Győr, Hungary
Published: April 2016

The design phase of B-spline neural networks is a highly computationally complex task. Existent heuristics have been found to be highly dependent on the initial conditions employed. Increasing interest in biologically inspired learning algorithms for control techniques such as Artificial Neural Networks and Fuzzy Systems is in progress. In this paper, the Bacterial Programming approach is presented, which is based on the replication of the microbial evolution phenomenon. This technique produces an efficient topology search, obtaining additionally more consistent solutions.
Logistic GMDH-Type Neural Network and its Application to Identification of X-Ray Film Characteristic Curve
Authors: Kondo Tadashi ,   School of Health Sciences, The University of Tokushima, 3-l8-15 Kuramoto-cho, Tokushima 770-8509, Japan, Ueno Junji
Published: April 2016

The logistic Group Method of Data Handing (GMDH)-type neural network identifying a complex nonlinear system we propose is automatically organized using heuristic self-organization that is basic to GMDH algorithm. In this neural network, structural parameters such as the number of layers, the number of neurons per layer, useful input variables, and optimum neuron architectures are automatically determined using a prediction error criterion defined as Akaike’s Information Criterion (AIC) to produce an optimum neural network architecture suiting the complexity of the nonlinear system. In applying this neural network to the identification problem of the X-ray film characteristic curve, we found that modeling with such a neural network is more accurate than applying multiple regression analysis, a conventional neural network, and GMDH algorithm.
Optimal Size Fuzzy Models
Authors: Gedeon Tamás D. ,   Dept. of Computer Science, Australian National University, Canberra 0200, Australia, Kóczy László T. , Zorat Alessandro ,   Dept. of Telecommunications and Media Informatics, Budapest University of Technology and Economics, Sztocek u. 2, H-1111 Budapest, Hungary,   Dept. of Information and Communication Technologies, University of Trento, Via Sommarive 14, Povo, I-38100 Italy
Published: April 2016

Approximate models using fuzzy rule bases can be made more precise by suitably increasing the size of the rule base and decreasing uncertainty in the rules. A large rule base, however, requires more time for its evaluation and hence the problem arises of determining the size that is good enough for the task at hand, but allows as fast as possible reasoning using the rule base. This trade-off between computation time and precision is significant whenever a prediction is made which can become “out of date” or “too old” if not used in time. The trade off is considered here in the context of tracking a moving target. In this problem, a higher degree of accuracy results in tighter precision of determining target location, but at the cost of longer computation time, during which the target can move further away, thus ultimately requiring a longer search for exact target localisation. This paper examines the problem of determining the optimal rule base size that will yield a minimum total time required to repeatedly re-acquire the moving target, as done by a cat that plays with a mouse. While this problem has no known solution in its general formulation, solutions are shown here for specific contexts.
A Hybrid System ASVR/NGARCH Tuned by Quantum-Based Minimization to Improve Forecasting Accuracy
Authors: Chang Bao Rong ,   Department of Computer Science and Information Engineering, National Taitung University, 684 Chunghua Rd., Sec. 1, Taitung City, Taitung, Taiwan
Published: April 2016

Adaptive support vector regression (ASVR) is very useful to act as a predictor for complex time series prediction. However, ASVR cannot avoid volatility clustering and thus worsen its predictive accuracy. Therefore, incorporating NGARCH model into ASVR is schemed for resolving the problem of volatility clustering to best fit the time series. Interestingly, quantum-based minimization algorithm is proposed in this study to tune the resulting weighted-average forecasts between ASVR and NGARCH to improve the forecast performance. Quantum optimization process tackles so-called NP-completeness problem outperforming artificial neural network or quadratic-programming so as to attain optimal or near-optimal result over the scope of search space. It follows that the proposed hybrid system can get the satisfactory results because of highly enhancing its generalization and then improving the accuracy.
Classification Rule Extraction Based on Relevant, Irredundant Attributes and Rule Enlargement
Authors: Lashkia George ,   School of Information Science and Technology, Chukyo University, 101 Tokodate, Kaizu-cho, Toyota 470-0393, Japan, Anthony Laurence , Koshimizu Hiroyasu ,   School of Science and Engineering, Waseda University, 3-4-1 Okubo, Shinjuku-ku, Tokyo 169-8555, Japan
Published: April 2016

In this paper we focus on the induction of classification rules from examples. Conventional algorithms fail in discovering effective knowledge when the database contains irrelevant information. We present a new rule extraction method, RGT, which tackles this problem by employing only relevant and irredundant attributes. Simplicity of rules is also our major concern. In order to create simple rules, we estimate the purity of patterns and propose a rule enlargement approach, which consists of rule merging and rule expanding procedures. In this paper, we describe the methodology for the RGT algorithm, discuss its properties, and compare it with conventional methods.
Mining Fuzzy Association Rules on Has-A and Is-A Hierarchical Structures
Authors: Chien Been-Chian ,   Department of Computer Science and Information Engineering, National University of Tainan, 33, Sec. 2, Su-Lin Street, Tainan 70005, Taiwan, R.O.C., Zhong Ming-Huang , Wang Jeng-Jung ,   Department of Information Engineering, I-Shou University Kaohsiung, 1, Section 1, Hsueh-Cheng Rd., Ta-Hsu Hsiang, Kaohsiung County, Taiwan 840, R.O.C.
Published: April 2016

Preliminary studies on data mining focus on finding association rules from transaction databases containing items without relationships among them. However, relationships among items often exist in real applications. Most of the previous works only concern about Is-A hierarchy. In this paper, hierarchical relationships include a Has-A hierarchy and multiple Is-A hierarchies are discussed. The proposed method first reduces a Has-A & Is-A hierarchy into an extended Has-A hierarchy using the IsA-Reduce algorithm. The quantitative data is transformed into fuzzy items. The RPFApriori algorithm is then applied to find fuzzy association rules from the fuzzy item data and the extended Has-A hierarchy.
Unfolding of Massive Laundry and Classification Types by Dual Manipulator
Authors: Osawa Fumiaki ,   Dept. of Electrical and Electronic Engineering, Daido Institute of Technology, 10-3 Takiharu-cho, Minami-ku, Nagoya 457-8530, Japan, Seki Hiroaki , Kamiya Yoshitsugu ,   Faculty of Engineering, Kanazawa University, Kakuma-machi, Kanazawa, Ishikawa 920-1192, Japan
Published: April 2016

With a progress of an aging society with fewer children, an introduction of robots for family use is expected. Focusing on laundry cleanup by robots, we are studying clothing classification for folding by a dual manipulator. Fabrics vary greatly in handling, making it difficult for robots to recognize and handle them easily. We propose determining clothing form and classifying types by alternately regrasping clothing and expanding them using a dual manipulator. Because clothing is classified and unfolded simultaneously, folding after this is easy. We confirmed the feasibility of our proposal in classifications using a real machine and eight types of laundry.
Analysis of New Aggregation Operators: Mean 3Π
Authors: Doncescu Andrei ,   LAAS CNRS UPR 8001 Toulouse, France, Regis Sebastien , Inoue Katsumi , Emilion Richard ,   National Institute of Informatics, 2-1-2 Hitotsubashi, Chiyoda-ku, Tokyo 101-8430, Japan,   University of West French Indies Point-a-Pitre, France,   University of Orleans, France
Published: April 2016

Knowledge based systems need to deal with aggregation and fusion of data with uncertainty. To use many sources of information in numerical forms for the purpose of decision or conclusion, systems suppose to have tools able to represent the knowledge in a mathematical form. One of the solutions is to use fuzzy logic operators. We present in this article an improvement of the triple Π operator introduced by Yager and Rybalov, which is calledmean3Π. Whereas triple Π is an operator completely reinforced, the presented operator is a mean operator, which makes it more robust to noise.
Neighbor Size of Initial Node Exchange and its Influence for SOM Learning
Authors: Miyoshi Tsutomu ,   Department of Information and Knowledge Engineering, Tottori University, 4-101 Koyama-cho Minami, Tottori-shi, Tottori 680-8552, Japan
Published: April 2016

The Self Organizing Map (SOM) involves neural networks, that learns the features of input data through unsupervised, competitive neighborhood learning. In the SOM learning algorithm, connection weights in a SOM feature map are initialized at random values, which also sets nodes at random locations in the feature map independent of input data space. The distance that output nodes move increases, slowing learning convergence. We propose solving this problem in initial node exchange using part of learning data. We investigated how the average move distance of all nodes, measured by convergence, changed with differences in the initial size of the neighbor area in node exchange. We clarified the influence of the initial neighbor size on the average move distance of all nodes, clarified the expression of relations, and showed the optimum domain of relations.
Neural Network Model for Word Sense Disambiguation Using Up/Down State and Morphoelectrotonic Transform
Authors: Watanabe Norifumi ,   Graduate School of Media and Governance, Keio University, 5233 Endo, Fujisawa, Kanagawa 252-8520, Japan, Ishizaki Shun
Published: April 2016

We propose a neural network model for word sense disambiguation using up/down state neurons and morphoelectrotonic transform. Relations between stimulus words and associated words are implemented in this neural network using an associative ontology. This new neural coding model disambiguates word senses in input sentences by using neural network firing dynamics. Whether to put a new link between two neurons is decided using the cooccurrence frequency between two words corresponding to the neurons and the attenuation rate of morphoelectrotonic potential between the two neurons. The distance of the new link is obtained with a learning mechanism by calculating morphoelectrotonic transform from the morphoelectrotonic potential of the two neurons. By analyzing learning behavior using average shortest path lengths and clustering coefficients, we show that this model has a small-world structure.
Hardware Feedback Self-Organizing Map and its Application to Mobile Robot Location Identification
Authors: Hikawa Hiroomi ,   Dept. of Computer Science and Intelligent Systems, Oita University, Oita 870-1192, Japan, Harada Kazutoshi , Hirabayashi Takenori ,   Shinko Electric Co., Ltd., Minato-ku, Tokyo 105-8564, Japan
Published: April 2016

We propose new hardware architecture for the self-organizing map (SOM) and feedback SOM (FSOM). Due to the parallel structure in the SOM and FSOM algorithm, customized hardware considerably speeds-up processing. Proposed hardware FSOM identifies the location of a mobile robot from a sequence of direction data. The FSOM is self-trained to cluster data to identify where the robot is. The proposed FSOM design is described in C and VHDL, and its performance is tested by simulation using actual sensor data from an experimental mobile robot. Results show that the hardware FSOM succeeds in self-learning to find the robot’s location. The hardware FSOM is estimated to process 6,992 million weight-vector elements per second.
A Hybrid Learning Strategy for Real Hardware of Swing-Up Pendulum
Authors: Nakamura Shingo ,   Dept. of Applied Physics, School of Science and Engineering, Waseda University, 3-4-1 Okubo, Shinjuku-ku, Tokyo 169-8555, Japan, Saegusa Ryo , Hashimoto Shuji
Published: April 2016

Generally, the bottom-up learning approaches, such as neural-network, to obtain the optimal controller of target task for mechanical system face a problem including huge number of trials, which require much time and give stress against the hardware. To avoid such problems, a simulator is often built and performed with a learning method. However, there are also problems that how simulator is constructed and how accurate it performs. In this paper, we are considering a construction of simulator directly from the real hardware. Afterward a constructed simulator is used for learning target task and the obtained optimal controller is applied to the real hardware. As an example, we picked up the pendulum swing-up task which was a typical nonlinear control problem. The construction of a simulator is performed by back-propagation method with neural-network and the optimal controller is obtained by reinforcement learning method. Both processes are implemented without using the real hardware after the data sampling, therefore, load against the hardware gets sufficiently smaller, and the objective controller can be obtained faster than using only the hardware. And we consider that our proposed method can be a basic learning strategy to obtain the optimal controller of mechanical systems.
Realization of Rapid Movement for Legged Entertainment Robots Using Two New Actuators, the Inertia Actuator and the Cam Charger
Authors: Nassiraei Amir Ali Forough ,   FAIS-Robotics Development Support Office, Collaboration Center 4F, 2-1 Hibikino, Wakamatsu-ku, Kitakyushu City, Fukuoka 808-0196, Japan, Ishii Kazuo , Masakado Seiji , Matsuo Takayuki , Ichikawa Kodai , Fukushima Hajime , Murata Masayuki , Sonoda Takashi , Takahira Isao , Miki Tsutomu ,   Graduate School of Life Science and Systems Engineering, Kyushu Institute of Technology, 2-4 Hibikino, Wakamatsu-ku, Kitakyushu City, Fukuoka 808-0196, Japan
Published: April 2016

We discuss the realization of rapid movement for legged entertainment robots using two new actuators, the Inertia Actuator and the Cam Charger. As an internal torque generator, the Inertia Actuator generates small internal torque by changing the rotor speed and large internal torque quickly by using a brake to stop the rotor at high speed. To realize jumping, we introduce the Cam Charger to fit to the robot foot. The key is to charge a series of strong torsion springs using a specially shaped cam. After detailing of the Cam Charger and the Inertia Actuator principles, we evaluate the feasibility of our approach through simulation. We show experimentally that our artistic “Jumping Joe” robot prototype including these two actuators demonstrates rapid movements such as fast wakeup, jumping, and somersaults.
Anthropomorphization Framework for Human-Object Communication
Authors: Osawa Hirotaka ,   Graduate School of Science and Technology, Keio University, 3-14-1 Hiyoshi, Kohoku-Ku, Yokohama 223-8522, Japan, Mukai Jun , Imai Michita ,   Faculty of Science and Technology, Keio University, 3-14-1 Hiyoshi, Kohoku-Ku, Yokohama 223-8522, Japan
Published: April 2016

We propose an anthropomorphization framework that determines an object’s body image. This framework directly intervenes and anthropomorphizes objects in ubiquitous-computing environments through robotic body parts shaped like those of human beings, which provide information through spoken directions and body language. Our purpose is to demonstrate that an object acquires subjective representations through anthropomorphization. Using this framework, people can more fully understand instructions given by an object. We designed an anthropomorphization framework that changes the body image by attaching body parts. We also conducted experiments to evaluate this framework. Results indicate that the site at which an anthropomorphization device is attached influences human perception of the object’s virtual body image, and participants in experiments understood several instructions given by the object more clearly. Results also indicate that participants better intuited their devices’ instructions and movement in ubiquitous-computing environments.
Predictive Control of Harmonic Drive in Automotive Application
Authors: Varga Ádam ,   Department of Control Engineering and Information Technology, Budapest University of Technology and Economics, H-1117 Magyar Tudósok krt. 2, Budapest, Hungary, Lantos Béla
Published: April 2016

This paper addresses the predictive control of the harmonic drive in an automotive application. The goal of the control was to provide good steering feel for the driver and satisfactory tracking performance in a steering system. The paper presents the dynamic model of the harmonic drive, a design framework and a two step algorithm for predictive controller design. The elaborated model predictive controller is similar to a cascade type controller with constraints in the performance function to ensure closed loop stability and a useful compromise between torque tracking and position tracking. The controller was developed and implemented in a real-time environment for rapid prototype design using Matlab, Simulink, Real-Time Workshop and dSPACE AutoBox hardware, then it was experimentally tuned for best steering feel and good tracking performance.
Effective Multiple Vector Quantization for Image Compression
Authors: Shigei Noritaka ,   Dept. of Electrical and Electronics Engineering, Kagoshima University, 1-21-40 Korimoto, Kagoshima-shi 890-0065, Japan, Miyajima Hiromi , Maeda Michiharu , Ma Lixin ,   Fukuoka Institute of Technology, 3-30-1 Wajiro-higashi, Higashi-ku, Fukuoka 811-0295, Japan,   University of Shanghai for Science and Technology, 516 Jungong Rd, Shanghai 200093, China
Published: April 2016

Multiple-VQ methods generate multiple independent codebooks to compress an image by using a neural network algorithm. In the image restoration, the methods restore low quality images from the multiple codebooks, and then combine the low quality ones into a high quality one. However, the naive implementation of these methods increases the compressed data size too much. This paper proposes two improving techniques to this problem: “index inference” and “ranking based index coding.” It is shown that index inference and ranking based index coding are effective for smaller and larger codebook sizes, respectively.
Intelligent Help System that Synchronizes Application Software Operation: Toward Intelligent Tailor-Made Information Provision
Authors: Kobayashi Ichiro ,   Graduate School of Humanities and Sciences, Advanced Sciences, Ochanomizu University, 2-1-1 Ootsuka Bunkyo-ku Tokyo 112-8610, Japan, Tsunawaki Tomoko ,   Hitachi Systems & Services, Ltd.
Published: April 2016

We propose providing tailor-made information to users having problems in operating application software. We conducted experiments to determine users’ operation features and constructed a user operation model to provide tailor-made information to users based on experiment results. We reconstructed a “Java word-processor” into a user operation model to provide appropriate, timely instructions when users had problems in operation, synchronizing instructions with user operation. We verified the feasibility of our proposal using this prototype in experiments.