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3rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2000

作者： Bzostek, Andrew Kumar, Rajesh Hata, Nobuhiko Schorr, Oliver Kikinis, Ron Taylor, Russell H. Department of Computer Science Johns Hopkins University BaltimoreMD United States Surgical Planning Laboratory Brigham and Women’s Hospital and Harvard Medical School BostonMA United States Institute of Process Control and Robotics University of Karlsruhe Germany Engineering Research Center for Computer Integrated Surgical Systems and Technology Germany

ISBN: (纸本)3540411895

We seek to build CIS research systems within a flexible, open architecture. In this paper, we outline our solutions to the problems of system design, construction, and integration in this environment: building distributed, modular systems for sensing, control, and processing. Based on our experience building these systems, we utilize distributed network architectures, modular software components, and intelligent object distribution to maximize flexibility while complying with the particular needs and interfaces of specific components. While a work in progress, these approaches have been integrated into several systems in development and have demonstrated significant utility, based on construction time and system flexibility. This paper will discuss the first two aspects: modular software and basic network architectures, while intelligent object distribution is presented in a companion paper. Based on the applications we have targeted and technologies we have used, this paper introduces our architecture and its components. It then presents the system’s current state and finally, discusses plans for future improvements and extensions. © Springer-Verlag Berlin Heidelberg 2000.

关键词： Network architecture

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Temporal-Hierarchical Emergency-Degree Inference System for Running Vehicles Using Image and Navigation Data

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Journal of Advanced computational Intelligence and Intelligent Informatics 2000年第1期4卷 76-86页

作者： Koizumi, Satoshi Matsushita, Masayuki Takama, Yasufumi Takahashi, Hiroshi Hirota, Kaoru Department of Computational Intelligence and Systems Science Interdisciplinary Graduate School of Science and Engineering Tokyo Institute of Technology 4259 Nagatsuta Midori-ku Yokohama226-8502 Japan Nissan Research Center Nissan Motor Co. Ltd. 1 Natsusima Yokosuka237-8523 Japan

A fuzzy inference system of emergency-degrees for operating vehicles is proposed. Three situations of joining, cutting-in, and approaching the vehicle ahead are selected as target situations. The system has a hierarchical structure with temporal effects, using vagueness concept and a sigmoid function, to achieve the continuity of inference values and to suppress the influence of noise. The proposed system is tested using video data, taken by CCD stereo cameras mounted on a car and navigation data. The difference between the emergency-degree and driver's perception of danger is evaluated by an expert for safety in an automobile company. Finally the validity of the fuzzy inference with temporal effects for the proposed system is shown through comparison with four other inference methods. © Fuji Technology Press Ltd. Creative Commons CC BY-ND: This is an Open Access article distributed under the terms of the Creative Commons Attribution-NoDerivatives 4.0 International License (http://***/licenses/by-nd/4.0/).

关键词： Fuzzy inference

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Automating component integration for web-based data analysis

Automating component integration for web-based data analysis

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IEEE Conference on Aerospace

作者： J. Penix B. Fischer J. Whittle G. Pour J. Van Baalen Computational Sciences Division NASA Ames Research Center Moffett Field CA USA NASA Ames Research Center Moffett Field CA US Computational Sciences Division Recom Technologies Corporation Moffett Field CA USA Department of Computer Information and Systems Engineering San Jose State University Foundation San Jose CA USA Computer Science Department University of Wurzburg Laramie WY USA

In this paper, we describe a component-based infrastructure for Web-based data analysis applications that reduces the need for custom integration programming; Our vision is that, to construct an application, scientists will give a specification of three kinds of components: the desired data source, a data analysis filter, and a visualization device. From this specification, our infrastructure will automatically generate the desired application. To support this vision, the infrastructure contains an intelligent component integration system (iCIS) that automates component retrieval, adaptation, and integration. Within iCIS, component knowledge is represented using formal mathematical specifications, to provide a common framework for reasoning about problem decomposition, component retrieval and adapter generation. Our current focus is on integration of data analysis filters with online data archives described using XML-based languages. We also discuss how iCIS is integrated with a 3-tier client/server application architecture to support in a heterogeneous and distributed computing environment.

关键词： Data analysis Application software Data visualization Filters NASA Intelligent systems Computer architecture Software engineering Maintenance Space technology

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Fuzzy Rule Interpolation by the Conservation of Relative Fuzziness

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Journal of Advanced computational Intelligence and Intelligent Informatics 2000年第1期4卷 95-101页

作者： Kóczy, László T. Hirota, Kaoru Gedeon, Tamás D. International Education Center Budapest University of Technology and Economics Building Z IF. 102 Bcrtalan L str 2 BudapestH-l111 Hungary Department of Computational Intelligence and Systems Sciences Interdisciplinary Graduate School of Science and Engineering Tokyo Institute of Technology 4259 Nagatsula Midori-ku Yokohama226 Japan School of Information Technology Murdoch University PerthWA6150 Australia

If the number of variables is growing the size of fuzzy rule bases increase exponentially, To reduce size and inference/control time, it is often necessary to deal with sparse rule bases. In such bases, classic algorithms such as the CRI of Zadeh and the Mamdani-method do not function. In such rule bases, rule interpolation techniques are necessary- The linear rule interpolation (KH-interpolation) based on the Fundamental Equation of Interpolation introduced by Koczy and Hirota is suitable for dealing with sparse bases, but this method often results in conclusions which are not directly interpretable, and need some further transformations. One of the possible ways to avoid this problem is the interpolation method based on the conservation of fuzziness, proposed recently by Gedeon and Koczy for trapezoidal fuzzy sets. In this paper, a refined version of that method will be presented that is fully in accordance with the Fundamental Equation, with extensions to multiple dimensions, and then to arbitrarily shaped membership functions. Several possibilities for the latter will be shown. © Fuji Technology Press Ltd. Creative Commons CC BY-ND: This is an Open Access article distributed under the terms of the Creative Commons Attribution-NoDerivatives 4.0 International License (http://***/licenses/by-nd/4.0/).

关键词： Interpolation

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Car Type/Name Recognition System Based on The Concept of Fixation

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Journal of Advanced computational Intelligence and Intelligent Informatics 1999年第4期3卷 274-281页

作者： Arai, Yoshinori Hirota, Kaoru Research and Education Center of Software Engineering Tokyo Institute of Polytechnics 1583 Iiyama Kanagawa Atsugi243-0297 Japan Department of Computational lntelligence and Systems Science Interdisciplinary Graduate School of Science and Engineering Tokvo Institute of Technology 4259 Nagatsuta-cho Midori-ku Kanagawa Yokohama226-8502 Japan

The concept of the "Fixation", meaning to monitor features for identifying transport vehicles is proposed. And a framework of the "Fuzzy Hierarchical Pattern Recognition using a Fixation Feedback (FHPR/FF)" algorithm is introduced. The experiment system, for identifying car types and car names where 70 images are taken for 14 miniature cars from 5 directions, is constructed. Forced recognition of 92.9% is achieved for car types and 89.7% for car names. The proposed experiment system is the basis for real running vehicle identification confirmed by perfect identification of 5 images for each model. © 1999 Fuji Technology Press. All rights reserved.

关键词： Fuzzy inference

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Advanced power plant start-up automation based on the integration of soft computing and hard computing techniques

Advanced power plant start-up automation based on the integr...

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IEEE International Conference on systems, Man and Cybernetics

作者： A. Kamiya A. Kakei K. Kawai S. Kobayashi Power and Industrial Research and Development Center Toshiba Corporation Tokyo Japan Thermal Power Overseas Plant Engineering Department Toshiba Corporation Yokohama Japan Management Innovation Group Toshiba Corporation Tokyo Japan Computational Intelligence and Systems Science Department Tokyo Institute of Technology Yokohama Japan

The conventional HC (Hard Computing) techniques, i.e., ES (Expert System) and DDC (Digital Direct Control), have so far played a key role in large-scale thermal power plant automation systems which are based on a hierarchy structure. In this paper, we propose to integrate these HC techniques with the emerging SC (Soft Computing) in order to achieve the next-generation optimal automation system. SC, implemented through the integration of reinforcement-learning-based NN (Neural Networks) and GA (Genetic Algorithms), is capable of stochastic searching, learning and generalization, in solving those online optimization problems that are highly non-linear and accompanied with local optima. During the start-up process, SC is applied to generate and search the optimal or near-optimal schedule for the ES, which in turn controls the DDC-based controllers and monitors the whole power plant process with the given schedule. In accordance with our previous research, it has been verified that the optimal or near-optimal schedule can be obtained within tens of seconds, a time range which should be acceptable in power plant operation. The optimal schedule reduces the start-up time by approximately 10% for warm start-up mode.

关键词： Power generation Automation Optimal scheduling Neural networks Optimal control Expert systems Digital control Automatic control Control systems Large-scale systems

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Relating H/sub 2/ and H/sub /spl infin//-norm bounds for sampled-data systems

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IEEE Transactions on Automatic Control 1997年第6期42卷 858-863页

作者： S. Hara B.D.O. Anderson H. Fujioka Department of Systems Engineering and Cooperative Research Center for Robust and Adaptive Systems School of Information Sciences & Engineering Australian National University Canberra Australia Department of Computational Intelligence and Systems Science Tokyo Institute of Technology Yokohama Japan Department of Mechanical Engineering for Computer Controlled Machinery Faculty of Engineering Osaka University Suita Osaka Japan

We relate the H/sub /spl infin// and H/sub 2/ norms for multi-input/multi-output sampled-data feedback control systems, where a continuous-time plant is controlled by a digital compensator with hold and sampler. Upper bounds on both H/sub 2/ and H/sub /spl infin// norms are obtained based on fundamental relations derived by two different approaches, namely the hybrid state-space approach and the fast sampling and lifting approach.

关键词： H infinity control Riccati equations Automatic control Robustness Output feedback Control system synthesis Optimal control Entropy Attenuation Control systems

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Steady‐state security evaluation of electrical power systems by means of artificial neural network

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European Transactions on Electrical Power 1995年第2期5卷 91-97页

作者： Chiodo, E. Menniti, D. Testa, A. Picardi, C. Elio Chiodo (1959) received the degree in Electronics Engineering in 1985 and the Ph.D. degree in Computational Statistics both from the University of Naplefltaly. He is a Researcher at the Department of Electrical Engineering of the University of Naples and a member of the Italian Statistical Society. His areas of interest include probabilistic methods applied to electric power systems analysis. (University of Naples Fedrrico 11. Electrical Engineering Dept.via Claudio 21 1-80125 Naplefltaly T +3981/7683226 Fax+3981/2396897) Daniele Menniti (1958) received the degree in Electrical Engineering from the University of Calabria. Cosenzataly and the Ph.D. degree in Electrical Engineering from the University of NapleslItaly in 1984 and 1989 respectively. He is a researcher at the Electronic. Computer and Systems Science Department of the University of Calabria. Italy. Hiscurrent research interests concern electric power system analysis real-time control and automation. (University of Calabria Electronic Computer and Systems Science Dep. Arcavacataji Rende (CS). 1-87036 CosenzdItaly T +39984/494707. Fax +39984/4947 13) Alfredo Testa (1950) received the degree in Electrical Engineering from the University of Naples/Italy in 1975. He is an Associate Professor in Electrical Power Systems at the Department of Electrical Engineering of the University of Naples. He is engaged in researches on electrical power systems reliability and harmonic analysis. (University of Naples Federico 11. Electrical Engineering Dep. via Claudio '2 1 1-80 I25 NapleslItaly T + 39 8 I/7 68 3'2 11. Fax+3981/2396897) Ciro Picardi (1949) received the degree in Electronics Engineering from the University of Naples/Italy in 1975. He is currently Associate Professor in Process Control at the Department of Electronic Computer and System Science of the University of Calabria. Italy. His current research interests are in the area of electrical drives robotics neural networks and fuzzy control. (University of Calabria Electronic. Compu

An artificial‐neural‐network (ANN) application for steady‐state security evaluation of electrical power systems is presented. Such application is based upon a combined use of a multilayer back‐propagation neural network and of an opportune electrical system model, previously proposed by some of the authors. In the paper, the definition of steady‐state security and the problems arising from its on‐line evaluation are briefly recalled. Then, the ANN utilized is described with particular reference to the solutions adopted by the authors. Afterwards, some remarks on the system model utilized are reported. Finally, numerical applications on the AEP‐14 bus system are presented and the results obtained with different ANN configurations are analyzed. Copyright © 1995 John Wiley & Sons, Ltd.

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AUTOMATED OPPOSING FORCES FOR TRAINING SIMULATIONS

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NAVAL ENGINEERS JOURNAL 1995年第3期107卷 245-252页

作者： ANTON, PS MCADOW, KL SMITH, DB LEHNER, PE SHARP, MA Dr. Philip S. Anton:is currently the Terhnology Area Manager of Modeling and Reasoning in the MITRE Advanced Information Technologies Center. His research at MITRE has included behavioral models of military doctrine and command environmental modeling and reasoning for command forces and intelligent simulations of military entities. His other interests include neural networks artificial intelligence and intelligent agents. Dr. Anton received a U.S. degree in Computer Engineering from UCLA and an M.S. degree and a Ph.D in Computational Neuroscience from the University of California. Irvine After finishing nuclear power training Mr. McAdow served as an Engineering Department Division Officer on USS Mariano G. Vallejo (SSBN 658). He then worked as a civilian far the Naval Coastal Systems Center and then far Martin Marietta Aero and Naval Systems in the development of various submarine systems and unmanned undersea vehicles. David Smith:has been a member of the technical staff at MITRE s Advanced Information Technologies center since 1993. He has a B.S. degree in Computer Science and Mathematics from Albright College and an M.S. degree in Information and Computer Science from the Georgia Institute of Technology. Dr. Paul Lehner:is a part-time Principal Scientist in the MITRE Advanced Information Technologies Center. In addition. Dr. Lehner is an Associate Professor of Systems Engineering at George Mason University. His research at MITRE has included automated planning and scheduling adversarial planning and reasoning against uncertainty. Dr. Lehner received a B.S. degree in Psychology from Bethany College. He also holds M.S. degrees in Psychology and Mathematics and a Ph.D. in Mathematical Psychology from the University of Michigan. Prior to his current employment at MITRE and at George Mason University Dr. Lehner served as the Technical Director of the Decision Sciences Section at PAR Government Systems Corporation. After finishing nuclear power training Cdr. Sharp served as an Engineering Department Divisi

Providing realistic opposing forces is critical to the successful use of military training simulations. Unfortunately, a number of issues can make the manual control of realistic opposing forces difficult or unattainable, This paper explores these issues while discussing how Automatic Interactive Targets (AITs) can assist Training Exercise Controllers (TECs) in providing validated and realistic opposing forces in highly interactive situations. The features of the prototype Remote AIT processing System (RAPS) are used to demonstrate how an AIT system can be designed to meet TEC requirements for automated entities, RAPS can provide remote control of AITs for existing or new systems while providing sufficient features to allow a TEC to appropriately select and control AITs for individual training exercises and crew proficiencies.

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The detection of micro-calcifications in mammographic images using high dimensional features

The detection of micro-calcifications in mammographic images...

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Annual IEEE Symposium on Computer-Based Medical systems

作者： J.L. Solka W.L. Poston C.E. Priebe G.W. Rogers R.A. Lorey D.J. Marchette K. Woods K. Bowyer Systems Research and Technology Department Computational Statistics Group Naval Surface Warfare Center Danlgren VA USA Weapons Systems Department Naval Surface Warfare Center Dahlgren VA USA Department of Computer Science and Engineering University of South Florida Tampa FL USA

This paper examines techniques for the efficient use of high dimensional feature sets in the detection of micro-calcifications in mammograms. The paper focuses on techniques for dimensionality reduction and discriminant analysis. The paper examines the use of principal components and Fisher's linear discriminant for dimensionality reduction along with parametric and nonparametric statistical techniques for discriminant analysis.< >

关键词： Feature extraction Pixel Weapons Breast cancer Diagnostic radiography Background noise Fluctuations Image segmentation Military computing Statistics

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