作者：
Djukanovic, M.B.Sobajic, D.J.Pao, Y.‐H.Miodrag B. Djukanovic (1959) received his B.S.
M.Sc. and Ph.D. degrees in Electrical Engineering from the University of Belgrade/Yugoslavia in 1982 1985 and 1992 respectively specializing in electric power systems. In 1984 he joined the Electrical Engineering Institute “Nikola Tesla” in Belgrade where he was working on the scientific studies in the field of power systems planning operation and control. In 1985 and 1990 he was appointed as a research scholar at the Royal Institute of Technology Stockholm and Case Western Reserve University Cleveland Ohio. His major in- terests are in the area of power system analysis steady-state and dynamic security and application of neural networks in electric power systems. (Electrical Engineering Institute “Nicola Tesla” ul. Koste Glavinica 8A YU-11000 Belgrad T +3811/2351-619 Fax + 3811/2351-823) Dejan J. Sobajic (1949) received the B.S.E.E. and the M.S.E.E. degrees from the University of Belgrade/Yugoslavia in 1972 and 1976
respectively and the Ph.D. degree from Case Western Reserve University Cleveland Ohio in 1988. At present he is with the Department of Electrical Engineering and Applied Physics Case Western Reserve University Cleveland. He is also the Engineering Manager of A1 WARE Inc. Cleveland. His current research interests include power system operation and control neuralnet systems and adaptive control. He is a member of the IEEE Task Force on Neural-Network Applications in Power Systems and of the IEEE Intelligent Controls Committee. He is the Chairman of the International Neural-Networks Society Special Interest Group on Power Engineering. (Case Western Reserve University Department of Electrial Engineering and Computer Sciences Glennan Building Ohio 44 106 USA T + 1216/421-2380 Fax +1216/368-8776) Yoh-Han Pao (1922) has been a Professor of Electrical Engineering and Computer Science at Case Westem Reserve University (CWRU)
Cleveland Ohio since 1967. He has served as chairman of the University's Electrical Engineering Department
The Transient Energy Function (TEF) method has been intensely investigated over the last decade as a reliable and accurate tool for transient stability assessment of multimachine power systems. In this paper we propos...
Our objective is to recognize parts in a structured environment (such as a factory) using inexpensive and widely available hardware. In this paper we consider the class of parts with constant polygonal cross section. ...
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We propose a new interprocessor communication network, named the Segmented Bus, for multiprocessor message passing computer architectures executing groups of processes with localized communication patterns and time va...
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The problem of computing volumetric shape from stereo is examined. It is argued that intermediate two-and-one-half-dimensional dense or wire-frame descriptions may not be always possible from stereo, especially when t...
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In automated feature-based motion analysis of multiple frames, correspondence data are usually noisy and fragmented. A technique that gradually refines the initial noisy correspondence data and links fragments of a si...
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An intelligent hybrid system architecture is proposed for the design of plastic parts. To facilitate the concept of concurrent engineering, an object-oriented design environment is recommended. Problems with previous ...
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An intelligent hybrid system architecture is proposed for the design of plastic parts. To facilitate the concept of concurrent engineering, an object-oriented design environment is recommended. Problems with previous intelligent design systems are discussed. Using neural networks in conjunction with expert systems may provide the route to a more flexible design space. The necessary architecture to achieve these goals is described. A working model of the system was created. This model is discussed in detail.
The problem of computing volumetric shape from stereo is examined. It is argued that intermediate two-and-one-half-dimensional dense or wire-frame descriptions may not be always possible from stereo, especially when t...
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The problem of computing volumetric shape from stereo is examined. It is argued that intermediate two-and-one-half-dimensional dense or wire-frame descriptions may not be always possible from stereo, especially when there are curved surfaces in the scene, and that 3D volumetric descriptions of objects may have to be derived directly from stereo correspondences. Methods are then presented for recovering volumetric shape with linear straight homogeneous generalized cones (LSHGCs) and straight homogeneous generalized cones (SHGCs) as the shape models, using some invariant properties in their monocular and stereo projections. Experimental results on images of objects with curved surfaces are given.< >
In automated feature-based motion analysis of multiple frames, correspondence data are usually noisy and fragmented. A technique that gradually refines the initial noisy correspondence data and links fragments of a si...
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In automated feature-based motion analysis of multiple frames, correspondence data are usually noisy and fragmented. A technique that gradually refines the initial noisy correspondence data and links fragments of a single trajectory using feedback from 3D motion estimation is presented. First, 3D motion parameters are estimated using the initial correspondence data. Then, each noisy trajectory is partitioned into subsets of points, each of which conforms to the estimated motion. The best set is used as the input to the next motion estimation. This process is repeated, and the gaps in the refined correspondence data are filled by guidance from the predicted motion. Test results for a standard real image sequence are presented.< >
An approach for tracing, representation, and recognition of a handwritten numeral in an offline environment is presented. A 2D spatial representation of a numeral is first transformed into a 3D spatiotemporal represen...
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An approach for tracing, representation, and recognition of a handwritten numeral in an offline environment is presented. A 2D spatial representation of a numeral is first transformed into a 3D spatiotemporal representation by identifying the tracing sequence based on a set of heuristic rules acting as transformation operators. Given the dynamic information of the tracing sequence, a multiresolution critical-point segmentation method is proposed to extract local feature points, at varying degrees of scale and coarseness. A neural network architecture, the hierarchically self-organizing learning (HSOL) network (S. Lee, J.C. Pan, 1989), especially for handwritten numeral recognition, is presented. Experimental results based on a bidirectional HSOL network indicated that the method is robust in terms of variations, deformations, and corruption, achieving about 99% recognition rate for the test patterns.< >
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