This paper reviews progress in developing a mathematical framework to evaluate the performance of mobile radio links. It takes into account multi-ray multipath fading with Rician or Nakagami probability density, logno...
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In this paper, a combined method of change detection and failure decision is proposed for the system under the adaptive control based on the self-tuning regulator. The controlled system is assumed encounter unexpected...
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In this paper, a combined method of change detection and failure decision is proposed for the system under the adaptive control based on the self-tuning regulator. The controlled system is assumed encounter unexpected parameter changes, which may be caused by a failure or a normal operation. Such a system change can effectively be detected by using Fullback Discrimination Information (KDI) as an index for model discrimination. In order to decide whether the detected system change is caused by a failure or not, a neural network approach to failure decision is introduced. Based on the knowledge about failure modes and system operations, the regulator parameter variations after the change detection are used as training data for the network learning. In this way an on-line monitoring scheme of adaptively controlled systems can be established. Simulation studies of a second-order damped oscillator have been earned out to demonstrate the effectiveness of the method.
作者：
Christopher AhlbergBen ShneidermanDept. of Computer Science
Chalmers University of Technology S-412 96 Göteborg Sweden and Department of Computer Science Human-Computer Interaction Laboratory & Institute for Systems Research University of Maryland College Park MD Department of Computer Science
Human-Computer Interaction Laboratory & Institute for Systems Research University of Maryland College Park MD
In this paper, a quick identification method based on the short time record of input-output data is introduced for joint state and parameter estimation of nonlinear continuous-time systems. The method can then be used...
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In this paper, a quick identification method based on the short time record of input-output data is introduced for joint state and parameter estimation of nonlinear continuous-time systems. The method can then be used for on-line monitoring the system with unknown parameters. An application way of the method to main-steam temperature control of a thermal power plant are developed in the framework of model reference adaptive control system (MRACS). Simulation studies on a super-heater model have been carried out to demonstrate the effectiveness of the proposed method.
作者：
Christopher AhlbergBen ShneidermanDept. of Computer Science
Chalmers University of Technology S-412 96 Göteborg Sweden and Department of Computer Science Human-Computer Interaction Laboratory & Institute for Systems Research University of Maryland College Park MD Department of Computer Science
Human-Computer Interaction Laboratory & Institute for Systems Research University of Maryland College Park MD
The purpose of this paper is to expose some inaccuracies presented in a paper by Ram and Curran. [Inform. Sci. 52(1):53-73 (1990)]. The synthesis approach described uses an algorithm, Synthesizer (Algorithm 3.2.1), to...
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The purpose of this paper is to expose some inaccuracies presented in a paper by Ram and Curran. [Inform. Sci. 52(1):53-73 (1990)]. The synthesis approach described uses an algorithm, Synthesizer (Algorithm 3.2.1), to provide more nonredundant covers, and it also includes several heuristics to aid the design of relational databases. However, the final design may not be minimal in regard to the number of 3NF relations. This paper improves the approach by considering the minimization of the final relational database schemata. Finally, an implementation is presented, and it is illustrated through an example.
This paper presents a fuzzy-logic approach to the efficient unsupervised character classification in order to increase the robustness, correctness, and speed of a follow-up optical character recognition system. The cl...
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This paper presents a fuzzy-logic approach to the efficient unsupervised character classification in order to increase the robustness, correctness, and speed of a follow-up optical character recognition system. The classification procedures are split into two stages. The first stage separates the characters into seven categories based on the word structure of a text line. The second stage, referring to pattern matching, is to classify all the characters in each category of stage one into a different set of prototypes. The existing methods of similarity measures and their problems are investigated, and a nonlinear weighted similarity function is proposed. A fuzzy model of unsupervised classification, which is more natural to represent the library of prototypes, is defined and the weighted fuzzy similarity measure is extended. Several propositions of the features of the fuzzy model are discussed. Finally, a preclassifier to speed up the classification is presented. The small set of prototypes can be recognized and postprocessed much easier and more efficient.
This paper presents a 4-move perfect ZKIP of knowledge with no cryptographic assumption for the random self reducible problems [TW87] whose domain is NP∩BPP. The certified discrete log problem is such an example. (Fi...
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This paper presents a new measure of the complexity of many to one functions. We study bit correlations among the preimages of an element of the range of many to one one-way functions. Especially, we investigate the c...
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