This paper gives a description of an adaptative on-line diagnosis system for the detection of slow varying changes in dynamical systems. This fault detection system is based on both a learning and a supervisor module....
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This paper gives a description of an adaptative on-line diagnosis system for the detection of slow varying changes in dynamical systems. This fault detection system is based on both a learning and a supervisor module. The learning module acts as an adaptatif fuzzy classifier i.e. its parameters are updated on-line. The supervisor module controls the learning periods of the classifier, it makes the adaptation of the classifier parameters on steady states, these parameters are saved without changes as evolutions occur (e.g. slow or abrupt). So, the diagnosis system is able to detect: new operation modes and slow or abrupt varying changes on the plant operation modes.
The aim of this sesion is the presentation of some activities and cooperation between researcher in Latin America. Some of the papers are original with new ideas in Robotics and Computer Vision.
The aim of this sesion is the presentation of some activities and cooperation between researcher in Latin America. Some of the papers are original with new ideas in Robotics and Computer Vision.
The Bessel distribution, one of the Cohen's class time-frequency distributions, has been reported, in many application areas and particularly on biomedical signal processing, as an efficient spectral estimator. It...
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The Bessel distribution, one of the Cohen's class time-frequency distributions, has been reported, in many application areas and particularly on biomedical signal processing, as an efficient spectral estimator. Its usage on practical applications has been deprecated due to its computational burden. This paper presents a Bessel distribution algorithm and the results of its implementation on a variety of commercial off-the-shelf personal computers. The analysis of these results indicates that the Bessel distribution may no longer be put aside for real-time applications. It is demonstrated that the real-time estimation of pulsed Doppler blood flow spectral parameters is achievable.
This is the second in a series of three articles extending the proof of the Abstract State Machine Thesis-that arbitrary algorithms are behaviorally equivalent to abstract state machines-to algorithms that can interac...
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This is the second in a series of three articles extending the proof of the Abstract State Machine Thesis-that arbitrary algorithms are behaviorally equivalent to abstract state machines-to algorithms that can interact with their environments during a step, rather than only between steps. As in the first article of the series, we are concerned here with ordinary, small-step, interactive algorithms.,This means that the algorithms: (1) proceed in discrete, global steps, (2) perform only a bounded amount of work in each step, (3) use only such information from the environment as can be regarded as answers to queries, and (4) never complete a step until all queries from that step have been answered. After reviewing the previous article's formal description of such algorithms and the definition of behavioral equivalence, we define ordinary, interactive, small-step abstract state machines (ASMs). Except for very minor modifications, these are the machines commonly used in the ASM literature. We define their semantics in the framework of ordinary algorithms and show that they satisfy the postulates for these algorithms. This material lays the groundwork for the final article in the series, in which we shall prove the Abstract State Machine thesis for ordinary, intractive, small-step algorithms: All such algorithms are equivalent to ASMs.
Time-division multiple access (TDMA) is a digital multiplexing technique that is widely used in some cellular communication systems. Although the user time slots within a cell are designed to be synchronized with each...
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Time-division multiple access (TDMA) is a digital multiplexing technique that is widely used in some cellular communication systems. Although the user time slots within a cell are designed to be synchronized with each other, they are asynchronous with time slots in nearby cells operating on the same carrier frequencies. The resulting cochannel interference is an important consideration in such systems because it influences the cell size and the quality of service. This paper provides an overview of an adaptive array that is capable of separating and demodulating cochannel signals in a sequential manner. Least-squares algorithms with training are employed to compute the array (beamformer) coefficients such that several cochannel signals are rejected. The array output contains an estimate of one cochannel signal that is equalized and processed by the detector.
The comparison between optimal sequential and non-sequential fixed size sample (FSS) strategies in the problem of abrupt change detection and isolation is discussed. The general case of non-orthogonal Gaussian hypothe...
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The comparison between optimal sequential and non-sequential fixed size sample (FSS) strategies in the problem of abrupt change detection and isolation is discussed. The general case of non-orthogonal Gaussian hypotheses is considered. Each hypothesis is characterized by its mean vector (the change signature) and it is desirable to detect/isolate a change subject to the constraints on a prefixed time between false alarm and a maximum probability of false isolation. It is theoretically established that the performance of the proposed FSS algorithm is directly related to the mutual geometry between the hypotheses through the Kullback-Leibler information. It is shown that a simple FSS algorithm is almost as efficient as an optimal sequential one but in contrast to the sequential strategy, the FSS strategy can be easily used for monitoring in the case of variable structure systems.
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