this paper presents an overview of an artificial neural network (ANN) based partial discharge (PD) distribution patternrecognition problem to power system application. After referring briefly to the developments of A...
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this paper presents an overview of an artificial neural network (ANN) based partial discharge (PD) distribution patternrecognition problem to power system application. After referring briefly to the developments of ANN technique-based PD measurements, the paper outlines how the introduction of new emerging technology has resulted in the design of a number of PD diagnostic systems for practical application in test laboratories and on site. the structure of a PD data base and selection of learning of PD datapattern, extraction of relevant characteristic feature or information for PD recognition are discussed. Some practical problems encountered in the neuro-fuzzy techniques based real time PD recognition are also addressed.
the proceedings contains 78 papers from the 1997 IEEE internationalconference on Tools with Artificial Intelligence. Topics discussed include: neural networks;knowledge representation and reasoning;artificial intelli...
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the proceedings contains 78 papers from the 1997 IEEE internationalconference on Tools with Artificial Intelligence. Topics discussed include: neural networks;knowledge representation and reasoning;artificial intelligence;software engineering;genetic algorithms;logic based reasoning systems;natural language processing;vision and patternrecognition;optimization problem solving tools;evolutionary computation;object-oriented methodologies;intelligent agents;knowledge based systems;intelligent user interfaces;datamining;and machinelearning.
the performance of complex systems such as the aircraft gas turbine engine deteriorates in time due to the degradation or failure of its components. Condition monitoring systems have been developed to provide advanced...
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ISBN:
(纸本)0852966903
the performance of complex systems such as the aircraft gas turbine engine deteriorates in time due to the degradation or failure of its components. Condition monitoring systems have been developed to provide advanced warning of impending failure of components. By correctly predicting that a component is failing it can be replaced at an appropriate time thereby saving time and money for the operator of the system. these condition monitoring systems use various approaches and techniques to evaluate system parameters and make judgements on the condition of various components. this paper focuses on the two general approaches being investigated for condition monitoring systems: static pattern analysis approach and the dynamical systems approach. Both techniques are applied to real engine data and their performance results given.
this paper presents an overview of an artificial neural network(ANN) based partial discharge (PD) distribution patternrecognition problem to power system application. After referring briefly to the developments of AN...
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ISBN:
(纸本)0780326512
this paper presents an overview of an artificial neural network(ANN) based partial discharge (PD) distribution patternrecognition problem to power system application. After referring briefly to the developments of ANN technique-based PD measurements, the paper outlines how the introduction of new emerging technology has resulted in the design of a number of PD diagnostic systems for practical application in test laboratories and on site. the structure of a PD data base and selection of learning of PD datapattern, extraction of relevant characteristic feature or information for PD recognition are discussed. Some practical problems encountered in the neuro-fuzzy techniques based real time PD recognition are also addressed.
datamining algorithms including machinelearning, statistical analysis, and patternrecognition techniques can greatly improve our understanding of data warehouses that are now becoming more widespread. In this paper...
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ISBN:
(纸本)0780337573
datamining algorithms including machinelearning, statistical analysis, and patternrecognition techniques can greatly improve our understanding of data warehouses that are now becoming more widespread. In this paper;we focus on classification algorithms and review the need for multiple classification algorithms. We describe a system called MLC++, which was designed to help choose the appropriate classification algorithm for a given dataset by making it easy to compare the utility of different algorithms on a specific dataset of interest. MLC++ not only provides a work-bench for such comparisons, but also provides a library of C++ classes to aid in the development of new algorithms, especially hybrid algorithms and multi-strategy algorithms. Such algorithms are generally hard to code from scratch. We discuss design issues, interfaces to other programs, and visualization of the resulting classifiers.
the logical neural architecture LAPART is used in a mode that allows through learningthe easy creation and extraction of IF-thEN inference rules from data. this paper first describes ART1 and the complement coded sta...
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the logical neural architecture LAPART is used in a mode that allows through learningthe easy creation and extraction of IF-thEN inference rules from data. this paper first describes ART1 and the complement coded stack input binary representations. Next, we present a more detailed discussion of LAPART. then we show how rules are learned and extracted from the memory templates of the ART1s. We present a pedagogical example of rules extracted from a simple data set. Finally, we note that a fundamental difference between LAPART rule-based systems and regular rule-based systems is the existence of a "rule attractor" that can enhance system generalization in a controlled manner.
We present an unsupervised technique for visual learning which is based on density estimation in high-dimensional spaces using an eigenspace decomposition. Two types of density estimates are derived for modeling the t...
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ISBN:
(纸本)0818670428
We present an unsupervised technique for visual learning which is based on density estimation in high-dimensional spaces using an eigenspace decomposition. Two types of density estimates are derived for modeling the training data: a multivariate Gaussian (for a unimodal distribution) and a multivariate Mixture-of-Gaussians model (for multimodal distributions). these probability densities are then used to formulate a maximum-likelihood estimation framework for visual search and target detection for automatic object recognition. this learning technique is tested in experiments with modeling and subsequent detection of human faces and non-rigid objects such as hands.
this work describes a statistical approach to deal withlearning and recognition problems in the field of computer vision. An abstract theoretical framework is provided, which is suitable for automatic model generatio...
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ISBN:
(纸本)0818670428
this work describes a statistical approach to deal withlearning and recognition problems in the field of computer vision. An abstract theoretical framework is provided, which is suitable for automatic model generation from examples, identification, and localization of objects. Both, the learning and localization stage are formalized as parameter estimation tasks. the statistical learning phase is unsupervised with respect to the matching of model and scene features. the general mathematical description yields algorithms which can even treat parameter estimation problems from projected data. the experiments show that this probabilistic approach is suitable for solving 2D and 3D object recognition problems using grey-level images. the method can also be applied to 3D image processing issues using range images, i.e. 3D input data.
A learning environment for maintenance of power equipment using Virtual Reality technology is described. the design concept is to support developing operator's mental model based on the investigations of the maint...
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A learning environment for maintenance of power equipment using Virtual Reality technology is described. the design concept is to support developing operator's mental model based on the investigations of the maintenance expertise and insights on human cognition. Currently, the system which is considered to be effective through demonstration as test usage is being evaluated.
In this paper an extension to the standard error back-propagation learning rule for multi-layer feed forward neural networks is proposed, that enables them to trained for context dependent information. the context dep...
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In this paper an extension to the standard error back-propagation learning rule for multi-layer feed forward neural networks is proposed, that enables them to trained for context dependent information. the context dependent learning is realised by using a different error function (called Average Risk: AVR) in stead of the sum of squared errors (SQE) normally used in error backpropagation and by adapting the update rules. It is shown that for applications where this context dependent information is important, a major improvement in performance is obtained.
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