Analysis of dissolved gases content in power transformer oil is very important to monitor transformer latent fault and ensure normal operation of entire power system. Analysis of dissolved gases content in power trans...
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ISBN:
(纸本)9781424455379
Analysis of dissolved gases content in power transformer oil is very important to monitor transformer latent fault and ensure normal operation of entire power system. Analysis of dissolved gases content in power transformer oil is a complicated problem due to its nonlinearity and the small quantity of training data. Support vector machine (SVM) has been successfully employed to solve classification problem of nonlinearity and small sample. However, SVM has rarely been applied to diagnosis transformer fault by analysis the dissolved gases content in power transformer. In this study, support vector machine is proposed to analysis dissolved gases content in power transformer oil, among which cross-validation is used to determine free parameters of support vector machine. The experimental data from the electric power company in Sichuan are used to illustrate the performance of proposed SVM model. The experimental results indicate that the proposed SVM model can achieve very good diagnosis accuracy under the circumstances of small sample. Consequently, the SVM model is a proper alternative for diagnosing power transformer fault.
An general decision layer text classification fusion model for higher precision, is proposed, which based on model theory of information fusion, and different classification algorithm of the feature layer fusion centr...
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An general decision layer text classification fusion model for higher precision, is proposed, which based on model theory of information fusion, and different classification algorithm of the feature layer fusion centre having different pre-processing, their classification results input into the decision layer fusion centre separately. And the final classification result output from decision layer fusion centre. KNN, SVM and BP Net are used in feature layer, and D-S Theory is used in decision layer. The model is realized in the experiment. From the experiment and contrast, the text classification fusion model can improve the classification precision effectively.
Analysis of dissolved gases content in power transformer oil is very important to monitor transformer latent fault and ensure normal operation of entire power *** of dissolved gases content in power transformer oil is...
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Analysis of dissolved gases content in power transformer oil is very important to monitor transformer latent fault and ensure normal operation of entire power *** of dissolved gases content in power transformer oil is a complicated problem due to its nonlinearity and the small quantity of training *** vector machine (SVM) has been successfully employed to solve classification problem of nonlinearity and small ***,SVM has rarely been applied to diagnosis transformer fault by analysis the dissolved gases content in power *** this study,support vector machine is proposed to analysis dissolved gases content in power transformer oil,among which cross-validation is used to determine free parameters of support vector *** experimental data from the electric power company in Sichuan are used to illustrate the performance of proposed SVM *** experimental results indicate that the proposed SVM model can achieve very good diagnosis accuracy under the circumstances of small ***,the SVM model is a proper alternative for diagnosing power transformer fault.
In a variety of text classification algorithm, KNN is a competitive one with simple implementation and high efficiency. However, with the expansion of the size of the text, the runtime of KNN will grow rapidly that ca...
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ISBN:
(纸本)9781424458219
In a variety of text classification algorithm, KNN is a competitive one with simple implementation and high efficiency. However, with the expansion of the size of the text, the runtime of KNN will grow rapidly that cannot be afford. In this paper, we improve the KNN by introducing the kd-tree storage structure and reducing the sample space through the sample clestering methods. And experiment shows that the runtime of improved KNN algorithm reduce apparently.
Failure of rectifier circuit has the characteristics of latency and complexity,which leads to the difficulty to fault diagnosis for rectifier circuit.A new method of optimizing support vector machine (SVM) by using an...
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Failure of rectifier circuit has the characteristics of latency and complexity,which leads to the difficulty to fault diagnosis for rectifier circuit.A new method of optimizing support vector machine (SVM) by using ant colony optimization algorithm is presented to fault diagnosis for rectifier circuit in the *** experimental object is provided and the six ACO-SVM classifiers are developed to identify the following seven states of the experimental *** testing results demonstrate that the ACO-SVM classifier has higher diagnostic accuracy than normal support vector machine and BP neural network.
In this paper, a general decision layer classification fusion model, based on information fusion for improving classification precision, is proposed, that is, different multi-classification algorithms as the feature l...
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ISBN:
(纸本)9780769537450
In this paper, a general decision layer classification fusion model, based on information fusion for improving classification precision, is proposed, that is, different multi-classification algorithms as the feature layer doing respective classification, and the results of classification algorithms are Input into decision level, the last classification result is output. This model is applied into improving precision of text classification. And the model is used to the Computer Center of some department. Through the experiment, the text classification fusion model can improve the classification precision effectively
classification is an important research topic in the field of image data mining. There have been many data classification methods studied, including decision-tree method, statistical methods, neural networks, rough se...
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ISBN:
(纸本)9780769538761
classification is an important research topic in the field of image data mining. There have been many data classification methods studied, including decision-tree method, statistical methods, neural networks, rough sets, etc. This paper proposed a method to classify the image with normal cloud model which is an uncertainty transformation model between quantities and qualities conception. We develop the algorithm for classification based on normal cloud model. Finally we perform experiments on an artificial trademark image database. The results show the advantages of the cloud model in the process of classification.
When working with real-world applications we often find imbalanced datasets, those for which there exists a majority class with normal data and a minority class with abnormal or important data. In this work, we make a...
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When working with real-world applications we often find imbalanced datasets, those for which there exists a majority class with normal data and a minority class with abnormal or important data. In this work, we make an overview of the class imbalance problem;we review consequences, possible causes and existing strategies to cope with the inconveniences associated to this problem. As an effort to contribute to the solution of this problem, we propose a new rule induction algorithm named Rule Extraction for MEdical Diagnosis (REMED), as a symbolic one-class learning approach. For the evaluation of the proposed method, we use different medical diagnosis datasets taking into account quantitative metrics, comprehensibility, and reliability. We performed a comparison of REMED versus C4.5 and RIPPER combined with over-sampling and cost-sensitive strategies. This empirical analysis of the REMED algorithm showed it to be quantitatively competitive with C4.5 and RIPPER in terms of the area under the Receiver Operating Characteristic curve (AUC) and the geometric mean, but overcame them in terms of comprehensibility and reliability. Results of our experiments show that REMED generated rules systems with a larger degree of abstraction and patterns closer to well-known abnormal values associated to each considered medical dataset.
In this paper, we propose a feature-based Chinese term relation extraction approach that combined the advantages of both naive bayes algorithm and perceptron algorithm. A subset of the features was estimated in traini...
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ISBN:
(纸本)9780769536545
In this paper, we propose a feature-based Chinese term relation extraction approach that combined the advantages of both naive bayes algorithm and perceptron algorithm. A subset of the features was estimated in training data;another subset of the features was trained by discriminative function. The results demonstrate that the proposed hybrid algorithm almost always outperforms the naive bayes algorithms and perceptron algorithms whether the training set is small or not. On the other hand, a novel feature representation was proposed, which included term sequence feature, term appearance features and context information features. Comparing the previous method, long-range dependence was considered in the proposed feature representation, which add the position of feature into vector space model (VSM) and promotes the capability of feature representation. Further, punctuation feature is the important character for terms relation extraction.
Intrusion Detection is one of network security area of technology main research directions. Data mining technology will be applied to Network Intrusion Detection System (NIDS), may automatically discover the new patte...
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ISBN:
(纸本)9780769536156
Intrusion Detection is one of network security area of technology main research directions. Data mining technology will be applied to Network Intrusion Detection System (NIDS), may automatically discover the new pattern from the massive network data, to reduce the workload of the manual compilation intrusion behavior patterns and normal behavior patterns. This article reviewed the current intrusion detection technology and the data mining technology briefly. Focus on data mining algorithm in anomaly detection and misuse detection of specific applications. For misuse detection, the main study the classification algorithm;For anomaly detection, the main study the pattern comparison and the cluster algorithm. In pattern comparison to analysis deeply the association rules and sequence rules. Finally, has analysised the difficulties which the current data mining algorithm in intrusion detection applications faced at present, and has indicated the next research direction.
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