The test selection and optimization （TSO） can improve the abilities of fault diagnosis, prognosis and health-state evalua- tion for prognostics and health management （PHM） systems. Traditionally, TSO mainly focuse...The test selection and optimization （TSO） can improve the abilities of fault diagnosis, prognosis and health-state evalua- tion for prognostics and health management （PHM） systems. Traditionally, TSO mainly focuses on fault detection and isolation, but they cannot provide an effective guide for the design for testability （DFT） to improve the PHM performance level. To solve the problem, a model of TSO for PHM systems is proposed. Firstly, through integrating the characteristics of fault severity and propa- gation time, and analyzing the test timing and sensitivity, a testability model based on failure evolution mechanism model （FEMM） for PHM systems is built up. This model describes the fault evolution- test dependency using the fault-symptom parameter matrix and symptom parameter-test matrix. Secondly, a novel method of in- herent testability analysis for PHM systems is developed based on the above information. Having completed the analysis, a TSO model, whose objective is to maximize fault trackability and mini- mize the test cost, is proposed through inherent testability analysis results, and an adaptive simulated annealing genetic algorithm （ASAGA） is introduced to solve the TSO problem. Finally, a case of a centrifugal pump system is used to verify the feasibility and effectiveness of the proposed models and methods. The results show that the proposed technology is important for PHM systems to select and optimize the test set in order to improve their performance level.展开更多
流分类技术在网络安全监控、QoS、入侵检测等应用领域起着重要的作用,是当前研究的热点。提出一种新的特征选择算法GATS-C4.5来构建轻量级的IP流分类器。该算法采用遗传算法与禁忌搜索相混合的搜索策略对特征子集空间进行随机搜索,然后...流分类技术在网络安全监控、QoS、入侵检测等应用领域起着重要的作用,是当前研究的热点。提出一种新的特征选择算法GATS-C4.5来构建轻量级的IP流分类器。该算法采用遗传算法与禁忌搜索相混合的搜索策略对特征子集空间进行随机搜索,然后利用提供的数据在C4.5上的分类正确率作为特征子集的评价标准来获取最优特征子集。在IP流数据集上进行了大量的实验,实验结果表明基于GATS-C4.5的流分类器在不影响检测准确度的情况下能够提高检测速度,并且基于GATS-C4.5的IP流分类器与NBK-FCBF(Nave Bayes method with Kernel densityesti mation after Correlation-Based Filter)相比具有更小的计算复杂性与更高的检测率。展开更多
Web文本特征获取是Web挖掘中重要而关键的前提工作,传统文本特征获取方法由于在确定文本词条的权重方面做得不够准确,从而直接影响了文本分类算法的精确度。为此,提出一种基于主题词典和遗传算法的文本特征获取方法(dic-tionary and GA-...Web文本特征获取是Web挖掘中重要而关键的前提工作,传统文本特征获取方法由于在确定文本词条的权重方面做得不够准确,从而直接影响了文本分类算法的精确度。为此,提出一种基于主题词典和遗传算法的文本特征获取方法(dic-tionary and GA-based feature selection algorithms,DGFSA),利用主题词典来调整词条权重,从而获取文本特征向量。实验结果表明,DGFSA比传统算法在文本分类的准确率和特征词的约简率方面分别提高了28.4%和16.3%。展开更多
基金supported by the National Natural Science Foundation of China(51175502)
文摘The test selection and optimization （TSO） can improve the abilities of fault diagnosis, prognosis and health-state evalua- tion for prognostics and health management （PHM） systems. Traditionally, TSO mainly focuses on fault detection and isolation, but they cannot provide an effective guide for the design for testability （DFT） to improve the PHM performance level. To solve the problem, a model of TSO for PHM systems is proposed. Firstly, through integrating the characteristics of fault severity and propa- gation time, and analyzing the test timing and sensitivity, a testability model based on failure evolution mechanism model （FEMM） for PHM systems is built up. This model describes the fault evolution- test dependency using the fault-symptom parameter matrix and symptom parameter-test matrix. Secondly, a novel method of in- herent testability analysis for PHM systems is developed based on the above information. Having completed the analysis, a TSO model, whose objective is to maximize fault trackability and mini- mize the test cost, is proposed through inherent testability analysis results, and an adaptive simulated annealing genetic algorithm （ASAGA） is introduced to solve the TSO problem. Finally, a case of a centrifugal pump system is used to verify the feasibility and effectiveness of the proposed models and methods. The results show that the proposed technology is important for PHM systems to select and optimize the test set in order to improve their performance level.
文摘流分类技术在网络安全监控、QoS、入侵检测等应用领域起着重要的作用,是当前研究的热点。提出一种新的特征选择算法GATS-C4.5来构建轻量级的IP流分类器。该算法采用遗传算法与禁忌搜索相混合的搜索策略对特征子集空间进行随机搜索,然后利用提供的数据在C4.5上的分类正确率作为特征子集的评价标准来获取最优特征子集。在IP流数据集上进行了大量的实验,实验结果表明基于GATS-C4.5的流分类器在不影响检测准确度的情况下能够提高检测速度,并且基于GATS-C4.5的IP流分类器与NBK-FCBF(Nave Bayes method with Kernel densityesti mation after Correlation-Based Filter)相比具有更小的计算复杂性与更高的检测率。
