In recent decades,many software reliability growth models（SRGMs） have been proposed for the engineers and testers in measuring the software reliability precisely.Most of them is established based on the non-homogene...In recent decades,many software reliability growth models（SRGMs） have been proposed for the engineers and testers in measuring the software reliability precisely.Most of them is established based on the non-homogeneous Poisson process（NHPP）,and it is proved that the prediction accuracy of such models could be improved by adding the describing of characterization of testing effort.However,some research work indicates that the fault detection rate（FDR） is another key factor affects final software quality.Most early NHPPbased models deal with the FDR as constant or piecewise function,which does not fit the different testing stages well.Thus,this paper first incorporates a multivariate function of FDR,which is bathtub-shaped,into the NHPP-based SRGMs considering testing effort in order to further improve performance.A new model framework is proposed,and a stepwise method is used to apply the framework with real data sets to find the optimal model.Experimental studies show that the obtained new model can provide better performance of fitting and prediction compared with other traditional SRGMs.展开更多
This paper focuses on the fast rate fault detection filter (FDF) problem for a class of multirate sampled-data (MSD) systems. A lifting technique is used to convert such an MSD system into a linear time-invariant disc...This paper focuses on the fast rate fault detection filter (FDF) problem for a class of multirate sampled-data (MSD) systems. A lifting technique is used to convert such an MSD system into a linear time-invariant discrete-time one and an unknown input observer (UIO) is considered as FDF to generate residual. The design of FDF is formulated as an H∞ optimization problem and a solvable condition as well as an optimal solution are derived. The causality of the residual generator can be guaranteed so that the fast rate residual can be implemented via inverse lifting. A numerical example is included to demonstrate the feasibility of the obtained results.展开更多
Typically,smart grid systems enhance the ability of conventional power system networks as it is vulnerable to several kinds of attacks.These vulnerabil-ities might cause the attackers or intruders to collapse the enti...Typically,smart grid systems enhance the ability of conventional power system networks as it is vulnerable to several kinds of attacks.These vulnerabil-ities might cause the attackers or intruders to collapse the entire network system thus breaching the confidentiality and integrity of smart grid systems.Thus,for this purpose,Intrusion detection system(IDS)plays a pivotal part in offering a reliable and secured range of services in the smart grid framework.Several exist-ing approaches are there to detect the intrusions in smart grid framework,however they are utilizing an old dataset to detect anomaly thus resulting in reduced rate of detection accuracy in real-time and huge data sources.So as to overcome these limitations,the proposed technique is presented which employs both real-time raw data from the smart grid network and KDD99 dataset thus detecting anoma-lies in the smart grid network.In the grid side data acquisition,the power trans-mitted to the grid is checked and enhanced in terms of power quality by eradicating distortion in transmission lines.In this approach,power quality in the smart grid network is enhanced by rectifying the fault using a FACT device termed UPQC(Unified Power Quality Controller)and thereby storing the data in cloud storage.The data from smart grid cloud storage and KDD99 are pre-pro-cessed and are optimized using Improved Aquila Swarm Optimization(IASO)to extract optimal features.The probabilistic Recurrent Neural Network(PRNN)classifier is then employed for the prediction and classification of intrusions.At last,the performance is estimated and the outcomes are projected in terms of grid voltage,grid current,Total Harmonic Distortion(THD),voltage sag/swell,accu-racy,precision,recall,F-score,false acceptance rate(FAR),and detection rate of the classifier.The analysis is compared with existing techniques to validate the proposed model efficiency.展开更多
为优化平衡现代装备的可靠性与测试性,提出一种考虑机内测试(BIT,Built In Test)特性的装备可靠性与测试性协同优化方法。建立了BIT特性模型、装备的可靠性模型和测试性模型;建立了以BIT数量为决策变量、装备可靠性指标最低要求为约束...为优化平衡现代装备的可靠性与测试性,提出一种考虑机内测试(BIT,Built In Test)特性的装备可靠性与测试性协同优化方法。建立了BIT特性模型、装备的可靠性模型和测试性模型;建立了以BIT数量为决策变量、装备可靠性指标最低要求为约束条件、测试性指标为优化目标的装备测试性优化模型和以BIT数量为决策变量、装备可靠性指标最低要求与测试性指标最低要求为约束条件、可靠性指标与测试性指标加权求和结果为优化目标的装备可靠性与测试性协同优化模型;通过求解模型,计算得出最佳的BIT数量、装备的可靠性指标和测试性指标。以雷达为例,给出了装备可靠性与测试性协同优化的仿真实验结果:设置BIT数量为48个时,装备可靠度为0.821,故障诊断率为88.2%,为不同装备确定合理BIT数量以达到可靠性与测试性优化平衡的目的提供了参考。展开更多
基金supported by the National Natural Science Foundation of China(61070220)the Anhui Provincial Natural Science Foundation(1408085MKL79)
文摘In recent decades,many software reliability growth models（SRGMs） have been proposed for the engineers and testers in measuring the software reliability precisely.Most of them is established based on the non-homogeneous Poisson process（NHPP）,and it is proved that the prediction accuracy of such models could be improved by adding the describing of characterization of testing effort.However,some research work indicates that the fault detection rate（FDR） is another key factor affects final software quality.Most early NHPPbased models deal with the FDR as constant or piecewise function,which does not fit the different testing stages well.Thus,this paper first incorporates a multivariate function of FDR,which is bathtub-shaped,into the NHPP-based SRGMs considering testing effort in order to further improve performance.A new model framework is proposed,and a stepwise method is used to apply the framework with real data sets to find the optimal model.Experimental studies show that the obtained new model can provide better performance of fitting and prediction compared with other traditional SRGMs.
基金Supported by National Natural Science Foundation of P. R. China (60374021)the Natural Science Foundation of Shandong Province (Y2002G05)the Youth Scientists Foundation of Shandong Province (03BS091, 05BS01007) and Education Ministry Foundation of P. R. China (20050422036)
文摘This paper focuses on the fast rate fault detection filter (FDF) problem for a class of multirate sampled-data (MSD) systems. A lifting technique is used to convert such an MSD system into a linear time-invariant discrete-time one and an unknown input observer (UIO) is considered as FDF to generate residual. The design of FDF is formulated as an H∞ optimization problem and a solvable condition as well as an optimal solution are derived. The causality of the residual generator can be guaranteed so that the fast rate residual can be implemented via inverse lifting. A numerical example is included to demonstrate the feasibility of the obtained results.
基金Supported by Nationai Natural Science Foundation of China （61074085）, Beijing Natural Science Foundation （4122029, 4142035）, and the Fundamental Research Funds for the Central Universities （F_RF-SD-12-008B, FRF-AS- 11-004B）
文摘Typically,smart grid systems enhance the ability of conventional power system networks as it is vulnerable to several kinds of attacks.These vulnerabil-ities might cause the attackers or intruders to collapse the entire network system thus breaching the confidentiality and integrity of smart grid systems.Thus,for this purpose,Intrusion detection system(IDS)plays a pivotal part in offering a reliable and secured range of services in the smart grid framework.Several exist-ing approaches are there to detect the intrusions in smart grid framework,however they are utilizing an old dataset to detect anomaly thus resulting in reduced rate of detection accuracy in real-time and huge data sources.So as to overcome these limitations,the proposed technique is presented which employs both real-time raw data from the smart grid network and KDD99 dataset thus detecting anoma-lies in the smart grid network.In the grid side data acquisition,the power trans-mitted to the grid is checked and enhanced in terms of power quality by eradicating distortion in transmission lines.In this approach,power quality in the smart grid network is enhanced by rectifying the fault using a FACT device termed UPQC(Unified Power Quality Controller)and thereby storing the data in cloud storage.The data from smart grid cloud storage and KDD99 are pre-pro-cessed and are optimized using Improved Aquila Swarm Optimization(IASO)to extract optimal features.The probabilistic Recurrent Neural Network(PRNN)classifier is then employed for the prediction and classification of intrusions.At last,the performance is estimated and the outcomes are projected in terms of grid voltage,grid current,Total Harmonic Distortion(THD),voltage sag/swell,accu-racy,precision,recall,F-score,false acceptance rate(FAR),and detection rate of the classifier.The analysis is compared with existing techniques to validate the proposed model efficiency.
文摘为优化平衡现代装备的可靠性与测试性,提出一种考虑机内测试(BIT,Built In Test)特性的装备可靠性与测试性协同优化方法。建立了BIT特性模型、装备的可靠性模型和测试性模型;建立了以BIT数量为决策变量、装备可靠性指标最低要求为约束条件、测试性指标为优化目标的装备测试性优化模型和以BIT数量为决策变量、装备可靠性指标最低要求与测试性指标最低要求为约束条件、可靠性指标与测试性指标加权求和结果为优化目标的装备可靠性与测试性协同优化模型;通过求解模型,计算得出最佳的BIT数量、装备的可靠性指标和测试性指标。以雷达为例,给出了装备可靠性与测试性协同优化的仿真实验结果:设置BIT数量为48个时,装备可靠度为0.821,故障诊断率为88.2%,为不同装备确定合理BIT数量以达到可靠性与测试性优化平衡的目的提供了参考。
