The bug tracking system is well known as the project support tool of open source software. There are many categorical data sets recorded on the bug tracking system. In the past, many reliability assessment methods hav...The bug tracking system is well known as the project support tool of open source software. There are many categorical data sets recorded on the bug tracking system. In the past, many reliability assessment methods have been proposed in the research area of software reliability. Also, there are several software project analyses based on the software effort data such as the earned value management. In particular, the software reliability growth models can </span><span style="font-family:Verdana;">apply to the system testing phase of software development. On the other</span><span style="font-family:Verdana;"> hand, the software effort analysis can apply to all development phase, because the fault data is only recorded on the testing phase. We focus on the big fault data and effort data of open source software. Then, it is difficult to assess by using the typical statistical assessment method, because the data recorded on the bug tracking system is large scale. Also, we discuss the jump diffusion process model based on the estimation method of jump parameters by using the discriminant analysis. Moreover, we analyze actual big fault data to show numerical examples of software effort assessment considering many categorical data set.展开更多
A large amount of information is frequently encountered when characterizing the sample model in chemical process.A fault diagnosis method based on dynamic modeling of feature engineering is proposed to effectively rem...A large amount of information is frequently encountered when characterizing the sample model in chemical process.A fault diagnosis method based on dynamic modeling of feature engineering is proposed to effectively remove the nonlinear correlation redundancy of chemical process in this paper.From the whole process point of view,the method makes use of the characteristic of mutual information to select the optimal variable subset.It extracts the correlation among variables in the whitening process without limiting to only linear correlations.Further,PCA(Principal Component Analysis)dimension reduction is used to extract feature subset before fault diagnosis.The application results of the TE(Tennessee Eastman)simulation process show that the dynamic modeling process of MIFE(Mutual Information Feature Engineering)can accurately extract the nonlinear correlation relationship among process variables and can effectively reduce the dimension of feature detection in process monitoring.展开更多
As condition monitoring of systems continues to grow in both complexity and application, an overabundance of data is amassed. Computational capabilities are unable to keep abreast of the subsequent processing requirem...As condition monitoring of systems continues to grow in both complexity and application, an overabundance of data is amassed. Computational capabilities are unable to keep abreast of the subsequent processing requirements. Thus, a means of establishing computable prognostic models to accurately reflect process condition, whilst alleviating computational burdens, is essential. This is achievable by restricting the amount of information input that is redundant to modelling algorithms. In this paper, a variable clustering approach is investigated to reorganise the harmonics of common diagnostic features in rotating machinery into a smaller number of heterogeneous groups that reflect conditions of the machine with minimal information redundancy. Na?ve Bayes classifiers established using a reduced number of highly sensitive input parameters realised superior classification powers over higher dimensional classifiers,demonstrating the effectiveness of the proposed approach. Furthermore, generic parameter capabilities were evidenced through confirmatory factor analysis. Parameters with superior deterministic power were identified alongside complimentary, uncorrelated, variables.Particularly, variables with little explanatory capacity could be eliminated and lead to further variable reductions. Their information sustainability is also evaluated with Na?ve Bayes classifiers, showing that successive classification rates are sufficiently high when the first few harmonics are used. Further gains were illustrated on compression of chosen envelope harmonic features. A Na?ve Bayes classification model incorporating just two compressed input variables realised an 83.3% success rate, both an increase in classification rate and an immense improvement volume-wise on the former ten parameter model.展开更多
The quantitatively/semi-quantitatively formation conditions of vertical dominant hydrocarbon migration pathways were analyzed based on the big data analysis of petroleum geological parameters of complex fault Zone zon...The quantitatively/semi-quantitatively formation conditions of vertical dominant hydrocarbon migration pathways were analyzed based on the big data analysis of petroleum geological parameters of complex fault Zone zone in the central-south Bohai Bay. According to this condition, the vertical dominant migration pathway and its charge points/segments are searched through structural modeling assistant analysis in the East Sag of Huanghekou. Under the constraints of charge points/segments, numerical simulation of hydrocarbon charge and migration is carried out to successfully predict hydrocarbon migration pathways and hydrocarbon enrichment blocks in shallow layers of complex fault zone. The main results are as follows:(1) The hydrocarbon charge in shallow layers of the active fault zone is differential, the charge points/sections of vertical dominant migration pathways are the starting points of shallow hydrocarbon migration and are very important for the hydrocarbon migration and accumulation in the shallow layers.(2) Among the shallow faults, those cutting the deep transfer bins or deep major migration pathways, with fault throw of more than 80 m in the accumulation period and the juxtaposition thickness between fault and caprock of the deep layers of less than 400 m are likely to be vertical dominant migration pathways in the sag area.(3) By controlling the vertical dominant migration pathways and charging points/segments in carrier layer, Neo-tectonic movement caused the differential hydrocarbon accumulation in the complex fault zone. The research results are of great significance for the fine exploration of the complex fault zone.展开更多
针对蓄电池设备故障诊断的复杂性和传统方法的局限性,提出一种基于大数据技术的智能诊断系统。该系统融合大数据技术,可实现对过充电、过放电、外部短路等典型故障模式的高精度识别和原因分析。实验结果表明,该系统的平均诊断准确率达95...针对蓄电池设备故障诊断的复杂性和传统方法的局限性,提出一种基于大数据技术的智能诊断系统。该系统融合大数据技术,可实现对过充电、过放电、外部短路等典型故障模式的高精度识别和原因分析。实验结果表明,该系统的平均诊断准确率达95.8%,电池健康状态(State Of Health,SOH)预测误差低于2%,优化后的电池循环寿命可提升15.6%,在保障蓄电池设备安全性、可靠性等方面具有重要应用价值。展开更多
文摘The bug tracking system is well known as the project support tool of open source software. There are many categorical data sets recorded on the bug tracking system. In the past, many reliability assessment methods have been proposed in the research area of software reliability. Also, there are several software project analyses based on the software effort data such as the earned value management. In particular, the software reliability growth models can </span><span style="font-family:Verdana;">apply to the system testing phase of software development. On the other</span><span style="font-family:Verdana;"> hand, the software effort analysis can apply to all development phase, because the fault data is only recorded on the testing phase. We focus on the big fault data and effort data of open source software. Then, it is difficult to assess by using the typical statistical assessment method, because the data recorded on the bug tracking system is large scale. Also, we discuss the jump diffusion process model based on the estimation method of jump parameters by using the discriminant analysis. Moreover, we analyze actual big fault data to show numerical examples of software effort assessment considering many categorical data set.
基金Supported by the National Natural Science Foundation of China(21576143).
文摘A large amount of information is frequently encountered when characterizing the sample model in chemical process.A fault diagnosis method based on dynamic modeling of feature engineering is proposed to effectively remove the nonlinear correlation redundancy of chemical process in this paper.From the whole process point of view,the method makes use of the characteristic of mutual information to select the optimal variable subset.It extracts the correlation among variables in the whitening process without limiting to only linear correlations.Further,PCA(Principal Component Analysis)dimension reduction is used to extract feature subset before fault diagnosis.The application results of the TE(Tennessee Eastman)simulation process show that the dynamic modeling process of MIFE(Mutual Information Feature Engineering)can accurately extract the nonlinear correlation relationship among process variables and can effectively reduce the dimension of feature detection in process monitoring.
文摘As condition monitoring of systems continues to grow in both complexity and application, an overabundance of data is amassed. Computational capabilities are unable to keep abreast of the subsequent processing requirements. Thus, a means of establishing computable prognostic models to accurately reflect process condition, whilst alleviating computational burdens, is essential. This is achievable by restricting the amount of information input that is redundant to modelling algorithms. In this paper, a variable clustering approach is investigated to reorganise the harmonics of common diagnostic features in rotating machinery into a smaller number of heterogeneous groups that reflect conditions of the machine with minimal information redundancy. Na?ve Bayes classifiers established using a reduced number of highly sensitive input parameters realised superior classification powers over higher dimensional classifiers,demonstrating the effectiveness of the proposed approach. Furthermore, generic parameter capabilities were evidenced through confirmatory factor analysis. Parameters with superior deterministic power were identified alongside complimentary, uncorrelated, variables.Particularly, variables with little explanatory capacity could be eliminated and lead to further variable reductions. Their information sustainability is also evaluated with Na?ve Bayes classifiers, showing that successive classification rates are sufficiently high when the first few harmonics are used. Further gains were illustrated on compression of chosen envelope harmonic features. A Na?ve Bayes classification model incorporating just two compressed input variables realised an 83.3% success rate, both an increase in classification rate and an immense improvement volume-wise on the former ten parameter model.
基金Supported by the National Science and Technology Major Project(2016ZX05024-003)
文摘The quantitatively/semi-quantitatively formation conditions of vertical dominant hydrocarbon migration pathways were analyzed based on the big data analysis of petroleum geological parameters of complex fault Zone zone in the central-south Bohai Bay. According to this condition, the vertical dominant migration pathway and its charge points/segments are searched through structural modeling assistant analysis in the East Sag of Huanghekou. Under the constraints of charge points/segments, numerical simulation of hydrocarbon charge and migration is carried out to successfully predict hydrocarbon migration pathways and hydrocarbon enrichment blocks in shallow layers of complex fault zone. The main results are as follows:(1) The hydrocarbon charge in shallow layers of the active fault zone is differential, the charge points/sections of vertical dominant migration pathways are the starting points of shallow hydrocarbon migration and are very important for the hydrocarbon migration and accumulation in the shallow layers.(2) Among the shallow faults, those cutting the deep transfer bins or deep major migration pathways, with fault throw of more than 80 m in the accumulation period and the juxtaposition thickness between fault and caprock of the deep layers of less than 400 m are likely to be vertical dominant migration pathways in the sag area.(3) By controlling the vertical dominant migration pathways and charging points/segments in carrier layer, Neo-tectonic movement caused the differential hydrocarbon accumulation in the complex fault zone. The research results are of great significance for the fine exploration of the complex fault zone.
文摘针对蓄电池设备故障诊断的复杂性和传统方法的局限性,提出一种基于大数据技术的智能诊断系统。该系统融合大数据技术,可实现对过充电、过放电、外部短路等典型故障模式的高精度识别和原因分析。实验结果表明,该系统的平均诊断准确率达95.8%,电池健康状态(State Of Health,SOH)预测误差低于2%,优化后的电池循环寿命可提升15.6%,在保障蓄电池设备安全性、可靠性等方面具有重要应用价值。
