Test selection is to select the test set with the least total cost or the least total number from the alternative test set on the premise of meeting the required testability indicators.The existing models and methods ...Test selection is to select the test set with the least total cost or the least total number from the alternative test set on the premise of meeting the required testability indicators.The existing models and methods are not suitable for system level test selection.The first problem is the lack of detailed data of the units’fault set and the test set,which makes it impossible to establish a traditional dependency matrix for the system level.The second problem is that the system level fault detection rate and the fault isolation rate(referred to as"two rates")are not enough to describe the fault diagnostic ability of the system level tests.An innovative dependency matrix(called combinatorial dependency matrix)composed of three submatrices is presented.The first problem is solved by simplifying the submatrix between the units’fault and the test,and the second problem is solved by establishing the system level fault detection rate,the fault isolation rate and the integrated fault detection rate(referred to as"three rates")based on the new matrix.The mathematical model of the system level test selection problem is constructed,and the binary genetic algorithm is applied to solve the problem,which achieves the goal of system level test selection.展开更多
It is very important to accurately recognize and locate pulverized and block coal seen in a coal mine's infrared image monitoring system. Infrared monitor images of pulverized and block coal were sampled in the ro...It is very important to accurately recognize and locate pulverized and block coal seen in a coal mine's infrared image monitoring system. Infrared monitor images of pulverized and block coal were sampled in the roadway of a coal mine. Texture statistics from the grey level dependence matrix were selected as the criterion for classification. The distributions of the texture statistics were calculated and analysed. A normalizing function was added to the front end of the BP network with one hidden layer. An additional classification layer is joined behind the linear layer. The recognition of pulverized from block coal images was tested using the improved BP network. The results of the experiment show that texture variables from the grey level dependence matrix can act as recognizable features of the image. The innovative improved BP network can then recognize the pulverized and block coal images.展开更多
Knowledge transfer among New Product Development(NPD)projects is beneficial for reducing project duration and promoting technological innovation.To support effective knowledge transfer,we propose a clustering method f...Knowledge transfer among New Product Development(NPD)projects is beneficial for reducing project duration and promoting technological innovation.To support effective knowledge transfer,we propose a clustering method for NPD projects based on similarity,integrating both structural and attribute similarities.First,to measure project structural similarity,we analyze both direct and indirect knowledge transfer relationships among project activities using the dependency structure matrix(DSM).Second,we measure project attribute similarity by calculating knowledge increments derived from sequential and iterative development processes.Finally,we apply a hierarchical clustering method to group similar projects,forming different programs.An industrial example is provided to demonstrate the proposed model.The results show that clustering projects into programs can enhance multi-project management by reducing coordination time for knowledge transfer within each program.Additionally,this approach provides some new insights,including quantifying project similarity based on knowledge transfer and understanding the influence of structural and attribute similarities on multi-project management.展开更多
基金supported by the National Natural Science Foundation of China(51605482)the Equipment Pre-research Project(41403020101).
文摘Test selection is to select the test set with the least total cost or the least total number from the alternative test set on the premise of meeting the required testability indicators.The existing models and methods are not suitable for system level test selection.The first problem is the lack of detailed data of the units’fault set and the test set,which makes it impossible to establish a traditional dependency matrix for the system level.The second problem is that the system level fault detection rate and the fault isolation rate(referred to as"two rates")are not enough to describe the fault diagnostic ability of the system level tests.An innovative dependency matrix(called combinatorial dependency matrix)composed of three submatrices is presented.The first problem is solved by simplifying the submatrix between the units’fault and the test,and the second problem is solved by establishing the system level fault detection rate,the fault isolation rate and the integrated fault detection rate(referred to as"three rates")based on the new matrix.The mathematical model of the system level test selection problem is constructed,and the binary genetic algorithm is applied to solve the problem,which achieves the goal of system level test selection.
基金Project 20050290010 supported by the Doctoral Foundation of Chinese Education Ministry
文摘It is very important to accurately recognize and locate pulverized and block coal seen in a coal mine's infrared image monitoring system. Infrared monitor images of pulverized and block coal were sampled in the roadway of a coal mine. Texture statistics from the grey level dependence matrix were selected as the criterion for classification. The distributions of the texture statistics were calculated and analysed. A normalizing function was added to the front end of the BP network with one hidden layer. An additional classification layer is joined behind the linear layer. The recognition of pulverized from block coal images was tested using the improved BP network. The results of the experiment show that texture variables from the grey level dependence matrix can act as recognizable features of the image. The innovative improved BP network can then recognize the pulverized and block coal images.
基金supported by the National Natural Science Foundation of China(Grant Nos.W2441021 and 72271022).
文摘Knowledge transfer among New Product Development(NPD)projects is beneficial for reducing project duration and promoting technological innovation.To support effective knowledge transfer,we propose a clustering method for NPD projects based on similarity,integrating both structural and attribute similarities.First,to measure project structural similarity,we analyze both direct and indirect knowledge transfer relationships among project activities using the dependency structure matrix(DSM).Second,we measure project attribute similarity by calculating knowledge increments derived from sequential and iterative development processes.Finally,we apply a hierarchical clustering method to group similar projects,forming different programs.An industrial example is provided to demonstrate the proposed model.The results show that clustering projects into programs can enhance multi-project management by reducing coordination time for knowledge transfer within each program.Additionally,this approach provides some new insights,including quantifying project similarity based on knowledge transfer and understanding the influence of structural and attribute similarities on multi-project management.
