For understanding acoustic emission （AE） activity and accumulation of micro-damage inside rock under pure tensile state, the AE signals has been monitored on the test of directly tension on two kinds of marble speci...For understanding acoustic emission （AE） activity and accumulation of micro-damage inside rock under pure tensile state, the AE signals has been monitored on the test of directly tension on two kinds of marble specimens. A tensile constitutive model was proposed with the damage factor calculated by AE energy rate. The tensile strength of marble was discrete obviously and was sensitive to the inside microdefects and grain composition. With increasing of loading, the tensile stress-strain curve obviously showed nonlinear with the tensile tangent modulus decreasing. In repeated loading cycle, the tensile elastic modulus was less than that in the previous loading cycle because of the generation of micro damage during the prior loading. It means the linear weakening occurring in the specimens. The AE activity was corresponding with occurrence of nonlinear deformation. In the initial loading stage which only elastic deformation happened on the specimens, there were few AE events occurred; while when the nonlinear deformation happened with increasing of loading, lots of AE events were generated. The quantity and energy of AE events were proportionally related to the variation of tensile tangent modulus. The Kaiser effect of AE activity could be clearly observed in tensile cycle loading. Based on the theory of damage mechanics, the damage factor was defined by AE energy rate and the tensile damage constitutive model was proposed which only needed two property constants. The theoretical stress-strain curve was well fitted with the curve plotted with tested datum and the two property constants were easily gotten by the laboratory testing.展开更多
Accurately identifying network traffics at the early stage is very important for the application of traffic identification.Recent years,more and more research works have tried to build effective machine learning model...Accurately identifying network traffics at the early stage is very important for the application of traffic identification.Recent years,more and more research works have tried to build effective machine learning models to identify traffics with the few packets at the early stage.However,a basic and important problem is still unresolved,that is how many packets are most effective in early stage traffic identification.In this paper,we try to resolve this problem using experimental methods.We firstly extract the packet size of the first 2-10 packets of 3 traffic data sets.And then execute crossover identification experiments with different numbers of packets using 11 well-known machine learning classifiers.Finally,statistical tests are applied to find out which number is the best performed one.Our experimental results show that 5-7are the best packet numbers for early stage traffic identification.展开更多
To solve the fault diagnosis problem of liquid propellant rocket engine ground testing bed,a fault diagnosis approach based on self-organizing map(SOM)is proposed.The SOM projects the multidimensional ground testing b...To solve the fault diagnosis problem of liquid propellant rocket engine ground testing bed,a fault diagnosis approach based on self-organizing map(SOM)is proposed.The SOM projects the multidimensional ground testing bed data into a two-dimensional map.Visualization of the SOM is used to cluster the ground testing bed data.The out map of the SOM is divided to several regions.Each region is represented for one fault mode.The fault mode of testing data is determined according to the region of their labels belonged to.The method is evaluated using the testing data of a liquid-propellant rocket engine ground testing bed with sixteen fault states.The results show that it is a reliable and effective method for fault diagnosis with good visualization property.展开更多
Aimed at the generation of high-quality test set in the shortest possible time, the test generation for combinational circuits （CC） based on the chaotic particle swarm optimization （CPSO） algorithm is presented ac...Aimed at the generation of high-quality test set in the shortest possible time, the test generation for combinational circuits （CC） based on the chaotic particle swarm optimization （CPSO） algorithm is presented according to the analysis of existent problems of CC test generation, and an appropriate CPSO algorithm model has been constructed. With the help of fault simulator, the test set of ISCAS＇ 85 benchmark CC is generated using the CPSO, and some techniques are introduced such as half-random generation, and simulation of undetected fauhs.with original test vector, and inverse test vector. Experimental results show that this algorithm can generate the same fault coverage and small-size test set in short time compared with other known similar methods, which proves that the proposed method is applicable and effective.展开更多
文摘For understanding acoustic emission （AE） activity and accumulation of micro-damage inside rock under pure tensile state, the AE signals has been monitored on the test of directly tension on two kinds of marble specimens. A tensile constitutive model was proposed with the damage factor calculated by AE energy rate. The tensile strength of marble was discrete obviously and was sensitive to the inside microdefects and grain composition. With increasing of loading, the tensile stress-strain curve obviously showed nonlinear with the tensile tangent modulus decreasing. In repeated loading cycle, the tensile elastic modulus was less than that in the previous loading cycle because of the generation of micro damage during the prior loading. It means the linear weakening occurring in the specimens. The AE activity was corresponding with occurrence of nonlinear deformation. In the initial loading stage which only elastic deformation happened on the specimens, there were few AE events occurred; while when the nonlinear deformation happened with increasing of loading, lots of AE events were generated. The quantity and energy of AE events were proportionally related to the variation of tensile tangent modulus. The Kaiser effect of AE activity could be clearly observed in tensile cycle loading. Based on the theory of damage mechanics, the damage factor was defined by AE energy rate and the tensile damage constitutive model was proposed which only needed two property constants. The theoretical stress-strain curve was well fitted with the curve plotted with tested datum and the two property constants were easily gotten by the laboratory testing.
基金This research was partially supported by National Natural Science Foundation of China under grant No.61472164,No.61402475,No.61173078,No.61203105,No.61173079,No.61070130,and No.60903176,the Provincial Natural Science Foundation of Shandong under grant No.ZR2012FM010,No.ZR2011FZ001,No.ZR2010FM047,No.ZR2010FQ028 and No.ZR2012FQ016
文摘Accurately identifying network traffics at the early stage is very important for the application of traffic identification.Recent years,more and more research works have tried to build effective machine learning models to identify traffics with the few packets at the early stage.However,a basic and important problem is still unresolved,that is how many packets are most effective in early stage traffic identification.In this paper,we try to resolve this problem using experimental methods.We firstly extract the packet size of the first 2-10 packets of 3 traffic data sets.And then execute crossover identification experiments with different numbers of packets using 11 well-known machine learning classifiers.Finally,statistical tests are applied to find out which number is the best performed one.Our experimental results show that 5-7are the best packet numbers for early stage traffic identification.
基金Sponsored by the National Natural Science Foundation of China(Grant No. NSFC-60572010)
文摘To solve the fault diagnosis problem of liquid propellant rocket engine ground testing bed,a fault diagnosis approach based on self-organizing map(SOM)is proposed.The SOM projects the multidimensional ground testing bed data into a two-dimensional map.Visualization of the SOM is used to cluster the ground testing bed data.The out map of the SOM is divided to several regions.Each region is represented for one fault mode.The fault mode of testing data is determined according to the region of their labels belonged to.The method is evaluated using the testing data of a liquid-propellant rocket engine ground testing bed with sixteen fault states.The results show that it is a reliable and effective method for fault diagnosis with good visualization property.
文摘Aimed at the generation of high-quality test set in the shortest possible time, the test generation for combinational circuits （CC） based on the chaotic particle swarm optimization （CPSO） algorithm is presented according to the analysis of existent problems of CC test generation, and an appropriate CPSO algorithm model has been constructed. With the help of fault simulator, the test set of ISCAS＇ 85 benchmark CC is generated using the CPSO, and some techniques are introduced such as half-random generation, and simulation of undetected fauhs.with original test vector, and inverse test vector. Experimental results show that this algorithm can generate the same fault coverage and small-size test set in short time compared with other known similar methods, which proves that the proposed method is applicable and effective.
