On the basis of investigating the statistical data of bus transport networks of three big cities in China,wepropose that each bus route is a clique(maximal complete subgraph)and a bus transport network(BTN)consists of...On the basis of investigating the statistical data of bus transport networks of three big cities in China,wepropose that each bus route is a clique(maximal complete subgraph)and a bus transport network(BTN)consists of alot of cliques,which intensively connect and overlap with each other.We study the network properties,which includethe degree distribution,multiple edges' overlapping time distribution,distribution of the overlap size between any twooverlapping cliques,distribution of the number of cliques that a node belongs to.Naturally,the cliques also constitute anetwork,with the overlapping nodes being their multiple links.We also research its network properties such as degreedistribution,clustering,average path length,and so on.We propose that a BTN has the properties of random cliqueincrement and random overlapping clique,at the same time,a BTN is a small-world network with highly clique-clusteredand highly clique-overlapped.Finally,we introduce a BTN evolution model,whose simulation results agree well withthe statistical laws that emerge in real BTNs.展开更多
This paper aims to design a controller to robustly stabilize uncertain Takagi-Sugeno fuzzy systems with time- varying input delay.Based on Lyapunov-Krasovskii functional approach,the sufficient conditions for robust s...This paper aims to design a controller to robustly stabilize uncertain Takagi-Sugeno fuzzy systems with time- varying input delay.Based on Lyapunov-Krasovskii functional approach,the sufficient conditions for robust stabilization of such systems are given in the form of linear matrix inequali- ties.The controller design does not have to require that the time-derivative of time-varying input delay be smaller than one. A numeric example is given to show that the proposed results are effective and less conservative.展开更多
We abstract the bus transport networks(BTNs)to two kinds of complex networks with space L and spaceP methods respectively.Using improved community detecting algorithm(PKM agglomerative algorithm),we analyzethe communi...We abstract the bus transport networks(BTNs)to two kinds of complex networks with space L and spaceP methods respectively.Using improved community detecting algorithm(PKM agglomerative algorithm),we analyzethe community property of two kinds of BTNs graphs.The results show that the BTNs graph described with space Lmethod have obvious community property,but the other kind of BTNs graph described with space P method have not.The reason is that the BTNs graph described with space P method have the intense overlapping community propertyand general community division algorithms can not identify this kind of community structure.To overcome this problem,we propose a novel community structure called N-depth community and present a corresponding community detectingalgorithm,which can detect overlapping community.Applying the novel community structure and detecting algorithmto a BTN evolution model described with space P,whose network property agrees well with real BTNs',we get obviouscommunity property.展开更多
With the increasing complexity of industrial processes, the high-dimensional industrial data exhibit a strong nonlinearity, bringing considerable challenges to the fault diagnosis of industrial processes. To efficient...With the increasing complexity of industrial processes, the high-dimensional industrial data exhibit a strong nonlinearity, bringing considerable challenges to the fault diagnosis of industrial processes. To efficiently extract deep meaningful features that are crucial for fault diagnosis, a sparse Gaussian feature extractor(SGFE) is designed to learn a nonlinear mapping that projects the raw data into the feature space with the fault label dimension. The feature space is described by the one-hot encoding of the fault category label as an orthogonal basis. In this way, the deep sparse Gaussian features related to fault categories can be gradually learned from the raw data by SGFE. In the feature space,the sparse Gaussian(SG) loss function is designed to constrain the distribution of features to multiple sparse multivariate Gaussian distributions. The sparse Gaussian features are linearly separable in the feature space, which is conducive to improving the accuracy of the downstream fault classification task. The feasibility and practical utility of the proposed SGFE are verified by the handwritten digits MNIST benchmark and Tennessee-Eastman(TE) benchmark process,respectively.展开更多
基金supported by National Natural Science Foundation of China under Grant Nos.60504027 and 60874080the Postdoctor Science Foundation of China under Grant No.20060401037
文摘On the basis of investigating the statistical data of bus transport networks of three big cities in China,wepropose that each bus route is a clique(maximal complete subgraph)and a bus transport network(BTN)consists of alot of cliques,which intensively connect and overlap with each other.We study the network properties,which includethe degree distribution,multiple edges' overlapping time distribution,distribution of the overlap size between any twooverlapping cliques,distribution of the number of cliques that a node belongs to.Naturally,the cliques also constitute anetwork,with the overlapping nodes being their multiple links.We also research its network properties such as degreedistribution,clustering,average path length,and so on.We propose that a BTN has the properties of random cliqueincrement and random overlapping clique,at the same time,a BTN is a small-world network with highly clique-clusteredand highly clique-overlapped.Finally,we introduce a BTN evolution model,whose simulation results agree well withthe statistical laws that emerge in real BTNs.
基金Supported by National Basic Research Program of China(973 Program)(2002CB312200)National Natural Science Foundation of China(60474045)
文摘This paper aims to design a controller to robustly stabilize uncertain Takagi-Sugeno fuzzy systems with time- varying input delay.Based on Lyapunov-Krasovskii functional approach,the sufficient conditions for robust stabilization of such systems are given in the form of linear matrix inequali- ties.The controller design does not have to require that the time-derivative of time-varying input delay be smaller than one. A numeric example is given to show that the proposed results are effective and less conservative.
基金Supported by the National Natural Science Foundation of China under Grant Nos.60504027 and 60874080the China Postdoctoral Science Foundation Funded Project under Grant No.20060401037
文摘We abstract the bus transport networks(BTNs)to two kinds of complex networks with space L and spaceP methods respectively.Using improved community detecting algorithm(PKM agglomerative algorithm),we analyzethe community property of two kinds of BTNs graphs.The results show that the BTNs graph described with space Lmethod have obvious community property,but the other kind of BTNs graph described with space P method have not.The reason is that the BTNs graph described with space P method have the intense overlapping community propertyand general community division algorithms can not identify this kind of community structure.To overcome this problem,we propose a novel community structure called N-depth community and present a corresponding community detectingalgorithm,which can detect overlapping community.Applying the novel community structure and detecting algorithmto a BTN evolution model described with space P,whose network property agrees well with real BTNs',we get obviouscommunity property.
基金Projects(62125306, 62133003) supported by the National Natural Science Foundation of ChinaProject(TPL2019C03) supported by the Open Fund of Science and Technology on Thermal Energy and Power Laboratory,ChinaProject supported by the Fundamental Research Funds for the Central Universities(Zhejiang University NGICS Platform),China。
文摘With the increasing complexity of industrial processes, the high-dimensional industrial data exhibit a strong nonlinearity, bringing considerable challenges to the fault diagnosis of industrial processes. To efficiently extract deep meaningful features that are crucial for fault diagnosis, a sparse Gaussian feature extractor(SGFE) is designed to learn a nonlinear mapping that projects the raw data into the feature space with the fault label dimension. The feature space is described by the one-hot encoding of the fault category label as an orthogonal basis. In this way, the deep sparse Gaussian features related to fault categories can be gradually learned from the raw data by SGFE. In the feature space,the sparse Gaussian(SG) loss function is designed to constrain the distribution of features to multiple sparse multivariate Gaussian distributions. The sparse Gaussian features are linearly separable in the feature space, which is conducive to improving the accuracy of the downstream fault classification task. The feasibility and practical utility of the proposed SGFE are verified by the handwritten digits MNIST benchmark and Tennessee-Eastman(TE) benchmark process,respectively.
