This paper presents a rapid and simple risk calculation method for large and complex engineering systems, the simulated maximum entropy method （SMEM）, which is based on integration of the advantages of the Monte Car...This paper presents a rapid and simple risk calculation method for large and complex engineering systems, the simulated maximum entropy method （SMEM）, which is based on integration of the advantages of the Monte Carlo and maximum entropy methods, thus avoiding the shortcoming of the slow convergence rate of the Monte Carlo method in risk calculation. Application of SMEM in the calculation of reservoir flood discharge risk shows that this method can make full use of the known information under the same conditions and obtain the corresponding probability distribution and the risk value. It not only greatly improves the speed, compared with the Monte Carlo method, but also provides a new approach for the risk calculation in large and complex engineering systems.展开更多
In complex networks,identifying influential spreader is of great significance for improving the reliability of networks and ensuring the safe and effective operation of networks.Nowadays,it is widely used in power net...In complex networks,identifying influential spreader is of great significance for improving the reliability of networks and ensuring the safe and effective operation of networks.Nowadays,it is widely used in power networks,aviation networks,computer networks,and social networks,and so on.Traditional centrality methods mainly include degree centrality,closeness centrality,betweenness centrality,eigenvector centrality,k-shell,etc.However,single centrality method is onesided and inaccurate,and sometimes many nodes have the same centrality value,namely the same ranking result,which makes it difficult to distinguish between nodes.According to several classical methods of identifying influential nodes,in this paper we propose a novel method that is more full-scaled and universally applicable.Taken into account in this method are several aspects of node’s properties,including local topological characteristics,central location of nodes,propagation characteristics,and properties of neighbor nodes.In view of the idea of the multi-attribute decision-making,we regard the basic centrality method as node’s attribute and use the entropy weight method to weigh different attributes,and obtain node’s combined centrality.Then,the combined centrality is applied to the gravity law to comprehensively identify influential nodes in networks.Finally,the classical susceptible-infected-recovered(SIR)model is used to simulate the epidemic spreading in six real-society networks.Our proposed method not only considers the four topological properties of nodes,but also emphasizes the influence of neighbor nodes from the aspect of gravity.It is proved that the new method can effectively overcome the disadvantages of single centrality method and increase the accuracy of identifying influential nodes,which is of great significance for monitoring and controlling the complex networks.展开更多
The Maximum-entropy Method(MEM)for determining the complete ODF(orientation distribution function),accompanied with the equal-volume partitioning technique for quantitative texture analysis,was first tested in analysi...The Maximum-entropy Method(MEM)for determining the complete ODF(orientation distribution function),accompanied with the equal-volume partitioning technique for quantitative texture analysis,was first tested in analysing the texture of a commercial purity titanium strip.The experimentally measured results indi- cate that the rolling planes of most grains in this sample are parallel to the{1010}and the{1210}with about ~±10°spread while the rolling directions nearly distribute uniformly and their volume fractions are 19.46％ and 18.70％ respectively.Besides,there are still two weaker texture components,(7526)[1544]and (1105)[2311],with 3.24％and 4.17％respectively.展开更多
基金supported by the National Water Pollution Control and Management Technology Major Projects(Grant No. 2009ZX07423-001)the National Natural Science Foundation of China (Grants No.51179069and 40971300)the Fundamental Research Funds for the Central Universities (Grants No.10QX43,09MG16,and 10QG23)
文摘This paper presents a rapid and simple risk calculation method for large and complex engineering systems, the simulated maximum entropy method （SMEM）, which is based on integration of the advantages of the Monte Carlo and maximum entropy methods, thus avoiding the shortcoming of the slow convergence rate of the Monte Carlo method in risk calculation. Application of SMEM in the calculation of reservoir flood discharge risk shows that this method can make full use of the known information under the same conditions and obtain the corresponding probability distribution and the risk value. It not only greatly improves the speed, compared with the Monte Carlo method, but also provides a new approach for the risk calculation in large and complex engineering systems.
基金Project support by the National Key Research and Development Program of China(Grant No.2018YFF0301000)the National Natural Science Foundation of China(Grant Nos.71673161 and 71790613)。
文摘In complex networks,identifying influential spreader is of great significance for improving the reliability of networks and ensuring the safe and effective operation of networks.Nowadays,it is widely used in power networks,aviation networks,computer networks,and social networks,and so on.Traditional centrality methods mainly include degree centrality,closeness centrality,betweenness centrality,eigenvector centrality,k-shell,etc.However,single centrality method is onesided and inaccurate,and sometimes many nodes have the same centrality value,namely the same ranking result,which makes it difficult to distinguish between nodes.According to several classical methods of identifying influential nodes,in this paper we propose a novel method that is more full-scaled and universally applicable.Taken into account in this method are several aspects of node’s properties,including local topological characteristics,central location of nodes,propagation characteristics,and properties of neighbor nodes.In view of the idea of the multi-attribute decision-making,we regard the basic centrality method as node’s attribute and use the entropy weight method to weigh different attributes,and obtain node’s combined centrality.Then,the combined centrality is applied to the gravity law to comprehensively identify influential nodes in networks.Finally,the classical susceptible-infected-recovered(SIR)model is used to simulate the epidemic spreading in six real-society networks.Our proposed method not only considers the four topological properties of nodes,but also emphasizes the influence of neighbor nodes from the aspect of gravity.It is proved that the new method can effectively overcome the disadvantages of single centrality method and increase the accuracy of identifying influential nodes,which is of great significance for monitoring and controlling the complex networks.
文摘The Maximum-entropy Method(MEM)for determining the complete ODF(orientation distribution function),accompanied with the equal-volume partitioning technique for quantitative texture analysis,was first tested in analysing the texture of a commercial purity titanium strip.The experimentally measured results indi- cate that the rolling planes of most grains in this sample are parallel to the{1010}and the{1210}with about ~±10°spread while the rolling directions nearly distribute uniformly and their volume fractions are 19.46％ and 18.70％ respectively.Besides,there are still two weaker texture components,(7526)[1544]and (1105)[2311],with 3.24％and 4.17％respectively.
