Eurasia Continental Bridge——An Important Link of the Coordinated Development between China's Eastern,Central and Western Regions

The new Eurasia Continental Bridgeruns from East China to Central andWest China, crossing 11 provincesand regions, namely Jiangsu, Shandong,Hubei, Anhui, Shaanxi, Gansu, Shanxi,Sichuan, Qinghai, Xinjiang and Ningxia.The total area is 3.6 million sq km and thepopulation is about 300 million, accountingfor a third and a quarter of the whole countryrespectively. The area is very rich in naturalresources, so it is called an "Economic goldbelt" and "Gold corridor". Aerial remote-control survey indicates that along thecontinental bridge from Lianyungang to theAla Mountains within China’s boundary,

Reliability and reliability sensitivity analysis of structure by combining adaptive linked importance sampling and Kriging reliability method

The application of reliability analysis and reliability sensitivity analysis methods to complicated structures faces two main challenges:small failure probability(typical less than 10-5)and time-demanding mechanical models.This paper proposes an improved active learning surrogate model method,which combines the advantages of the classical Active Kriging–Monte Carlo Simulation(AK-MCS)procedure and the Adaptive Linked Importance Sampling(ALIS)procedure.The proposed procedure can,on the one hand,adaptively produce a series of intermediate sampling density approaching the quasi-optimal Importance Sampling(IS)density,on the other hand,adaptively generate a set of intermediate surrogate models approaching the true failure surface of the rare failure event.Then,the small failure probability and the corresponding reliability sensitivity indices are efficiently estimated by their IS estimators based on the quasi-optimal IS density and the surrogate models.Compared with the classical AK-MCS and Active Kriging–Importance Sampling(AK-IS)procedure,the proposed method neither need to build very large sample pool even when the failure probability is extremely small,nor need to estimate the Most Probable Points(MPPs),thus it is computationally more efficient and more applicable especially for problems with multiple MPPs.The effectiveness and engineering applicability of the proposed method are demonstrated by one numerical test example and two engineering applications.

Operational Risk-averse Routing Optimization for Cyber-physical Power Systems

The extensive application of modern information and communication technology in the power system through the in-depth integration of the information system and the power system has led to the gradual development of the cyberphysical power system(CPPS).While advanced information technology increases the safety and reliability of power system operations,it also increases the risks of fault propagation.To improve the reliability of CPPS from the perspective of power communication routing,it is proposed that the CPPS model and vulnerability assessment of power node reflect the correlation between information and energy flows with the service impact on power grid operation,which is an important index for evaluating communication services.According to the distribution of services at the different important levels on the links,the importance of the cross-layer link is established as the vulnerability evaluation index of the communication network.Then,the routing optimization model is proposed in combination with the service transmission risk under cyber-attack and the operating characteristics of the information system,which is solved through an improved fast-convergent genetic algorithm.The simulation results show that the proposed method allocates the alternate route to the low-risk link without significantly increasing the delay of the main route,which effectively improves the power supply reliability of CPPS in extreme cyber-attack scenarios.