Data Fusion about Serviceability Reliability Prediction for the Long-Span Bridge Girder Based on MBDLM and Gaussian Copula Technique

This article presented a new data fusion approach for reasonably predicting dynamic serviceability reliability of the long-span bridge girder.Firstly,multivariate Bayesian dynamic linear model(MBDLM)considering dynamic correlation among the multiple variables is provided to predict dynamic extreme deflections;secondly,with the proposed MBDLM,the dynamic correlation coefficients between any two performance functions can be predicted;finally,based on MBDLM and Gaussian copula technique,a new data fusion method is given to predict the serviceability reliability of the long-span bridge girder,and the monitoring extreme deflection data from an actual bridge is provided to illustrated the feasibility and application of the proposed method.

Application reliability for communication networks and its analysis method

The network reliability is difficult to be evaluated because of the complex relationship among the network components.It can be quite different for different users running different applications on the same network.This paper proposes a new concept and a model of application reliability.Different from the existing models that ignores the effects of applications,the proposed application reliability model considers the effects of different applications on the network performance and different types of network faults and makes the analysis of network components relationship possible.This paper also provides a method to evaluate the application reliability when the data flow satisfies Markov properties.Finally,a case study is presented to illustrate the proposed network reliability model and the analysis method.

Progress in high-speed train technology around the world

This is a review of high-speed train development in the sense of technology advances all over the world. Three generations of high-speed trains are classified according to their technical characteristics and maximum operating speed. Emphasis is given to the newly developed high-speed train in China, CRH380. The theoretical foundations and future development of CRH380 are briefly discussed.

A new method for optimal FTU placement in distribution network under consideration of power service reliability

Modern distribution network with high penetration of intermittent renewable sources and the so-called prosumers requires more reliable distribution automation(DA) system for safe operation and control. The design of optimal feeder terminal units(FTU)placement is critical and economical for the effective DA application. Previously proposed solutions of optimal FTU placement aiming to ensure the accuracy of state estimation(SE), typically include the following two main shortcomings: 1) only to obtain the optimal FTU placement in quantity, and the analysis of FTU location is not considered yet; 2) few consider the uncertainty of intermittent power injections in the analysis of state estimation. In this paper, a modified methodology of FTU placement is proposed not only aiming to ensure the accuracy of state estimation with the minimum number of meters, but also finding those specific FTU locations to guarantee the power service reliability. Moreover, the uncertainty models of those intermittent power injections are also considered by using probability density function(PDF). The resultant optimization problem is addressed by using the covariance matrix adaptation evolution strategy(CMA-ES). Case studies demonstrate the feasibility and effectiveness of the proposed methodology.

An effective communication and computation model based on a hybridgraph-deeplearning approach for SIoT

Social Edge Service(SES)is an emerging mechanism in the Social Internet of Things(SIoT)orchestration for effective user-centric reliable communication and computation.The services are affected by active and/or passive attacks such as replay attacks,message tampering because of sharing the same spectrum,as well as inadequate trust measurement methods among intelligent devices(roadside units,mobile edge devices,servers)during computing and content-sharing.These issues lead to computation and communication overhead of servers and computation nodes.To address this issue,we propose the HybridgrAph-Deep-learning(HAD)approach in two stages for secure communication and computation.First,the Adaptive Trust Weight(ATW)model with relation-based feedback fusion analysis to estimate the fitness-priority of every node based on directed graph theory to detect malicious nodes and reduce computation and communication overhead.Second,a Quotient User-centric Coeval-Learning(QUCL)mechanism to formulate secure channel selection,and Nash equilibrium method for optimizing the communication to share data over edge devices.The simulation results confirm that our proposed approach has achieved effective communication and computation performance,and enhanced Social Edge Services(SES)reliability than state-of-the-art approaches.

Indeterminacy-aware service selection for reliable service composition

With the development of Intemet and Web service technology, Web service composition has been an effective way to construct software applications; service selection is the crucial element in the composition process. However, the existing selection methods mostly generate static plans since they neglect the inherent stochastic and dynamic nature of Web services. As a result, Web service composition often inevitably terminates with failure. An indeterminacy-aware service selection algorithm based on an improved Markov decision process （IMDP） has been designed for reliable service composition, but it suffers from higher computation complexity. Therefore, an efficient method is proposed, which can reduce the computation cost by converting the service selection problem based on IMDP into solving a nonhomogeneous linear equation set. Experimental results demonstrate the success rate of service composition has been improved greatly, whilst also reducing computation cost.