Earticle

It is one of the effective methods to improve the reliability of the distribution system by using looped network power supply. With the development of new energy technology, distributed generation has been more widely used. But a small power system named micro-grid which is assembled by the device such as distributed generation, energy storage device and controllable load in a certain operating rules catches the attention of people. It can operate in islanded mode with the power grid failure, which can greatly improve the reliability of the system. Traditional analytical method and Monte Carlo method are difficult to evaluate its reliability accurately. In this paper, a new artificial intelligence method based on Bayesian networks is used for the system reliability analysis and evaluation. The corresponding Bayesian networks are established for the reliability test bus 2 which containing looped network and the improve reliability test bus 6 which containing micro-grids separately. With the help of causal reasoning and diagnostic reasoning, the supply influence of different types of DG output intermittent, operation mode on loads are detected, the key factors which have an impact on important load power supply reliability are found out, which is the theoretical foundation for practical engineering decision, so as to improve system reliability, identify failures and make maintenance scheduling.

This paper proposes the community detection in complex networks based on improved genetic algorithm and local optimization (IGALO) in terms of the defect that traditional community detection approaches based on genetic algorithm have strong randomness and weak searching ability in the process of community detection. Taking modularity function Q as the objective function, IGALO algorithm adopts label propagation method of one-iteration to initialize population so as to generate initial population with certain precision. Then, anti-destructive one-way crossover strategy is proposed to ensure the crossover operation to develop in the direction of making community structure increase to modularity function. Finally, mutation strategy of node local optimization is proposed to improve the searching efficiency of algorithm. This algorithm effectively overcomes the defect that traditional algorithms have weak searching ability and improves the community detection accuracy. Tests are made on benchmark networks and real-world networks and comparative analysis is also made with various classic algorithms. The results show that IGALO algorithm is effective and feasible.

Vehicular Ad Hoc Networks (VANETs) have emerged as new technology with an aim of providing safety and security for the persons sitting in the vehicles. Consequently, wide variety of research contributions have been presented for VANET intrinsic characteristics. This paper provides comprehensive details of various types of routing protocol which is used to route the packets amongst various vehicles in high mobility scenarios. A detailed categorization of various routing techniques is provided in this paper with respect to the advantages, disadvantages and their various applications. The routing techniques have been compared to make the Intelligent Transportation System.

A growing number of enterprises have been moving their works to encapsulate system functions, business logics and processing modules into Web service because of its flexibility and low-cost. However, service-oriented software calls for constantly adjusting its architecture in order to respond to varying user requirements and instable runtime environments. One of the most challenging issues is how to effectively implement a reconfiguration to ensure the business-critical application is trustworthy. In this paper, it proposes a method to dynamic composition for service replacement, which focuses on the probabilistic optimization to service planning of candidate compositions when the service failure is occurred. First, the input and output data specification is defined to describe interface behaviors, and then the probabilistic solution graph is introduced to formalize replacement strategies. Second, corresponding algorithms are discussed for optimization selection purpose, which includes reliability calculation process and model modification process. The former computes the probability value of each service planning generated from probabilistic solution graph. The latter modifies probabilistic solution graph model to recommend Top-k solutions, pruning the service planning which does not satisfy the specified probability value. Third, the architecture of prototype is presented to demonstrate the feasibility of the proposed method. Our method provides a reference to guarantee the reliability of service process in E-commerce.

Compressed sensing is an innovative technology, which provides a new sampling mode. The key problem in compressed sensing is the construction of sensing matrix, which has an important influence on the signal sampling and reconstruction algorithm. At present, in most cases the sensing matrix is a random structure, and is difficult to realize due to its huge storage in practical applications. In this paper, we introduce a novel deterministic construction of sensing matrix via Q-matrix, which is calculated by solving the N-queens problem. The proposed sensing matrix has good orthogonality and circularity. Using the circularity of Q-matrix, we can construct sensing matrix for compressed sensing. A large number of simulation results show that the proposed sensing matrix in this paper can obtain a better quality of the reconstructed image, and it is easily realized owing to its cyclic characteristic.