Applied Mechanics and Materials Vols. 635-637

Abstract: In this work we present a computational method for for solving a class of nonlinear Volterra integro-differential equations of fractional order which is based on Adomian Decom-position Method. Convergence analysis is dependable enough to estimate the maximum absolute truncated error of the Adomian series solution. Numerical example is included to demonstrate the validity and applicability of the method.

Abstract: To solve the problem of design and manufacturing on the production of many varieties of small batch, the parts grouping method that based on clustering algorithm, clustering validity index and BP neural network method for new parts is proposed. At first, mathematical model of part clustering is built, and parts grouping is based on the similarity of data and cluster centers which is calculated by Euclidean distance, then the effectiveness of parts group is tested by Function Index and optimal number of clusters group can be found. The algorithm is achieved by Matlab clustering toolbox, so the best part family structure is built. Furthermore, the grouped parts are used to train the BP neural network toolbox in Matlab, then simulate new parts on network to find the match group. At last, a case study was also presented to verify the feasibility of this method．

Abstract: Appraising is an important work of Tactical Internet (TI) simulative training. As a new type of network, the appraising of TI training needs to be solved by new method. Therefore, the paper set up a new index system firstly, laid the foundation for the appraising of Tactical Internet simulative training. Secondly, the paper used a Petri net method, which solved the acquisition problem for those indexes that are dynamic changed and not easy to be directly calculated. Thirdly, the paper used a BP neural network method, which solved the calculation of index weights. Finally, based on the method of satisfaction degree, the paper set up an appraising model which realized the appraising of trainees’ training level。The paper’s work has been applied in practice and achieved good results.

Abstract: Introduced key technologies of Automated Testing Framework. Firstly, started from the opinion of software reusing, combined with the characteristic of Object-Oriented software and based on analyzing the feasibility of software reusing, this paper discusses the significance of Design Pattern. Secondly, in order to make the framework reusable, expansive, maintainable, referenced the idea of XUnit framework, Pattern generating Framework, this paper gives detailed introduction about the key technologies that are used in the ATF framework. The framework is implemented by using and applying many existing related design patterns and techniques such as Command, Observer, Composite, Adapter and Template Method, etc. In addition to the above, this paper also describes (Automated Testing Framework for N.E.W.), which is apply ATF to the functionality testing of N.E.W. It also analyses the test results, the framework’s shortage and provides the improved advices.

Abstract: The recent years saw a rapid development in information technology, which is closely bound up with the world as well as our daily lives. Concerning security, stability and development of a country, cyberspace, the five-dimensional space coexisted with land, sea, air, and space domain, is worthy of being paid more attention to. Therefore, dozens of studies have been done in this field and people began to know more about the concept and structure of cyberspace. This paper mainly describes the development of cyberspace, elaborates its structure and summarizes the importance of cyberspace technology.

Abstract: This paper proposed a remote monitoring system based on Web Services .In the system, the data form different kinds of sensors and cameras are managed by the sub station program. The system provides services by using Web Services. The problems appeared in the test run of the system are analyzed. The solutions which solve the problem are proposed. The controlling system can meet the needs of the enterprises. The proposed system has good openness and expansibility.

Abstract: Nowadays, fuzzing is one of the most effective ways to identify software security vulnerabilities, especially when we want to discover vulnerabilities about documents. According to the principles and ideas of Fuzzing, a vulnerability discovery system named WFuzzer is developed. This system can overcome the disadvantage of old ways; it also effectively improves the detection of potential unknown security vulnerabilities. This system is more automated and performs better in finding new security vulnerabilities.

Abstract: Cloud computing needs to manage a large number of computing resources, while resources scheduling strategy plays a key role in determining the efficiency of cloud computing. evolutionary algorithms (EA) as appropriate tools to optimize multi-objective problems have been applied to optimize Resources Scheduling of cloud computing ,However, studies on improving the convergence ratio and processing time in the most applied algorithms such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) in Resources Scheduling domains remain poorly understood. the resource schedule algorithmbased on Artificial Fish Swarm Optimization(AFSA) for Cloud Computing Environment has been designed and implemented after the study on the resource schedule of Cloud Computing. The main idea of improved AFSA is to extend Fish Swarm Optimization to the interacting swarms model by cooperative Models . The improved AFSA probability analysis indicates that searching solution is much more efficient and speeds up the multi-swarm to converge to the global optimum.Finally, the result of the experiment indicates that the scheduling system can improve the efficiency of dispatching resource and the utilization ratio in the Cloud Computing system.

Abstract: With the rapid development of information science and technology, data-driven approaches are already being the research tide in many fields. BP neural network (BPNN), support vector machine (SVM) and least squares support vector machine (LS-SVM) are introduced and adopted to simulate fluctuating time-series wind speeds in this paper. The regression-prediction models developed by implementing machine interpolation learning are established respectively. And the original speeds used as learning and forecast samples for the simulation of the data-driven approaches are obtained through AR numerical modeling. Based on the comparison of evaluation index, the results show that the simulated fluctuating wind speeds through SVM and LS-SVM are more accurate than the simulated speeds through BPNN, but the simulation time of LS-SVM and BPNN are shorter than the SVM.

Abstract: A new kind of network public opinion classification method based on K_ nearest neighbor (K_NN) classification algorithm in Hadoop environment is studied in this paper. In the light of distributed storage and parallel processing Characteristics of Hadoop platform, the parallel K_NN classification algorithm in the frame of MapReduce is designed. The classification ability and execution efficiency of proposed scheme is verified and the results show that the parallel K_NN algorithm enhances the network public opinion classification precision and execution efficiently.