Applied Mechanics and Materials Vols. 513-517

Abstract: Optimizing the energy consumption of sensor node and prolonging the network lifetime are the key steps to enable the wireless sensor network to enter into practical applications. This paper focuses on studying the energy efficient wireless sensor network routing protocol from the perspective of node energy consumption efficiency and balance, designing the Energy Balanced Adaptive Clustering Hierarchical Protocol (EBACH) and two-layer transceiver model, improving data transmission process to conform to the energy-saving principle of wireless multi-hop data transmission, and analyzing the feasibility and accuracy of the algorithm through simulation.

Abstract: The feature extraction and detection problem of network information abnormal behavior was researched in this paper. The network attack tended to ambiguity in hidden, and the abnormal behavior of network information is referred as a data signal series, and it was existed in the network information space with strong interference. Traditional detection method was hard to detect the abnormal signal. On the basis of fractional Fourier transform (FRFT), an improved abnormal behavior feature detection algorithm of network information was proposed. The properties such as energy gathering and noise suppression of 4-order cumulant slice were taken in advantage. In the post processing of fractional Fourier transform detection, the post processing operator of 4 order cumulant was introduced in the detection algorithm, the post energy was gathered in the fractional Fourier domain, the signal accumulation was likely to be increased, and the interference noise could be restrained effectively. Simulation results show that the improved algorithm has perfect noise suppression performance, and it can detect and extract the abnormal behavior feature in the network space to maximum. The detection performance is improved greatly, and the research result can be applied in the network information warfare and network security areas.

Abstract: Based on the glowworm swarm optimization (GSO) and BP neural network (BPNN), an algorithm for BP neural network optimized glowworm swarm optimization (GSOBPNN) is proposed. In the algorithm, GSO is used to generate better network initial thresholds and weights so as to compensate the random defects for the thresholds and weights of BPNN, thus it can make BPNN have faster convergence and greater learning ability. The efficiency of the proposed prediction method is tested by the simulation of the chaotic time series generated by Lorenz system. The simulations results show that the proposed method has higher forecasting accuracy compared with the BPNN, so prove it is feasible and effective in the chaotic time series.

Abstract: The knowledge management system of teaching case corpus adopts case reasoning technology in the field of artificial intelligence. The whole system includes altogether ten modules. They are case uploading, case modification, case analysis, case algorithm and critical case management. The basic function is to assist the trained teachers to get the teaching case knowledge from other teachers, so as to develop teachersspecialty. In the module of case algorithm , in the application of the algorithm of case-searching based on AHP, the case needed by the users can be sorted out in an objective and fair way.

Abstract: Nowadays, many digital archive systems have been developed to facilitate archive operations such as archive query and archive update. However, for many enterprises traditional archive management methods are still utilized, and paper files are the most important materials for archives. To cope with this problem, we present an enterprise-wide digital archive management system infrastructure in this paper. Based on this infrastructure, an archive query management is developed. Through the infrastructure and the query management, it can be expected that more production efficiency can be achieved.

Abstract: This article is in force reality, based on the military establishment of the internal network based on B/S standard parts management system to achieve effective protection of non-standard pieces of equipment. In the management system, simulation software VIRTOOLS, designed equipment fee standard virtual simulation model, user observation, through the non-standard parts of the virtual display, the user can require non-standard units pieces for a good match. Meanwhile, the simulation software can also provide virtual assembly capabilities, through the virtual switch feature, users can better on non-standard parts needed for effective selection.

Abstract: The increasing development of social networks provides a unique source for analyzing human dynamics in the modern age. In recently years, many models have been introduced to study on that how those microcosmic individuals with different opinions tend to format macroscopic consensus in the opinion interaction. In this paper, we propose a simple model to describe the process of opinion evolving base of the parameter of opinion update threshold. In this model, there have little influence under the different network parameters such as network size, average degree, and value of threshold. At the same time, the initial distribution of opinion and individual refer to the history value can decide the last state of opinion.

Abstract: In recent 15 years, the study of complex networks has been gradually becoming an important issue. Community structure is an interesting property of complex networks. Researchers have made much exciting and important progress in community detection methods. The paper introduced the definition and significance of community structure; elaborates on the overview of community discovery algorithms and a proposed taxonomy according to the basic principle that they used. Modularity function was recommended briefly. Finally, described several popular test methods and benchmarks.

Abstract: To study the organizational structure of particles in particle swarm optimization (PSO), we have proposed the family PSO (FPSO) previously. To further study the internal structure of FPSO, this paper introduced the animal collective behavior into the FPSO. It made the interaction ruling among particles was not based on random selection but topological distance. Each family interacted on average with a fixed number of neighbors, rather than with all neighbors within a fixed metric distance. Simulations for four benchmark functions demonstrated that the interaction ruling based on topological distance among particles was more reasonable than that on random selection.

Abstract: Understanding the topological structure of scale-free networks or small world networks is required and useful for investigation of complex networks. We will build up a class of edge-growing network models and provide an algorithm for finding spanning trees of edge-growing network models in this article.