Applied Mechanics and Materials Vols. 373-375

Abstract: Urban crises bring enormous threat to peoples life and property. Emergency exercises are the efficient way of anti-crisis training. However, the existing emergency exercises are lack of flexibility and costly. We put forward a novel emergency exercise system framework based on wargaming. First, the framework of wargaming system is introduced. Then, the application of wargaming on emergency decision exercise of urban crisis is described in detail. The advantage of wargaming exercise system is noticeable. It can simulate crisis affairs with low cost, can practice the decision-making abilities, and can help to find weak points of current anti-crisis plans, etc.

Abstract: Aimed at the high dynamics and uncertainty of road traffic, we propose a method combine BDD (binary decision diagram)-Based heuristic algorithm which used to do the initial path planning with BDD-Based incremental to solve the route replanning problem. In order to get the optimal path set, BDD-Based heuristic Search is firstly used for global planning. BDD is a compact data structure, the BDD-Based heuristic Search use this characteristic to represent state space and compress the search space through heuristic information at the same time; when the road network information changes, incremental replanning was used in difference type of congestion and the optimum path set again. The simulation results show that the BDD-Based heuristic Search and incremental replanning method has high efficiency and practicability in solving vehicle routing problem under dynamic and uncertain environment.

Abstract: For the issues of inferior local search ability and premature convergence in the later evolution stage of the traditional particle swarm optimization, an improved particle swarm optimization is proposed and applied to the parameter estimation. Firstly, in the evolutionary process of particle swarm optimization, the particles which have crossed the border are buffered according to the speed. Then, each particle is performed mutation in different probability according to the evolutionary generations, which can keep the diversity of the particle swarm and avoid the premature convergence effectively. Thirdly, a crossover operation is conducted between the current best particle and the particle which is selected from the particle swarm with a certain probability, which can lead particles gradually approaching to the extreme point and hence, the local search ability of the algorithm will be improved. The advanced particle swarm optimization is applied to the parameter estimation of the kinetic model in the Hg oxidation process and the application result show the effectiveness of the suggested algorithm.

Abstract: The detection of hydrophobicity is an important way to evaluate the performance of composite insulator, which is helpful to the safe operation of composite insulator. In this paper, the image processing technology and Back Propagation neural network is introduced to recognize the composite insulator hydrophobicity grade. First, hydrophobic image is preprocessed by histogram equalization and adaptive median filter, then the image was segmented by Ostu threshold method, and four features associated with hydrophobicity are extracted. Finally, the improved Back Propagation neural network is adopted to recognize composite insulator hydrophobicity grade. The experimental results show that the improved Back Propagation neural network can accurately recognize the composite insulator hydrophobicity

Abstract: This paper presents the new algorithm which is an improved normalized variable step size LMS adaptive filtering algorithm. A normalized LMS algorithm with variable step size iterative formula is deduced and at the same time the simulation results prove that the new algorithm has good performance. The LMS adaptive filtering algorithm has been widely used in many applications such as system identification, noise cancellation and the adaptive notch filter ,the paper analyses the application and implement the simulation by matlab. the result shows the proposed algorithm has been applied well.

Abstract: The sustainable forestry economic development is a typical non-linear complex system, so traditional statistical methods are hard to be used for the pre-warning. This paper constructs the pre-warning index system for the sustainable forestry economic development, and then builds a pre-warning model for it by applying the BP artificial neural network technology. This model provides a technical means for the pre-warning analysis of the sustainable forestry economic development as well as the decision-making basis for the forestry economic management and planning.

Abstract: Snowfall process is analyzed from the perspective of computer simulation in this paper, the dynamic properties and the static properties of snow particles are abstracted based on particle system theory. The snow particles movement is simulated with wind and no wind. To solve the problems of the blur of snowing environment and firn phenomena, the paper puts forward a method of applying pulverization to snowing virtual environment. The tests show that the simulation methods of snowfall and pulverization are effective and real, which are suit for real-time virtual environment.

Abstract: Because of the complexity of the software development, some software developers may plagiarize source code that comes from other projects or open source software in order to shorten development cycle. Usually the copyist would modify and disguise the source code copied to escape plagiarism detection. So far, most algorithms cant completely detect the source disguised by the copyist, especially cant exactly distinguish between the source code and the plagiaristic code. In this paper, we summarize and analyze the effect of disguised source to the detection process, design the strategy to remove the effect of disguised source, and propose a PDG-based software source code plagiarism detection algorithm. The algorithm can detect the existence of disguised source, so as to find out source code plagiarism. And we propose a heuristic rule to make the detection algorithm have the ability to give the plagiarism direction. Any existing algorithm does not have this function. We prove the availability of the algorithm by experiment.

Abstract: An improved Quantum-behaved particle swarm optimization algorithm is proposed for 0-1 knapsack problem.In the new algorithm, the inertia weight is expressed as functions of particle evolution velocity and particle aggregation by defining particle evolution velocity and particle aggregation so that the inertia weight has adaptability. At the same time, slowly varying function is introduced to the traditional location updating formula so that the local optimal solution can be effectively overcome. The simulation experiments show that improved Quantum-behaved particle swarm optimization algorithm has better convergence and stability in solving knapsack problem.

Abstract: With the development of multimedia network, more and more real-time multimedia applications require a network capable of satisfying QoS constraints such as delay and delay-variation bound so that the messages reach each destination node at almost the same time within a certain specified time limit, and certain application also demand that different streams for each different destination node should reach with minimum cost under the constraints. Therefore, the delay and delay-variation constraint multicast routing problem is studied and a new strategy in core selection methods is proposed. The algorithm we propose guarantees that the delay from the source to any destination does not exceed a real-time constraint satisfying the delay-variation constraint under cost minimization. In addition, this core selection methods achieve a balance of optimizing cost and delay of the multicast tree. Simulation results show that the algorithm has low complexity and balances between the computational complexity and performance, it can also meet the requirements of real-time network communication.