Applied Mechanics and Materials Vols. 556-562

Abstract: M/M/1 model was the most important and basic of all queue models. The paper combined Monte Carlo simulation with traditional M/M/1 model. The paper set an example of traditional M/M/1 system and calculates its indicators of performance firstly. Secondly, define the parameters with combination of the observation data and Delphi results. Thirdly, do Monte Carlo simulation with computer and software. In the end, compare the results by two methods. The results showed that the performance could be acceptable and did not need to make any improvements in case of traditional calculation. But, lots of things had been changed when the parameters had become uncertain. All indicators showed a big risk after the Monte Carlo simulation. Compare the results of traditional M/M/1 model and Monte Carlo simulation; it was found that it was necessary to treat this kind of problem as an uncertain problem in order to improve the accuracy of decision making.

Abstract: Compressed Sensing is very efficient in reducing the relatively high sampling rate. But when it comes to the channel estimation of uncooperative communication, the common CS reconstruction algorithms seem impractical to implement since a pilot is required, which is difficult for uncooperative communication. In this paper, we combine the sparsity transform dictionary, which is formed by a sequence of delays of the template signal, together with the idea of alternative minimization to improve the traditional CoSaMP algorithm to reconstruct under-sampled UWB-2PPM signal transmitted by unkown complex channel without a knowledge of pilot. The theoretical analysis and simulations show that the proposed algorithm is capable of reconstructing the original transmitted signal without a pilot.

Abstract: The key of background subtraction which is widely used in moving object detecting is to set up and update the background model. This paper presents a block background subtraction method based on ViBe, using the spatial correlation and time continuity of the video sequence. Set up the video sequence background model firstly. Then, update the background model through block processing. Finally employ the difference between the current frame and background model to extract moving objects.

Abstract: In this paper, we present a multiscale diffusion entropy analysis (DEA) for describing the traffic fractal dynamics with a spectrum of scale exponents . The method combines DEA with moving fitting window to analyze the traffic index (TI) series in different scales and shows more details of scale properties and provides a reliable analysis. We also quantify the effects of weather, traffic peaks on scale spectrum. The results indicate clearly that at large scales, the exponents show large volatility and they all have their own scale patterns. The multiscale DEA method provides new ways to measure the TI series and distinguishes groups in different conditions.

Abstract: In the vast ocean of data, how people can learn from the best, to eliminate the dross, the Internet age has become a major issue, also facing great challenge for data processing, and it is the key to develop the national network economy. For sensitive information filtering time lag, low accuracy, poor self-adaptability to the Internet, Chinese text media (webpage, micro-blogging, forum, etc.) for the study, using the technology of the opinion mining and natural language processing, study of sensitive information feature extraction algorithm to reveal the interrelationship of sensitive information and sensitive dictionary, providing technical support for sensitive dictionary and sensitive information recognition.

Abstract: In this paper, an improved artificial bee colony algorithm (IABC) for dynamic environment optimization has been proposed. As we compared the IABC with greedy algorithm (GA), Particle swarm optimization (PSO) and original artificial bee colony algorithm (ABC), the result of dynamic function optimization shows that the IABC can obtain satisfactory solutions and good tracing performance for dynamic function in time.

Abstract: The behavior of hybrid systems is determined by combining continuous variables with discrete switching logic. The identification of a hybrid systems aims to find an accurate model of the system's dynamics based on its past inputs and outputs. For a class of hybrid systems in the switched form, a model identification method based on affinity propagation clustering is presented in this paper. In this method, the model identification issue is equivalent to the problems of classification of the cluster data of the system and regression of the classification data. The clustering algorithm is successively applied to group the sampled data into clusters, and the sub-models are trained by least squares support vector machines according to corresponding sub-class samples. The last model of switched systems is obtained by combining each sub-model. Simulation results show the effectiveness and feasibility of the proposed method.

Abstract: The profit of raising fatstock is subordinated to the random variables of the normal distribution. The average feeding profit is established by using the statistical methods. Moreover, the function between the feeding profit and raising amount is determined by adopting the method of linear regression. The function relationship among the purchase price, demand and production capacity of raising fatstock is gained by using the binary regression method. Therefore, the price of the meat livestock in the future will be forecast. However, the amount of raising fatstock can be controlled according to the differences between the production capacity and the demand.

Abstract: In this paper, we focus on the problem of how to obtain 3D cloud point data from different data source in ground fissure. We mainly analyzed the spatial morphology and the data characteristics of the ground fissure. The curve formula is constructed in engineering geological section and designed the algorithm of obtaining 3D point cloud data. This algorithm has been verified in Xi’an area and obtained satisfactory results. The research achievement provides a new method for using multi-source data on 3D modeling ground fissure.

Abstract: For the high real-time demands of multi-sensor target tracking system, a global optimum data association algorithm based on time constraint is presented. Firstly, the statistical distance between measurement and prediction center of the track is calculated. An optimal association model among all measurements and all tracks is founded. Then, an improved auction algorithm is proposed to solve the association model. Multi-time auction is conducted in the constrained time, and the optimal solution is obtained by comparing the value of objective function. Simulation shows that the proposed method has a perfect association performance in the constrained time.