Applied Mechanics and Materials Vols. 713-715

Abstract: In wireless sensor network, it is necessary to make effective prediction of sensor node’s data during its sleep period. In this paper a model of rational cubic spline weight function (SWF) neural network with linear denominator was established for sensor node’s temperature prediction. This kind of rational spline function is denoted by 3/1 rational splines. Then we trained and tested the network, the simulation results showed that, compared to the traditional BP neural network, the training speed is higher and the error is smaller. Therefore the prediction model can effectively predict the sensor’s temperature.

Abstract: We describe a sorting algorithm,by which uniform-distributed data can be sorted,and sorting time is. This algorithm has been implemented in IBM PC using C language.

Abstract: In this paper, a wavelet-based Bayesian fusion framework is presented, in which a low spatial resolution hyperspectral (HS) image is fused with a high spatial resolution multispectral (MS) image. Particularly, a multivariate model, Gaussian Scale Mixture (GSM) model, is employed, which is believed to be capable of modeling the distribution of wavelet coefficients more accurately. A practical implementation scheme is also presented for feasible calculations. The proposed approach is validated by simulation experiments for HS and MS image fusion. The experimental results of the proposed approach are also compared with its counterpart employing a Gaussian model for performance evaluation.

Abstract: Stereo matching methods are widely used in computer vision and stereo reconstruction, from the perspective of improving the matching accuracy, this paper focuses on the global optimization algorithm. An improved Belief Propagation method is proposed in this paper, by involving more pixels into information transmission, our method improves the accuracy ofstereo matching. The experimental results verify the efficiencyand reliability of our method.

Abstract: Some key technologies for clustering the radio advertising are introduced firstly. Then the design and implementation of the system are presented. The system analyzes the cluster characters for radio advertising by principal component analysis. It could be used to capture the radio ads’ time slots. This system shows a way to analyze audio data, and could be used to classify and identify different audio ads. Therefore, it has a wonderful application prospect.

Abstract: Taking the grain yield data from 1980 to 2012 of Jilin Province for example, this paper analyzes the main factors that influences the grain yield based on the principle component analysis method. According to these main factors, the input samples of BP neutral network are definite. Thereby, the BP neutral networks could be trained to predict. The results show that the fertilizer consumption, large cattle head number, end grain sowing area, effective irrigation area and rural per capita living space are the main effect factor on grain yield. The BP neural network was built by using it as the input samples. The number of input nodes of the network is determined. Then build the prediction model of grain production in Jilin province. The simulation results show that, the average error of prediction results of BP neural network model based on principal component analysis is 4.48%.

Abstract: According to the difference in grain production capacity of the region in Jilin province, to formulate the strategy of sustainable development of grain production in Jilin province, to provide scientific basis for related departments to establish food production decision. In this paper, cluster analysis of multivariate statistical method was used, analyzed the regional difference characteristics of grain production capacity of 25 regions of Jilin Province in 2012, and elaborated the reasons for the formation of regional differences. Through cluster analysis, the 25 area of Jilin province is divided into 4 categories. Several factors of regional location, quantity of cultivated land area and population, agricultural modernization is the difference in the main area of grain production capacity in Jilin Province.

Abstract: Image segmentation and feature extraction are the premise for machine vision system to analyze and identify the image. Threshold image segmentation algorithm according to the method of two dimension threshold has a lot of calculation in calculating the threshold, and the minimum error threshold method can not use the spatial information of image. This paper presents an improved quantum-behaved particle swarm optimization based on the night segmentation and feature extraction technology. This paper introduces the QPSO algorithm based on multi group and multi stage improvement. The QPSO optimizing algorithm gradually approaches the global optimum threshold value to achieve better convergence and stability. An algorithm of vision image segmentation and feature extraction based on improved quantum-behaved particle swarm optimization is designed. Experimental results show that the optimization process of this algorithm has less control parameters and faster convergence speed.

Abstract: This paper discusses about the analysis and evaluation of different transit fare patterns. In the previous studies, most of the analyses concerned about the transportation economics issues. Recently, the methods of transportation modelling have been widely used in evaluating transit network. In this paper, a bi-level programming model is presented to evaluate the differentiated transit fare structures. The upper-level problem aims to minimize passengers’ total travel cost, whereas the lower-level problem is a stochastic user equilibrium transit assignment model with capacity constraints, which can be changed to different fare structures.

Abstract: The research of time series analysis model to predict the effect of rice blast, provides the reference for the prediction of the disease. In successive years of planting area and the rice blast incidence area as the original data,were used to establish the forecast model of time series analysis of moving average, exponential mean and variance analysis of periodic extrapolation of rice blast incidence, analysis its forecast results, and built at the beginning ofthe prediction was verified in application. The experimental results show that the time sequence analysis model can be used for long-term forecasting of rice blast.