Applied Mechanics and Materials Vol. 733

Abstract: The accuracy of short-term wind power forecast is important for the power system operation. Based on the real-time wind power data, a wind power prediction model using wavelet neural network (WNN) is proposed. In order to overcome such disadvantages of WNN as easily falling into local minimum, this paper put forward using Particle Swarm Optimization (PSO) algorithm to optimize the weight and threshold of WNN. It’s advisable to use Support Vector Machine (SVM) to comparatively do prediction and put two outcomes as input vector for Generalized Regression Neural Network (GRNN) to do nonlinear combination forecasting. Simulation shows that combination prediction model can improve the accuracy of the short-term wind power prediction.

Abstract: Optimization problem is the problem which can be often encountered mostly in industrial design, and the key of optimization is to find the global optimum and higher constriction speed. This paper proposes a PSO algorithm based on BP neural network by neural network trains and selects individual extreme best randomly, to make the particle follow the optimal particle in the solution space search, and obtain the optimum extreme best in the whole situation. Through the application of the simulation experiment on image segmentation showed that the algorithm is suitable in dealing with multiple types function and constraint, with fast convergence speed, and easy combination with traditional optimization methods, thus improving its own limitations, and solving problems more efficiently.

Abstract: In this paper, we propose the group synchronization of fractional-order network for the first time. The stability analysis of group synchronization is discussed by matrix theorey. Moreover, schemes and criteria for group synchronization is derived. Illustrative examples are presented to show the validity of the proposed methods.

Abstract: Harmonic analysis is the foundation of harmonic control and compensation. The voltage signals with harmonic component is difficult to analysis under noise environment. This paper proposed a new approach for harmonic analysis based on Hyperbolic S-transform. Firstly, the affection for harmonic analysis by different characters of the hyperbolic window including forward-taper parameter and backward-taper parameter is compared. Secondly, the modified Hyperbolic S-transform with optimal characters is used for harmonic analysis. Finally, the analysis result of the new approach is compared with other methods. Simulation results show the effectiveness and advantages of the new method. It is very satisfied for harmonic analysis under high noise environments.

Abstract: As partially linear regression model contains parameters part and the nonparametric part, it is better than the linear model. Partially linear regression model is more freedom, flexible, and can seize the characteristics of data. This passage first reduces the dimension of expenditure index data using principal component analysis. Then based on the dimension-reduced data, a partial linear model is established to forecast expenditure on army. The results show a great advantage over those by stepwise linear regression analysis.

Abstract: In this paper, considering the nonlinear and non-stationary properties of extreme high-temperature time series, we introduce Empirical Mode Decomposition to analyze the extreme high-temperature time series from 1959 to 2012 in Fengxian district of Shanghai. The scale characteristics and oscillating mode characteristics were mainly investigated. The trend of extreme high-temperature also shows periodic variation from decreasing to increasing for the recent fifty years. Analyze the reconstructed modes with the wave pattern: It shows that variability are quite large from 1997 to 1999 and from 1977 to 1982, which shows extreme high-temperature rose and fell dramatically in these periods. The volatility from 2006 to 2008 is far more dramatic than the other times. And it is the most remarkable in the recent fifty years.

Abstract: TSP problem optimization is a combinatorial optimization model studied which is NP hard, and it has been solved by a lot of algorithms. A new improved cuckoo optimization algorithm (KF-CS) has been put forward to solve the routing optimization problem of logistics distribution vehicle. Kalman Filter Cuckoo search (KF-CS) is a new intelligent algorithm which used to estimate the state of a stochastic phenomenon which has Gaussian distribution. The problem of travelling salesman was experimented. To demonstrate the effectiveness and efficiency of the proposed algorithm, the benchmark problems from TSPLIB were tested and compared with PSO, DE, ACO and standard CS. The results showed that the KF-CS algorithm achieved shorter distances in all cases within fewer generations, and it has obvious effects to find the optimal solution frequency and time.

Abstract: ZOOM FFT technology is widely used in signal processing and modal analysis, it can improve frequency resolution, and amplify the characteristic curve in selected regions, makes the frequency characteristic of the system more clearly shown. ZOOM FFT technology based on analytic signal and band-pass filter has greatly improved than traditional methods, provides a new way to virtual instrument based on computer platform. This article discusses the application of Matlab to achieve the ZOOM FFT.

Abstract: With the development of image hiding technique, it has become more and more mature. Least significant bit (LSB) image hiding algorithm is the most classic image hiding algorithm, which is simple, efficient and easy-to-extract. However, due to the change of LSB, the statistical characteristic of image carrier changes, which makes the hiding easy to be detected. On the analysis of the deficiency by using the conventional LSB algorithm, the paper propose an improved LSB algorithm of image hiding based on randomness. This algorithm could implement times of randomness on the bytes hiding, volume hiding and bit hiding. In addition, the algorithm could improve the invisibility with a jot modification. Finally, the experiment results indicate that the algorithm could hide the image well and amplify the hiding volume, which could resist the SPA analysis effectively.

Abstract: This paper presents a new algorithm to retrieve 3D model on distance classification histogram. First, we select the certain number of random points on the model surface and compute the distance between two random points. Secondly, we sort the distance into two types which is based on the different geometry properties of these distance and construct the distance classification histogram. Finally, we measure the similarity of 3D models by comparing distance classification histogram. The experimental results on PSB show that our method has a good performance in precision and computational complication.