Applied Mechanics and Materials Vols. 644-650

Abstract: Using the quantitative geophysical model function (GMF) between the radar backscatter coefficient and the sea surface wind speed, wind direction, radar parameters and environmental parameters, the wind vector can be retrieved from backscattering measurement. In this paper, Extreme learning machine (ELM) approach is used to develop a unified GMF respectively using the simulated training data-set generated by the empirical GMF CMOD5.N and the wind data gained from the ASCAT. Analysis indicates that the method based on extreme learning machine showing a good inversion result compared with CMOD5.N with fast training and high accuracy. The new method provides a novel feasible way for future surface wind field inversion.

Abstract: The key of knowledge resources polymerization is to cluster the knowledge resources exist in document form, knowledge document set is divided into some clusters, require the similarity of document content within the same clusters as large as possible, and the similarity between different clusters as small as possible. In this paper, using the k-mean algorithm to cluster research knowledge resources, according to the characteristics of knowledge resources, and people in the query data mainly use keywords to query characteristics, first to clustering the keywords, map directly by the clustering results of keywords to get the initial clustering of knowledge resources, and then based on the membership degree of knowledge resources optimization clustering set.

Abstract: The intensity of a product is regard as the degradation process during the whole product life, and as the confrontation process with the stress imposed on the product. Dynamic interfering strength theory proposed for structural reliability, we constructed the model for a element or a system in a continuous degenerate procedure varying with time t. A method of reliability evaluation is given for a kind of common sight strength and stress distribution.

Abstract: Twin support vector machine (TWSVM), as a variant of the generalized eigenvalue proximal support vector machine (GEPSVM), attempts to improve the generalization of GEPSVM, whose solution follows from solving two quadratic programming problems (QPPs), each of which is smaller than in a standard SVM. Unfortunately, TWSVM fails to fully consider the local geometry structure and the local underlying descriminant information inside the samples that may be important for classification performance and only preserves the global data structure. In this paper, a novel TWSVM with manifold regularization is proposed by introducing the basic idea of the locality preserving within-class scatter matrix (LPWSM) into TWSVM. We termed this method manifold TWSVM (MTWSVM). MTWSVM not only retains the superior characteristics of TWSVM, but also preserves the local geometry structure between samples and shows the local underlying discriminant information. Experimental results confirm the effectiveness of our method.

Abstract: Classic N-S equation has first order accuracy in both of time and space. It has only the terms of first order, without the terms of second or higher order. These terms are relative in time and space steps. The time and space steps, as basic elements of fluid research, should be only some finite quantities and not be infinitely near to zero as defined in mathematics. If the terms of second or higher order can be ignored depends on the value of the corresponding derivative multiplied. Compared with terms of first order, the terms of second or higher order can be ignored under the condition of laminar flow. However, under the condition of turbulent flow, these can’t be ignored yet. When turbulent flow develops fully, the terms of first order, compared with terms of second order, can be ignored. So, it is why classic N-S equations aren’t closed when they are used to analyze turbulent flow. On the basic, many different special forms of the second order accuracy N-S equations of incompressible fluid are derived.

Abstract: In this paper, an algebraic algorithm is developed with Min-algebra for the path planning problem of a simple weighted directed graph. According to the algebraic algorithm, the shortest path and its minimum steps will be concluded through the direct distance matrix A. Experimental results show that the algebraic algorithm is suitable for the path planning problem. The shortest path and its length can be gotten rapidly based on the proposed algorithm. Finally, the results are compared to those obtained by the conventional Dijkstra algorithm.

Abstract: To describe the performance for a new kind of neural network, This paper discusses the approximation of the neural network using a kind of rational spline weight function. The rational spline consists of piecewise rational functions with cubic numerators and linear denominators. The theoretic formula of approximation is proposed and an example is also given. It can be concluded that this new neural network can get very high training accuracy.

Abstract: This paper aims to obtain the time complexity for a new kind of neural network using rational spline weight functions. In this paper, we introduce the architecture of the neural network, and analyze the time complexity in detail. Finally, some examples are also given to verify the theoretical analysis. The results show that the time complexity depends on the number of patterns, the input and out dimensions of the neural networks.

Abstract: Ground penetrating radar (GPR) is a powerful tool for detecting defects behind reinforced concrete (RC) structures. However, the received data from GPR includes a large number of clutters which are easy overwhelming the signal of target. In order to successfully extract the target signature, these clutters effects need to be eliminated. In this article, a clutter suppression algorithm based on Principal Component Analysis (PCA) combining with gradient magnitude is presented. PCA clutter suppression algorithm is applied to the data and removes most of the echoes from ground surface and portion of other clutters with weak energy. Then gradient magnitude clutter suppression is used to remove majority of the residue clutters. It is demonstrated from simulation that the proposed algorithm is able to significantly suppress the clutters and is superior to the PCA clutter suppression, magnitude clutter suppression and means subtraction method.

Abstract: A kind of classical product codes based on repeated-root cyclic codes are considered in this paper, and the classical product codes are applied to construct the asymmetric quantum product codes. The novel asymmetric quantum product codes have great asymmetry for correcting Z errors and X errors, and the parameters of asymmetric quantum product codes can be precisely determined by the properties of the repeated-root cyclic codes and their dual codes. The examples show that these asymmetric quantum product codes based repeated-root cyclic codes are more efficient than some existent asymmetric quantum product codes.