摘要：
In this paper, we deal with a class of inequality problems for dynamic frictional contact between a piezoelectric body and a foundation. The model consists of a system of the hemivariational inequality of hyperbolic type for the displacement, the time dependent elliptic equation for the electric potential. The friction condition is described to be the Clarke subdifferential relations of nonmonotone and multivalued character in the tangential directions on the boundary. The existence of a weak solution to the model is proved by embedding the problem into a class of second-order evolution inclusions, and by applying a surjectivity result for multivalued operators.
期刊：
Journal of Computational and Theoretical Nanoscience,2009年6(3):662-666 ISSN：1546-1955
通讯作者：
Chen, Shubo
作者机构：
[Xiao, Zhengming] Hunan Univ Technol, Zhuzhou 412008, Hunan, Peoples R China.;[Li, Junfeng; Chen, Shubo] Hunan City Univ, Dept Math & Comp Sci, Yiyang 413000, Hunan, Peoples R China.
通讯机构：
[Chen, Shubo] H;Hunan City Univ, Dept Math & Comp Sci, Yiyang 413000, Hunan, Peoples R China.
关键词：
Distance;Modified Schultz index;Nanotube;Schultz index
摘要：
The modified Schultz index of a graph G is defined as S*(G) = Σ{u, v⊂V(G)(d(u)·d(v))dG(u, v) where d(u) (or d(v)) is the degree of the vertex u (or v), and dG (u, v) is the distance between u and v. Explicit formulas for calculating the modified Schultz index of nanotubes covered by C4 are provided in this report.
摘要：
Learning-based image steganalysis is an effective and universal approach to cope with the following two difficulties: unknown statistics and steganographic algorithms. A crucial part of the learning-based process is the selection of low-dimensional features, which strongly impacts the accuracy of classification. A novel principal feature selection and fusion (PFSF) method is presented to reduce features, and then it is applied to image steganalysis. First, we analyze the multicollinearity among features to eliminate redundant features. Next, we implement the linear transform based on principal components analysis (PCA) and use Savage decision-making to eliminate insignificant features. Last, in order to further reduce features, we fuse the selected features, followed by selecting the principal features from the fused features to form a new feature set. The advantage of the proposed method is that it needs the cover images only, without requiring the availability of the stego-images in the process of the features selection. Moreover, the proposed method greatly reduces the computational time. Our method has been tested on two feature sets from Moulin's and Fridrich's features. The experimental results show that our method not only reduces the feature number by 90%, but also provides more reliable detection results than the previous steganalysis methods do.
