The prediction accuracy of link prediction based on node similarity is always reduced without considering the asymmetric information. In order to solve the problem, a novel method for node similarity measurement with asymmetric information was proposed. Firstly, the disadvantage of the similarity measure algorithm based on Common Neighbor (CN) was analyzed, which it only considered the number of CNs without considering the number of all neighbors of each node. Secondly, the similarity measure between nodes was defined as the ratio of the common nodes to all the neighbor nodes. Then, the symmetric similar information and the asymmetric similar information between nodes were combined, and the similarity between nodes was described in detail. Finally, the proposed method was applied to predict the link relationship in complex networks. The experimental results on the real datasets show that, compared with the previous common neighbor-based similarity measurement methods such as CN, Adamic Adar (AA) and Resource Allocation (RA), the proposed method can improve the accuracy of node similarity measurement and improve the accuracy of link relationship prediction in complex networks.

Considering the LCD-Mura defect background reconstructed by current background suppression methods was vulnerable to introduced noise and target defects, a kind of defect image background modeling method based on Singular Value Decomposition (SVD) and maximum entropy was proposed. The singular value sequence was obtained by the SVD of the image pixel matrix. The correspondence between the image components and the singular values was derived by the matrix norm, and the entropy of each component of the image was calculated by the ratio of each component singular value, then effective singular values of background reconstruction was determined by the maximum entropy. Finally, the background was got by the matrix reconstruction, and the general method of evaluating the effect of background reconstruction was put forward. Compared with the three B spline curve fitting methods, the proposed method can improve the contrast of region Mura by 0.59 times at least and the line Mura contrast by 7.71 times at most; and compared with the Discrete Cosine Transform (DCT) method, it reduces the noise of the point Mura by 33.8 percent at least and the line Mura noise by 76.76 percent. The simulation results show that, the model has the advantages of low noise, low loss and high brightness, and can be used to construct the background information of the defect image more accurate.