Aiming at problem of additive Gauss noise removal, an improved image restoration algorithm based on the existing K-means Singular Value Decomposition (K-SVD) method was proposed by integrating dictionary learning and variational model. Firstly, according to geometric and photometric information, image blocks were clustered into different groups, and these groups were classified into different types according to the texture and edge categories, then an adaptive dictionary was trained according to the types of these groups and the size of the atoms determined by the noise level. Secondly, a variational model was constructed by fusing the sparse representation priori obtained from the dictionary with the non-local similarity priori of the image itself. Finally, the final denoised image was obtained by solving the variational model. The experimental results show that compared with similar denoising algorithms, when the noise ratio is high, the proposed method has better visual effect, solving the problems of poor accuracy, serious texture loss and visual artifacts; the structural similarity index is also significantly improved, and the Peak Signal-to-Noise Ratio (PSNR) is increased by an average of more than 10%.

Aiming at the problem that the traditional entity alignment algorithm may lead to bad performance in entity alignment task of Chinese heterogeneous encyclopedia knowledge base, an entity alignment method based on entity attributes and the features of context topics was proposed. First, a Chinese heterogeneous encyclopedia knowledge base was constructed based on Baidu encyclopedia and Hudong encyclopedia data. Next, the Resource Description Framework Schema (RDFS) vocabulary list was made to normalize the entity attributes. Then the entity context information was extracted and the Chinese word segmentation was used on the contexts. The contexts were modelled by using the topic model and the parameters were computed by Gibbs sampling method. After that the topic-word probability matrix, the characteristic word collection and the corresponding feature matrix were calculated. Last, the Longest Common Subsequence (LCS) algorithm was used to compute the entity attribute similarity. When the similarity was between the lower and the upper bounds, the topic features of the entities' context were combined to resolve the entity alignment problem. Finally, according to the standard method, an entity alignment data set of Chinese heterogeneous encyclopedia was constructed for simulation experiments. In comparison with the traditional property similarity algorithm, weighted-property algorithm, context term frequency feature model and topic model algorithm, the experimental results show that the proposed method achieves 97.8% accuracy, 88.0% recall, 92.6% F-score in people class and 98.6% accuracy, 73.0% recall, 83.9% F-score in movie class. It outperformed the other entity alignment algorithms. The experimental results also indicate that the proposed method can improve the entity alignment results in constructing the Chinese heterogeneous encyclopedia knowledge base, and it can be applied to the entity alignment tasks with context information.

The properties of the measured objects in 3D profile using the grating projection are more and more complex, there are a large number of splits in the extracted refinement grating stripes, and the refinement stripe encoding is very difficult. An automatic coding algorithm based on color structure light was proposed. The paper designed a new model of color structure light, introduced its design principle and implemented a new automatic stripe coding algorithm. First, the algorithm extracted the refinement grating stripe with color information from the color structure grating. Then, orderly encoded the refined stripes of each color by judging the best connected domain. Finally, the article got the stripe coding of the total image through combined coding by using the periodicity of grating model. The simulation experiment results show that the model design of color structure light is simple, the automatic coding algorithm of stripe has high accuracy and the error is decreased to 10 percent. The ideal 3D points cloud data model can be reconstructed through the strip coded data.

〖JP2〗With different way of link established, the high frequency IP wide-area network shows a different topological feature during the network operation process. Modeling study and simulation analysis were made with a modified harmonious unifying hybrid preferential model on high frequency IP wide-area network. And the results were found that the degree distribution, shortest path and clustering coefficient would be impacted by the different node selection method for new connection. Mean-while, the average shortest path of the network would be affected by the different edge deletion method. What's more the topological characteristics of the network were determined by the proportion of different node selection method.〖JP〗

Inspired by the structures and ideas of natural immune system, a new instruction detection modeling was proposed to improve the capability of detection system. There may be some nonself strings that were not detected by any detector in the modeling, so named as "holes", which would lead to performance decline and increase the ratio of leak. After analyzing the formation reason and characteristics of the holes, some methods were presented to eliminate holes. It is proved by simulation experiments that different kinds of detectors can repair hole effectively, thereby false negative rate decreases.