At present, there are many research results in Tang poems and Song lyrics from the perspective of literature, but there are few research results in Tang poems and Song lyrics by using the hypergraph based hypernetwork method, and the only researches of this kind are also limited to the study of Chinese character frequency and word frequency. The analysis and study of Tang poems and Song lyrics by using the method of hypernetwork data analysis is helpful to explore the breadth that cannot be reached by the traditional perspective of literature, and to discover the law of word composition laws in literatures and historical backgrounds reflected by Tang poems and Song lyrics. Therefore, based on two ancient text corpuses:Quan Tang Shi and Quan Song Ci, the hypernetworks of Tang poems and Song lyrics were established respectively. In the construction of the hypernetworks, a Tang poem or a Song lyrics was taken as a hyperedge, and the characters in Tang poems or Song lyrics were taken as the nodes within the hyperedge. Then, the topological indexes and network characteristics of the hypernetworks of Tang poems and Song lyrics, such as node hyperdegree, node hyperdegree distribution, hyperedge node degree, and hyperedge node degree distribution, were experimentally analyzed, in order to find out the characters use, word use and aesthetic tendency of poets in Tang dynasty and lyricists in Song dynasty. Finally, based on the works of poems and lyrics of Li Bai, Du Fu, Su Shi and Xin Qiji, the work hypernetworks were constructed, and the relevant network parameters were calculated. The analysis results show that there is a great difference between the maximum and minimum hyperdegrees of the two hypernetwork, and the distribution of the hyperdegrees is approximate to the power-law distribution, which indicates the scale-free property of the two hypernetworks. In addition, the degrees of hyperedge nodes in Tang poem hypernetwork are also have obvious distribution characteristics. In specific, the degrees of hyperedge nodes in Tang poems and Song lyrics are more distributed between 20 and 100, and the degrees of hyperedge nodes in Song lyric hypernetwork are more distributed between 30 and 130. Moreover, it is found that the work hypernetworks have smaller average path length and a larger clustering coefficient, which reflects the small-world characteristics of the work hypernetworks.

Calligraphy appreciation is widely considered to require sequence restoration, while the sequence restoration of painting is ignored in long time. Moreover, the detail feature of brush strokes is considered to enhance the perception of sequentiality. In order to quantify the sequentiality perception and to explore the influence of detail features of strokes on sequentiality perception, a sequentiality perception quantization method based on Markov chain entropy rate was proposed. Firstly, the perceived sequentiality of an individual to the markers on the artwork was modeled as a Markov chain. Then, the entropy rate of the Markov model was calculated to measure the uncertainty of the perceived sequentiality. Finally, the negentropy was used to measure the order of the perceived sequentiality and was normalized to obtain the measurement index:sequentiality perception. The feasibility of this method was verified through the actual measurement of the sequentiality perception of a group of artworks. And based on the proposed sequentiality measurement, the effect of graph transformation on the sequentiality perception of artworks was studied. Experimental results show that the sequentiality consistency keeps high level when rotation or mirror transformation is performed to the original image, while the correctness varies significantly. This means that the feature of brush strokes is not the primary factor in forming the sequentiality experience, and the viewer's own experience of order of strokes is more important in the formation, but this conclusion needs to be further verified.

Most of the existing link prediction algorithms only study the first-order similarity between nodes and their neighbor nodes, without considering the high-order similarity between nodes and the neighbor nodes of their neighbor nodes. In order to solve this problem, a Link Prediction algorithm based on High-Order Proximity Approximation (LP-HOPA) was proposed. Firstly, the normalized adjacency matrix and similarity matrix of a network were solved. Secondly, the similarity matrix was decomposed by the method of matrix decomposition, and the representation vectors of the network nodes and their contexts were obtained. Thirdly, the original similarity matrix was high-order optimized by using Network Embedding Update (NEU) algorithm of high-order network representation learning, and the higher-order similarity matrix representation was calculated by using the normalized adjacency matrix. Finally, a large number of experiments were carried out on four real datasets. Experiments results show that, compared with the original link prediction algorithm, the accuracy of most of the link prediction algorithms optimized by LP-HOPA is improved by 4% to 50%. In addition, LP-HOPA can transform the link prediction algorithm based on local structure information of low-order network into the link prediction algorithm based on high-order characteristics of nodes, which confirms the validity and feasibility of the link prediction algorithm based on high order proximity approximation to a certain extent.

Aiming at the disadvantages of high fixing cost and partial observability of system environment in the process of repairing Autonomous Underwater Vehicle (AUV) software faults, a method was proposed based on micro-reboot mechanism and Partially Observable Markov Decision Process (POMDP) model for failure repair of AUV. To facilitate the implementation of the fine-grained self-repair micro-reboot strategy, a hierarchical structure was built based on micro-reboot combined with the characteristics of AUV software. Meanwhile, a self-repair model was put forward according to the theory of POMDP. With the goal of minimizing the fixing cost, the repair strategy was solved by Point Based Value Iteration (PBVI) algorithm to allow the repair action to execute in the partially observable environment at a lower cost.The simulation results show that the proposed repairing method can solve the AUV software failures caused by the software-aging and system calls. Compared with two-tier micro-repair strategy and three-tier micro-repair fixing strategy, this method is obviously superior to the contrast method in cumulative fault repair time and operational stability.

In order to create novel artistic effects, a period-dynamic-image model was proposed, in which each element is a periodic function. Instead of using an array of color pixels to represent a digital image, a Fourier model was used to represent a periodic dynamic image as an array of functional pixels, and the output of each pixel was computed by a Fourier synthesis process. Then three applications with three rendering styles were put forward, including dynamic painting, dynamic distortion effects and dynamic speech balloons, to visually display the periodic dynamic images. A prototype system was constructed and a series of experiments were performed. The results demonstrate that the proposed method can effectively explore the novel artistic effects of periodic dynamic images, and it can be used as a new art media.