The current link prediction algorithms based on network representation learning mainly construct feature vectors by capturing the neighborhood topology information of network nodes for link prediction. However, those algorithms usually only focus on learning information from the single neighborhood topology of network nodes, while ignore the researches on similarity between multiple nodes in link structure. Aiming at these problems, a new Link Prediction model based on Densely connected convolutional Network (DenseNet-LP) was proposed. Firstly, the node representation vectors were generated by the network representation learning algorithm called node2vec, and the structure information of the network nodes was mapped into three dimensional feature information by these vectors. Then, DenseNet was used to to capture the features of link structure and establish a two-category classification model to realize link prediction. The experimental results on four public datasets show that, the Area Under the Receiver Operating Characteristic (ROC) Curve (AUC) value of the prediction result of the proposed model is increased by up to 18 percentage points compared to the result of network representation learning algorithm.

Existing Word2vec-based Network Representation Learning (NRL) algorithms use a Random Walk (RW) to generate node sequence. The RW tends to select nodes with larger degrees, so that the node sequence can not reflect the network structure information well, decreasing the performance of the algorithm. To solve the problem, a new network representation learning algorithm based on improved random walk was proposed. Firstly, RLP-MHRW (Remove self-Loop Probability for Metropolis-Hastings Random Walk) was used to generate node sequence. This algorithm would not favor nodes with larger degrees while forming a node sequence, so that the obtained sequence can efficiently reflect the network structure information. Then, the node sequence was put into Skip-gram model to obtain the node representation vector. Finally, the performance of the network representation learning algorithm was measured by a link prediction task. Contrast experiment has been performed in four real network datasets. Compared with LINE (Large-scale Information Network Embedding) and node2vec on arXiv ASTRO-PH, the AUC (Area Under Curve) value of link prediction has increased by 8.9% and 3.5% respectively, and so do the other datasets. Experimental results show that RLP-MHRW can effectively improve the performance of the network representation learning algorithm based on Word2vec.

Concerning that the samples are treated equally and the difference of samples is ignored in the conventional feature selection algorithms, as well as the learning model cannot effectively avoid the influence from the noise samples, an Unsupervised Feature Selection algorithm based on Self-Paced Learning (UFS-SPL) was proposed. Firstly, a sample subset containing important samples for training was selected automatically to construct the initial feature selection model, then more important samples were added gradually into the former model to improve its generalization ability, until a robust and generalized feature selection model was constructed or all samples were selected. Compared with Convex Semi-supervised multi-label Feature Selection (CSFS), Regularized Self-Representation (RSR) and Coupled Dictionary Learning method for unsupervised Feature Selection (CDLFS), the clustering accuracy, normalized mutual information and purity of UFS-SPL were increased by 12.06%, 10.54% and 10.5%, respectively. The experimental results show that UFS-SPL can effectively remove the effect of irrelevant information from original data sets.

For the traditional player skill estimation algorithms based on probabilistic graphical model neglect the first-move advantage (or home play advantage) which affects estimation accuracy, a new method to model the first-move advantage was proposed. Based on the graphical model, the nodes of first-move advantage were introduced and added into player's skills. Then, according to the game results, true skills and first-move advantage of palyers were caculated by Bayesian learning method. Finally, predictions for the upcoming matches were made using those estimated results. Two real world datasets were used to compare the proposed method with the traditional model that neglect the first-move advantage. The result shows that the proposed method can improve average estimation accuracy noticeably.

In view of the correction error problem which is caused by some factors such as dithering, the authors presented a new optical tilt correction method based on grating projection. The method was based on the analysis of each pixel of the data array in a sequence of fringe patterns having multiple frequencies, and setup model for pixel coordinates and pixel-slope. Then skew angles of fringes were calculated by trigonometry with the relationship between tilt angle and pixel-slope. At last, tilt correction was realized. The experimental results show that, the algorithm is capable of accurately detecting angle within the range [-90°,90°],accuracy is 99%. Compared with other algorithms such as Hough transform, the proposed algorithm improves precision and accuracy significantly.

Concerning the large volume of data of multi-view video transmission system, an Unequal Error Protection (UEP) method based on depth-map and Distributed Video Coding (DVC) was proposed. Firstly, the depth-map based on multi-view was extracted. Secondly, one point view and the depth-map were transmitted. Finally, through the network transmission, other point views were generated by the point view and the depth-map at the decoder. Considering their different importance at the decoder, different distributed video coding methods were used in the point view and the depth-map to realize UEP. The simulation results show that the proposed transmission method provides stronger error resilience and higher transmission reliability, and it improves the Peak Signal-to-Noise Ratio (PSNR) of images about 1. 5 dB than traditional distributed multi-view video coding transmission system.

This paper proposes a classified strain limiting method which deals with unreal stretch deformation in clothing simulation with physical-based mass-spring model. The method primarily include two processing module. The first module is classification. it uses velocity computed by integrating system as input parameters first, and then judges some point whether needs strain limiting by the energy method, finally through the judged result divides point set into two types : needing strain limiting and not. The second module is strain limiting. It defines the threshold value of spring deformation and three variables for representing the restrictive proportions in principal strain direction, and then by the line strain theory computes the strain tensor of spring, finally obtains specific restrictive proportions and updates the position of corresponding point. The method could guarantee natural simulation results and eliminate unreal stretch deformation, and does not require all elements of the strain limiting for processing, reducing the computational cost to ensure real-time. Results indicate that the method have good effect and efficiency.

To balance the energy consumption of nodes in Wireless Sensor Network (WSN), the authors proposed a routing protocol to transmit the network health state information based on clustering routing algorithm, named Event-Driven State Monitoring Information Routing (ED-SMIR). ED-SMIR adopted the way with single-hop turning to multi-hop in rotation according to energy consumption rate. For the route from cluster head to the sink, ED-SMIR took multi-hop routing. The simulation results show that, compared with LEACH and EDBCM, ED-SMIR has less energy consumption, and it can balance the energy of the entire network and extend the survival time of network effectively.

A situation plotting system was designed and implemented. It could provide two plotting method: 2D plotting in electronic maps and 3D plotting in virtual battle environment. The key technology of 3D plotting such as terrain elevation matching, collision detection and response, special information and effect displaying were discussed. A realtime conversion method between 2D plotting and 3D plotting was presented to realize integrated display of 2D situation and 3D situation.