In view of the existing rumor propagation model, the impact of non-supportive comments on internet rumor propagation has not been analyzed in detail. An SII _CR ₁R ₂(Susceptible-Infected-Infected with non-supportive comment-Removed1-Removed2) internet rumor propagation model was proposed by introducing rumor spreaders with non-supportive comments. Firstly, the steady-state analysis of the model was performed to prove the stability of rumor-free equilibrium and rumor propagation equilibrium. Secondly, the theoretical results were verified by the numerical simulation, and the impacts of non-supportive comment probability, recovery probability, propagation probability and the persuasiveness of non-supportive comments on internet rumor propagation were analyzed. The analysis results show that increasing non-supportive comment probability has an inhibitory effect on internet rumor propagation, but the effect is affected by the recovery probability, and enhancing the persuasiveness of non-supportive comments and reducing the propagation probability can effectively reduce the influence range of internet rumor. Simulations results on WS(Watts-Strogatz) small-world network and BA(Barabási-Albert) scale-free network confirm that non-supportive comments can suppress internet rumor propagation. Finally, according to the analysis results, the prevention and control strategies of rumor were put forward.

The global map cannot be accurately known in the path planning of mobile robots. In order to solve the problem, a local path planning algorithm based on fuzzy rules and artificial potential field method was proposed. Firstly, the ranging group and fuzzy rules were used to classify the shape of obstacles and construct the local maps. Secondly, a modified repulsive force function was introduced in the artificial potential field method. Based on the local maps, the local path planning was performed by using the artificial potential field method. Finally, with the movement of robot, time breakpoints were set to reduce path oscillation. For the maps of random obstacles and bumpy obstacles, the traditional artificial potential field method and the improved artificial potential field method were respectively used for simulation. The experimental results show that, in the case of random obstacles, compared with the traditional artificial potential field method, the improved artificial potential field method can significantly reduce the collision of obstacles; in the case of bumpy obstacles, the improved artificial potential field method can successfully complete the goal of path planning. The proposed algorithm is adaptable to terrain changes, and can realize the path planning of six-legged robot under unknown maps.

Aiming at the urgent requirement of rapid and efficient 3D model shape analysis and retrieval technology, a new method of 3D model shape correspondence analysis by combining the intrinsic heat kernel features and local volume features was proposed. Firstly, the intrinsic shape features of the model were extracted by using Laplacian Eigenmap and heat kernel signature. Then, the feature matching matrix was established by combining the stability of the model heat kernel feature and the significance of local space volume. Finally, the model registration and shape correspondence matching analysis was implemented through feature matrix similarity measurement and short path searching. The experimental results show that, the proposed shape correspondence analysis method with the combination of heat kernel distance and local volume constraint can not only effectively improve the efficiency of model shape matching, but also identify the structural features of the same class models. The proposed method can be applied to further realize the co-segmentation and shape retrieval of multigroup models.

The existing link prediction methods based on node similarity usually ignore the link strength of network topology and the weight value in the typological path method with weight is difficult to set. To solve these problems, a new prediction algorithm based on link importance and data field was proposed. Firstly, this method assigned different weight for each link according to the topology graph. Secondly, it took into account the interaction between potential link nodes and pre-estimated the link values for the partial nodes without links. Finally, it calculated the similarity between two nodes with data field potential function. The experimental results on some typical data sets of the real-world network show that, the proposed method has good performance with both classification index and recommended index. In comparison to the Local Path (LP) algorithm with the same complexity, the proposed algorithm raises Area Under Curve (AUC) by 3 to 6 percentages, and raises Discounted Cumulative Gain (DCG) by 1.5 to 2.5 points. On the whole, it improves the prediction accuracy. Because of its easy parameter determination and low time complexity, this new approach can be deployed simply.