The reasonable control of trajectory is an important factor affecting intelligent decision-making of Unmanned Aerial Vehicle (UAV). Focusing on the local observability and the complexity of upper air of mission environment, a method of semantic entity construction and trajectory control for UAV electric power inspection was proposed. Firstly, a spatial topology network based on entity knowledge of electric power inspection field was built, and the semantic trajectory sequence network about position nodes and its semantic interfaces were generated. Then, based on the result set of similarity measure of spatial topology structures, the security licensing mechanism and reinforcement learning based trajectory control strategy were proposed to realize the UVA electric power inspection on the basis of consensus concept connotation and position structure. Experimental results show that for an example of UAV electric power inspection, the optimal strategy obtained by the proposed method can satisfy the maximum robust performance, and at the same time, the fitness of the target network can stably converge and the physical area coverage is higher than 95% through the reinforcement learning of this method, so that the method provides flight basis for the decision-making of UVA electric power inspection tasks.

To solve the problem that traditional image classification methods deal with the whole image in a non-hierarchical way, an image classification method based on visual saliency detection was proposed. Firstly, the visual attention model was employed to generate the salient region. Secondly, the texture feature and time signature feature of the image were extracted by Gabor filter and pulse coupled neural network, respectively. Finally, the support vector machine was adopted to accomplish image classification according to the features of the salient region. The experimental results show that the image classification precision rates of the proposed method in SIMPLIcity and Caltech are 94.26% and 95.43%, respectively. Obviously, saliency detection and efficient image feature extraction are significant to image classification.

This paper studied consensus problems for second-order multi-Agent systems with random time-delays. Both networks under the fixed and switching topologies were taken into consideration. The stability criteria of the time-delay dependence of multi-Agent systems was analyzed by constructing a proper Lyapunov function. And sufficient conditions for all Agents achieving the consensus were given in the forms of Liner Matrix Inequality (LMI). Finally, the simulation results show the correctness and effectiveness of the conclusions.

Plenty of security problems may occur when servers adopt Agent to deploy mobile codes so as to realize interactive storage between different business clients. In order to pursue a higher reliability of the software, and to make sure those Agents healthily running in an untrusted complex environment, after analyzing traditional integrity validating mechanism, combining I&A, PCC and reflection techniques, a new classified mechanism of enabling the integrity of trusted terminal Agents was proposed, and an efficient validating model with multiple interacting modules was designed, aiming at improving the reliability of the mobile codes by realizing its behaviors-monitoring.