In order to study the intelligent voice control of aircraft towing vehicle, realize accurate and efficient recognition of the voice command of pilot in the airport environment, and solve the problems of large computation, high time complexity and low recognition efficiency of the traditional Dynamic Time Warping (DTW) algorithm, a new optimization algorithm of DTW with constraint of hexagonal warping window for vehicle speech recognition was proposed. Firstly, the influence of warping window on the accuracy and efficiency of DTW algorithm was analyzed from three aspects such as the principles of DTW algorithm, the speech characteristics of towing vehicle instruction and the airport environment. Then, on the basis of DTW optimization algorithm with constraint of Itakura Parallelogram rhombic warping window, a DTW global optimization algorithm with the constraint of hexagonal warping window was further proposed. Finally, by varying the optimization coefficient, the optimal DTW algorithm with the constraint of hexagonal warping window was realized. The experimental results based on isolated-word recognition show that, compared with the traditional DTW algorithm and the DTW algorithm with rhombic warping window constraint, the recognition error rate of the proposed optimal algorithm is reduced by 77.14% and 69.27% respectively, and its recognition efficiency is increased by 48.92% and 27.90% respectively. The proposed optimal algorithm is more robust and timeliness, and can be used as an ideal instruction input port for intelligent control of aircraft towing vehicle.

Concerning the slow convergence and local optimum of the traditional robot path planning algorithms in complicated enviroment, a new path planning algorithm for mobile robots based on Particle Swarm Optimization (PSO)algorithm in repulsion potential field was proposed. Firstly, the grid method was used to give a preliminary path planning of robot, which was regarded as the initial particle population. The size of grids was determined by the obstacles of different shapes and sizes and the total area of obstacles in the map, then mathematical modeling of the planning path was completed. Secondly, the particle position and speed were constantly updated through the cooperation between particles. Finally, the high-security fitness function was constructed using the repulsion potential field of obstacles to obtain an optimal path from starting point to target of robot. Simulation experiment was carried out with Matlab. The experimental results show that the proposed algorithm can implement path optimization and safely avoid obstacles in a complex environment; the contrast experimental results indicat that the proposed algorithm converges fast and can solve the local optimum problem.

Unattended Wireless Sensor Network (UWSN) is usually deployed in a bad or hostile environment. The deployment of static aggregation node (or base station) is difficult, making real-time information aggregation impossible. Hence the node should undertake the mission of self-sustaining the data temporarily. In order to enhance the probability of data survival, this paper proposed a dependable data survival scheme based on Chinese Remainder Theorem (CRT) weighted threshold secret sharing in UWSN. The scheme built a node reputation model in the network, improving the efficiency and effectiveness of key division, distribution and reconstruction. In addition, this paper also discussed the good homomorphism properties of CRT based multi-secret sharing, and the key combination operation scheme, which further ensures the reliable survival of data.