In order to solve the task allocation problem of multi-Automated Guided Vehicle (AGV) storage system, a Variable Neighborhood_Simulated Annealing (VN_SA) algorithm was proposed. Firstly, according to the system operation process and operating characteristics of AGV, with the path cost, time cost and task equilibrium value cost of AGV during the task execution as the goals, and adding the power consumption situations of AGV driving with and without load to the constraints, a more practical multi-objective optimization model of task allocation for multi-AGV storage system was built. Then, aiming at the characteristics of the problem, a VN_SA algorithm was designed. The search range of the simulated annealing algorithm was expanded by the neighborhood perturbation operation in the algorithm, and the local optimum was jumped out by the algorithm and the global development effect was obtained by combining the probability mutation characteristics. The simulation experiments were carried out on works with the number of tasks of 20, 50, 100 respectively. Experimental results show that, the optimized total cost of the proposed algorithm is reduced by 6.4, 7.5 and 13.2 percentage points respectively compared with Genetic Algorithm (GA), which verifies the effectiveness of the proposed algorithm under different task sizes. It can be seen that the proposed algorithm has better convergence and search efficiency.

To solve the problem of privacy leakage in crowdsourced location data collection, a locally differentially private location data collection method with crowdsourcing was proposed. Firstly, a Voronoi diagram constructed by point-by-point insertion method was used to partition the road network space. Secondly, a random disturbance satisfying local differential privacy was used to disturb the original location data in each Voronoi grid. Thirdly, a designed spatial range query method was applied to noisy datasets to get the unbiased estimation of the actual result. Finally, experiments were carried out on spatial range queries to compare the proposed algorithm with PTDC (Privacy-preserving Trajectory Data Collection) algorithm. The results show that the query error rate is no more than 40%, and less than 20%in the best situation, and the running time is less than 8 seconds, which are better than those of PTDC algorithm while the proposed method has a higher degree of privacy preserving.

Concerning the problem of trajectory tracking control for an Unmanned Aerial Vehicle (UAV) when the nominal values of autopilot parameters deviate from the actual values, a three-dimensional trajectory adaptive tracking control law was proposed. First, with no parameter deviation of autopilot parameters, a guidance law was derived and commands were obtained for the UAV's airspeed, track angle and flight-path angle. Global asymptotic stability of the closed-loop tracking system was proved by Lyapunov stability theory. Then regarding the deviation of autopilot parameters, a parameter adaption algorithm was designed to estimate the actual autopilot parameters online, and an adaptive tracking control law for UAV's three-dimensional trajectory was obtained. Simulation results show that the proposed adaptive tracking control law can achieve UAV's three-dimensional trajectory tracking effectively in spite of autopilot parameter deviation.

Focusing on the issue that edges extracted by state-of-the-art exist some deficiencies including non-continuity, incompleteness, incline, jitter and notches etc., an edge extraction method based on graph theory was proposed, which considered the image as an undirected graph by regarding each pixel as a node and connecting two adjacent nodes in horizontal or vertical direction to constitute a side. The proposed method included three phases:in pixels similarity calculation phase, the weights were given to sides in undirected graph, which represented pixels similarity; in threshold determination phase, the mean of all the weights (the similarity of the whole image) was determined as a threshold; in edge determination phase, when weights on horizontal or vertical sides were smaller than the threshold, the left nodes of horizontal side and the upper nodes of vertical side were retained to constitute edges of the image. The experimental results show that the proposed edge extraction method based on graph theory is suitable for the images with obvious target and background, and can overcome deficiencies including non-continuity, incompleteness, incline, jitter and notches etc., and has anti-noise ability to Speckle noise and Gaussian noise.

In order to solve the problems of having nonlinear observation equations and being susceptible to initial value of filtering in bearings-only target tracking, a range-parameterized hybrid coordinates Square Root Cubature Kalman Filter (SRCKF) algorithm was proposed. Firstly,it applied the SRCKF to hybrid coordinates,obtained better tracking effect than the SRCKF under Cartesian coordinates. And then it combined the range parameterization strategy with the SRCKF under hybrid coordinates, and eliminated the impact of unobservable range. The simulation results show that the proposed algorithm can significantly improve the accuracy and robustness although the computational complexity increases slightly.

In order to enhance the performance of the synchronization and demodulation, a Very high frequency (VHF) Data Broadcast (VDB) signal demodulation algorithm based on the solution of differential equation was proposed. This algorithm eliminated the synchronization performance deterioration caused by the frequency offset. And frame synchronization, bit synchronization, frequency offset estimation and correction could be completed within a single set of synchronization symbols. The simulation results show that the method is effective to enhance the VDB signal demodulation performance.

Existing results of auxiliary word are difficult to use in the automatic annotation of natural language processing. Based on the auxiliary words knowledge base, rule-based method is used in automatic annotation of auxiliary words usage. Contrast to the results of test, it shows that refining, extension and adjusting the matching order of the rules can promote the precision and recall effectively. It is also benefit for improve the quality of Chinese Corpus, deepen the processing depth, and reduce the artificial work.

In view of the difficult in management of airborne multi-sensor, a fuzzy decision tree algorithm is applied to multi-sensor intelligent management, combined with operational processes, and combat stage will be merging with the target type, different target attributes is built reasonable. The fuzzy decision trees model are established based on target type, then the airborne multi-sensor management model is established. Finally, the simulation results show that the method can compose battlefield objectives and tactical effectively, complete airborne multi-sensor management rapidly, and the method is reasonable and effective.

In open service grid,to establish trust relationships among different domains is a basic problem so as to cooperate between them.In this paper a two-layer trust model based on behavior was proposed to enhance grid security and extensibility.This trust model was used to resolve trust issues between entities in different domains.The upper layer established and maintained recommendation trust relationships between different domains in grid.Lower layer was in charge of how domain manager evaluated trust of entities in his domain.An algorithm was given to adjust trust relationships between domains based on entities interactions,and a practical method to process recommendation trust.At last,the experiment results show that two-layer trust model is effective and extensible.