In the scenario of using Unmanned Aerial Vehicle (UAV) as the data collector for computation offloading to provide Mobile Edge Computing (MEC) services to User Equipment (UE), a wireless communication strategy to achieve efficient UE coverage through UAV was designed. Firstly, under the condition of a given UE distribution, for the UAV flight trajectory and communication strategy, an optimization method of Successive Convex Approximation (SCA) was used to obtain an approximate optimal solution that was able to minimize the global energy. In addition, for scenarios with large-scale distribution of UEs or a large number of tasks, an adaptive clustering algorithm was proposed to divide the UEs on the ground into as few clusters as possible, and to ensure the offloading data of all UEs in each cluster was able to be collected in one flight. Finally, the computation offloading data collection tasks of the UEs in each cluster were allocated to one flight, so that the goal of reducing the number of dispatches required for a single UAV or the UAV number of dispatches required for multiple UAVs to complete the task was achieved. The simulation results show that the proposed method can generate fewer clusters than the K-Means algorithm and converge quickly, and is suitable for UAV-assisted computation offloading scenarios with widely distributed UEs.

Many network structure information-based link prediction algorithms estimate the similarity between nodes and perform link prediction by using the clustering degree of nodes. However, these algorithms only focus on the clustering coefficient of nodes in network, and do not consider the influence of link clustering coefficient between the predicted nodes and their common neighbor nodes on the similarity between nodes. Aiming at the problem, a link prediction algorithm combining node clustering coefficient and asymmetric link clustering coefficient was proposed. Firstly, the clustering coefficient of common neighbor node was calculated, and the average link clustering coefficient of the predicted nodes was obtained by using two asymmetric link clustering coefficients of common neighbor node. Then, a comprehensive measurement index was obtained by fusing these two clustering coefficients based on Dempster-Shafer(DS) theory, and by applying the index to Intermediate Probability Model (IMP), a new node similarity index, named IMP_DS, was designed. The experimental results on the data of nine networks show that the proposed algorithm achieves performance in terms of Area Under the Curve (AUC) of Receiver Operating Characteristic (ROC) and Precision in comparison with Common Neighbor (CN), Adamic-Adar (AA), Resource Allocation (RA) indexes and InterMediate Probability model based on Common Neighbor (IMP_CN).

Focusing on low accuracy and high costs of backtracking-based subgraph query algorithm applied to large-scale graphs, a Spark-based Subgraph Query Matching (SQM) algorithm was proposed to improve query accuracy and reduce query overhead for large graphs. The data graph was firstly filtered according to structure information, and the query graph was divided into basic query units. Then each basic query unit was matched and joined together. Finally, the algorithm's efficiency was improved and search space was reduced by parallelization. The experimental results show that compared with Stwig (Sub twig) algorithm and TurboISO algorithm, SQM algorithm can increase the speed by 50% while ensuring the same query results.

Concerning premature convergence and low searching capability of Differential Evolutionary (DE) algorithm, the dynamic adjustment of control parameters was dicussed, and a self-adaptive differential evolution algorithm based on opposition-based learning was proposed. In the proposed algorithm, opposition-based elite learning was used to enhance the local search ability of the population and obtain more accurate optimal individuals; meanwhile, Gaussian distribution was used to improve the exploitation ability of each individual and increase the diversity of the population, which avoids premature convergence of the algorithm and achieves the balance of the global exploitation and local exploitation. Comparison experiments with some other differential evolution algorithms were conducted on six test functions in CEC 2014. The experimental results show that the proposed algorithm outperforms the compared differential evolution algorithms in terms of convergence speed, solution accuracy and reliability.

To deal with the emitter identification problem in Multi-Function Radar (MFR) based on Stochastic Context-Free Grammar (SCFG) model, a MFR emitter identification method based on Stochastic Infinite State Automata (SISA) was proposed on the basis of syntactic modeling. The grammar production rules in "Mercury" MFR control module and the characteristic production rules in "Mercury" MFR system were used in this method to reconstruct an SCFG, which was further used to construct an SISA for identification subsequently. Theoretical analysis and simulation results show that the proposed method can realize MFR emitter identification. Within a certain range, the average recognition rate can be improved by adding the amount of grammar production rules, and the identification performance is superior to Stochastic Push-Down Automata (SPDA) constructed by SCFG. The experimental results validate the reliability and effectiveness of the proposed method.

To solve the problem of Fine Particulate Matter (PM2.5) concentration prediction, a PM2.5 concentration prediction model was proposed. First, through introducing the comprehensive meteorological index, the factors of wind, humidity, temperature were comprehensively considered; then the feature vector was conducted by combining the actual concentration of SO2, NO2, CO and PM10; finally the Least Squares Support Vector Machine (LS-SVM) prediction model was built based on feature vector and PM2.5 concentration data. The experimental results using the data from the city A and city B environmental monitoring centers in 2013 show that, the forecast accuracy is improved after the introduction of a comprehensive weather index, error is reduced by nearly 30%. The proposed model can more accurately predict the PM2.5 concentration and it has a high generalization ability. Furthermore, the author analyzed the relationship between PM2.5 concentration and the rate of hospitalization, hospital outpatient service amount, and found a high correlation between them.

Introducing the velocity inertia, memory capacity of each individual and learning or communicating capacity of Particle Swarm Optimization (PSO) into the Artificial Fish-Swarm Algorithm (AFSA), a new algorithm called the “Fish-Swarm Algorithm optimized by PSO(PSO-FSA)” was put forward. In this new algorithm, the swimming of each fish has velocity inertia, and the PSO-FSA has totally five kinds of behavior pattern as follows: swarming, following, remembering, communicating and searching. The simulation analysis shows that PSO-FSA has more stable and higher performance in convergence speed and searching precision than PSO and AFSA. Finally, the PSO-FSA was applied to the maximum power point tracking of photovoltaic power generation system under partially shaded condition, and the experimental results show that PSO-FSA can find the maximum power point under partially shaded insolation conditions quickly and precisely.

In order to improve the accuracy and stability of the players in the movement of RoboCup3D simulation platform, a kind of real-time resolution of inverse kinematics for humanoid NAO model was proposed. Firstly, the lower limb topology of NAO model was analyzed, and its forward kinematics model was established. Secondly, the equations of every joint angle in all lower limbs were derived by real-time inverse kinematics analytic method. Finally, the algorithm was realized by coding. The experimental results validate the numerical stability and feasibility of online execution of the method, and the overall competitive level of the RoboCup3D simulation soccer team has been enhanced.

In a large-scale Wireless Sensor Network (WSN), the nodes closer to the single sink node use up their energy more quickly than others because of relaying more packets so that the network is invalid rapidly. In order to elongate the network lifetime, it is required to deduce the hops from sensor node to sink node. An efficient method is to deploy multiple sink nodes instead of single one. Therefore, it needs to be considered that how many sink nodes should be deployed on minimizing network cost and maximizing network lifetime. A network lifetime model and a cost model were proposed in WSN with multiple sink nodes and a new method was presented to determine the optimal number of sink nodes by computing the Ratio of Lifetime to Cost (RLC). The theoretical studies show that the number of sink nodes is related to the cost of sensor nodes and sink nodes, the network scale, the number of critical sensor nodes and the transmission power of sensor node. The simulation results prove the theoretical conclusion.