In order to solve the problem of feature information loss caused by the introduction of a large number of pooling layers in traditional convolutional neural networks, based on the feature of Capsule Network (CapsNet)——using vector neurons to save feature space information, a network model 3DSPNCapsNet (3D Small Pooling No dense Capsule Network) was proposed for recognizing 3D models. Using the new network structure, more representative features were extracted while the model complexity was reduced. And based on Dynamic Routing (DR) algorithm, Dynamic Routing-based algorithm with Length information (DRL) algorithm was proposed to optimize the iterative calculation process of capsule weights. Experimental results on ModelNet10 show that compared with 3DCapsNet (3D Capsule Network) and VoxNet, the proposed network achieves better recognition results, and has the average recognition accuracy on the original test set reached 95%. At the same time, the recognition ability of the network for the rotation 3D models was verified. After the rotation training set is appropriately extended, the average recognition rate of the proposed network for rotation models of different angles reaches 81%. The experimental results show that 3DSPNCapsNet has a good ability to recognize 3D models and their rotations.

To solve the sparse and cold start problems of recommendation system, a Trust Regularization Euclidean Embedding (TREE) algorithm by fusing trust information was proposed. Firstly, the Euclidean embedding model was employed to embed the user and project in the unified low-dimensional space. Secondly, to measure the trust information, both the project participation degree and user common scoring factor were brought into the user similarity calculation formula. Finally, a regularization term of social trust relationship was added to the Euclidean embedding model, and trust users with different preferences were used to constrain the location vectors of users and generate the recommendation results. In the experiments, the proposed TREE algorithm was compared with the Probabilistic Matrix Factorization (PMF), Social Regularization (SoReg), Social Matrix Factorization (SocialMF) and Recommend with Social Trust Ensemble (RSTE) algorithms. When dimensions are 5 and 10, TREE algorithm has the Root Mean Squared Error (RMSE) decreased by 1.60% and 5.03% respectively compared with the optimal algorithm RSTE on the dataset Filmtrust.While on the dataset Epinions, the RMSE of TREE algorithm was respectively 1.12% and 1.29% lower than that of the optimal algorithm SocialMF. Experimental results show that TREE algorithm further alleviate the sparse and cold start problems and improves the accuracy of scoring prediction.

The key link of air defense command and control system is to evaluate the threat degree of air target according to target situation information, the accuracy of the assessment will have a significant impact on air defense operations. Aiming at the shortcomings of traditional evaluation methods, such as poor real-time performance, heavy workload, low evaluation accuracy, and unable to evaluate multiple objectives simultaneously, a method of air target threat assessment based on Adaptive Crossbreeding Particle Swarm Optimization (ACPSO) and Least Squares Support Vector Machine (LSSVM) was proposed. Firstly, according to the air target situation information, the framework of threat assessment system was constructed. Then, ACPSO algorithm was used to optimize the regularization parameter and kernel function parameter in LSSVM. In order to overcome the disadvantages of the traditional crossbreeding mechanism, an improved cross-hybridization mechanism was proposed, and the crossbreeding probability was adjusted adaptively. Finally, the training and evaluation results of the systems were compared and analyzed, and the multi-target real-time dynamic threat assessment was realized by the optimized system. Simulation results show that the proposed method has the advantages of high accuracy and short time required, and can be used to evaluate multiple targets simultaneously. It provides an effective solution to evaluate the threat of air targets.

Intelligent air combat is a hot research topic in military aviation field and missile hit prediction is an important part of intelligent air combat. Aiming at the shortcomings of insufficient research on missile hit prediction, poor optimization ability of the algorithm, and low prediction accuracy of the model, a missile hit prediction model based on Adaptively-Mutated Chaotic Particle Swarm Optimization (AMCPSO) and Support Vector Machine (SVM) was proposed. Firstly, feature extraction of air combat data was carried out to build sample library for model training; then, the improved AMCPSO algorithm was used to optimize the penalty factor C and the kernel function parameter g in SVM, and the optimized model was used to predict the samples; finally, comparison tests with classical PSO algorithm, the BP neural network method and the method based on lattice were made. The results show that the global and local optimization ability of the proposed algorithm are both stronger, and the prediction accuracy of the proposed model is higher, which can provide a reference for missile hit prediction research.

Concerning ranging difficulties with 60GHz signals in Non Line of Sight (NLOS) conditions, a new positioning algorithm based on beamforming in Wireless Local Area Network (WLAN) was proposed. Firstly, the beamforming technology was applied to search the strongest path by adjusting receiving antennas along the channel path with the maximum power.The searching robustness was enhanced and the location coverage was expanded. Secondly, the time delay bias in NLOS conditions was modeled as a Gaussian random variable to reconstruct the NLOS measurements. Finally, to further improve the positioning accuracy, the outlier detection mechanism was introduced by setting a reasonable detection threshold. The localization simulation experiments were conducted on Matlab using STAs-STAs (STAtions-STAtions) channel model, the Time of Arrival (TOA) localization algorithm based on traditional coherent estimation method achieved the average positioning error at about 2m, and the probability of 1m localization accuracy was just 0.5% under NLOS conditions, while the proposed algorithm achieved the average positioning error at 1.02cm, and the probability of 1m localization accuracy reached 94%. Simulation results show that the beamforming technology is an effective solution to 60GHz localization in NLOS conditions, and the localization accuracy and the probability of successful localization are effectively improved.

Based on the research of two-dimensional code and recognition of mobile phone, the authors have developed a two-dimensional code recognition system on Android platform. In the system architecture design, the two-dimensional code recognition system platform support layer, client application layer, cloud handle layer were hierarchically designed to ensure the system security. According to the requirements, the system function models and cases analyses were given. Finally, the two-dimensional code core encode and decode functions of mobile phone were achieved.

A secure model was proposed which not only relied on prevention controls but also incorporated detect intrusion and tolerant intrusion. The model can detect intrusions and locate and repair the damage caused by intrusion based on transactions, which protect the database system and provide secure services to legitimate clients. The techniques of both redundancy and diversity were utilized for secret data storage. Threshold secret share approach was presented to protect confidential data based on Chinese remainder theorem.