Tracking of the person crossing the cameras is a technical challenge for smart city and intelligent security. And person re-identification is the most important technology for cross-camera person tracking. Due to the domain bias, applying person re-identification algorithms for cross-scenario application leads to the dramatic accuracy reduction. To address this challenge, a method based on Generative Adversarial Network (GAN) Uniting with Spatial-Temporal pattern (STUGAN) was proposed. First, training samples of the target scenario generated by the GAN were introduced to enhance the stability of the recognition model. Second, the spatio-temporal features were used to construct the spatio-temporal pattern of the target scenario, so as to screen low-probability matching samples. Finally, the recognition model and the spatio-temporal pattern were combined to realize the person re-identification task. On classic datasets of this field named Market-1501 and DukeMTMC-reID, the proposed method was compared with BoW (Bag-of-Words), PUL (Progressive Unsupervised Learning), UMDL (Unsupervised Multi-task Dictionary Learning) and other advanced unsupervised algorithms. The experimental results show that the proposed method achieves 66.4%, 78.9% and 84.7% recognition accuracy for rank-1, rank-5 and rank-10 indicators on the Market-1501 dataset respectively, which are 5.7, 5.0 and 4.4 percentage points higher than the best results of the comparison algorithm, respectively; and the mean Average Precision (mAP) higher than the comparison algorithms except Similarity Preserving cycle-consistent Generative Adversarial Network (SPGAN).

Aiming at the problem that traditional single detection algorithm models have low detection rate and slow detection speed on different types of attacks in industrial control system, an intrusion detection model combining optimized Support Vector Machine (SVM) and K-means++algorithm was proposed. Firstly, the original dataset was preprocessed by Principal Component Analysis (PCA) to eliminate its correlation. Secondly, an adaptive mutation process was added to Particle Swarm Optimization (PSO) algorithm to avoid falling into local optimal solution during the training process. Thirdly, the PSO with Adaptive Mutation (AMPSO) algorithm was used to optimize the kernel function and penalty parameters of the SVM. Finally, a K-means algorithm improved by density center method was united with the optimized support vector machine to form the intrusion detection model, achieving anomaly detection of industrial control system. The experimental results show that the proposed method can significantly improve the detection speed and the detection rate of various attacks.

Due to the interference of various factors in the complex situation of reality, the errors may occur in the person re-identification. To improve the accuracy of person re-identification, a person re-identification algorithm based on spatial-temporal regularization was proposed. Firstly, the ResNet-50 network was used to extract the features of the input video sequence frame by frame, and the series of frame-level features were input into the spatial-temporal regularization network to generate corresponding weight scores. Then the weighted average was performed on the frame-level features to obtain the sequence-level features. To avoid weight scores from being aggregated in one frame, frame-level regularization was used to limit the difference between frames. Finally, the optimal results were obtained by minimizing the losses. A large number of tests were performed on MARS and DukeMTMC-ReID datasets. The experimental results show that the mean Average Precision (mAP) and the accuracy can be effectively improved by the proposed algorithm compared with Triplet algorithm. And the proposed algorithm has excellent performance for human posture variation, viewing angle changes and interference with similar appearance targets.

Concerning the problems of estimating the service time of airport flight support, and the particularity, complexity, and influence factors' uncertainty of flight support service process, an estimation model of flight support service time based on Bayesian Network (BN) was proposed. The knowledge of aviation experts and the machine learning of historical data were combined by the proposed model, and the incremental learning characteristic of BN was used to adjust the BN model dynamically, so as to make itself adapt to new conditions and constantly update the service time estimates of flight support. By using the data selected from a large domestic hub airport information system, the proposed BN model was trained via the Expectation Maximization (EM) algorithm to obtain the test results. The analysis of experimental results and model evaluation show that the proposed method can effectively estimate the service time of flight support and has higher accuracy. In addition, the sensitivity analysis demonstrates that the flight density during flight arrival time has the strongest influence on flight support service time.

The existing methods of constructing Voronoi diagram have low efficiency and high complexity, to remedy the disadvantages, a new method of constructing and updating Voronoi diagram based on the hybrid methods was given to query the nearest neighbor of the given spatial data effectively, and a new method of searching the nearest neighbor based on Voronoi diagram and the minimum inscribed circle was presented. To deal with the frequent, changes of the query point position, the method based on Voronoi diagram and the minimum bounding rectangle was proposed. To improve the efficiency of the dual nearest neighbor pair and closest pair query, a new method was given based on Voronoi polygons and their minimum inscribed circles. The experimental results show that the proposed methods reduce the additional computation caused by the uneven distribution of data and have a large advantage for the big dataset and the frequent query.