The rapid growth of Location-Based Social Networks (LBSN) provides a vast amount of Point-of-Interest (POI) data, which facilitates the research of POI recommendation. To solve the low recommendation accuracy caused by the extreme sparseness of user-POI matrix and the lack of POI features, by integrating information such as tags, geography, socialization, score, and image information of POI, a POI recommendation method integrating social networks and image contents called SVPOI was proposed. Firstly, with the analysis of POI dataset, a distance factor was constructed based on power law distribution and a tag factor was constructed based on term frequency, and the existing historical score data was merged to construct a new user-POI matrix. Secondly, VGG16 Deep Convolutional Neural Network (DCNN) was used to process the images of POI to construct the POI image content matrix. Thirdly, the user social matrix was constructed according to the social network information of POI data. Finally, with the use of Probabilistic Matrix Factorization (PMF) model, the POI recommendation list was obtained with the integration of user-POI matrix, image content matrix and user social matrix. On real-world datasets, the accuracy of SVPOI is improved significantly compared to PMF, SoRec (Social Recommendation using probabilistic matrix factorization), TrustMF (Social Collaborative Filtering by Trust) and TrustSVD (Social Collaborative Filtering by Trust with SVD) while Mean Absolute Error (MAE) and Root-Mean-Square Error (RMSE) of SVPOI are decreased by 5.5% and 7.82% respectively compared to those of TrustMF which was the best of the comparison methods. The experimental results demonstrate the recommendation effectiveness of the proposed method.

To solve the problems of traditional regression-based methods such as the loss of texture caused by face scale normalization, costing more time for retraining expanded data set to improve the generalization ability of the original model, and even potential non-convergence and incomputability, a face alignment method based on scale self-adaption and Incremental Learning (IL) was proposed. Firstly, the mapping relationship between the initial face points and the standard face points was established. Secondly, the extraction of the texture features on the original image and the normalization of the face scales were achieved via the mapping relationship. At last, incremental learning was applied to the new data set by using the existing models, which improved the generalization ability of the original model quickly. The experimental results show that the proposed method performs higher alignment accuracy than traditional regression-based methods. On the AFW (Annotated Faces in the Wild) dataset (68 feature points), the accuracy is increased by 2 to 4 percentage points; and on a 100000-level large dataset (5 feature points), the robustness is increased by 1 to 2 percentage points compared to the methods based on Deep Learning (DL). In addition, the proposed method is not only suitable for face alignment regression model, but also applicable for solving other regression models.

To avoid conflict incurring in airport taxiway and ease air traffic controller load, a control strategy of taxiway conflict based on sensor network was proposed. The dynamic model of taxiway based on sensor network was proposed according to taxiway movement process and the taxiway movement controlling regulations are defined, then the problems of taxiway conflict are transformed into state forbidden problems. Controllers were designed using the partial correlation matrix method and logical mutual exclusion method for state forbidden problems, and a way is proposed for control command of lights decided based on the state of transition. Taxiway conflict can be avoided and guided automatically using navigation light. A simulation was provided to testify the effectiveness of the control strategy finally.