The existing semantic segmentation algorithms have too many parameters and huge memory usage, so that it is difficult to meet the requirements real-world applications such as automatic driving. In order to solve the problem, a novel, effective and lightweight real-time semantic segmentation algorithm based on Separable Pyramid Module (SPM) was proposed. Firstly, factorized convolution and dilated convolution were adopted in the form of a feature pyramid to construct the bottleneck structure, providing a simple but effective way to extract local and contextual information. Then, the Context Channel Attention (CCA) module based on computer vision attention was proposed to modify the channel weights of shallow feature maps by utilizing deep semantic features, thereby optimizing the segmentation results. Experimental results show that without pre-training or any additional processing, the proposed algorithm achieves mean Intersection-over-Union (mIoU) of 71.86% on Cityscapes test set at the speed of 91 Frames Per Second (FPS). Compared to Efficient Residual Factorized ConvNet (ERFNet), the proposed algorithm has the mIoU 3.86 percentage points higher, and the processing speed of 2.2 times. Compared with the latest Light-weighted Network with Efficient Reduced Non-local operation for real-time semantic segmentation (LRNNet), the proposed algorithm has the mIoU slightly lower by 0.34 percentage points, but the processing speed increased by 20 FPS. The experimental results show that the proposed algorithm has great value for completing tasks such as efficient and accurate street scene image segmentation required in automatic driving.

Concerning the problems of single fare, low revenue rate of passenger transport and unbalanced passenger flow in different sections of high-speed railway, an adjustment strategy of high-speed railway fare based on passenger flow assignment was proposed. Firstly, the related factors affecting passenger travel choice behavior were analyzed, and a generalized travel cost function including four indicators of economy, rapidity, convenience and comfort was constructed. Secondly, a bilevel programming model considering the maximization of revenue of railway passenger transport management department and the minimization of passenger travel cost was established, in which the upper level programming achieved the maximum revenue of high-speed railway passenger transport by formulating fare adjustment strategy, the lower-level programming took the minimum passenger generalized travel cost as the goal, and used the competition and cooperation relationship between different trains of section to construct Stochastic User Equilibrium (SUE) model, and the model was solved by Method of Successive Averages (MSA) based on the improved Logit assignment model. Finally, the case study shows that the proposed fare adjustment strategy can effectively balance the section passenger flow, reduce passenger travel cost and improve passenger transport revenue to a certain extent. The experimental results show that the fare adjustment strategy can provide decision support and methodological guidance for railway passenger transport management departments to optimize fare system and formulate fare adjustment schemes.

Aiming at the problems of data sparseness and cold start in social network recommendation systems, a hybrid social network recommendation algorithm based on feature Transform and Probabilistic Matrix Factorization (TPMF) was proposed. Using Probability Matrix Factorization (PMF) method as recommendation framework, trust network, the relationship between the recommended items, user-item score matrix and adaptive weight were combined to balance the impact of individual and social potential characteristics on users. The trust feature transfer was introduced into the recommendation system as valid basis for recommendation. Compared to the User-Based Collaborative Filtering (UBCF), TidalTrust, PMF and SoRec, the experimental results show that the Mean Absolute Error (MAE) of TPMF was decreased by 4.1% to 20.8%, and the Root Mean Square Error (RMSE) of TPMF was decreased by 3.3% to 18.5%. Compared with the above four algorithms, for the cold start problem, the Mean Absolute Error was decreased by 1.6 to 14.7%, and the RMSE was decreased by 1.2% to 9.7%, which verifies TPMF effectively alleviates cold start problem and improves the robustness of the algorithm.

In data mining, mining maximum frequent itemsets instead of mining frequent itemsets can greatly improve the operating efficiency of system. The running time consumption of existing maximum frequent itemset mining algorithms is still very large. In order to solve the problem, a new DiffNodeset Maxmal Frequent Itemset Mining (DNMFIM) algorithm was proposed. Firstly, a new data structure DiffNodeset was adopted to realize the fast calculation of intersection and support degree. Secondly, the connection method with linear time complexity was adopted to reduce the complexity of connecting two DiffNodesets and avoid multiple invalid calculations. Then, the set-enumeration tree was adopted as the search space, and a variety of optimal pruning strategies were used to reduce the search space. Finally, the superset detection technology used in the MAximal Frequent Itemset Algorithm (MAFIA) algorithm was adopted to improve the accuracy of algorithm effectively. The experimental results show that, DNMFIM algorithm outperforms MAFIA and N-list based MAFIA (NB-MAFIA) in terms of time efficiency. The proposed algorithm has a good performance when mining the maximal frequent itemsets in different types of datasets.

The forecasted concentration of PM2.5 forecasting model greatly deviate from the measured concentration. In order to solve this problem, the data (from February 2015 to July 2015), consisting of measured PM2.5 concentration, PM2.5 model (WRF-Chem) forecasted concentration and model forecasted data of 5 main meteorological factors, were provided by Shanghai Pudong Meteorological Bureau. Support Vector Machine (SVM) and Particle Swarm Optimization (PSO) algorithm were combined to build rolling forecasting model of hourly PM2.5 concentration in 24 hours in advance. Meanwhile, the nighttime average concentration, daytime average concentration and daily average concentration during the upcoming day were forecasted by rolling model. Compared with Radical Basis Function Neural Network (RBFNN), Multiple Linear Regression (MLR) and WRF-Chem, the experimental results show that the proposed SVM model improves the forecasting accuracy of PM2.5 concentration one hour in advance (according with the results concluded from finished research), and can comparatively well forecast PM2.5 concentration in 24 hours in advance, and effectively forecast the nighttime average concentration, daytime average concentration and daily average concentration during the upcoming day. In addition, the proposed model has comparatively high forecasting accuracies of hourly PM2.5 concentration in 12 hours in advance and nighttime average concentration during the upcoming day.

In order to resolve the security issues of Mobile-Radio Frequency Identification (M-RFID), the author designed a lightweight authentication protocol based on Hash. It can achieve the mutual authentication between tags, readers and servers in the environment of wireless communication. It can prevent a series of security issues such as replay attacks, unauthorized reading, and location tracking. GNY logic was applied to prove that the agreement is sufficient to meet the security needs.