Traditional location service recommendation schemes lack consideration of user preferences and potential social relationships， resulting in recommendation results that fail to meet user’s personalized needs. Graph Neural Networks （GNNs） are widely used in the field of location recommendation by its good graph structure data processing capabilities. However， the previous studies’ centralized data paradigm is easy to lead to the issue of location privacy leakage. Therefore， a Location Privacy-preserving Recommendation scheme based on Federated Graph Neural Network （FedGNN-LPR） was proposed. Firstly， the user’s social relationship embedding and Point-Of-Interest （POI） embedding were learned through the graph attention network. Secondly， a POI-based pseudo labelling model was developed to predict the number of user visits to an unknown location， so as to protect user privacy and alleviate the cold-start problem. Finally， a clustered federated learning strategy based on differential privacy was proposed to protect client interaction data and solve the problem of data heterogeneity. Experiments were conducted on two publicly available real datasets， and the results demonstrate that the proposed scheme is reduced by 7.89% and 9.29% respectively compared to the Federated Averaging （FedAvg） algorithm， and 2.32% and 2.75% respectively compared to the FL+HC algorithm， in terms of Mean Absolute Error （MAE） and Root Mean Square Error （RMSE）. Moreover， it is shown that FedGNN-LPR exhibits better performance on federated learning location recommendation. Therefore， FedGNN-LPR not only protects user location privacy， but also improves location recommendation performance.

Aiming at the problems of low linear combination efficiency and too much attention to hard examples of the base classifiers of Adjusts Adaptive Boosting （AdaBoost） algorithm， two improved algorithms based on margin theory， named sample Weight and Parameterization of Improved AdaBoost （WPIAda） and sample Weight and Parameterization of Improved AdaBoost-Multitude （WPIAda.M）， were proposed. Firstly， the updates of sample weights were divided into four situations by both WPIAda and WPIAda.M algorithms， which increased the sample weights with the margin changing from positive to negative to suppress the negative movement of the margin and reduce the number of samples with the margin at zero. Secondly， according to the error rates of the base classifiers and the distribution of the sample weights， a new method to solve the coefficients of base classifiers was given by WPIAda.M algorithm， thereby improving the combination efficiency of base classifiers. On 10 UCI datasets， compared with algorithms such as WLDF_Ada （dfAda）， skAda， SWA-Adaboost （swaAda）， WPIAda and WPIAda.M algorithms had the test error reduced by 7.46 percentage points and 7.64 percentage points on average respectively， and the Area Under Curve （AUC） increased by 11.65 percentage points and 11.92 percentage points respectively. Experimental results show that WPIAda and WPIAda.M algorithms can effectively reduce the attention to hard examples， and WPIAda.M algorithm can integrate base classifiers more efficiently， so that the two algorithms can both further improve the classification performance.

To solve the instantaneous overcurrent of the large power inductive load, a body control system based on soft start was established. The abundant peripherals of Micro Control Unit (MCU) and the scalability of Field Programmable Gate Array (FPGA) were fully utilized to implement the rapid sampling of the multiplex switching signals and the output control of the Pulse Width Modulation (PWM) signals. In the software, the program was designed by the modular, which completed the task and met the need of the car. At last, the test shows the Electromagnetic Interference (EMI) on the vehicle is reduced, soft start of the large inductive equipment is realized, and the instantaneous overcurrent of the load is reduced by 40%, so the body control system can be applied with the unmanned vehicle's control

In view of the problem of attribute mapping techniques in materialized data integration, the authors proposed a search algorithm of attribute mapping based on combined similarity calculation (SACS). The proposed algorithm was established through intuitive calculation factors and combined formula to traverses attribute mapping in data sources. The algorithm avoids the sample selection problem of machine learning in traditional attribute mapping techniques, and improves the precision rate and recall rate for attribute mapping.

An encryption algorithm which combined the process of MPEG video compressing with video encryption based on Lorenz chaotic system was put forward to deal with the security problem of video information. Three dimensional chaotic sequences of Lorenz system were used to encrypt DC, AC and motion vector coefficients during the compressing of I frame, B frame and P frame. The luminance information of I frame was encrypted among blocks by the chaotic sequence. The algorithm is secure and real-time because the encryption is done during the process of video compressing.

The paper defined fractal art based on the essential theory of fractal-chaos, expressed structural means of fractal figures in line with its track and distribution, and made use of these methods to construct a series of M-J fractal-chaos figures, showing the beautiful fine structure of the fractal set. The paper provided a definitely new view and an application base for applying fractal-chaos theory and technique in the area of digital media.

Location-Based Social Network （LBSN） combines social network with geographical locations， providing users with novel personalized experiences. The protection of user location privacy is crucial for the secure operation of LBSN systems. To address the problem of rigid location privacy protection methods leading to low data utility and decreased quality of Location-Based Service （LBS） experiences， a User Preference-based and Differentially Private Location Privacy Protection （UPDP-LPP） method was proposed. Firstly， the set of user stay points was obtained by using a stay point extraction algorithm. Secondly， the types of stay points were labeled by using a feature fusion method. Finally， by dynamically obtaining privacy budget and noise sensitivity through user preferences， Laplace noise was added to the privacy radius to protect sensitive user location information. Experimental results on two public real datasets show that the proposed method improves the data utility of privacy protection by more than 10% compared to TLDP （Trajectory Location Data Protection）， DPLPA （Differential Privacy-based Location Privacy protection Algorithm）， and LPPM （Location Privacy Protection Mechanism） when the privacy budget is the same. It can be seen that UPDP-LPP not only protects user location privacy， but also enhances data utility.