To improve the efficiency of wave power generation technology, a mathematical model of the system from wave input to motor output was established. The key parameters that affected the system′s power generation capacity were analyzed by using theoretical modeling and simulating, so as to provide theoretical guidance for the research of the constant speed control strategy of the hydraulic energy conversion system. The hydraulic energy storage system of wave energy generation was composed of 3 parts. The mathematical model of the system was established by analyzing each component′s motion equation and energy equation, and finding the connection parameters between the two components. The key parameters and characteristics of the system were determined qualitatively by analyzing the system′s power equation. To confirm the accuracy of theoretical analysis, the AMEsim simulation platform was used to design and imitate the system. The results showed that the motor′s output power was affected by the height of the wave, period, flow area of the proportional flow valve and the motor displacement. The highest order of the influence was 1, 4, 4 and 2, respectively. The results also verified that the precharging pressure of the accumulator had little influence on the motor′s output power.

A cross social network user alignment method by fusing node state information was proposed. The local characteristics of nodes and node state information were captured through network representation to obtain the embedded vector of each account, and the aligned users were found by calculating the similarity between corresponding representations of different accounts. Experimental results on two real data sets showed that the proposed method could align more users than other methods. When predicting top-k of different scales, the proposed method could achieve an alignment precision of 50% at top-9 on the data set Twitter-Foursquare with dense network structure. Compared with other methods on the sparse and large network data set DM-ML, the improvement on alignment precision was 12.06%-36.62%. The analysis of F1-score also showed that the proposed method could effectively improve the performance of user alignment.

With vehicle platoon in intelligent transportation system as the background, this research established a vehicle platoon control model based on the intelligent driver model and analyzed the influence of delay in vehicle-to-vehicle communication on the stability of platoon control. A fault diagnosis method based on the update delay of vehicle-to-vehicle communication was proposed. The statistical characteristics of median and average were used to calculate the decision variables which were used to judge whether a fault had occurred. A two-layer sliding window was designed to smooth the decision variables and adaptively calculate the decision variable in real time. The Jarque-Bera algorithm was used to test the normality of the statistical distribution of the receiver's update delay within a period of time. If the distribution significantly deviated from the normal distribution, it was considered that the communication quality had deteriorated. Collecting vehicle speed data and vehicle-to-vehicle communication delay data at the testbed, simulations were conducted to analyze the statistical distribution characteristics of the vehicle-vehicle communication update delay in different scenarios and verify the influence on vehicle platoon control of vehicle-to-vehicle communication delay. The research results showed that the vehicle-to-vehicle communication delay caused drastic changes in the control rate during the collaborative control process. The communication fault diagnosis method based on the update delay could effectively diagnose whether the vehicle-to-vehicle communication quality had deteriorated.