The rheological property of asphalt is an important factor affecting the pavement performance of asphalt binder, and the fundamental reason for the change of asphalt rheological property is the strong evolution of asphalt material meso structure. However, the internal mechanism of rejuvenated asphalt mastic system is complex and its rules are difficult to grasp. Aiming to study the relationship between meso mechanical parameters and rheological parameters of rejuvenated asphalt mastic, the meso structure model of rejuvenated asphalt mastic was established and improved based on the discrete element method. Moreover, the meso parameters of the model were obtained by the objective function method, and then the influences of various factorswere studied to construct the mathematical constitutive model of rheological parameter modulus and meso mechanical parameters. Combing with the reliability of the improved Burgers model was verified based on the rheological test results of rejuvenated asphalt mastic. In addition, the virtual test of dynamic shear rheological dynamic frequency scanning was carried out on the asphalt mastic sample by particle flow software. By adjusting the mesomechanical parameters, the simulation results (complex shear modulus and phase angle)were consistent with the test results. This study clarified the relationship between mesomechanics and macro performance, and this model could be used to obtain the complex shear modulus of rejuvenated asphalt mastic under different types, filler-asphalt ratio and external force environments by adjusting particle flow, wall boundary and other conditions, which can greatly save the workload for the later research and provide a theoretical basis for production experiments.

Radial basis function neural networks (RBF NNs) are one of the most useful tools in the classification of the sonar targets. Despite many abilities of RBF NNs, low accuracy in classification, entrapment in local minima, and slow convergence rate are disadvantages of these networks. In order to overcome these issues, the sine-cosine algorithm (SCA) has been used to train RBF NNs in this work. To evaluate the designed classifier, two benchmark underwater sonar classification problems were used. Also, an experimental underwater target classification was developed to practically evaluate the merits of the RBFbased classifier in dealing with high-dimensional real world problems. In order to have a comprehensive evaluation, the classifier is compared with the gradient descent (GD), gravitational search algorithm (GSA), genetic algorithm (GA), and Kalman filter (KF) algorithms in terms of entrapment in local minima, the accuracy of the classification, and the convergence rate. The results show that the proposed classifier provides a better performance than other compared classifiers as it classifies the sonar datasets 2.72% better than the best benchmark classifier, on average.

Energy storage technology (EST) is an effectiveway to improve the power quality of renewable energy generation (such as solar energy and wind energy), but a single energy storage system (ESS) is difficult to meet the demand for the safe operation of the grid. According to the structure and operation characteristics of the existing battery/super-capacitor hybrid energy storage system (HESS), a battery/super-capacitor HESS is proposed. The working principle and three working modes (the super-capacitor pre-charging cold stand-by mode, the boost mode and buck mode) of the HESS are analyzed in detail. The state equations of the boost mode and buck mode are derived. The state space average method is used to establish the small signal equivalent model under the buck/boost mode. More-over, the charge and discharge control strategy of the HESS is obtained by combining the voltage closed-loop control. The simulation model is built in Matlab/Simulink to verify the effectiveness of the proposed HESS and its control strategy. The results show that the HESS and its control strategy can ensure the DC bus voltage has good stability and superior anti-interference, and it can simultaneously provide large current, increase the battery life, and improve the technical economy of energy storage.