Summary Studies on the effect of different shape strategies on wind‐induced responses of super tall buildings have been extensive. However, little systematic research on the influence of aerodynamic shapes on wind pressure distributions of super high‐rise building having a height more than 500 m is reported in the literature. In this paper, a series of wind tunnel tests are conducted on models simulating tapered buildings taller than 500 m with an aspect ratio of 9:1 by applying synchronous pressure measurement technology to investigate the influence of different shape strategies on the wind force coefficients of the cross section (Cs) and on the peak negative pressure distributions on surfaces. The shape strategies considered include tapering of the cross section of a building along its height, chamfered modification, and opening ventilation slots. It is found that the wind force coefficient Cs increase with an increase of the tapering ratio. It is shown that chamfered modification can effectively reduce most of the wind force coefficients Cs to less than 0.9. As for peak wind pressures, a zone having a higher negative pressure is found to locate at the bottom of the side faces of the model. With an increase of the tapering ratio, the peak negative pressure of side faces of the model slightly decreases. Chamfered modification can significantly increase the peak negative pressure at the chamfered location. Furthermore, it is demonstrated that opening ventilation slots had less effect on Cs, but the peak negative pressure can significantly increase at the area of opening ventilation slots and adjacent areas.

Self-piercing riveting is a connection technology to achieve efficient connection of aluminum alloy plates, and the quality of the connection mainly depends on the rivet and mold parameters. This paper establishes a self-piercing riveting simulation analysis model based on the ALE method, and verifies the accuracy of the model by comparing the simulation and experimental results. Five parameters such as rivet length and mould radius are selected, and the undercut amount, bottom thickness, remaining thickness, and tensile shear capacity are used as evaluation indexes to research the main factors that affect the joint forming quality, and an effective optimization scheme is proposed.

In order to improve the quality of clinching joint, the parameters of mould should be considered synthetically. In this paper, the Hollomon flow stress model is used to simulate material deformation, while Arbitrary lagrangain-Eulerian method to adjust the distorted meshes for improving the accuracy of finite element simulation. The experiment of multi-objective optimization and finite element simulation method are combined to study the relationship underneath between the mould parameters and quality of clinching joint. As two of the most important quality related indicators, the neck thickness and self-lock value of clinching joint are optimized by the genetic algorithm and the optimal solution of the mould parameters are obtained accordingly. The final experimental results show the obvious quality improvement of clinching point after the optimization of the mould parameters. This progress provides reference for the multi-parameter optimization of the mould. 1

Efficient and accurate object detection is crucial for the widespread use of low-cost unmanned sweepers. This paper focuses on the low-cost sweeper in practical working scenarios and proposes a traffic participant detection method based on an enhanced YOLO-v5 model. To train the model on noise knowledge, three types of noise were added to the data set based on the mathematical model's vibration response. The loss function was optimized to balance detection accuracy and real-time performance while focusing on traffic participant detection using YOLO-v5. CTDS and BFSA modules were proposed based on the attention mechanism to enhance the YOLO-v5 model. Comparative experiments demonstrated the effectiveness of the proposed method, with the enhanced YOLO-v5 model achieving a 4.5% higher mean average precision than the traditional YOLO-v5 network. Moreover, the proposed method can process images at a frame per second (FPS) of 89 while ensuring real-time performance, meeting the object detection requirements of actual sweeper.

Automated valet parking is a part of autonomous vehicles. Path tracking is a vital capability of autonomous vehicles. In the scenario of automatic valet parking, the existing control algorithm will produce a high tracking error and a high computational burden. This paper proposes a path-tracking algorithm based on model predictive control to adapt to low-speed driving. By using the model predictive control method and vehicle kinematics model, a path tracking controller is designed. Combining the dual algorithm to further optimize the solver, a new QPKWIK solver is proposed. The simulation results show that the solution time of the QPKWIK solver is 25% less than that of the QP solver, and the tracking error is reduced by up to 41% compared with the QP solver. In the parking lot, the tracking performance is tested under four common scenarios, and the experimental results show that this controller has better tracking performance.