In recent years, the mechanical clinching method plays an increasingly important role in the building of thin-walled structures. The clinched joint can be employed to join the lightweight materials. Compared with other joining methods, the clinched joint has better mechanical behavior. However, the clinched joint may be deformed during use when it bears a high shear force. In this research, a process to join aluminum alloy and restore deformed joint was proposed and investigated. The clinched joint was deformed in the deforming process. Then, a customized rivet and two flat restoring tools were utilized for restoring the deformed joint to join aluminum alloy. Different restoring forces such as 45, 40, 35, 30, 25, and 20 kN were employed to produce diverse restored joints. Some shearing tests on the restored joint were utilized for understanding joint material flow, mode of failure, thickness of neck, shear strength, and absorption of energy. The thickness of neck can be increased in restoring process, which contributes to improve the shear strength. The rivet embedded in a pit also helps restored joint bear shear force, so all of the restored joints have higher absorption of energy and shear strength than the clinched joints. The restoring process effectively restores the deformed joint to obtain better mechanical behavior.
City-scale traffic speed prediction provides significant data foundation for the intelligent transportation system, which enriches commuters with up-to-date information about traffic condition. However, predicting on-road vehicle speed accurately is challenging, as the speed of the vehicle on the urban road is affected by various types of factors. These factors can be categorized into three main aspects, which are temporal, spatial, and other latent information. In this paper, we propose a novel spatio-temporal model named L-U-Net based on U-Net as well as long short-term memory architecture and develop an effective speed prediction model, which is capable of forecasting city-scale traffic conditions. It is worth noting that our model can avoid the high complexity and uncertainty of subjective features extraction and can be easily extended to solve other spatio-temporal prediction problems such as flow prediction. The experimental results demonstrate that the prediction model we proposed can forecast urban traffic speed effectively. INDEX TERMS Convolutional neural network, long short-term memory neural network, spatio-temporal modeling, traffic speed prediction.
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