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In order to ensure the secondary part at the front end of vehicles absorb the impact energy as much as possible in case of collision, reduce the impact on main parts, and ensure the safety of passengers and drivers, the energy absorption structure is set at the front end of the vehicle to improve the passive safety. Firstly, compared different standards at home and abroad and their requirements for collision conditions. Secondly, ASME RT-2 standard is selected as the standard, two energy absorbing structures are designed respectively. Structures mainly include head girders, corner posts and crash elements. The result shows that both structures can absorb more than 1.22 MJ of energy through the simulation by Ls-Dyna. At last, energy absorbing structures which has two energy absorb stages is tested to verify the reliability of the simulation results, and it is proved that the structure can meet the requirements of ASME RT-2.

The development of neuromorphic optoelectronic systems opens up the possibility of the next generation of artificial vision. In this work, the novel broadband (from 365 to 940 nm) and multilevel storage optoelectronic synaptic thin‐film transistor (TFT) arrays are reported using the photosensitive conjugated polymer (poly[(9,9‐dioctylfluorenyl‐2,7‐diyl)‐co‐(bithiophene)], F8T2) sorted semiconducting single‐walled carbon nanotubes (sc‐SWCNTs) as channel materials. The broadband synaptic responses are inherited to absorption from both photosensitive F8T2 and sorted sc‐SWCNTs, and the excellent optoelectronic synaptic behaviors with 200 linearly increasing conductance states and long retention time > 103 s are attributed to the superior charge trapping at the AlOx dielectric layer grown by atomic layer deposition. Furthermore, the synaptic TFTs can achieve IOn/IOff ratios up to 106 and optoelectronic synaptic plasticity with the low power consumption (59 aJ per single pulse), which can simulate not only basic biological synaptic functions but also optical write and electrical erase, multilevel storage, and image recognition. Further, a novel Spiking Neural Network algorithm based on hardware characteristics is designed for the recognition task of Caltech 101 dataset and multiple features of the images are successfully extracted with higher accuracy (97.92%) of the recognition task from the multi‐frequency curves of the optoelectronic synaptic devices. [ABSTRACT FROM AUTHOR]

Travel patterns reflect the regularity of residents' mobility, and it is a crucial factor to evaluate the reasonability of urban spatial structure and connectivity of road networks. Therefore, exploring travel patterns is of practical significance for urban planning, traffic management, and improvement of the operational efficiency of the transportation system. In this study, we apply the tensor model to explore travel patterns under temporal and spatial dimensions based on the license plate recognition (LPR) data collected from the Changsha city, China. As travel patterns are influenced by many variables, a method framework based on the tensor model is proposed to explore the influence of variables on travel characteristics. Firstly, we apply clustering algorithms and the principal component analysis method to extract main feature variables, which can achieve the purpose of dimensionality reduction and eliminate the complex collinearity among variables. Then, the tensor decomposition and reconstruction algorithms are performed based on extracted feature variables to analyze their influence on travel patterns. The experiments demonstrate the advantages of the proposed method framework., Author(s): Zhaoxin Liu [1]; Xiaolu Wang [2]; Yufeng Bi [3]; Jun Kong [1,4]; Run Xu [3]; Yuanpei Chen [1,4]; Jinjun Tang (corresponding author) [2] 1. Introduction The transportation system is [...]

Simulating three-dimensional acoustic problems with traditional finite element models (FEMs) may result in a large dispersion error that is difficult to manage. To control dispersion error caused by "overly stiff" features of FEM, a face-based smoothed FEM (FS-FEM) model was used to analyze the sound field of a high-speed train. Based on the acoustic Galerkin method's weak form, smooth regions were reconstructed within the original finite element area, and numerical simulations were performed in MATLAB. Modal analysis of a test example revealed that results obtained using FS-FEM are more accurate than those obtained using the traditional FEM. Finally, it was used to calculate the sound field of China Railway High-speed 380B (CRH380B) high-speed trains in time and frequency domains, achieving higher accuracy than the traditional approach. Results were more akin to the realistic solution, which demonstrated the performance of the FS-FEM. [ABSTRACT FROM AUTHOR]