L
Long Chen
Researcher at Jiangsu University
Publications - 255
Citations - 4388
Long Chen is an academic researcher from Jiangsu University. The author has contributed to research in topics: Suspension (vehicle) & Control theory. The author has an hindex of 24, co-authored 244 publications receiving 2576 citations.
Papers
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Journal ArticleDOI
YOLOv4-5D: An Effective and Efficient Object Detector for Autonomous Driving
Yingfeng Cai,Luan Tianyu,Hongbo Gao,Hai Wang,Long Chen,Li Yicheng,Miguel Angel Sotelo,Zhixiong Li +7 more
TL;DR: In this article, the authors proposed a one-stage object detection framework for improving the detection accuracy while supporting a true real-time operation based on the YOLOv4.
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Improved design of dynamic vibration absorber by using the inerter and its application in vehicle suspension
TL;DR: In this article, a new vehicle suspension structure called ISD suspension, including the inerter, spring and damper has been created, which can effectively improve the damping performance of the suspension system, especially at the offset frequency of the vehicle body.
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Precise control of a four degree-of-freedom permanent magnet biased active magnetic bearing system in a magnetically suspended direct-driven spindle using neural network inverse scheme
TL;DR: In this article, a decoupling control scheme for a 4-DOF PMBAMB in a direct-driven spindle system based on the neural network inverse (NNI) and 2-degree-of-freedom (DOF) internal model control method was proposed.
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A Comparative Study of State-of-the-Art Deep Learning Algorithms for Vehicle Detection
TL;DR: Five mainstream deep learning object detection algorithms in vehicle detection, namely the faster RCNN, R-FCN, SSD, RetinaNet, and YOLOv3 on the KITTI data are compared and analyzed and the PR curve and AP value are used to evaluate the detection accuracy.
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Robust Target Recognition and Tracking of Self-Driving Cars With Radar and Camera Information Fusion Under Severe Weather Conditions
TL;DR: The fusion algorithm improves the robustness of the environment perception system and provides accurate environment perception information for the decision-making system and control system of autonomous vehicles.