Yulin Zhou

Dr. Yulin Zhou


Research Focus & Metrics

Academic Career Length

5Years (2022-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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29 June 2022 in Processes
Verified
Journal Article
Open access
PDF file
CWChun-Guang Wang
Chun-Guang Wang
YZYulin Zhou
Yulin Zhou
JLJunjie Li
Junjie Li
For the task of garbage classification, to overcome the main disadvantages of the Yolov4 target detection algorithm, such as the large network model and lower detection accuracy for small objects, a lightweight Yolov4 target detection network based on the EfficientNet-B0 fusion ECA mechanism is presented. The lightweight EfficientNet was used to replace the original backbone network, which reduces the parameters of the network model and improves the detection accuracy. Moreover, a deep detachable convolution block replaced the common convolution block in the original network, which further reduced the number of parameters in the model. In the feature pyramid model PANet, a lightweight ECA attention mechanism was introduced to realize the weight analysis of the importance of different channel feature maps through cross-channel interaction, allowing the network to extract more obvious features with which to distinguish categories. Finally, a Soft-NMS algorithm was introduced in the post-processing stage of the detection frame to reduce the missed target detection rate in dense areas, which can improve the detection accuracy of the network and detection efficiency. As shown in the results, the size of the model was only 48 MB, and the mAP was 91.09%. Compared with the original Yolov4 network, the mAP was increased by 5.77% based on the 80% reduction in the model size. The recognition of small targets was also improved, which proved the effectiveness and robustness of the improved algorithm.

2 December 2022 in Energies
Verified
Journal Article
Open access
PDF file
CWChun-Guang Wang
Chun-Guang Wang
YZYulin Zhou
Yulin Zhou
BXBing Xie
Bing Xie
JXJiuming Xie
Jiuming Xie
JZJunsheng Zhang
Junsheng Zhang
As the working tool of the robot, the importance of the gripper becomes more prominent with the extensive use of the robot. This paper proposes a new type of underactuated universal gripper that can be applied to handle lightweight parts of any shape. It integrates a crank train and a four-bar mechanism to grasp objects. The kinematics and statics analysis of the proposed gripper were carried out; and in this paper, we briefly introduce the concept and control system design. Then, the motion characteristics and grasping ability of the underactuated gripper are presented. A prototype of the gripper was designed and manufactured based on the simulation analysis, and relevant grasping experiments were carried out. The experimental results verify that the proposed universal gripper has the advantages of safe design, easy manufacturing, effective gripping, and stable holding of objects.