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928 results on '"Lu, Zhenyu"'

153. LightNILM

Author

Lu, Zhenyu, primary, Cheng, Yurong, additional, Zhong, Mingjun, additional, Luan, Wenpeng, additional, Ye, Yuan, additional, and Wang, Guoren, additional

Published
2022
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159. Fast Orthogonal Nonnegative Matrix Tri-Factorization for Simultaneous Clustering

Author

Li, Zhao, Wu, Xindong, Lu, Zhenyu, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Goebel, Randy, editor, Siekmann, Jörg, editor, Wahlster, Wolfgang, editor, Zaki, Mohammed J., editor, Yu, Jeffrey Xu, editor, Ravindran, B., editor, and Pudi, Vikram, editor

Published
2010
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170. Global contextual attention augmented YOLO with ConvMixer prediction heads for PCB surface defect detection.

Author

Xia, Kewen, Lv, Zhongliang, Liu, Kang, Lu, Zhenyu, Zhou, Chuande, Zhu, Hong, and Chen, Xuanlin

Subjects
SURFACE defects, FEATURE extraction, PRINTED circuits, FORECASTING, PROBLEM solving
Abstract

To solve the problem of missed and false detection caused by the large number of tiny targets and complex background textures in a printed circuit board (PCB), we propose a global contextual attention augmented YOLO model with ConvMixer prediction heads (GCC-YOLO). In this study, we apply a high-resolution feature layer (P2) to gain more details and positional information of small targets. Moreover, in order to suppress the background noisy information and further enhance the feature extraction capability, a global contextual attention module (GC) is introduced in the backbone network and combined with a C3 module. Furthermore, in order to reduce the loss of shallow feature information due to the deepening of network layers, a bi-directional weighted feature pyramid (BiFPN) feature fusion structure is introduced. Finally, a ConvMixer module is introduced and combined with the C3 module to create a new prediction head, which improves the small target detection capability of the model while reducing the parameters. Test results on the PCB dataset show that GCC-YOLO improved the Precision, Recall, mAP@0.5, and mAP@0.5:0.95 by 0.2%, 1.8%, 0.5%, and 8.3%, respectively, compared to YOLOv5s; moreover, it has a smaller model volume and faster reasoning speed compared to other algorithms. [ABSTRACT FROM AUTHOR]

Published
2023
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171. A stability locomotion-control strategy for quadruped robots with center-of-mass dynamic planning.

Author

Han, Yangyang, Liu, Guoping, Lu, Zhenyu, Zong, Huaizhi, Zhang, Junhui, Zhong, Feifei, and Gao, Liyu

Abstract

Copyright of Journal of Zhejiang University: Science A is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Published
2023
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