Zobrazeno 1 - 10
of 260
pro vyhledávání: '"ZHU Wenbing"'
Publikováno v:
Zhejiang dianli, Vol 43, Iss 1, Pp 117-125 (2024)
Arc discharge caused by short-circuit faults is one of the most serious faults in UHV transformers， with the potential for inducing deformation， rupture， burning， or even explosion of the oil tank. In order to analyze the stress-strain levels
Externí odkaz:
https://doaj.org/article/80661c316c4b4ca2a508d61cf68ce92f
Autor:
Zhu Wenbing, Hasan Hafnida
Publikováno v:
Applied Mathematics and Nonlinear Sciences, Vol 7, Iss 1, Pp 557-564 (2021)
To study the mathematical simulation analysis of shot-putter throwing optimal path.
Externí odkaz:
https://doaj.org/article/af44f78b081447599c5e0756a275d875
Autor:
Mao, Xiaofeng, Jiang, Zhengkai, Wang, Fu-Yun, Zhu, Wenbing, Zhang, Jiangning, Chen, Hao, Chi, Mingmin, Wang, Yabiao
Video diffusion models have shown great potential in generating high-quality videos, making them an increasingly popular focus. However, their inherent iterative nature leads to substantial computational and time costs. While efforts have been made t
Externí odkaz:
http://arxiv.org/abs/2409.11367
Autor:
Jin, Ying, Peng, Jinlong, He, Qingdong, Hu, Teng, Chen, Hao, Wu, Jiafu, Zhu, Wenbing, Chi, Mingmin, Liu, Jun, Wang, Yabiao, Wang, Chengjie
The performance of anomaly inspection in industrial manufacturing is constrained by the scarcity of anomaly data. To overcome this challenge, researchers have started employing anomaly generation approaches to augment the anomaly dataset. However, ex
Externí odkaz:
http://arxiv.org/abs/2408.13509
Positive and Unlabeled (PU) learning, a binary classification model trained with only positive and unlabeled data, generally suffers from overfitted risk estimation due to inconsistent data distributions. To address this, we introduce a pseudo-superv
Externí odkaz:
http://arxiv.org/abs/2407.06698
Autor:
Du, Qiangang, Peng, Jinlong, Chen, Xu, He, Qingdong, He, Liren, Nie, Qiang, Zhu, Wenbing, Chi, Mingmin, Wang, Yabiao, Wang, Chengjie
Change detection is widely applied in remote sensing image analysis. Existing methods require training models separately for each dataset, which leads to poor domain generalization. Moreover, these methods rely heavily on large amounts of high-qualit
Externí odkaz:
http://arxiv.org/abs/2404.11326
Autor:
Du, Qiangang, Peng, Jinlong, Wang, Changan, Chen, Xu, He, Qingdong, Zhu, Wenbing, Chi, Mingmin, Wang, Yabiao, Wang, Chengjie
Change detection aims to identify remote sense object changes by analyzing data between bitemporal image pairs. Due to the large temporal and spatial span of data collection in change detection image pairs, there are often a significant amount of tas
Externí odkaz:
http://arxiv.org/abs/2404.11318
Autor:
Wang, Chengjie, Zhu, Wenbing, Gao, Bin-Bin, Gan, Zhenye, Zhang, Jianning, Gu, Zhihao, Qian, Shuguang, Chen, Mingang, Ma, Lizhuang
Industrial anomaly detection (IAD) has garnered significant attention and experienced rapid development. However, the recent development of IAD approach has encountered certain difficulties due to dataset limitations. On the one hand, most of the sta
Externí odkaz:
http://arxiv.org/abs/2403.12580
Autor:
He, Liren, Jiang, Zhengkai, Peng, Jinlong, Liu, Liang, Du, Qiangang, Hu, Xiaobin, Zhu, Wenbing, Chi, Mingmin, Wang, Yabiao, Wang, Chengjie
In the field of multi-class anomaly detection, reconstruction-based methods derived from single-class anomaly detection face the well-known challenge of "learning shortcuts", wherein the model fails to learn the patterns of normal samples as it shoul
Externí odkaz:
http://arxiv.org/abs/2403.11561
Change detection is a widely adopted technique in remote sense imagery (RSI) analysis in the discovery of long-term geomorphic evolution. To highlight the areas of semantic changes, previous effort mostly pays attention to learning representative fea
Externí odkaz:
http://arxiv.org/abs/2305.18714