Zobrazeno 1 - 10
of 108
pro vyhledávání: '"Rugang Wang"'
Autor:
Weibin Kong, Haonan Zhang, Xiaofang Yang, Zijian Yao, Rugang Wang, Wenwen Yang, Jiachen Zhang
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-26 (2024)
Abstract Traditional Proportional-Integral-Derivative (PID) control systems often encounter challenges related to nonlinearity and time-variability. Original dung beetle optimizer (DBO) offers fast convergence and strong local exploitation capabiliti
Externí odkaz:
https://doaj.org/article/78f65baeba8d4f269ed5f7cf5c7212f5
Publikováno v:
PeerJ Computer Science, Vol 10, p e2494 (2024)
Salient object detection aims to identify the most prominent objects within an image. With the advent of fully convolutional networks (FCNs), deep learning-based saliency detection models have increasingly leveraged FCNs for pixel-level saliency pred
Externí odkaz:
https://doaj.org/article/25e9385a0f154879a622ee9f939a872b
Publikováno v:
IEEE Access, Vol 12, Pp 140809-140822 (2024)
Aiming at the YOLO (You Only Look Once) algorithm’s low detection accuracy caused by the complex background environment and large target scale difference in optical remote sensing image detection, the lightweight convolution fusion attention mechan
Externí odkaz:
https://doaj.org/article/c3cb9830801642858cd9b9e6dd312aad
Autor:
Rongfeng Zhou, Ping Li, Mingyang Zhang, Qingyao Lin, Yuanyuan Wang, Xuesheng Bian, Feng Zhou, Rugang Wang
Publikováno v:
IEEE Access, Vol 12, Pp 45773-45784 (2024)
Aiming at the conventional low-light enhancement algorithms for low-light image enhancement with problems of loss of details, low contrast and low color saturation, a detection algorithm called CE-Retinex(Cross Expansion Retinex) by incorporating the
Externí odkaz:
https://doaj.org/article/b4645a8503f844a3ab4d87d95847e8d0
Publikováno v:
IEEE Access, Vol 12, Pp 34741-34751 (2024)
Aiming at the YOLO (You Only Look Once) algorithm’s low detection accuracy caused by the complex background environment and large target scale difference in the detection of optical remote sensing images, the Deformable Convolutional Fusion Attenti
Externí odkaz:
https://doaj.org/article/0d8e5da444c74428ac1256a16debbb59
Publikováno v:
PeerJ Computer Science, Vol 10, p e2021 (2024)
To resolve the challenges of low detection accuracy and inadequate real-time performance in road scene detection, this article introduces the enhanced algorithm SDG-YOLOv5. The algorithm incorporates the SIoU Loss function to accurately predict the a
Externí odkaz:
https://doaj.org/article/4703e09678794edfb6398b7356735371
Autor:
Xinsheng Wu, Guohui Wu, Ping Ma, Rugang Wang, Linghua Li, Yuanyi Chen, Junjie Xu, Yuwei Li, Quanmin Li, Yuecheng Yang, Lijing Wang, Xiaoli Xin, Ying Qiao, Gengfeng Fu, Xiaojie Huang, Bin Su, Tong Zhang, Hui Wang, Huachun Zou
Publikováno v:
Nature Communications, Vol 14, Iss 1, Pp 1-10 (2023)
Abstract Despite the proven virological advantages, there remains some controversy regarding whether first-line integrase strand transfer inhibitors (INSTIs)-based antiretroviral therapy (ART) contributes to reducing mortality of people living with H
Externí odkaz:
https://doaj.org/article/cde7a281a7b1451b9c34aa888209f16c
Autor:
Xinsheng Wu, Guohui Wu, Ping Ma, Rugang Wang, Linghua Li, Yinghui Sun, Junjie Xu, Yuwei Li, Tong Zhang, Quanmin Li, Yuecheng Yang, Lijing Wang, Xiaoli Xin, Ying Qiao, Bingxue Fang, Zhen Lu, Xinyi Zhou, Yuanyi Chen, Qi Liu, Gengfeng Fu, Hongxia Wei, Xiaojie Huang, Bin Su, Hui Wang, Huachun Zou
Publikováno v:
Infectious Diseases of Poverty, Vol 12, Iss 1, Pp 1-14 (2023)
Abstract Background In 2003, China implemented free antiretroviral therapy (ART) for people living with HIV (PLHIV), establishing an eligibility threshold of CD4 50 (+ 7.8%, IRR = 1.078, 95% CI: 1.000–1.161; P = 0.046), heterosexual transmission (+
Externí odkaz:
https://doaj.org/article/7435601dd32c47e38807c9fa82fb5729
Publikováno v:
PLoS ONE, Vol 19, Iss 7, p e0305260 (2024)
Aiming at the problem that the SSD algorithm does not fully extract the feature information contained in each feature layer, as well as the feature information is easily lost during the sampling process, which makes the feature expression ineffective
Externí odkaz:
https://doaj.org/article/52a117fe0bd7446e8a15b41eaa963366
Publikováno v:
PeerJ Computer Science, Vol 10, p e1727 (2024)
The detection of surface defects on metal products during the production process is crucial for ensuring high-quality products. These defects also lead to significant losses in the high-tech industry. To address the issues of slow detection speed and
Externí odkaz:
https://doaj.org/article/8b990c096a0640a09b1ac41402009a8e