Zobrazeno 1 - 10
of 200
pro vyhledávání: '"Liu, Danfeng"'
Autor:
Tao, Xian, Qu, Zhen, Luo, Hengliang, Han, Jianwen, He, Yonghao, Liu, Danfeng, Lv, Chengkan, Shen, Fei, Zhang, Zhengtao
The Vision Challenge Track 1 for Data-Effificient Defect Detection requires competitors to instance segment 14 industrial inspection datasets in a data-defificient setting. This report introduces the technical details of the team Aoi-overfifitting-Te
Externí odkaz:
http://arxiv.org/abs/2306.14116
Autor:
Lv, Beibei, Teng, Dong, Huang, Xinzheng, Liu, Xiaohe, Liu, Danfeng, Khashaveh, Adel, Pan, Hongsheng, Zhang, Yongjun
Publikováno v:
In International Journal of Biological Macromolecules November 2024 281 Part 1
Autor:
Yang Lei-Lei, Li Hangshuo, Liu Danfeng, Li Kaiyuan, Li Songya, Li Yuhan, Du Pengxi, Yan Miaochen, Zhang Yi, He Wei
Publikováno v:
Nanotechnology Reviews, Vol 12, Iss 1, Pp 399-411 (2023)
Externí odkaz:
https://doaj.org/article/470c3e9f19a1440f9563f8a5161d7b8f
Autor:
Liu, Danfeng1 (AUTHOR) 202211051003@dlnu.edu.cn, Wang, Enyuan1 (AUTHOR) wangliguo@hrbeu.edu.cn, Wang, Liguo1 (AUTHOR) 202211051005@dlnu.edu.cn, Benediktsson, Jón Atli2 (AUTHOR) benedikt@hi.is, Wang, Jianyu1 (AUTHOR) 202211051004@dlnu.edu.cn, Deng, Lei1 (AUTHOR)
Publikováno v:
Remote Sensing. Aug2024, Vol. 16 Issue 16, p2941. 25p.
Publikováno v:
In Materials Today Communications December 2024 41
Publikováno v:
In Cancer Letters 28 April 2024 588
Autor:
Teng, Dong, Liu, Danfeng, Khashaveh, Adel, Lv, Beibei, Sun, Peiyao, Geng, Ting, Cui, Hongzhi, Wang, Yi, Zhang, Yongjun
Publikováno v:
In Journal of Advanced Research May 2024
Autor:
Liu, Danfeng1 (AUTHOR), Liu, Yunshan1 (AUTHOR), Liu, Maoye1 (AUTHOR), Geng, Yupeng1 (AUTHOR), Zhang, Yongjun2 (AUTHOR), Siemann, Evan3 (AUTHOR), Li, Bo1 (AUTHOR), Wang, Yi1 (AUTHOR) yiwang@ynu.edu.cn
Publikováno v:
Journal of Pest Science. Mar2024, Vol. 97 Issue 2, p793-807. 15p.
Akademický článek
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Autor:
ZHANG Xueyuan, XU Hongyan, DONG Yueming, LIU Danfeng, SUN Pengrui, YAN Rui, CUI Hongliang, LEI Hong, REN Fei
Publikováno v:
Xiehe Yixue Zazhi, Vol 14, Iss 1, Pp 139-147 (2023)
Objective To establish a fungal image-assisted classification model using deep learning technology. Methods The microscope images of people infected with Aspergillus, Saccharomyces and Cryptococcus neoformans were retrospectively collected from the E
Externí odkaz:
https://doaj.org/article/45ac9620b99442d28b245cbc02dc3d69