Zobrazeno 1 - 10
of 277
pro vyhledávání: '"LI, FUPENG"'
Autor:
Yang, Kairui, Guo, Zihao, Lin, Gengjie, Dong, Haotian, Zuo, Die, Peng, Jibin, Huang, Zhao, Xu, Zhecheng, Li, Fupeng, Bai, Ziyun, Lin, Di
We advocate the idea of the natural-language-driven(NLD) simulation to efficiently produce the object interactions between multiple objects in the virtual road scenes, for teaching and testing the autonomous driving systems that should take quick act
Externí odkaz:
http://arxiv.org/abs/2312.04008
Autor:
Kevin F. A. Darras, Marcel Balle, Wenxiu Xu, Yang Yan, Vincent G. Zakka, Manuel Toledo‐Hernández, Dong Sheng, Wei Lin, Boyu Zhang, Zhenzhong Lan, Li Fupeng, Thomas C. Wanger
Publikováno v:
Methods in Ecology and Evolution, Vol 15, Iss 12, Pp 2262-2275 (2024)
Abstract We need comprehensive information to manage and protect biodiversity in the face of global environmental challenges, and artificial intelligence is required to generate that information from vast amounts of biodiversity data. Currently, visi
Externí odkaz:
https://doaj.org/article/73c79530b01444ec9d59cf8a7f548d38
Publikováno v:
In Journal of Hazardous Materials 5 December 2024 480
Publikováno v:
In Environmental Technology & Innovation November 2024 36
Publikováno v:
In Environmental Technology & Innovation November 2024 36
Autor:
WANG Shiyuan, WANG Jing, LI Fupeng, TAO Zhigang, LIANG Mingjian, LIU Shao, QU Miao, ZHANG Liwen, ZENG Weizu, JIN Yunxia
Publikováno v:
Dizhi lixue xuebao, Vol 30, Iss 2, Pp 275-288 (2024)
Objective The Litang-Yidun fault is a left-lateral strike-slip active fault zone extending approximately 130 km in the Sichuan-Yunnan rhombic block in the Holocene. As a significant seismogenic structure controlling seismic activity in the Litang are
Externí odkaz:
https://doaj.org/article/cf784be3f88a4769a439e598abf3d44e
Autor:
Liu, Zheng, Chao, Nengfang, Chen, Gang, Zhang, Guoqing, Wang, Zhengtao, Li, Fupeng, Ouyang, Guichong
Publikováno v:
In Science of the Total Environment 20 September 2024 944
Publikováno v:
In Science of the Total Environment 20 September 2024 944
Publikováno v:
In Environmental Technology & Innovation August 2024 35
Autor:
Tsang, C. Y., Wang, Yongjia, Tsang, M. B., Estee, J., Isobe, T., Kaneko, M., Kurata-Nishimura, M., Lee, J. W., Li, Fupeng, Li, Qingfeng, Lynch, W. G., Murakami, T., Wang, R., Cozma, Dan, Kumar, Rohit, Ono, Akira, Zhang, Ying-Xun
Machine Learning (ML) algorithms have been demonstrated to be capable of predicting impact parameter in heavy-ion collisions from transport model simulation events with perfect detector response. We extend the scope of ML application to experimental
Externí odkaz:
http://arxiv.org/abs/2107.13985