Zobrazeno 1 - 10
of 48
pro vyhledávání: '"Wang, Cunshi"'
Autor:
Wang, Cunshi, Hu, Xinjie, Zhang, Yu, Chen, Xunhao, Du, Pengliang, Mao, Yiming, Wang, Rui, Li, Yuyang, Wu, Ying, Yang, Hang, Li, Yansong, Wang, Beichuan, Mu, Haiyang, Wang, Zheng, Tian, Jianfeng, Ge, Liang, Mao, Yongna, Li, Shengming, Lu, Xiaomeng, Zou, Jinhang, Huang, Yang, Sun, Ningchen, Zheng, Jie, He, Min, Bai, Yu, Jin, Junjie, Wu, Hong, Shang, Chaohui, Liu, Jifeng
With the rapid advancements in Large Language Models (LLMs), LLM-based agents have introduced convenient and user-friendly methods for leveraging tools across various domains. In the field of astronomical observation, the construction of new telescop
Externí odkaz:
http://arxiv.org/abs/2412.06412
Light curves serve as a valuable source of information on stellar formation and evolution. With the rapid advancement of machine learning techniques, it can be effectively processed to extract astronomical patterns and information. In this study, we
Externí odkaz:
http://arxiv.org/abs/2404.10757
Context. Variability carries physical patterns and astronomical information of objects, and stellar light curve variations are essential to understand the stellar formation and evolution processes. The studies of variations in stellar photometry have
Externí odkaz:
http://arxiv.org/abs/2305.13745
Autor:
Wang, Cunshi, Bai, Yu, Yuan, Haibo, Liu, Jifeng, Fernández-Ontiveros, J. A., Coelho, Paula R. T., Jiménez-Esteban, F., Galarza, Carlos Andrés, Angulo, R. E., Cenarro, A. J., Cristóbal-Hornillos, D., Dupke, R. A., Ederoclite, A., Hernández-Monteagudo, C., López-Sanjuan, C., Marín-Franch, A., Moles, M., Sodré Jr., L., Ramió, H. Vázquez, Varela, J.
Publikováno v:
A&A 664, A38 (2022)
Context. Stellar parameters are among the most important characteristics in studies of stars, which are based on atmosphere models in traditional methods. However, time cost and brightness limits restrain the efficiency of spectral observations. The
Externí odkaz:
http://arxiv.org/abs/2205.02595
Autor:
Wang, Cunshi, Zhu, Jianzhong, Xiao, Gongkui, Zhou, Xiaobin, Wang, Hailong, Zhu, Qiuzi, Gao, Zhimin, Cao, Yanyan
Publikováno v:
In Chemical Engineering Journal 15 September 2024 496
Autor:
Wang, Hailong, Gao, Zhimin, Zhu, Qiuzi, Wang, Cunshi, Cao, Yanyan, Chen, Liang, Liu, Jianlong, Zhu, Jianzhong
Publikováno v:
In Environmental Pollution 15 August 2024 355
Autor:
Wang, Cunshi, Bai, Yu, López-Sanjuan, C., Yuan, Haibo, Wang, Song, Liu, Jifeng, Sobral, David, Baqui, P. O., Martín, E. L., Galarza, Carlos Andres, Alcaniz, J., Angulo, R. E., Cenarro, A. J., Cristóbal-Hornillos, D., Dupke, R. A., Ederoclite, A., Hernández-Monteagudo, C., Marín-Franch, A., Moles, M., Sodré Jr., L., Ramió, H. Vázquez, Varela, J.
Publikováno v:
A&A 659, A144 (2022)
Context. In modern astronomy, machine learning has proved to be efficient and effective to mine the big data from the newesttelescopes. Spectral surveys enable us to characterize millions of objects, while long exposure time observations and wide sur
Externí odkaz:
http://arxiv.org/abs/2106.12787
Publikováno v:
In Journal of Environmental Chemical Engineering April 2024 12(2)
Autor:
Zhu, Qiuzi, Chen, Liang, Zhu, Tiancheng, Gao, Zhimin, Wang, Cunshi, Geng, Ruiwen, Bai, Wangjun, Cao, Yanyan, Zhu, Jianzhong
Publikováno v:
In Environmental Pollution 1 February 2024 342
Autor:
Wang, Cunshi, Xiao, Gongkui, Zhou, Xiaobin, Zhu, Qiuzi, Chen, Yuanyi, Gao, Zhimin, Liu, Chao, Zhu, Jianzhong
Publikováno v:
In Separation and Purification Technology 15 October 2023 323