Zobrazeno 1 - 10
of 347
pro vyhledávání: '"Li Yongqian"'
Autor:
Jia Zhicheng, Li Yongqian, Wang Peixuan, Yang Kai, Shi Mengyu, Chen Wen, Liang Qihui, Guo Ying
Publikováno v:
Journal of Ovarian Research, Vol 17, Iss 1, Pp 1-12 (2024)
Abstract Aim This study was designed to investigate the pharmacological effects and mechanisms of ErZhiTianGui Decoction (EZTG) for age-related ovarian aging in mice. Methods This study used naturally aging mice as a model, and EZTG was used for intr
Externí odkaz:
https://doaj.org/article/89108174806b47e7b5da0d48248c02ca
Publikováno v:
Dianzi Jishu Yingyong, Vol 47, Iss 5, Pp 54-58 (2021)
In traditional Doppler velocity measurement, mixing quadrature and low-pass filtering are generally implemented in software, which results in relatively long measurement time and low accuracy. In order to solve the above-mentioned, STM32H743 single c
Externí odkaz:
https://doaj.org/article/fa27a7e5abcf4b18941769e4a4750cee
Autor:
Han, Jun, Chen, Zixiang, Li, Yongqian, Kou, Yiwen, Halperin, Eran, Tillman, Robert E., Gu, Quanquan
Electronic health records (EHRs) are a pivotal data source that enables numerous applications in computational medicine, e.g., disease progression prediction, clinical trial design, and health economics and outcomes research. Despite wide usability,
Externí odkaz:
http://arxiv.org/abs/2404.12314
Discrete diffusion models have emerged as powerful tools for high-quality data generation. Despite their success in discrete spaces, such as text generation tasks, the acceleration of discrete diffusion models remains under explored. In this paper, w
Externí odkaz:
http://arxiv.org/abs/2312.09193
Detecting hand actions in videos is crucial for understanding video content and has diverse real-world applications. Existing approaches often focus on whole-body actions or coarse-grained action categories, lacking fine-grained hand-action localizat
Externí odkaz:
http://arxiv.org/abs/2306.10858
Autor:
Liu, Zijuan1 (AUTHOR) Lzijuan@ncepu.edu.cn, Li, Yongqian1,2 (AUTHOR) lxzhang@ncepu.edu.cn, Zhang, Lixin1,2 (AUTHOR), Wang, Lei3 (AUTHOR) 1172101044@ncepu.edu.cn
Publikováno v:
Sensors (14248220). Sep2024, Vol. 24 Issue 18, p6023. 16p.
Autor:
Qi, Jiahao, Gong, Zhiqiang, Liu, Xingyue, Bin, Kangcheng, Chen, Chen, Li, Yongqian, Xue, Wei, Zhang, Yu, Zhong, Ping
Deep learning methodology contributes a lot to the development of hyperspectral image (HSI) analysis community. However, it also makes HSI analysis systems vulnerable to adversarial attacks. To this end, we propose a masked spatial-spectral autoencod
Externí odkaz:
http://arxiv.org/abs/2207.07803
Autor:
Zhang, Yu, Gong, Zhiqiang, Zhang, Yichuang, Li, YongQian, Bin, Kangcheng, Qi, Jiahao, Xue, Wei, Zhong, Ping
Transferable adversarial attack is always in the spotlight since deep learning models have been demonstrated to be vulnerable to adversarial samples. However, existing physical attack methods do not pay enough attention on transferability to unseen m
Externí odkaz:
http://arxiv.org/abs/2205.09592
Publikováno v:
In Sensors and Actuators: A. Physical 1 October 2024 376