Zobrazeno 1 - 10
of 1 038
pro vyhledávání: '"CHEN, Yonghong"'
Autor:
Lan, Peng, Chen, Donglai, Xie, Chong, Chen, Keshu, He, Jinyuan, Zhang, Juntao, Chen, Yonghong, Xu, Yan
Federated learning is an approach to collaboratively training machine learning models for multiple parties that prohibit data sharing. One of the challenges in federated learning is non-IID data between clients, as a single model can not fit the data
Externí odkaz:
http://arxiv.org/abs/2306.16703
Pumped storage hydro units (PSHU) are great sources of flexibility in power systems. This is especially valuable in modern systems with increasing shares of intermittent renewable resources. However, the flexibility from PSHUs, particularly in the re
Externí odkaz:
http://arxiv.org/abs/2304.03821
While deep neural networks (DNNs) have strengthened the performance of cooperative multi-agent reinforcement learning (c-MARL), the agent policy can be easily perturbed by adversarial examples. Considering the safety critical applications of c-MARL,
Externí odkaz:
http://arxiv.org/abs/2204.07932
Publikováno v:
In Information Sciences May 2024 669
Autor:
She, Xinqi, Xiong, Tongqiang, Wang, Zhibin, Cai, Guoji, Chen, Yonghong, Sun, Yong, Zheng, ZhiPeng, Zhou, Guopeng, Feng, Bo
Publikováno v:
In Results in Physics May 2024 60
Autor:
Jackson, Rosemary J., Keiser, Megan S., Meltzer, Jonah C., Fykstra, Dustin P., Dierksmeier, Steven E., Hajizadeh, Soroush, Kreuzer, Johannes, Morris, Robert, Melloni, Alexandra, Nakajima, Tsuneo, Tecedor, Luis, Ranum, Paul T., Carrell, Ellie, Chen, YongHong, Nishtar, Maryam A., Holtzman, David M., Haas, Wilhelm, Davidson, Beverly L., Hyman, Bradley T.
Publikováno v:
In Molecular Therapy 1 May 2024 32(5):1373-1386
Autor:
Yang, Fan, Hong, Zhichao, Song, Yunxiong, Chen, Yonghong, Yan, Shiguang, Lin, Zhisheng, Chen, Ying, Wang, Genshui
Publikováno v:
In Ceramics International 1 September 2024 50(17) Part B:31482-31490
Publikováno v:
In Information Sciences August 2024 677
This work proposes a method of wind farm scenario generation to support real-time optimization tools and presents key findings therein. This work draws upon work from the literature and presents an efficient and scalable method for producing an adequ
Externí odkaz:
http://arxiv.org/abs/2106.09105