Zobrazeno 1 - 10
of 208
pro vyhledávání: '"Zhou Lidong"'
Publikováno v:
Frontiers in Energy Research, Vol 12 (2024)
To enhance the resilience of power distribution networks against extreme natural disasters, this article introduces a robust fault recovery strategy for multi-source, flexible interconnected power distribution networks, particularly under scenarios o
Externí odkaz:
https://doaj.org/article/2fe3aee8ad314001950ff72babe39f3f
Publikováno v:
Zhejiang dianli, Vol 41, Iss 7, Pp 94-100 (2022)
Given the increasingly severe security situation at home and abroad， the security of power system has received growing attention. Power monitoring system is one of the most important systems of power system. This paper proposes a security protectio
Externí odkaz:
https://doaj.org/article/eca929da22f5413c9f96f40cfaae87ba
Autor:
Chen, Tianyu, Lu, Shuai, Lu, Shan, Gong, Yeyun, Yang, Chenyuan, Li, Xuheng, Misu, Md Rakib Hossain, Yu, Hao, Duan, Nan, Cheng, Peng, Yang, Fan, Lahiri, Shuvendu K, Xie, Tao, Zhou, Lidong
Ensuring correctness is crucial for code generation. Formal verification offers a definitive assurance of correctness, but demands substantial human effort in proof construction and hence raises a pressing need for automation. The primary obstacle li
Externí odkaz:
http://arxiv.org/abs/2410.15756
Autor:
Xiong, Yifan, Jiang, Yuting, Yang, Ziyue, Qu, Lei, Zhao, Guoshuai, Liu, Shuguang, Zhong, Dong, Pinzur, Boris, Zhang, Jie, Wang, Yang, Jose, Jithin, Pourreza, Hossein, Baxter, Jeff, Datta, Kushal, Ram, Prabhat, Melton, Luke, Chau, Joe, Cheng, Peng, Xiong, Yongqiang, Zhou, Lidong
Reliability in cloud AI infrastructure is crucial for cloud service providers, prompting the widespread use of hardware redundancies. However, these redundancies can inadvertently lead to hidden degradation, so called "gray failure", for AI workloads
Externí odkaz:
http://arxiv.org/abs/2402.06194
Autor:
Zheng, Ningxin, Jiang, Huiqiang, Zhang, Quanlu, Han, Zhenhua, Yang, Yuqing, Ma, Lingxiao, Yang, Fan, Zhang, Chengruidong, Qiu, Lili, Yang, Mao, Zhou, Lidong
Dynamic sparsity, where the sparsity patterns are unknown until runtime, poses a significant challenge to deep learning. The state-of-the-art sparsity-aware deep learning solutions are restricted to pre-defined, static sparsity patterns due to signif
Externí odkaz:
http://arxiv.org/abs/2301.10936
Autor:
Lin, Zhiqi, Miao, Youshan, Liu, Guodong, Shi, Xiaoxiang, Zhang, Quanlu, Yang, Fan, Maleki, Saeed, Zhu, Yi, Cao, Xu, Li, Cheng, Yang, Mao, Zhang, Lintao, Zhou, Lidong
With the growing model size, deep neural networks (DNN) are increasingly trained over massive GPU accelerators, which demands a proper parallelization plan that transforms a DNN model into fine-grained tasks and then schedules them to GPUs for execut
Externí odkaz:
http://arxiv.org/abs/2301.08984
Autor:
Lei, Doudou, Li, Lingling, Song, Pengyue, Xu, QingBin, Huang, Lihua, Ma, Xiao, Zhou, Lidong, Kong, Weijun
Publikováno v:
In Food Chemistry: X 30 December 2024 24
Publikováno v:
In Ultramicroscopy December 2024 267
Autor:
Zhang, Xian, Guo, Xiaobing, Zeng, Zixuan, Liu, Wenyan, Guo, Zhongxin, Chen, Yang, Chen, Shuo, Yin, Qiufeng, Yang, Mao, Zhou, Lidong
Anti-piracy is fundamentally a procedure that relies on collecting data from the open anonymous population, so how to incentivize credible reporting is a question at the center of the problem. Industrial alliances and companies are running anti-pirac
Externí odkaz:
http://arxiv.org/abs/2107.06049
Autor:
Li, Ying, Jia, Boyu, Song, Pengyue, Long, Nan, Shi, Linchun, Li, Peng, Wang, Jiabo, Zhou, Lidong, Kong, Weijun
Publikováno v:
In Food Chemistry 15 March 2024 436