no code implementations • 30 Mar 2024 • Yuji Cao, Huan Zhao, Yuheng Cheng, Ting Shu, Guolong Liu, Gaoqi Liang, Junhua Zhao, Yun Li
With extensive pre-trained knowledge and high-level general capabilities, large language models (LLMs) emerge as a promising avenue to augment reinforcement learning (RL) in aspects such as multi-task learning, sample efficiency, and task planning.
no code implementations • 28 Jan 2024 • Chao Yang, Gaoqi Liang, Steven R. Weller, Shaoyan Li, Junhua Zhao, ZhaoYang Dong
Fast and reliable transmission network reconfiguration is critical in improving power grid resilience to cyber-attacks.
no code implementations • 28 Nov 2023 • Jiaqi Ruan, Xiangrui Meng, Yifan Zhu, Gaoqi Liang, Xianzhuo Sun, Huayi Wu, Huijuan Xiao, Mengqian Lu, Pin Gao, Jiapeng Li, Wai-kin Wong, Zhao Xu, Junhua Zhao
Modern society's reliance on power systems is at risk from the escalating effects of wind-related climate change.
no code implementations • 22 Nov 2023 • Jiaqi Ruan, Gaoqi Liang, Huan Zhao, Guolong Liu, Xianzhuo Sun, Jing Qiu, Zhao Xu, Fushuan Wen, Zhao Yang Dong
Applying large language models (LLMs) to modern power systems presents a promising avenue for enhancing decision-making and operational efficiency.
no code implementations • 6 Sep 2018 • Jinjin Gu, Haoyu Chen, Guolong Liu, Gaoqi Liang, Xinlei Wang, Junhua Zhao
In this paper, we present the problem formulation and methodology framework of Super-Resolution Perception (SRP) on industrial sensor data.