Zobrazeno 1 - 10
of 339
pro vyhledávání: '"Li Lanqing"'
Publikováno v:
发电技术, Vol 45, Iss 4, Pp 684-695 (2024)
ObjectivesDistributed photovoltaic power prediction is of great significance for the operation and scheduling of photovoltaic power plants. Point prediction methods are difficult to comprehensively describe the uncertainty of distributed photovoltaic
Externí odkaz:
https://doaj.org/article/53432ce182f54b819d4d0d2cde8f49a7
Publikováno v:
Applied Mathematics and Nonlinear Sciences, Vol 9, Iss 1 (2024)
Optimizing the active control of the power system and improving the stability of the system in the face of cyber-attacks are necessary to secure the power supply and achieve energy saving and emission reduction. The article proposes an improved granu
Externí odkaz:
https://doaj.org/article/9928eb155a49415eb3f88a5e64785d2c
Publikováno v:
Applied Mathematics and Nonlinear Sciences, Vol 9, Iss 1 (2024)
This study employs An evolutionary algorithm to set up a multilayer BP neural network. The goal is to solve the issue that BP neural systems converge slowly and readily fall into local optimal solutions. The genetic algorithm reaches an initial set o
Externí odkaz:
https://doaj.org/article/f4e61d9bae734f3fa39cda850a9de0ec
Offline meta reinforcement learning (OMRL) has emerged as a promising approach for interaction avoidance and strong generalization performance by leveraging pre-collected data and meta-learning techniques. Previous context-based approaches predominan
Externí odkaz:
http://arxiv.org/abs/2405.12001
Autor:
Chen, Kexin, Cao, Hanqun, Li, Junyou, Du, Yuyang, Guo, Menghao, Zeng, Xin, Li, Lanqing, Qiu, Jiezhong, Heng, Pheng Ann, Chen, Guangyong
Chemical synthesis, which is crucial for advancing material synthesis and drug discovery, impacts various sectors including environmental science and healthcare. The rise of technology in chemistry has generated extensive chemical data, challenging r
Externí odkaz:
http://arxiv.org/abs/2402.12993
As a marriage between offline RL and meta-RL, the advent of offline meta-reinforcement learning (OMRL) has shown great promise in enabling RL agents to multi-task and quickly adapt while acquiring knowledge safely. Among which, Context-based OMRL (CO
Externí odkaz:
http://arxiv.org/abs/2402.02429
Autor:
Chen, Kexin, Li, Junyou, Wang, Kunyi, Du, Yuyang, Yu, Jiahui, Lu, Jiamin, Li, Lanqing, Qiu, Jiezhong, Pan, Jianzhang, Huang, Yi, Fang, Qun, Heng, Pheng Ann, Chen, Guangyong
Recent AI research plots a promising future of automatic chemical reactions within the chemistry society. This study proposes Chemist-X, a transformative AI agent that automates the reaction condition recommendation (RCR) task in chemical synthesis w
Externí odkaz:
http://arxiv.org/abs/2311.10776
How to effectively represent molecules is a long-standing challenge for molecular property prediction and drug discovery. This paper studies this problem and proposes to incorporate chemical domain knowledge, specifically related to chemical reaction
Externí odkaz:
http://arxiv.org/abs/2305.01912
Autor:
Han, Zongbo, Liang, Zhipeng, Yang, Fan, Liu, Liu, Li, Lanqing, Bian, Yatao, Zhao, Peilin, Hu, Qinghua, Wu, Bingzhe, Zhang, Changqing, Yao, Jianhua
Subpopulation shift exists widely in many real-world applications, which refers to the training and test distributions that contain the same subpopulation groups but with different subpopulation proportions. Ignoring subpopulation shifts may lead to
Externí odkaz:
http://arxiv.org/abs/2304.04148
Reinforcement learning (RL) has shown promise for decision-making tasks in real-world applications. One practical framework involves training parameterized policy models from an offline dataset and subsequently deploying them in an online environment
Externí odkaz:
http://arxiv.org/abs/2303.07046