Zobrazeno 1 - 10
of 218
pro vyhledávání: '"Liang Zihao"'
Publikováno v:
Frontiers in Psychiatry, Vol 14 (2023)
BackgroundPrevious clinical studies have found that negative mental states such as depression and anxiety are closely related to COVID-19 infection. We used Mendelian randomization (MR) to explore the relationship between depression, anxiety, and COV
Externí odkaz:
https://doaj.org/article/95fd1a72033e479492dab9d9aa278aee
Autor:
Somers Paul, Liang Zihao, Chi Teng, Johnson Jason E., Pan Liang, Boudouris Bryan W., Xu Xianfan
Publikováno v:
Nanophotonics, Vol 12, Iss 8, Pp 1571-1580 (2023)
The systems for multiphoton 3D nanoprinting are rapidly increasing in print speed for larger throughput and scale, unfortunately without also improvement in resolution. Separately, the process of photoinhibition lithography has been demonstrated to e
Externí odkaz:
https://doaj.org/article/0db39296ed41441192eb684fa53da5fa
Publikováno v:
E3S Web of Conferences, Vol 528, p 02021 (2024)
Energy-saving buildings have become a topical issue and widely used the energy-saving ventilation design, which aims to maximize the use of natural ventilation while reducing demand on mechanical ventilation. In this research, natural ventilation per
Externí odkaz:
https://doaj.org/article/0ae7e10e321847148010207594d07c7f
This paper proposes an Online Control-Informed Learning (OCIL) framework, which synthesizes the well-established control theories to solve a broad class of learning and control tasks in real time. This novel integration effectively handles practical
Externí odkaz:
http://arxiv.org/abs/2410.03924
This paper develops a policy learning method for tuning a pre-trained policy to adapt to additional tasks without altering the original task. A method named Adaptive Policy Gradient (APG) is proposed in this paper, which combines Bellman's principle
Externí odkaz:
http://arxiv.org/abs/2305.15193
This paper proposes a policy learning algorithm based on the Koopman operator theory and policy gradient approach, which seeks to approximate an unknown dynamical system and search for optimal policy simultaneously, using the observations gathered th
Externí odkaz:
http://arxiv.org/abs/2305.15188
This paper proposes a data-driven, iterative approach for inverse optimal control (IOC), which aims to learn the objective function of a nonlinear optimal control system given its states and inputs. The approach solves the IOC problem in a challengin
Externí odkaz:
http://arxiv.org/abs/2304.00100
Autor:
Liang, Zihao, Lo, Jason King Ching
Control barrier function (CBF) has recently started to serve as a basis to develop approaches for enforcing safety requirements in control systems. However, constructing such function for a general system is a non-trivial task. This paper proposes an
Externí odkaz:
http://arxiv.org/abs/2211.09854
Pre-training over mixtured multi-task, multi-domain, and multi-modal data remains an open challenge in vision perception pre-training. In this paper, we propose GPPF, a General Perception Pre-training Framework, that pre-trains a task-level dynamic n
Externí odkaz:
http://arxiv.org/abs/2208.02148
Publikováno v:
In Journal of Building Engineering 1 June 2024 86