Machine learning methods for small data challenges in molecular science
B Dou, Z Zhu, E Merkurjev, L Ke, L Chen… - Chemical …, 2023 - ACS Publications
Small data are often used in scientific and engineering research due to the presence of
various constraints, such as time, cost, ethics, privacy, security, and technical limitations in …
various constraints, such as time, cost, ethics, privacy, security, and technical limitations in …
Applications of Reinforcement Learning for maintenance of engineering systems: A review
AP Marugán - Advances in Engineering Software, 2023 - Elsevier
Nowadays, modern engineering systems require sophisticated maintenance strategies to
ensure their correct performance. Maintenance has become one of the most important tasks …
ensure their correct performance. Maintenance has become one of the most important tasks …
High-accuracy model-based reinforcement learning, a survey
Deep reinforcement learning has shown remarkable success in the past few years. Highly
complex sequential decision making problems from game playing and robotics have been …
complex sequential decision making problems from game playing and robotics have been …
Deep model-based reinforcement learning for high-dimensional problems, a survey
Deep reinforcement learning has shown remarkable success in the past few years. Highly
complex sequential decision making problems have been solved in tasks such as game …
complex sequential decision making problems have been solved in tasks such as game …
A review: machine learning for combinatorial optimization problems in energy areas
Combinatorial optimization problems (COPs) are a class of NP-hard problems with great
practical significance. Traditional approaches for COPs suffer from high computational time …
practical significance. Traditional approaches for COPs suffer from high computational time …
Frequency regulation capacity offering of district cooling system: An intrinsic-motivated reinforcement learning method
District cooling system (DCS), a type of large-capacity air conditioning system that supplies
cooling for multiple buildings, is an ideal resource to provide frequency regulation services …
cooling for multiple buildings, is an ideal resource to provide frequency regulation services …
[HTML][HTML] A review on machine learning in flexible surgical and interventional robots: Where we are and where we are going
Abstract Minimally Invasive Procedures (MIPs) emerged as an alternative to more invasive
surgical approaches, offering patient benefits such as smaller incisions, less pain, and …
surgical approaches, offering patient benefits such as smaller incisions, less pain, and …
Deep reinforcement learning for the heat transfer control of pulsating impinging jets
This research study explores the applicability of Deep Reinforcement Learning (DRL) for
thermal control based on Computational Fluid Dynamics. To accomplish that, the forced …
thermal control based on Computational Fluid Dynamics. To accomplish that, the forced …
Design and implementation of reinforcement learning‐based intelligent jamming system
S Zhang, H Tian, X Chen, Z Du, L Huang… - IET …, 2020 - Wiley Online Library
Here the intelligent jammer issue is studied. With the rapid development of cognitive radio
technology, current cognitive terminals can adaptively or intelligently switch channel by …
technology, current cognitive terminals can adaptively or intelligently switch channel by …
Comparison of model-based and model-free reinforcement learning for real-world dexterous robotic manipulation tasks
D Valencia, J Jia, R Li, A Hayashi… - … on robotics and …, 2023 - ieeexplore.ieee.org
Model Free Reinforcement Learning (MFRL) has shown significant promise for learning
dexterous robotic manipulation tasks, at least in simulation. However, the high number of …
dexterous robotic manipulation tasks, at least in simulation. However, the high number of …