Zobrazeno 1 - 10
of 35
pro vyhledávání: '"Lee, Xian Yeow"'
Autor:
Lee, Xian Yeow, Kumar, Aman, Vidyaratne, Lasitha, Rao, Aniruddha Rajendra, Farahat, Ahmed, Gupta, Chetan
Publikováno v:
2023 IEEE International Conference on Prognostics and Health Management (ICPHM)
This paper focuses on solving a fault detection problem using multivariate time series of vibration signals collected from planetary gearboxes in a test rig. Various traditional machine learning and deep learning methods have been proposed for multiv
Externí odkaz:
http://arxiv.org/abs/2305.05532
Regret has been widely adopted as the metric of choice for evaluating the performance of online optimization algorithms for distributed, multi-agent systems. However, data/model variations associated with agents can significantly impact decisions and
Externí odkaz:
http://arxiv.org/abs/2209.10105
Autor:
Lin, Jiabin, Lee, Xian Yeow, Jubery, Talukder, Moothedath, Shana, Sarkar, Soumik, Ganapathysubramanian, Baskar
Many physical systems have underlying safety considerations that require that the strategy deployed ensures the satisfaction of a set of constraints. Further, often we have only partial information on the state of the system. We study the problem of
Externí odkaz:
http://arxiv.org/abs/2203.15629
Autor:
Jiang, Zhanhong, Lee, Xian Yeow, Tan, Sin Yong, Tan, Kai Liang, Balu, Aditya, Lee, Young M., Hegde, Chinmay, Sarkar, Soumik
We propose a novel policy gradient method for multi-agent reinforcement learning, which leverages two different variance-reduction techniques and does not require large batches over iterations. Specifically, we propose a momentum-based decentralized
Externí odkaz:
http://arxiv.org/abs/2112.02813
Volt-var control (VVC) is the problem of operating power distribution systems within healthy regimes by controlling actuators in power systems. Existing works have mostly adopted the conventional routine of representing the power systems (a graph wit
Externí odkaz:
http://arxiv.org/abs/2109.12073
We introduce PowerGym, an open-source reinforcement learning environment for Volt-Var control in power distribution systems. Following OpenAI Gym APIs, PowerGym targets minimizing power loss and voltage violations under physical networked constraints
Externí odkaz:
http://arxiv.org/abs/2109.03970
Publikováno v:
In AI Open 2024 5:126-141
Advances in computing resources have resulted in the increasing complexity of cyber-physical systems (CPS). As the complexity of CPS evolved, the focus has shifted from traditional control methods to deep reinforcement learning-based (DRL) methods fo
Externí odkaz:
http://arxiv.org/abs/2011.07114
Adoption of machine learning (ML)-enabled cyber-physical systems (CPS) are becoming prevalent in various sectors of modern society such as transportation, industrial, and power grids. Recent studies in deep reinforcement learning (DRL) have demonstra
Externí odkaz:
http://arxiv.org/abs/2007.07176
Robustness of Deep Reinforcement Learning (DRL) algorithms towards adversarial attacks in real world applications such as those deployed in cyber-physical systems (CPS) are of increasing concern. Numerous studies have investigated the mechanisms of a
Externí odkaz:
http://arxiv.org/abs/1909.02583