23 citations · 52 across the 10 of their papers we have counts for
12 papers
An ensemble of convolution-based methods for fault detection using vibration signals
Xian Yeow Lee, Aman Kumar, Lasitha Vidyaratne +3
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 ma…
Distributed Online Non-convex Optimization with Composite Regret
Zhanhong Jiang, Aditya Balu, Xian Yeow Lee +3
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 va…
Stochastic Conservative Contextual Linear Bandits
Jiabin Lin, Xian Yeow Lee, Talukder Jubery +3
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 pa…
MDPGT: Momentum-based Decentralized Policy Gradient Tracking
Zhanhong Jiang, Xian Yeow Lee, Sin Yong Tan +5
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 ove…
A Graph Policy Network Approach for Volt-Var Control in Power Distribution Systems
Xian Yeow Lee, Soumik Sarkar, Yubo Wang
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 t…
PowerGym: A Reinforcement Learning Environment for Volt-Var Control in Power Distribution Systems
Ting-Han Fan, Xian Yeow Lee, Yubo Wang
We introduce PowerGym, an open-source reinforcement learning environment for Volt-Var control in power distribution systems. Following OpenAI Gym APIs, PowerGym targets minimizing…
Query-based Targeted Action-Space Adversarial Policies on Deep Reinforcement Learning Agents
Xian Yeow Lee, Yasaman Esfandiari, Kai Liang Tan +1
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…
Robustifying Reinforcement Learning Agents via Action Space Adversarial Training
Kai Liang Tan, Yasaman Esfandiari, Xian Yeow Lee +2
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 gri…
Spatiotemporally Constrained Action Space Attacks on Deep Reinforcement Learning Agents
Xian Yeow Lee, Sambit Ghadai, Kai Liang Tan +2
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 incr…
Learning to Cope with Adversarial Attacks
Xian Yeow Lee, Aaron Havens, Girish Chowdhary +1
The security of Deep Reinforcement Learning (Deep RL) algorithms deployed in real life applications are of a primary concern. In particular, the robustness of RL agents in cyber-ph…
Flow Shape Design for Microfluidic Devices Using Deep Reinforcement Learning
Xian Yeow Lee, Aditya Balu, Daniel Stoecklein +2
Microfluidic devices are utilized to control and direct flow behavior in a wide variety of applications, particularly in medical diagnostics. A particularly popular form of microfl…
Multi-level 3D CNN for Learning Multi-scale Spatial Features
Sambit Ghadai, Xian Lee, Aditya Balu +2
3D object recognition accuracy can be improved by learning the multi-scale spatial features from 3D spatial geometric representations of objects such as point clouds, 3D models, su…