19 citations · 77 across the 16 of their papers we have counts for
16 papers
Data-driven Residual Generation for Early Fault Detection with Limited Data
Hamed Khorasgani, Ahmed Farahat, Chetan Gupta
Traditionally, fault detection and isolation community has used system dynamic equations to generate diagnosers and to analyze detectability and isolability of the dynamic systems.…
An Offline Deep Reinforcement Learning for Maintenance Decision-Making
Hamed Khorasgani, Haiyan Wang, Chetan Gupta +1
Several machine learning and deep learning frameworks have been proposed to solve remaining useful life estimation and failure prediction problems in recent years. Having access to…
Deep Reinforcement Learning with Adjustments
Hamed Khorasgani, Haiyan Wang, Chetan Gupta +1
Deep reinforcement learning (RL) algorithms can learn complex policies to optimize agent operation over time. RL algorithms have shown promising results in solving complicated prob…
Deep Time Series Models for Scarce Data
Qiyao Wang, Ahmed Farahat, Chetan Gupta +1
Time series data have grown at an explosive rate in numerous domains and have stimulated a surge of time series modeling research. A comprehensive comparison of different time seri…
A Non-linear Function-on-Function Model for Regression with Time Series Data
Qiyao Wang, Haiyan Wang, Chetan Gupta +2
In the last few decades, building regression models for non-scalar variables, including time series, text, image, and video, has attracted increasing interests of researchers from…
Wisdom of the Ensemble: Improving Consistency of Deep Learning Models
Lijing Wang, Dipanjan Ghosh, Maria Teresa Gonzalez Diaz +5
Deep learning classifiers are assisting humans in making decisions and hence the user's trust in these models is of paramount importance. Trust is often a function of constant beha…