457 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- University of CambridgeGB45 papers
- University of NottinghamGB23 papers
- FZU ‒ Institute of Physics of the Academy of Sciences of the Czech RepublicCZ21 papers
- Hitachi (Japan)JP13 papers
- Commissariat à l'Énergie Atomique et aux Énergies AlternativesFR8 papers
- Laboratoire d'Électronique des Technologies de l'InformationFR8 papers
- Donostia International Physics CenterES7 papers
- Hitachi Global Storage Technologies (United States)US7 papers
- CEA GrenobleFR6 papers
- Texas A&M UniversityUS6 papers
- University of ExeterGB6 papers
- Chinese Academy of SciencesCN5 papers
9 papers · 1 filter
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…
Efficient Bayesian Optimization using Multiscale Graph Correlation
Takuya Kanazawa
Bayesian optimization is a powerful tool to optimize a black-box function, the evaluation of which is time-consuming or costly. In this paper, we propose a new approach to Bayesian…
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…
Challenges of Applying Deep Reinforcement Learning in Dynamic Dispatching
Hamed Khorasgani, Haiyan Wang, Chetan Gupta
Dynamic dispatching aims to smartly allocate the right resources to the right place at the right time. Dynamic dispatching is one of the core problems for operations optimization i…
Dynamic Dispatching for Large-Scale Heterogeneous Fleet via Multi-agent Deep Reinforcement Learning
Chi Zhang, Philip Odonkor, Shuai Zheng +3
Dynamic dispatching is one of the core problems for operation optimization in traditional industries such as mining, as it is about how to smartly allocate the right resources to t…
DeepCO: Offline Combinatorial Optimization Framework Utilizing Deep Learning
Wenpeng Wei, Toshiko Aizono
Combinatorial optimization serves as an essential part in many modern industrial applications. A great number of the problems are offline setting due to safety and/or cost issues.…
Health Indicator Forecasting for Improving Remaining Useful Life Estimation
Qiyao Wang, Ahmed Farahat, Chetan Gupta +1
Prognostics is concerned with predicting the future health of the equipment and any potential failures. With the advances in the Internet of Things (IoT), data-driven approaches fo…
Regularized Operating Envelope with Interpretability and Implementability Constraints
Qiyao Wang, Haiyan Wang, Chetan Gupta +1
Operating envelope is an important concept in industrial operations. Accurate identification for operating envelope can be extremely beneficial to stakeholders as it provides a set…
Remaining Useful Life Estimation Using Functional Data Analysis
Qiyao Wang, Shuai Zheng, Ahmed Farahat +2
Remaining Useful Life (RUL) of an equipment or one of its components is defined as the time left until the equipment or component reaches its end of useful life. Accurate RUL estim…