| Contrastive learning based self-supervised time-series analysis J Pöppelbaum, GS Chadha, A Schwung Applied Soft Computing 117, 108397, 2022 | 97 | 2022 |
| Bidirectional deep recurrent neural networks for process fault classification GS Chadha, A Panambilly, A Schwung, SX Ding ISA transactions 106, 330-342, 2020 | 97 | 2020 |
| A sequence-to-sequence approach for remaining useful lifetime estimation using attention-augmented bidirectional LSTM SRB Shah, GS Chadha, A Schwung, SX Ding Intelligent Systems with Applications 10, 200049, 2021 | 41 | 2021 |
| Reinforcement learning on job shop scheduling problems using graph networks MSA Hameed, A Schwung arXiv preprint arXiv:2009.03836 154, 2020 | 39 | 2020 |
| Deep convolutional clustering-based time series anomaly detection GS Chadha, I Islam, A Schwung, SX Ding Sensors 21 (16), 5488, 2021 | 38 | 2021 |
| Comparison of deep neural network architectures for fault detection in Tennessee Eastman process GS Chadha, A Schwung 2017 22nd IEEE International Conference on Emerging Technologies and Factory …, 2017 | 37 | 2017 |
| Comparison of semi-supervised deep neural networks for anomaly detection in industrial processes GS Chadha, A Rabbani, A Schwung 2019 IEEE 17th international conference on industrial informatics (INDIN) 1 …, 2019 | 35 | 2019 |
| Time series based fault detection in industrial processes using convolutional neural networks GS Chadha, M Krishnamoorthy, A Schwung IECON 2019-45th Annual Conference of the IEEE Industrial Electronics Society …, 2019 | 30 | 2019 |
| PLC-based real-time realization of flatness-based feedforward control for industrial compression systems S Dominic, Y Löhr, A Schwung, SX Ding IEEE Transactions on Industrial Electronics 64 (2), 1323-1331, 2016 | 30 | 2016 |
| Decentralized learning of energy optimal production policies using PLC-informed reinforcement learning D Schwung, S Yuwono, A Schwung, SX Ding Computers & Chemical Engineering 152, 107382, 2021 | 29 | 2021 |
| Generalized dilation convolutional neural networks for remaining useful lifetime estimation GS Chadha, U Panara, A Schwung, SX Ding Neurocomputing 452, 182-199, 2021 | 28 | 2021 |
| Graph neural networks-based scheduler for production planning problems using reinforcement learning MSA Hameed, A Schwung Journal of Manufacturing Systems 69, 91-102, 2023 | 23 | 2023 |
| Optimization of DEM parameters using multi-objective reinforcement learning F Westbrink, A Elbel, A Schwung, SX Ding Powder Technology 379, 602-616, 2021 | 21 | 2021 |
| An application of reinforcement learning algorithms to industrial multi-robot stations for cooperative handling operation D Schwung, F Csaplar, A Schwung, SX Ding 2017 IEEE 15th International Conference on Industrial Informatics (INDIN …, 2017 | 21 | 2017 |
| Shared temporal attention transformer for remaining useful lifetime estimation GS Chadha, SRB Shah, A Schwung, SX Ding Ieee Access 10, 74244-74258, 2022 | 19 | 2022 |
| Distributed self-optimization of modular production units: A state-based potential game approach D Schwung, A Schwung, SX Ding IEEE Transactions on Cybernetics 52 (4), 2174-2185, 2020 | 19 | 2020 |
| Fault detection assessment using an extended fmea and a rule-based expert system F Arévalo, C Tito, MR Diprasetya, A Schwung 2019 IEEE 17th International Conference on Industrial Informatics (INDIN) 1 …, 2019 | 19 | 2019 |
| Self learning in flexible manufacturing units: A reinforcement learning approach D Schwung, JN Reimann, A Schwung, SX Ding 2018 International Conference on Intelligent Systems (IS), 31-38, 2018 | 19 | 2018 |
| MLPro 1.0-Standardized reinforcement learning and game theory in Python D Arend, S Yuwono, MR Diprasetya, A Schwung Machine Learning with Applications 9, 100341, 2022 | 18 | 2022 |
| Predicting rigid body dynamics using dual quaternion recurrent neural networks with quaternion attention J Pöppelbaum, A Schwung IEEE Access 10, 82923-82943, 2022 | 18 | 2022 |
