| 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 |
| 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 |
| 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 |
| 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 |
| Gradient monitored reinforcement learning MSA Hameed, GS Chadha, A Schwung, SX Ding IEEE Transactions on Neural Networks and Learning Systems, 2021 | 10 | 2021 |
| Learning the non-linearity in convolutional neural networks GS Chadha, A Schwung arXiv preprint arXiv:1905.12337, 2019 | 6 | 2019 |
| Optimal dosing of bulk material using mass-flow estimation and DEM simulation GS Chadha, F Westbrink, T Schütte, A Schwung 2018 IEEE International Conference on Industrial Technology (ICIT), 256-261, 2018 | 5 | 2018 |
| Regularizing neural networks with gradient monitoring GS Chadha, E Meydani, A Schwung Recent Advances in Big Data and Deep Learning: Proceedings of the INNS Big …, 2020 | 4 | 2020 |
| Integrated IPC for data-driven fault detection F Westbrink, GS Chadha, A Schwung 2018 IEEE Industrial Cyber-Physical Systems (ICPS), 277-282, 2018 | 4 | 2018 |
| Generalized Dilation Neural Networks GS Chadha, JN Reimann, A Schwung arXiv preprint arXiv:1905.02961, 2019 | 3 | 2019 |
| Generalized Dilation Structures in Convolutional Neural Networks. GS Chadha, JN Reimann, A Schwung ICPRAM, 79-88, 2021 | 2 | 2021 |
| Permutation Learning in Convolutional Neural Networks for Time-Series Analysis GS Chadha, J Kim, A Schwung, SX Ding Artificial Neural Networks and Machine Learning–ICANN 2020: 29th …, 2020 | 2 | 2020 |
| Traffic sign detection using R-CNN P Rehlaender, M Schroeer, G Chadha, A Schwung Recent Advances in Big Data and Deep Learning: Proceedings of the INNS Big …, 2020 | 2 | 2020 |
| Intelligent Systems with Applications SRB Shah, GS Chadha, A Schwung, SX Ding | 1 | 2021 |
| Remaining useful lifetime estimation with sobolev training GS Chadha, SMN Sakeib, A Schwung 2020 25th IEEE international conference on emerging technologies and factory …, 2020 | 1 | 2020 |
Ding, Steven X.AKS, University of Duisburg-EssenE-mailová adresa ověřena na: uni-due.de
