Publications

Ageing-aware battery discharge prediction with deep learning

L. Biggio; T. Bendinelli; C. Kulkarni; O. Fink 

Applied Energy. 2023-09-15. Vol. 346, p. 121229. DOI : 10.1016/j.apenergy.2023.121229.

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Multi-agent reinforcement learning with graph convolutional neural networks for optimal bidding strategies of generation units in electricity markets

P. Rokhforoz; M. Montazeri; O. Fink 

Expert Systems With Applications. 2023-04-13. Vol. 225, p. 120010. DOI : 10.1016/j.eswa.2023.120010.

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Incentive Mechanism in the Sponsored Content Market With Network Effects

M. Montazeri; P. Rokhforoz; H. Kebriaei; O. Fink 

Ieee Transactions On Computational Social Systems. 2023-03-27. DOI : 10.1109/TCSS.2023.3257233.

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Filter-Informed Spectral Graph Wavelet Networks for Multiscale Feature Extraction and Intelligent Fault Diagnosis

T. Li; C. Sun; O. Fink; Y. Yang; X. Chen et al. 

Ieee Transactions On Cybernetics. 2023-03-23. DOI : 10.1109/TCYB.2023.3256080.

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Fully learnable deep wavelet transform for unsupervised monitoring of high-frequency time series

G. Michau; G. M. Frusque; O. Fink 

Proceedings of the National Academy of Sciences. 2023-02-18. Vol. 119, num. 8, p. e2106598119. DOI : 10.1073/pnas.2106598119.

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Controlled generation of unseen faults for Partial and Open-Partial domain adaptation

K. Rombach; G. Michau; O. Fink 

Reliability Engineering & System Safety. 2023-02-01. Vol. 230, p. 108857. DOI : 10.1016/j.ress.2022.108857.

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Fusing physics-based and deep learning models for prognostics

M. Arias Chao; C. kulkarni; K. Goebel; O. Fink 

Reliability Engineering & System Safety. 2023-01-22. Vol. 217, p. 107961. DOI : 10.1016/j.ress.2021.107961.

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Multi-agent Actor-Critic with Time Dynamics Based Opponent Model, Neurocomputing

Y. Tian; K. Kladny; Q. Wang; Z. Huang; O. Fink 

Neurocomputing. 2023-01-14. Vol. 517, p. 165-172. DOI : 10.1016/j.neucom.2022.10.045.

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Multi-agent actor-critic with time dynamical opponent model

Y. Tian; K. -R. Kladny; Q. Wang; Z. Huang; O. Fink 

Neurocomputing. 2023-01-14. Vol. 517, p. 165-172. DOI : 10.1016/j.neucom.2022.10.045.

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Safe multi-agent deep reinforcement learning for joint bidding and maintenance scheduling of generation units

P. Rokhforoz; M. Montazeri; O. Fink 

Reliability Engineering & System Safety. 2023-01-10. Vol. 232, p. 109081. DOI : 10.1016/j.ress.2022.109081.

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A comprehensive review of digital twin-part 2: roles of uncertainty quantification and optimization, a battery digital twin, and perspectives (vol 66, 1, 2022)

A. Thelen; X. Zhang; O. Fink; Y. Lu; S. Ghosh et al. 

Structural And Multidisciplinary Optimization. 2023-01-01. Vol. 66, num. 1, p. 23. DOI : 10.1007/s00158-022-03476-7.

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A comprehensive review of digital twin-part 2: roles of uncertainty quantification and optimization, a battery digital twin, and perspectives

A. Thelen; X. Zhang; O. Fink; Y. Lu; S. Ghosh et al. 

Structural And Multidisciplinary Optimization. 2023-01-01. Vol. 66, num. 1, p. 1. DOI : 10.1007/s00158-022-03410-x.

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Acceleration-Guided Acoustic Signal Denoising Framework Based on Learnable Wavelet Transform Applied to Slab Track Condition Monitoring

B. Dai; G. Frusque; Q. Li; O. Fink 

Ieee Sensors Journal. 2022-12-15. Vol. 22, num. 24, p. 24140-24149. DOI : 10.1109/JSEN.2022.3218182.

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A comprehensive review of digital twin – part 1: modeling and twinning enabling technologies

A. Thelen; X. Zhang; O. Fink; Y. Lu; S. Ghosh et al. 

Structural And Multidisciplinary Optimization. 2022-12-01. Vol. 65, num. 12, p. 354. DOI : 10.1007/s00158-022-03425-4.

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Learning physics-consistent particle interactions

Z. Han; D. S. Kammer; O. Fink 

PNAS Nexus. 2022-11-18. Vol. 1, num. 5, p. 264. DOI : 10.1093/pnasnexus/pgac264.

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Generalization of an Encoder-Decoder LSTM model for flood prediction in ungauged catchments

Y. Zhang; S. Ragettli; P. Molnar; O. Fink; N. Peleg 

Journal Of Hydrology. 2022-11-01. Vol. 614, p. 128577. DOI : 10.1016/j.jhydrol.2022.128577.

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Multi-agent maintenance scheduling of generation unit in electricity market using safe deep reinforcement learning algorithm

P. Rokhforoz; O. Fink 

2022-09-01. 32nd European Safety and Reliability Conference (ESREL 2022), Dublin, Ireland, August 28 – September 1, 2022.

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Contrastive Feature Learning for Railway Infrastructure Fault Diagnostic

O. Fink; K. Rombach; G. Michau; K. Ratnasabapathy; W. Bürzle et al. 

2022-08-22. 32nd European Safety and Reliability Conference (ESREL 2022), Dublin, Ireland, August 28 – September 1, 2022. p. 1875 – 1881. DOI : 10.3850/978-981-18-5183-4_S02-07-645-cd.

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Vacuum Circuit Breaker Closing Time Key Moments Detection via Vibration Monitoring: A Run-to-Failure Study

C-C. Hsu; G. M. Frusque; M. Muratovic; C. M. Franck; O. Fink 

2022-08-16. IEEE International Conference on Systems, Man and Cybernetics, Prague, Czech Republic, October 9-12, 2022. p. 254-260. DOI : 10.1109/SMC53654.2022.9945354.

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A prescriptive Dirichlet power allocation policy with deep reinforcement learning

Y. Tian; M. Han; C. Kulkarni; O. Fink 

Reliability Engineering & System Safety. 2022-08-01. Vol. 224, p. 108529. DOI : 10.1016/j.ress.2022.108529.

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Learnable Wavelet Packet Transform for Data-Adapted Spectrograms

G. M. Frusque; O. Fink 

2022-04-27. ICASSP 2022 – IEEE International Conference on Acoustics, Speech and Signal Processing, Singapore, May 23-27, 2022. p. 3119-3123. DOI : 10.1109/ICASSP43922.2022.9747491.

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Real-time model calibration with deep reinforcement learning

Y. Tian; M. A. Chao; C. Kulkarni; K. Goebel; O. Fink 

Mechanical Systems and Signal Processing. 2022-02-15. Vol. 165, p. 108284. DOI : 10.1016/j.ymssp.2021.108284.

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Artificial intelligence across company borders

O. Fink; T. Netland; S. Feuerriegelc 

Communications of the ACM. 2022-01-03. Vol. 65, num. 1, p. 34-36. DOI : 10.1145/3470449.

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Maintenance scheduling of manufacturing systems based on optimal price of the network

P. Rokhforoz; O. Fink 

Reliability Engineering & System Safety. 2022-01-03. Vol. 217, p. 108088. DOI : 10.1016/j.ress.2021.108088.

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Continual Test-Time Domain Adaptation

Q. Wang; O. Fink; L. Van Gool; D. Dai 

2022-01-01. IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), New Orleans, LA, Jun 18-24, 2022. p. 7191-7201. DOI : 10.1109/CVPR52688.2022.00706.

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