1 | Date | Publication | Source |
|---|---|---|---|
2 | 2022 | Marx, D., & Gryllias, K. (2022). Domain Knowledge Informed Unsupervised Fault Detection for Rolling Element Bearings. PHM Society European Conference, 7(1), 338–350. https://doi.org/10.36001/phme.2022.v7i1.3348 | https://doi.org/10.36001/phme.2022.v7i1.3348 |
3 | 2022 | Marx, D. G., Liu, C., Antoni, J., & Gryllias, K. (2022). Deep learning implementations of cyclo-stationary signal processing methods. In 2022 Leuven Conference on Noise and Vibration Engineering (pp. 661–678). | https://kuleuven.limo.libis.be/discovery/fulldisplay?docid=lirias3909561&context=SearchWebhook&vid=32KUL_KUL:Lirias&search_scope=lirias_profile&tab=LIRIAS&adaptor=SearchWebhook&lang=en |
4 | 2022 | Yang, J., Soltan, A. A. S., & Clifton, D. A. (2022). Machine learning generalizability across healthcare settings: insights from multi-site COVID-19 screening. NPJ digital medicine, 5(1), 69. https://doi.org/10.1038/s41746-022-00614-9 | https://doi.org/10.1038/s41746-022-00614-9 |
5 | 2022 | Yang, J., Clifton, D., Hirst, J. E., Kavvoura, F. K., Farah, G., Mackillop, L., & Lu, H. (2022). Machine Learning-Based Risk Stratification for Gestational Diabetes Management. Sensors (Basel, Switzerland), 22(13), 4805. https://doi.org/10.3390/s22134805 | https://doi.org/10.3390/s22134805 |
6 | 2023 | Yang, J., Soltan, A. A., Eyre, D. W., Yang, Y., & Clifton, D. A. (2023). An adversarial training framework for mitigating algorithmic biases in clinical machine learning. npj Digital Medicine, 6(1), 55. https://doi.org/10.1038/s41746-023-00805-y | https://doi.org/10.1038/s41746-023-00805-y |
7 | 2021 | Shiri, H., Wodecki, J., Ziętek, B., & Zimroz, R. (2021). Inspection Robotic UGV Platform and the Procedure for an Acoustic Signal-Based Fault Detection in Belt Conveyor Idler. Energies, 14(22), 7646. https://doi.org/10.3390/en14227646 | https://doi.org/10.3390/en14227646 |
8 | 2023 | Żuławiński, W., Maraj-Zygmąt, K., Shiri, H., Agnieszka, W., Zimroz, R. (2023). Framework for stochastic modelling of long-term non-homogeneous data with non-Gaussian characteristics for machine condition prognosis. Mechanical Systems and Signal Processing. 184. 109677. 10.1016/j.ymssp.2022.109677. | https://doi.org/10.1016/j.ymssp.2022.109677 |
9 | 2022 | Moosavi, F., Shiri, H., Wodecki, J., Wyłomańska, A., & Zimroz, R. (2022). Application of Machine Learning Tools for Long-Term Diagnostic Feature Data Segmentation. Applied Sciences, 12(13), 6766. https://doi.org/10.3390/app12136766 | https://doi.org/10.3390/app12136766 |
10 | 2022 | Krot, P., Shiri, H., Zimroz, R. (2022). Using the natural modes of transient vibrations in predictive maintenance of industrial machines. 30th Conference Vibrations in Physical Systems VIBSYS 2022 | https://www.researchgate.net/publication/364570570_Using_the_natural_modes_of_transient_vibrations_in_predictive_maintenance_of_industrial_machines |
11 | 2022 | Wodecki, J., Shiri, H., Siami, M., Zimroz, R.. (2022). Acoustic-based diagnostics of belt conveyor idlers in real life mining conditions by mobile inspection robot. 2022 Leuven Conference on Noise and Vibration Engineering (ISMA). 12-14 September 2022 https://doi.org/10.5281/zenodo.7864612 | https://doi.org/10.5281/zenodo.7864612 |
12 | 2021 | Shiri, H., Wodecki, J..(2021) Analysis of the sound signal to fault detection of bearings based on Variational Mode Decomposition. IOP Conf. Ser.: Earth Environ. Sci. 942 012020 | https://iopscience.iop.org/article/10.1088/1755-1315/942/1/012020/meta |
13 | 2021 | Zimroz, P., Shiri, H., Wodecki1, J.. (2021) Analysis of the vibro-acoustic data from test rig -comparison of acoustic and vibrational methods. IOP Conf. Ser.: Earth Environ. Sci. 942 012017 | https://iopscience.iop.org/article/10.1088/1755-1315/942/1/012017 |
14 | 2022 | Paz, B., Sorrosal, G., Mancisidor, A.. Intelligent Adaptative Robotic System for Physical Interaction Tasks. In 2022 Sixth IEEE International Conference on Robotic Computing (IRC), Italy, 2022 pp. 429-430. doi: 10.1109/IRC55401.2022.00082. | https://www.computer.org/csdl/proceedings-article/irc/2022/726000a429/1KckgCdyJSU |
15 | 2022 | Sal, B.T., Sorrosal, G., Mancisidor, A. Control System for Robotic Interaction Tasks. European Robotics Forum, Rotterdam, Holanda, 2022. (poster) | |
16 | 2022 | Kunte, D., Colangeli, C., Cornelis, B., Gryllias, K., De Veuster, C., Janssens, K. (2022). Transfer Learning for Booming Noise Classification. DAGA 2022 - 48. JAHRESTAGUNG FÜR AKUSTIK, 21 - 24 March 2022. | https://www.researchgate.net/publication/365471696_Transfer_Learning_for_Booming_Noise_Classification |
17 | 2022 | Kunte, D., Cornelis, B., Colangeli, C., De Veuster, C., and Gryllias, K.. Transfer learning for unsupervised booming noise classification. 2022 Leuven Conference on Noise and Vibration Engineering (ISMA), 12-14 September 2022. | https://kuleuven.limo.libis.be/discovery/fulldisplay?docid=lirias4064866&context=SearchWebhook&vid=32KUL_KUL:Lirias&search_scope=lirias_profile&tab=LIRIAS&adaptor=SearchWebhook&lang=en |
18 | 2022 | Deuschle, F., Cornelis, B., Lanslots, J., and Gryllias, K.. Overload detection in MEMS Microphone-based acoustic arrays. 2022 Leuven Conference on Noise and Vibration Engineering (ISMA), 12-14 September 2022. | http://past.isma-isaac.be/downloads/isma2022/proceedings/Contribution_423_proceeding_3.pdf |
19 | 2022 | Deuschle, F., Cornelis, B., Gryllias, K.. (2022). Robust sensor spike detection method based on Dynamic Time Warping. 4th International Conference on Advances in Signal Processing and AI, 19-21 October 2022. | shorturl.at/fyT15 |
20 | 2022 | Siami, M., Barszcz, T., Wodecki, J., & Zimroz, R. (2022). Design of an Infrared Image Processing Pipeline for Robotic Inspection of Conveyor Systems in Opencast Mining Sites. Energies, 15(18), 6771. https://doi.org/10.3390/en15186771 | https://doi.org/10.3390/en15186771 |
21 | 2022 | Siami, M., Barszcz, T., Wodecki, J., & Zimroz, R. (2022). Automated Identification of Overheated Belt Conveyor Idlers in Thermal Images with Complex Backgrounds Using Binary Classification with CNN. Sensors, 22(24), 10004. https://doi.org/10.3390/s222410004 | https://doi.org/10.3390/s222410004 |
22 | 2022 | Siami, M., Barszcz, T., Zimroz, R., Wodecki, J.. (2022). Robot-based Damage Assessment Method for Identification of Overheated Idlers in Conveyor Systems Using Histogram Analysis Techniques. IOP Conf. Ser.: Earth Environ. Sci. | https://www.mdpi.com/1424-8220/22/24/10004 |
23 | 2023 | Achilleos, A., Leclere, Q., & Antoni, J. (2023, July). Feature Extraction in non-stationary conditions. Surveillance, Vibrations, Shock and Noise. Retrieved from https://hal.science/hal-04165648 | https://hal.science/hal-04165648 |
24 | 2023 | Ahani, M., Bourdon, A., & Rémond, D. (2023, July). Effective Identification of Cyclic Excitation and Resonance in Non-stationary Gearbox Vibration Monitoring. Surveillance, Vibrations, Shock and Noise. Retrieved from https://hal.science/hal-04165641 | https://hal.science/hal-04165641 |
25 | 2023 | Karkafi, F., Raad, A., Abboud, D., Marnissi, Y., Doquet, G., & El Badaoui, M. (2023, July). Automated domain adaptation for bearings fault detection and classification. Surveillance, Vibrations, Shock and Noise. Retrieved from https://hal.science/hal-04165945 | https://hal.science/hal-04165945 |
26 | 2023 | Jabbar, A., Cocconcelli, M., d’Elia, G., Strozzi, M., & Rubini, R. (2023, July). Results on Experimental Data Analysis of Independent Cart Systems in Non-Stationary Conditions. Surveillance, Vibrations, Shock and Noise. Retrieved from https://hal.science/hal-04165905 | https://hal.science/hal-04165905 |
27 | 2023 | Cornelis, B., Deuschle, F., & Gryllias, K. (2023, July). Performance study of DTW-based spike measurement anomaly detection in sensors on real-world tests. Surveillance, Vibrations, Shock and Noise. Retrieved from https://hal.science/hal-04166051 | https://hal.science/hal-04166051 |
28 | 2023 | De Fabritiis, F., & Gryllias, K. (2023, July). A federated learning approach for rolling bearing fault diagnosis on data sources with imbalanced class distribution. In Surveillance, Vibrations, Shock and Noise. | https://hal.science/hal-04165856v1/document |
29 | 2023 | Kunte, D., Cornelis, B., Colangeli, C., & Gryllias, K. (2023, July). Investigating the usage of Proxy-A-Distance as a measure of dataset shift detection and quantification in an automotive booming noise classification setting. In Surveillance, Vibrations, Shock and Noise. | https://hal.science/hal-04166097/document |
30 | 2023 | Yang, J., Soltan, A. A., Eyre, D. W., & Clifton, D. A. (2023). Algorithmic fairness and bias mitigation for clinical machine learning with deep reinforcement learning. Nature Machine Intelligence, 1-11. | https://www.nature.com/articles/s42256-023-00697-3 |
31 | 2023 | Yang, J., Eyre, D.W., Lu, L., & Clifton, D. A. (2023). Interpretable machine learning-based decision support for prediction of antibiotic resistance for complicated urinary tract infections. npj Antimicrob Resist 1, 14. | https://www.nature.com/articles/s44259-023-00015-2 |
32 | 2023 | Yang, J., Kunte, D. S., Cornelis, B., & Clifton, D. A. (2023, December). Reinforcement Learning for Imbalanced Vehicle Booming Noise Classification. In 2023 2nd International Conference on Machine Learning, Control, and Robotics (MLCR) (pp. 24-29). IEEE. | https://ieeexplore.ieee.org/abstract/document/10475441 |
33 | 2023 | Yang, J., El-Bouri, R., O’Donoghue, O., Lachapelle, A. S., Soltan, A. A., Eyre, D. W., ... & Clifton, D. A. (2023). Deep reinforcement learning for multi-class imbalanced training: applications in healthcare. Machine Learning, 1-20. | https://link.springer.com/article/10.1007/s10994-023-06481-z |
34 | 2024 | Kunte, D., Marx, D., Cornelis, B., Colangeli, C., & Gryllias, K. (2024). Data-efficient fault detection in vehicle end-of-line testing through fault location-informed classification. In 2024 Leuven Conference on Noise and Vibration Engineering, Leuven, Belgium (September 9–11, 2024). | https://www.researchgate.net/publication/387053707_Data-efficient_fault_detection_in_vehicle_end-of-line_testing_through_fault_location-informed_classification |
35 | 2024 | Marx, D., Kunte, D., Cornelis, B., & Gryllias, K. (2024). A method for adding domain knowledge to semi-supervised fault detection using signal processing gradients. In 2024 Leuven Conference on Noise and Vibration Engineering, Leuven, Belgium (September 9–11, 2024). | https://www.researchgate.net/publication/387053960_A_method_for_adding_domain_knowledge_to_semi-supervised_fault_detection_using_signal_processing_gradients |
36 | 2024 | Siami, M., Barszcz, T., & Zimroz, R. (2024). Advanced Image Analytics for Mobile Robot-Based Condition Monitoring in Hazardous Environments: A Comprehensive Thermal Defect Processing Framework. Sensors, 24(11), 3421. | https://doi.org/10.3390/s24113421 |
37 | 2024 | Siami, M., Barszcz, T., Wodecki, J. and Zimroz, R., 2024. Semantic segmentation of thermal defects in belt conveyor idlers using thermal image augmentation and U-Net-based convolutional neural networks. Scientific Reports, 14(1), p.5748. | https://doi.org/10.1038/s41598-024-55864-2 |
38 | 2024 | Mostafavi, A., Siami, M., Friedmann, A., Barszcz, T. and Zimroz, R., 2024, June. Probabilistic Uncertainty-Aware Decision Fusion of Neural Network for Bearing Fault Diagnosis. In Prognostics and Health Management Society (PHM European Conference) 2024 (Vol. 8, No. 1). | |
39 | 2024 | Yang, J., Clifton, L., Dung, N. T., Phong, N. T., Yen, L. M., Thy, D. B. X., ... & Clifton, D. A. (2024). Mitigating machine learning bias between high income and low–middle income countries for enhanced model fairness and generalizability. Scientific Reports, 14(1), 13318. | https://www.nature.com/articles/s41598-024-64210-5 |
40 | 2024 | Yang, J., Triendl, H., Soltan, A. A., Prakash, M., & Clifton, D. A. (2023). Addressing label noise for electronic health records: Insights from computer vision for tabular data. medRxiv. | https://link.springer.com/article/10.1186/s12911-024-02581-5 |
41 | 2024 | Yang, J., Dung, N. T., Thach, P. N., Phong, N. T., Phu, V. D., Phu, K. D., ... & Clifton, D. A. (2024). Generalizability assessment of AI models across hospitals in a low-middle and high income country. Nature Communications, 15(1), 8270. | https://www.nature.com/articles/s41467-024-52618-6 |
42 | 2024 | Jia, L., Cornelis, B., & Gryllias, K. Validation of time series anomaly detection methods for automated detection of measurement anomalies in noise and vibration testing. | https://www.researchgate.net/publication/387054249_Validation_of_time_series_anomaly_detection_methods_for_automated_detection_of_measurement_anomalies_in_noise_and_vibration_testing |