| Data-driven ship digital twin for estimating the speed loss caused by the marine fouling A Coraddu, L Oneto, F Baldi, F Cipollini, M Atlar, S Savio Ocean Engineering 186, 106063, 2019 | 133 | 2019 |
| Condition-based maintenance of naval propulsion systems with supervised data analysis F Cipollini, L Oneto, A Coraddu, AJ Murphy, D Anguita Ocean Engineering 149, 268-278, 2018 | 68 | 2018 |
| Condition-based maintenance of naval propulsion systems: Data analysis with minimal feedback F Cipollini, L Oneto, A Coraddu, AJ Murphy, D Anguita Reliability Engineering & System Safety 177, 12-23, 2018 | 63 | 2018 |
| Predicting the cavitating marine propeller noise at design stage: A deep learning based approach L Miglianti, F Cipollini, L Oneto, G Tani, S Gaggero, A Coraddu, M Viviani Ocean Engineering 209, 107481, 2020 | 28 | 2020 |
| Unsupervised deep learning for induction motor bearings monitoring F Cipollini, L Oneto, A Coraddu, S Savio Data-Enabled Discovery and Applications 3, 1-13, 2019 | 27 | 2019 |
| Model scale cavitation noise spectra prediction: combining physical knowledge with data science F Miglianti, F Cipollini, L Oneto, G Tani, M Viviani Ocean Engineering 178, 185-203, 2019 | 16 | 2019 |
| Physical, data-driven and hybrid approaches to model engine exhaust gas temperatures in operational conditions A Coraddu, L Oneto, F Cipollini, M Kalikatzarakis, GJ Meijn, R Geertsma Ships and Offshore Structures 17 (6), 1360-1381, 2022 | 12 | 2022 |
| Unintrusive monitoring of induction motors bearings via deep learning on stator currents F Cipollini, L Oneto, A Coraddu, S Savio, D Anguita Procedia computer science 144, 42-51, 2018 | 10 | 2018 |
| Marine safety and data analytics: Vessel crash stop maneuvering performance prediction L Oneto, A Coraddu, P Sanetti, O Karpenko, F Cipollini, T Cleophas, ... Artificial Neural Networks and Machine Learning–ICANN 2017: 26th …, 2017 | 7 | 2017 |
| Randomized learning: Generalization performance of old and new theoretically grounded algorithms L Oneto, F Cipollini, S Ridella, D Anguita Neurocomputing 298, 21-33, 2018 | 6 | 2018 |
| Deep learning for cavitating marine propeller noise prediction at design stage L Oneto, F Cipollini, L Miglianti, G Tani, S Gaggero, M Viviani, A Coraddu 2020 International Joint Conference on Neural Networks (IJCNN), 1-10, 2020 | 5 | 2020 |
| Simple continuous optimal regions of the space of data A Carrega, F Cipollini, L Oneto Neurocomputing 349, 91-104, 2019 | 4 | 2019 |
| Crash stop maneuvering performance prediction: a data-driven solution for safety and collision avoidance L Oneto, A Coraddu, F Cipollini, O Karpenko, K Xepapa, P Sanetti, ... Data-Enabled Discovery and Applications 2, 1-11, 2018 | 4 | 2018 |
| Cavitation noise spectra prediction with hybrid models F Cipollini, F Miglianti, L Oneto, G Tani, M Viviani, D Anguita Recent Advances in Big Data and Deep Learning: Proceedings of the INNS Big …, 2020 | 2 | 2020 |
| Hybrid model for cavitation noise spectra prediction F Cipollini, F Miglianti, L Oneto, G Tani, M Viviani 2019 International Joint Conference on Neural Networks (IJCNN), 1-8, 2019 | 2 | 2019 |
| Data driven models for propeller cavitation noise in model scale F Miglianti, G Tani, M Viviani, F Cipollini, L Oneto Proceedings of the 6th International Symposium on Marine Propulsors, Rome, Italy, 2019 | 2 | 2019 |
| A Deep Learning Approach to Marine Propulsion System Maintenance F Cipollini, A Coraddu, L Oneto 3rd International Symposium on Naval Architecture and Maritime (INT-NAM …, 2018 | | 2018 |
Luca OnetoDIBRIS - University of GenoaVerified email at unige.it
