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AGOSTINI, FEDERICO
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Onset time detection of acoustic emission signals for structural monitoring with deep learning
In corso di stampa Melchiorre, Jonathan; Agostini, Federico; D'Amato, Leo; Rosso, MARCO MARTINO
Acoustic emission onset time detection for structural monitoring with U-Net neural network architecture
2024 Melchiorre, Jonathan; D'Amato, Leo; Agostini, Federico; Rizzo, Antonino Maria
Deep-Learning-Based Onset Time Precision in Acoustic Emission Non-Destructive Testing
2024 Melchiorre, J.; D'Amato, L.; Agostini, F.; Manuello, A.
A post processing pipeline to prepare raw data for machine learning algorithms in cardiac magnetic resonance imaging
2022 Agostini, F; Campese, S; Vianello, R; Pizzi, M; Cipriani, A; Zanetti, M
Beyond Transformers: fault type detection in maintenance tickets with Kernel Methods, Boost Decision Trees and Neural Networks
2022 Campese, Stefano; Agostini, Federico; Pazzini, Jacopo; Pozza, Davide
Myocardial fibrosis detection using kernel methods: preliminary results from a cardiac magnetic resonance study
2022 Campese, S; Agostini, F; Sciarretta, T; Pizzi, M; Cipriani, A; Zanetti, M
| Citazione | Data di pubblicazione | Autori | File |
|---|---|---|---|
| Onset time detection of acoustic emission signals for structural monitoring with deep learning / Melchiorre, Jonathan; Agostini, Federico; D'Amato, Leo; Rosso, MARCO MARTINO - In: Titolo volume non avvalorato[s.l] : Springer, In corso di stampa. | In corso di stampa | Jonathan MelchiorreFederico AgostiniLeo D'AmatoMarco Martino Rosso | WIRN_2023___Onset_Time_Detection_of_Acoustic_Emission_Signals_for_Structural_Monitoring_with_Deep_Learning__FULL_PAPER_.pdf |
| Acoustic emission onset time detection for structural monitoring with U-Net neural network architecture / Melchiorre, Jonathan; D'Amato, Leo; Agostini, Federico; Rizzo, Antonino Maria. - In: DEVELOPMENTS IN THE BUILT ENVIRONMENT. - ISSN 2666-1659. - ELETTRONICO. - 18:(2024), pp. 1-13. [10.1016/j.dibe.2024.100449] | 1-gen-2024 | Melchiorre, JonathanD'Amato, LeoAgostini, FedericoRizzo, Antonino Maria | Post-print version.pdf |
| Deep-Learning-Based Onset Time Precision in Acoustic Emission Non-Destructive Testing / Melchiorre, J.; D'Amato, L.; Agostini, F.; Manuello, A.. - 2004:(2024), pp. 367-372. (Intervento presentato al convegno 2024 IEEE International Workshop on Metrology for Living Environment, MetroLivEnv 2024 tenutosi a Chania (GRC) nel 12-14 June 2024) [10.1109/MetroLivEnv60384.2024.10615695]. | 1-gen-2024 | Melchiorre J.D'Amato L.Agostini F.Manuello A. | Metrolivenv2024_Deep_learning_based_onset_time_precision_in_acoustic_emission_non_destructive_testing (6).pdf; Deep-Learning-Based_Onset_Time_Precision_in_Acoustic_Emission_Non-Destructive_Testing.pdf |
| A post processing pipeline to prepare raw data for machine learning algorithms in cardiac magnetic resonance imaging / Agostini, F; Campese, S; Vianello, R; Pizzi, M; Cipriani, A; Zanetti, M. - In: EUROPEAN HEART JOURNAL. CARDIOVASCULAR IMAGING. - ISSN 2047-2404. - 23:Supplement_2(2022). [10.1093/ehjci/jeac141.017] | 1-gen-2022 | Agostini, F + | jeac141.017.pdf; ESC_raw_data_pipeline.pdf |
| Beyond Transformers: fault type detection in maintenance tickets with Kernel Methods, Boost Decision Trees and Neural Networks / Campese, Stefano; Agostini, Federico; Pazzini, Jacopo; Pozza, Davide. - (2022), pp. 1-8. (Intervento presentato al convegno 2022 IEEE World Congress on Computational Intelligence tenutosi a Padua (Italy) nel 18-23 July 2022) [10.1109/IJCNN55064.2022.9892980]. | 1-gen-2022 | Agostini,Federico + | poster_a1.pdf; Beyond_Transformers_fault_type_detection_in_maintenance_tickets_with_Kernel_Methods_Boost_Decision_Trees_and_Neural_Networks.pdf |
| Myocardial fibrosis detection using kernel methods: preliminary results from a cardiac magnetic resonance study / Campese, S; Agostini, F; Sciarretta, T; Pizzi, M; Cipriani, A; Zanetti, M. - In: EUROPEAN HEART JOURNAL. CARDIOVASCULAR IMAGING. - ISSN 2047-2404. - 23:Supplement_2(2022). [10.1093/ehjci/jeac141.005] | 1-gen-2022 | Agostini, F + | jeac141.005.pdf; ESC_analysis.pdf |