Gian Antonio Susto
Gian Antonio Susto
Associate Professor @ University of Padova, Co-founder @ Statwolf
Bestätigte E-Mail-Adresse bei - Startseite
Zitiert von
Zitiert von
Machine learning for predictive maintenance: A multiple classifier approach
GA Susto, A Schirru, S Pampuri, S McLoone, A Beghi
IEEE transactions on industrial informatics 11 (3), 812-820, 2014
Control of PDE-ODE cascades with Neumann interconnections
GA Susto, M Krstic
Journal of the Franklin Institute 347 (1), 284-314, 2010
An explainable artificial intelligence approach for unsupervised fault detection and diagnosis in rotating machinery
LC Brito, GA Susto, JN Brito, MAV Duarte
Mechanical Systems and Signal Processing 163, 108105, 2022
Time-series classification methods: Review and applications to power systems data
GA Susto, A Cenedese, M Terzi
Big data application in power systems, 179-220, 2018
A predictive maintenance system for epitaxy processes based on filtering and prediction techniques
GA Susto, A Beghi, C De Luca
IEEE Transactions on Semiconductor Manufacturing 25 (4), 638-649, 2012
A convolutional autoencoder approach for feature extraction in virtual metrology
M Maggipinto, C Masiero, A Beghi, GA Susto
Procedia Manufacturing 17, 126-133, 2018
Anomaly detection through on-line isolation forest: An application to plasma etching
GA Susto, A Beghi, S McLoone
2017 28th Annual SEMI Advanced Semiconductor Manufacturing Conference (ASMC …, 2017
Explainable machine learning in industry 4.0: Evaluating feature importance in anomaly detection to enable root cause analysis
M Carletti, C Masiero, A Beghi, GA Susto
2019 IEEE international conference on systems, man and cybernetics (SMC), 21-26, 2019
Supervised aggregative feature extraction for big data time series regression
GA Susto, A Schirru, S Pampuri, S McLoone
IEEE Transactions on Industrial Informatics 12 (3), 1243-1252, 2015
A one-class svm based tool for machine learning novelty detection in hvac chiller systems
A Beghi, L Cecchinato, C Corazzol, M Rampazzo, F Simmini, GA Susto
IFAC Proceedings Volumes 47 (3), 1953-1958, 2014
Anomaly detection approaches for semiconductor manufacturing
GA Susto, M Terzi, A Beghi
Procedia Manufacturing 11, 2018-2024, 2017
Multi-step virtual metrology for semiconductor manufacturing: A multilevel and regularization methods-based approach
GA Susto, S Pampuri, A Schirru, A Beghi, G De Nicolao
Computers & Operations Research 53, 328-337, 2015
Data-driven anomaly recognition for unsupervised model-free fault detection in artificial pancreas
L Meneghetti, M Terzi, S Del Favero, GA Susto, C Cobelli
IEEE Transactions on Control Systems Technology 28 (1), 33-47, 2018
A computer vision-inspired deep learning architecture for virtual metrology modeling with 2-dimensional data
M Maggipinto, M Terzi, C Masiero, A Beghi, GA Susto
IEEE Transactions on Semiconductor Manufacturing 31 (3), 376-384, 2018
Dealing with time-series data in predictive maintenance problems
GA Susto, A Beghi
2016 IEEE 21st International Conference on Emerging Technologies and Factory …, 2016
Algorithmic fairness datasets: the story so far
A Fabris, S Messina, G Silvello, GA Susto
Data Mining and Knowledge Discovery 36 (6), 2074-2152, 2022
A hidden-Gamma model-based filtering and prediction approach for monotonic health factors in manufacturing
GA Susto, A Schirru, S Pampuri, A Beghi, G De Nicolao
Control Engineering Practice 74, 84-94, 2018
Gender stereotype reinforcement: Measuring the gender bias conveyed by ranking algorithms
A Fabris, A Purpura, G Silvello, GA Susto
Information Processing & Management 57 (6), 102377, 2020
Automatic control and machine learning for semiconductor manufacturing: Review and challenges
GA Susto, S Pampuri, A Schirru, G De Nicolao, SF McLoone, A Beghi
Proceedings of the 10th European Workshop on Advanced Control and Diagnosis …, 2012
DeepVM: A Deep Learning-based approach with automatic feature extraction for 2D input data Virtual Metrology
M Maggipinto, A Beghi, S McLoone, GA Susto
Journal of Process Control 84, 24-34, 2019
Das System kann den Vorgang jetzt nicht ausführen. Versuchen Sie es später erneut.
Artikel 1–20