Umberto Albertin

Ph.D. candidate in Ingegneria Elettrica, Elettronica E Delle Comunicazioni , 38^th cycle (2022-2025)
Department of Electronics and Telecommunications (DET)

Docente esterno e/o collaboratore didattico
Department of Electronics and Telecommunications (DET)

umberto.albertin@polito.it

Profile

PhD

Research topic

EDGE real-time predictive maintenance framework

Tutors

Marcello Chiaberge
Riccardo Lacagnina

Research presentation

Poster

Research interests

Analog, Power and Mixed-Signal Circuits and Embedded Systems

Big Data, Machine Learning, Neural Networks and Data Science

Mechatronics and robotics

Modeling, simulation and CAD

Systems, Automation and Control

Biography

PhD student in Electrical, Electronics and Communications Engineering at Politecnico di Torino. He achieved Bachelor’s Degree in Mechanical Engineering in 2018 and Master’s Degree in Mechatronic Engineering in 2020. The core of his research activity concerns the study of real-time Predictive Maintenance techniques based on Machine Learning algorithms to deploy in embedded devices for monitoring complex industrial systems.

Teaching

Teachings

Master of Science

Electronic systems for mechatronics. A.A. 2023/24, MECHATRONIC ENGINEERING (INGEGNERIA MECCATRONICA). Collaboratore del corso
Electronics Fundamentals and Applications. A.A. 2023/24, MECHATRONIC ENGINEERING (INGEGNERIA MECCATRONICA). Collaboratore del corso
Electronic systems for mechatronics. A.A. 2022/23, MECHATRONIC ENGINEERING (INGEGNERIA MECCATRONICA). Collaboratore del corso

MostraNascondi A.A. passati

Publications

Latest publications View all publications in Porto@Iris

Cena, Carlo; Albertin, Umberto; Martini, Mauro; Bucci, Silvia; Chiaberge, Marcello (2024)
Physics-Informed Real NVP for Satellite Power System Fault Detection. In: IEEE International Conference on Advanced Intelligent Mechatronics (AIM), Boston (USA), 15-19 July 2024, pp. 679-684. ISBN: 979-8-3503-5537-6
Contributo in Atti di Convegno (Proceeding)
Albertin, Umberto; Pedone, Giuseppe; Brossa, Matilde; Squillero, Giovanni; Chiaberge, ... (2023)
A Real-Time Novelty Recognition Framework Based on Machine Learning for Fault Detection. In: ALGORITHMS, vol. 16, pp. 1-26. ISSN 1999-4893
Contributo su Rivista