Zobrazeno 1 - 10
of 42
pro vyhledávání: '"Michele Luvisotto"'
Autor:
Yufeng Wang, Qun Jin, Jianhua Ma, Klimis Ntalianis, MD. Zakirul Alam Bhuiyan, Michele Luvisotto
Publikováno v:
IEEE Access, Vol 8, Pp 169106-169109 (2020)
The mobile revolution is changing the way we interact with the people and things around us. Proximity awareness, the ability to actively/passively and continuously search for relevant value in one’s physical/virtual proximity, is at the core of thi
Externí odkaz:
https://doaj.org/article/d456fbbac0744ccab923b4a856b86e20
Clock Synchronization for Wireless Time-Sensitive Networking: A March From Microsecond to Nanosecond
Publikováno v:
IEEE Industrial Electronics Magazine. 16:35-43
Publikováno v:
IFAC-PapersOnLine. 55:49-54
Publikováno v:
2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA).
Publikováno v:
2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA).
Autor:
Khaoula Oueslati, Nabila Dhahbi-Megriche, Federica Bragone, Kateryna Morozovska, Tor Laneryd, Michele Luvisotto
Publikováno v:
2022 IEEE International Conference on Electrical Sciences and Technologies in Maghreb (CISTEM).
Publikováno v:
IEEE Transactions on Industrial Informatics. 16:5554-5564
To meet the extremely low latency constraints of industrial wireless control in critical applications, the wireless high-performance scheme (WirelessHP) has been introduced as a promising solution. The proposed design showed great improvements in ter
Autor:
Md. Zakirul Alam Bhuiyan, Michele Luvisotto, Yufeng Wang, Qun Jin, Klimis Ntalianis, Jianhua Ma
Publikováno v:
IEEE Access, Vol 8, Pp 169106-169109 (2020)
The mobile revolution is changing the way we interact with the people and things around us. Proximity awareness, the ability to actively/passively and continuously search for relevant value in one’s physical/virtual proximity, is at the core of thi
Publikováno v:
Electric Power Systems Research. 211:108447
This paper focuses on the thermal modelling of power transformers using physics-informed neural networks (PINNs). PINNs are neural networks trained to consider the physical laws provided by the general nonlinear partial differential equations (PDEs).
Publikováno v:
IEEE Transactions on Industrial Informatics. 15:6481-6491
Wireless industrial cyber-physical systems are increasingly popular in critical manufacturing processes. These kinds of systems, besides high performance, require strong security and are constrained by low computational capabilities. Physical layer a