no code implementations • 9 May 2023 • Kevin Mika, René Griessl, Nils Kucza, Florian Porrmann, Martin Kaiser, Lennart Tigges, Jens Hagemeyer, Pedro Trancoso, Muhammad Waqar Azhar, Fareed Qararyah, Stavroula Zouzoula, Jämes Ménétrey, Marcelo Pasin, Pascal Felber, Carina Marcus, Oliver Brunnegard, Olof Eriksson, Hans Salomonsson, Daniel Ödman, Andreas Ask, Antonio Casimiro, Alysson Bessani, Tiago Carvalho, Karol Gugala, Piotr Zierhoffer, Grzegorz Latosinski, Marco Tassemeier, Mario Porrmann, Hans-Martin Heyn, Eric Knauss, Yufei Mao, Franz Meierhöfer
The VEDLIoT project aims to develop energy-efficient Deep Learning methodologies for distributed Artificial Intelligence of Things (AIoT) applications.
no code implementations • 11 Oct 2021 • Shen Zhang, Oliver Wallscheid, Mario Porrmann
This review paper systematically summarizes the existing literature on utilizing machine learning (ML) techniques for the control and monitoring of electric machine drives.