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pro vyhledávání: '"Awad, Ramez"'
In the domain of computer vision, deep residual neural networks like EfficientNet have set new standards in terms of robustness and accuracy. One key problem underlying the training of deep neural networks is the immanent lack of a sufficient amount
Externí odkaz:
http://arxiv.org/abs/2202.10099
Autor:
Schönhof, Raoul, Werner, Artem, Elstner, Jannes, Zopcsak, Boldizsar, Awad, Ramez, Huber, Marco
Publikováno v:
2021, Procedia CIRP 100(7):331-336
Not only automation of manufacturing processes but also automation of automation procedures itself become increasingly relevant to automation research. In this context, automated capability assessment, mainly leveraged by deep learning systems driven
Externí odkaz:
http://arxiv.org/abs/2201.12107
Publikováno v:
In Procedia CIRP 2022 107:845-850
Autor:
Zander, Stefan, Heppner, Georg, Neugschwandtner, Georg, Awad, Ramez, Essinger, Marc, Ahmed, Nadia
This paper presents a novel ontology-driven software engineering approach for the development of industrial robotics control software. It introduces the ReApp architecture that synthesizes model-driven engineering with semantic technologies to facili
Externí odkaz:
http://arxiv.org/abs/1601.03998
Publikováno v:
In Procedia CIRP 2021 104:494-499
Publikováno v:
In Procedia CIRP 2021 104:488-493
Publikováno v:
In Procedia CIRP 2021 104:74-79
Autor:
Neb, Alexander, Brandt, David, Rauhöft, Greg, Awad, Ramez, Scholz, Johannes, Bauernhansl, Thomas
Publikováno v:
In Procedia CIRP 2021 104:68-73
Autor:
Schönhof, Raoul, Werner, Artem, Elstner, Jannes, Zopcsak, Boldizsar, Awad, Ramez, Huber, Marco
Publikováno v:
In Procedia CIRP 2021 100:331-336
Autor:
Awad, Ramez
Wirtschaftlicher produzieren mithilfe eines höheren Automatisierungsgrads - das ist das Ziel der Automatisierungs-Potenzialanalyse des Fraunhofer-Instituts für Produktionstechnik und Automatisierung IPA.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od_______610::1f88a5e2750e317394f7139879b07941
https://publica.fraunhofer.de/handle/publica/257044
https://publica.fraunhofer.de/handle/publica/257044