Ferndiagnose und Deep Learning bei der Zentralbahn – Mehrwert dank Daten und Experten-Know-how
dc.contributor.author | Barmettler, Marco | |
dc.contributor.author | Lee, Jihyun | |
dc.contributor.author | Burri, Florian | |
dc.contributor.author | Tschannen, Roman | |
dc.contributor.author | Wagner, Severin | |
dc.date.accessioned | 2024-10-30T14:50:44Z | |
dc.date.available | 2024-10-30T14:50:44Z | |
dc.date.issued | 2024-10 | |
dc.description.sponsorship | Innosuisse | |
dc.identifier.citation | ETR Eisenbahntechnische Rundschau (DVV Media Group GmbH), Vol Nr. 10, pp.56-60 | |
dc.identifier.uri | https://hdl.handle.net/20.500.12839/1533 | |
dc.language.iso | de | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 International | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.title | Ferndiagnose und Deep Learning bei der Zentralbahn – Mehrwert dank Daten und Experten-Know-how | |
dc.type | Media | |
dc.type | Article | |
dc.type.csemdivisions | BU-R | |
dc.type.csemresearchareas | Data & AI | |
dc.type.csemresearchareas | Industry 4.0 |