Predictive Bridge Information Deep Learning Modell zur Anomalieerkennung
Schlussbericht P-BIM
| dc.contributor.author | Schaller, Melanie | |
| dc.contributor.author | Hotho, Andreas | |
| dc.date.accessioned | 2025-07-28T10:18:16Z | |
| dc.date.available | 2025-07-28T10:18:16Z | |
| dc.date.issued | 2024 | |
| dc.description.version | publishedVersion | |
| dc.identifier.uri | https://oa.tib.eu/renate/handle/123456789/20265 | |
| dc.identifier.uri | https://doi.org/10.34657/19282 | |
| dc.language.iso | ger | |
| dc.publisher | Hannover : Technische Informationsbibliothek | |
| dc.relation.affiliation | Julius-Maximilians-Universität Würzburg - Lehrstuhl für Informatik X - Data Science | |
| dc.rights.license | Creative Commons Attribution-NonDerivs 3.0 Germany | |
| dc.rights.uri | https://creativecommons.org/licenses/by-nd/3.0/de/ | |
| dc.subject.ddc | 600 | |
| dc.title | Predictive Bridge Information Deep Learning Modell zur Anomalieerkennung | ger |
| dc.title.subtitle | Schlussbericht P-BIM | |
| dc.type | Report | |
| dcterms.extent | 12 Seiten | |
| dtf.duration | 01.06.2022 bis 31.03.2024 | |
| dtf.funding.funder | BMV | |
| dtf.funding.program | 19F1108A | |
| dtf.funding.program | 01F1108A | |
| tib.accessRights | openAccess |
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