Experimentation on NN Models for Hazard Identification in Machinery Functional Safety
DC Element | Wert | Sprache |
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dc.contributor.author | Iyenghar, Padma | |
dc.contributor.author | Kieviet, Michael | |
dc.contributor.author | Pulvermueller, Elke | |
dc.contributor.author | Wuebbelmann, Juergen | |
dc.contributor.editor | Dorksen, H. | |
dc.contributor.editor | Scanzio, S. | |
dc.contributor.editor | Jasperneite, J. | |
dc.contributor.editor | Wisniewski, L. | |
dc.contributor.editor | Man, K.F. | |
dc.contributor.editor | Sauter, T. | |
dc.contributor.editor | Seno, L. | |
dc.contributor.editor | Trsek, H. | |
dc.contributor.editor | Vyatkin, V. | |
dc.date.accessioned | 2024-01-04T10:29:09Z | - |
dc.date.available | 2024-01-04T10:29:09Z | - |
dc.date.issued | 2023 | |
dc.identifier.isbn | 9781665493130 | |
dc.identifier.issn | 1935-4576 | |
dc.identifier.uri | http://osnascholar.ub.uni-osnabrueck.de/handle/unios/72985 | - |
dc.description | Cited by: 0; Conference name: 21st IEEE International Conference on Industrial Informatics, INDIN 2023; Conference date: 17 July 2023 through 20 July 2023; Conference code: 192026 | |
dc.description.abstract | The use of Artificial Intelligence (AI) in machinery functional safety can enhance efficiency and accuracy by automating tasks previously carried out by humans. This paper presents an experimental evaluation of Neural Network (NN) models for hazard identification in machinery functional safety. The systematic study includes own implementations of NN models using open source building blocks and the use of an open source conversational AI framework with various pipeline configurations. The paper provides a comparative analysis of the qualitative and quantitative parameters for the models and configurations. © 2023 IEEE. | |
dc.language.iso | en | |
dc.publisher | Institute of Electrical and Electronics Engineers Inc. | |
dc.relation.ispartof | IEEE International Conference on Industrial Informatics (INDIN) | |
dc.subject | AI | |
dc.subject | Experimental evaluation | |
dc.subject | Functional Safety | |
dc.subject | hazard identification | |
dc.subject | Hazards | |
dc.subject | machinery functional safety | |
dc.subject | Model configuration | |
dc.subject | model configurations | |
dc.subject | Neural Network | |
dc.subject | Neural network model | |
dc.subject | Neural-networks | |
dc.subject | Open-source | |
dc.subject | Rasa | |
dc.subject | Systematic study | |
dc.title | Experimentation on NN Models for Hazard Identification in Machinery Functional Safety | |
dc.type | conference paper | |
dc.identifier.doi | 10.1109/INDIN51400.2023.10218319 | |
dc.identifier.scopus | 2-s2.0-85171158300 | |
dc.identifier.url | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85171158300&doi=10.1109%2fINDIN51400.2023.10218319&partnerID=40&md5=c8ab04eb15d6b4ba86759d6c999e52b8 | |
dc.description.volume | 2023-July | |
dcterms.isPartOf.abbreviation | IEEE Int. Conf. Ind. Informatics (INDIN) | |
local.import.remains | affiliations : Innotec GmbH, Erlenweg 12, Melle, 49324, Germany; Osnabrueck University of Applied Sciences, Faculty of Engineering and Computer Science, Germany; University of Osnabrueck, Software Engineering Research Group, Germany | |
local.import.remains | correspondence_address : P. Iyenghar; Innotec GmbH, Melle, Erlenweg 12, 49324, Germany; email: piyengha@uos.de | |
local.import.remains | publication_stage : Final | |
crisitem.author.dept | Institut für Informatik | - |
crisitem.author.deptid | institute12 | - |
crisitem.author.parentorg | FB 06 - Mathematik/Informatik/Physik | - |
crisitem.author.grandparentorg | Universität Osnabrück | - |
crisitem.author.netid | PuEl525 | - |
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geprüft am 03.06.2024