Publication:

Prediction of follower jumps in cam-follower mechanisms: The benefit of using physics-inspired features in recurrent neural networks

Date

2022
Journal article
https://doi.org/10.1016/j.ymssp.2021.108453
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dc.contributor.authorDe Groote, Wannes
dc.contributor.authorVan Hoecke, Sofie
dc.contributor.authorCrevecoeur, Guillaume
dc.contributor.imecauthorVan Hoecke, Sofie
dc.contributor.orcidimecVan Hoecke, Sofie::0000-0002-7865-6793
dc.date.accessioned2022-12-15T13:21:45Z
dc.date.available2021-11-02T15:56:02Z
dc.date.available2022-12-15T13:21:45Z
dc.date.issued2022
dc.description.wosFundingTextWannes De Groote holds a doctoral grant strategic basic research (3S07219) of the Research Foundation -Flanders (FWO), Belgium. This research received funding from the Flemish Government (AI Research Program), Belgium.
dc.identifier.doi10.1016/j.ymssp.2021.108453
dc.identifier.issn0888-3270
dc.identifier.urihttps://imec-publications.be/handle/20.500.12860/37481
dc.publisherACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD
dc.source.beginpage108453
dc.source.endpagena
dc.source.issuena
dc.source.journalMECHANICAL SYSTEMS AND SIGNAL PROCESSING
dc.source.numberofpages20
dc.source.volume166
dc.subject.keywordsSYSTEM
dc.subject.keywordsBIFURCATIONS
dc.subject.keywordsFORCE
dc.title

Prediction of follower jumps in cam-follower mechanisms: The benefit of using physics-inspired features in recurrent neural networks

dc.typeJournal article
dspace.entity.typePublication
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