Train Type Identification at S&C

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dc.contributor.authorFloriánová, Martinacs
dc.contributor.authorPodroužek, Jancs
dc.contributor.authorApeltauer, Jiřícs
dc.contributor.authorVukušič, Ivancs
dc.contributor.authorPlášek, Ottocs
dc.coverage.issue1cs
dc.coverage.volume2020cs
dc.date.issued2020-11-24cs
dc.description.abstractThe presented paper concerns the development of condition monitoring system for railroad switches and crossings that utilizes vibration data. Successful utilization of such system requires a robust and efficient train type identification. Given the complex and unique dynamical response of any vehicle track interaction, the machine learning was chosen as a suitable tool. For design and validation of the system, real on-site acceleration data were used. The resulting theoretical and practical challenges are discussed.en
dc.formattextcs
dc.format.extent1-12cs
dc.format.mimetypeapplication/pdfcs
dc.identifier.citationJOURNAL OF ADVANCED TRANSPORTATION. 2020, vol. 2020, issue 1, p. 1-12.en
dc.identifier.doi10.1155/2020/8849734cs
dc.identifier.issn0197-6729cs
dc.identifier.orcid0000-0002-8001-6349cs
dc.identifier.orcid0000-0003-0493-5922cs
dc.identifier.orcid0000-0002-9791-4655cs
dc.identifier.orcid0000-0002-6713-7146cs
dc.identifier.orcid0000-0003-2799-1521cs
dc.identifier.other168010cs
dc.identifier.researcheridP-9965-2015cs
dc.identifier.scopus25121877100cs
dc.identifier.scopus56566688300cs
dc.identifier.scopus56301300500cs
dc.identifier.urihttp://hdl.handle.net/11012/196331
dc.language.isoencs
dc.publisherHindawics
dc.relation.ispartofJOURNAL OF ADVANCED TRANSPORTATIONcs
dc.relation.urihttps://www.hindawi.com/journals/jat/2020/8849734/cs
dc.rightsCreative Commons Attribution 4.0 Internationalcs
dc.rights.accessopenAccesscs
dc.rights.sherpahttp://www.sherpa.ac.uk/romeo/issn/0197-6729/cs
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/cs
dc.subjectSVMen
dc.subjectTrain type Identificationen
dc.subjectRailway Switches and Crossingsen
dc.subjectAccelerometer Dataen
dc.titleTrain Type Identification at S&Cen
dc.type.driverarticleen
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen
sync.item.dbidVAV-168010en
sync.item.dbtypeVAVen
sync.item.insts2025.02.03 15:44:13en
sync.item.modts2025.01.17 15:17:46en
thesis.grantorVysoké učení technické v Brně. Fakulta stavební. Ústav železničních konstrukcí a stavebcs
thesis.grantorVysoké učení technické v Brně. Fakulta stavební. Ústav automatizace inženýrských úloh a informatikycs
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