Ändra sökning
RefereraExporteraLänk till posten
Permanent länk

Direktlänk
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annat format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annat språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf
Vehicle Classification using Road Side Sensors and Feature-free Data Smashing Approach
Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.ORCID-id: 0000-0002-6032-6155
Department of Electrical Engineering and Automation, Aalto University.
School of Information Technology, Halmstad University.
Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.ORCID-id: 0000-0002-5888-8626
Visa övriga samt affilieringar
Antal upphovsmän: 62016 (Engelska)Ingår i: 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC 2016), Piscataway, NJ: Institute of Electrical and Electronics Engineers (IEEE), 2016, s. 1988-1993, artikel-id 7795877Konferensbidrag, Publicerat paper (Refereegranskat)
Abstract [en]

The main contribution of this paper is a study of the applicability of data smashing – a recently proposed data mining method – for vehicle classification according to the “Nordic system for intelligent classification of vehicles” standard, using measurements of road surface vibrations and magnetic field disturbances caused by passing vehicles. The main advantage of the studied classification approach is that it, in contrast to the most of traditional machine learning algorithms, does not require the extraction of features from raw signals. The proposed classification approach was evaluated on a large dataset consisting of signals from 3074 vehicles. Hence, a good estimate of the actual classification rate was obtained. The performance was compared to the previously reported results on the same problem for logistic regression. Our results show the potential trade-off between classification accuracy and classification method’s development efforts could be achieved.

Ort, förlag, år, upplaga, sidor
Piscataway, NJ: Institute of Electrical and Electronics Engineers (IEEE), 2016. s. 1988-1993, artikel-id 7795877
Serie
IEEE International Conference on Intelligent Transportation Systems, ISSN 2153-0009, E-ISSN 2153-0017
Nationell ämneskategori
Datavetenskap (datalogi) Reglerteknik Signalbehandling
Forskningsämne
Signalbehandling; Kommunikations- och beräkningssystem
Identifikatorer
URN: urn:nbn:se:ltu:diva-59788DOI: 10.1109/ITSC.2016.7795877ISI: 000392215500310Scopus ID: 2-s2.0-85010042316ISBN: 9781509018895 (digital)OAI: oai:DiVA.org:ltu-59788DiVA, id: diva2:1037576
Konferens
2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC 2016), Rio de Janeiro, Brazil, 1-4 Nov 2016
Tillgänglig från: 2016-10-17 Skapad: 2016-10-17 Senast uppdaterad: 2018-03-28Bibliografiskt granskad

Open Access i DiVA

Fulltext saknas i DiVA

Övriga länkar

Förlagets fulltextScopus

Personposter BETA

Kleyko, DenisBirk, WolfgangOsipov, Evgeny

Sök vidare i DiVA

Av författaren/redaktören
Kleyko, DenisBirk, WolfgangOsipov, Evgeny
Av organisationen
DatavetenskapSignaler och system
Datavetenskap (datalogi)ReglerteknikSignalbehandling

Sök vidare utanför DiVA

GoogleGoogle Scholar

doi
isbn
urn-nbn

Altmetricpoäng

doi
isbn
urn-nbn
Totalt: 217 träffar
RefereraExporteraLänk till posten
Permanent länk

Direktlänk
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annat format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annat språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf