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Vehicle Classificat...
Vehicle Classification using Road Side Sensors and Feature-free Data Smashing Approach
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- Kleyko, Denis (författare)
- Luleå tekniska universitet,Datavetenskap,Luleå University of Technology, Luleå, Sweden
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- Hostettler, Roland (författare)
- Aalto University, Helsinki, Finland,Department of Electrical Engineering and Automation, Aalto University
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- Lyamin, Nikita, 1989- (författare)
- Högskolan i Halmstad,Centrum för forskning om inbyggda system (CERES),School of Information Technology, Halmstad University
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- Birk, Wolfgang (författare)
- Luleå tekniska universitet,Signaler och system,Luleå University of Technology, Luleå, Sweden
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- Wiklund, Urban (författare)
- Umeå universitet,Institutionen för strålningsvetenskaper,Department of Biomedical Engineering and Informatics, Umeå University,Umea Univ, Dept Biomed Engn & Informat, Umea, Sweden.
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- Osipov, Evgeny (författare)
- Luleå tekniska universitet,Datavetenskap,Luleå University of Technology, Luleå, Sweden
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(creator_code:org_t)
- Piscataway : IEEE, 2016
- 2016
- Engelska.
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Ingår i: 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC). - Piscataway : IEEE. - 9781509018895 - 9781509018888 - 9781509018901 ; , s. 1988-1993
- Relaterad länk:
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https://urn.kb.se/re...
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https://doi.org/10.1...
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https://urn.kb.se/re...
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https://urn.kb.se/re...
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Abstract
Ämnesord
Stäng
- 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.
Ämnesord
- TEKNIK OCH TEKNOLOGIER -- Maskinteknik -- Farkostteknik (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Mechanical Engineering -- Vehicle Engineering (hsv//eng)
- TEKNIK OCH TEKNOLOGIER -- Elektroteknik och elektronik -- Signalbehandling (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Electrical Engineering, Electronic Engineering, Information Engineering -- Signal Processing (hsv//eng)
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datorseende och robotik (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Vision and Robotics (hsv//eng)
- TEKNIK OCH TEKNOLOGIER -- Elektroteknik och elektronik -- Reglerteknik (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Electrical Engineering, Electronic Engineering, Information Engineering -- Control Engineering (hsv//eng)
- TEKNIK OCH TEKNOLOGIER -- Medicinteknik -- Annan medicinteknik (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Medical Engineering -- Other Medical Engineering (hsv//eng)
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Sciences (hsv//eng)
Nyckelord
- Data mining
- Economic and social effects
- Intelligent systems
- Intelligent vehicle highway systems
- Learning algorithms
- Learning systems
- Magnetic levitation vehicles
- Roads and streets
- Transportation
- Vehicles
- Mechanical vibrations
- Signalbehandling
- Dependable Communication and Computation Systems
Publikations- och innehållstyp
- ref (ämneskategori)
- kon (ämneskategori)
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