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Anomaly detection using one-class SVM with wavelet packet decomposition

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Authors
Hautakangas, Hannu |
Nieminen, Jukka
Date
2011
Discipline
TietotekniikkaMathematical Information Technology

 
Anomaly detection has become a popular research topic in the field of machine learning. Support vector machine is one anomaly detection technique and it is coming one the most widely used. In this research, anomaly detection is applied to road condition monitoring, especially pothole detection, using accelerometer data. The proposed concept includes data preprocessing, feature extraction, feature selection and classification. Accelerometer data was first filtered and segmented, after which features were extracted with frequency- and time-domain functions, with genetic programming and with wavelet packet decomposition. A classification model was built using support vector machine and the calculated features. The results with actual accelerometer data demonstrates that potholes can be detected reliably. Features from wavelet packet decomposition yielded the best classification results.
 
Poikkeavuuksien havaitsemisesta on tullut suosittu tutkimusalue koneoppimisen alalla. Tukivektorikone on yksi poikkeavuuksien havaitsemismenetelmä ja siitä on tulossa yksi alan käytetyimmistä tekniikoista. Tässä tutkielmassa poikkeavuuksien havaitsemista sovelletaan tien pinnan kuoppien tunnistamiseen kiihtyvyysanturin mittausarvoista. Kiihtyvyysanturin mittausarvoja esikäsiteltiin suodattimen ja ikkunoinnin avulla, minkä jälkeen arvoista laskettiin piirteitä aika- ja taajuustason funktioiden, geneettisen ohjelmoinnin ja aallokemuunnoksen avulla. Parhaiden piirteiden valinnan jälkeen luotiin ennustava malli tukivektorikoneella. Luokittelutulokset osoittavat, että kuopat voidaan havaita luotettavasti kiihtyvyysanturin mittausarvoista. Parhaat tulokset saavutetiin allokemuunnoksella lasketuilla piirteillä.
 
Keywords
accelerometer anomaly detection feature selection one-class support vector machine wavelet packet decomposition koneoppiminen tietotekniikka poikkeavuus
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http://urn.fi/URN:NBN:fi:jyu-201202291321

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