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[{"key": "dc.contributor.author", "value": "Hautakangas, Hannu", "language": null, "element": "contributor", "qualifier": "author", "schema": "dc"}, {"key": "dc.contributor.author", "value": "Nieminen, Jukka", "language": null, "element": "contributor", "qualifier": "author", "schema": "dc"}, {"key": "dc.date.accessioned", "value": "2012-02-29T17:01:23Z", "language": "", "element": "date", "qualifier": "accessioned", "schema": "dc"}, {"key": "dc.date.available", "value": "2012-02-29T17:01:23Z", "language": "", "element": "date", "qualifier": "available", "schema": "dc"}, {"key": "dc.date.issued", "value": "2011", "language": null, "element": "date", "qualifier": "issued", "schema": "dc"}, {"key": "dc.identifier.other", "value": "oai:jykdok.linneanet.fi:1198566", "language": null, "element": "identifier", "qualifier": "other", "schema": "dc"}, {"key": "dc.identifier.uri", "value": "https://jyx.jyu.fi/handle/123456789/37465", "language": "", "element": "identifier", "qualifier": "uri", "schema": "dc"}, {"key": "dc.description.abstract", "value": "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.", "language": "", "element": "description", "qualifier": "abstract", "schema": "dc"}, {"key": "dc.description.abstract", "value": "Poikkeavuuksien havaitsemisesta on tullut suosittu tutkimusalue koneoppimisen alalla. Tukivektorikone on yksi poikkeavuuksien havaitsemismenetelm\u00e4 ja siit\u00e4 on tulossa yksi alan k\u00e4ytetyimmist\u00e4 tekniikoista. T\u00e4ss\u00e4 tutkielmassa poikkeavuuksien havaitsemista sovelletaan tien pinnan kuoppien tunnistamiseen kiihtyvyysanturin mittausarvoista. Kiihtyvyysanturin mittausarvoja esik\u00e4siteltiin suodattimen ja ikkunoinnin avulla, mink\u00e4 j\u00e4lkeen arvoista laskettiin piirteit\u00e4 aika- ja taajuustason funktioiden, geneettisen ohjelmoinnin ja aallokemuunnoksen avulla. Parhaiden piirteiden valinnan j\u00e4lkeen luotiin ennustava malli tukivektorikoneella.\r\nLuokittelutulokset osoittavat, ett\u00e4 kuopat voidaan havaita luotettavasti kiihtyvyysanturin mittausarvoista. Parhaat tulokset saavutetiin allokemuunnoksella\r\nlasketuilla piirteill\u00e4.", "language": "", "element": "description", "qualifier": "abstract", "schema": "dc"}, {"key": "dc.description.provenance", "value": "Submitted using Plone Publishing form by Hannu Hautakangas (hahautak) on 2012-02-29 17:01:21.271054. Form: Pro gradu -lomake (2 tekij\u00e4\u00e4) (https://kirjasto.jyu.fi/julkaisut/julkaisulomakkeet/pro-gradu-lomake-2-tekijaa). JyX data:", "language": "en", "element": "description", "qualifier": "provenance", "schema": "dc"}, {"key": "dc.description.provenance", "value": "Submitted by jyx lomake-julkaisija (jyx-julkaisija@noreply.fi) on 2012-02-29T17:01:22Z\r\nNo. of bitstreams: 2\r\nURN:NBN:fi:jyu-201202291321.pdf: 1196632 bytes, checksum: 8ef536daf1a353a814c9f47eddfbf571 (MD5)\r\nlicense.html: 5563 bytes, checksum: df18d34eec2cc73e507de17f2b36c553 (MD5)", "language": "en", "element": "description", "qualifier": "provenance", "schema": "dc"}, {"key": "dc.description.provenance", "value": "Made available in DSpace on 2012-02-29T17:01:23Z (GMT). No. of bitstreams: 2\r\nURN:NBN:fi:jyu-201202291321.pdf: 1196632 bytes, checksum: 8ef536daf1a353a814c9f47eddfbf571 (MD5)\r\nlicense.html: 5563 bytes, checksum: df18d34eec2cc73e507de17f2b36c553 (MD5)\r\n Previous issue date: 2011", "language": "en", "element": "description", "qualifier": "provenance", "schema": "dc"}, {"key": "dc.format.extent", "value": "68 sivua", "language": null, "element": "format", "qualifier": "extent", "schema": "dc"}, {"key": "dc.format.mimetype", "value": "application/pdf", "language": null, "element": "format", "qualifier": "mimetype", "schema": "dc"}, {"key": "dc.language.iso", "value": "eng", "language": null, "element": "language", "qualifier": "iso", "schema": "dc"}, {"key": "dc.rights", "value": "In Copyright", "language": "en", "element": "rights", "qualifier": null, "schema": "dc"}, {"key": "dc.subject.other", "value": "accelerometer", "language": null, "element": "subject", "qualifier": "other", "schema": "dc"}, {"key": "dc.subject.other", "value": "anomaly detection", "language": null, "element": "subject", "qualifier": "other", "schema": "dc"}, {"key": "dc.subject.other", "value": "feature selection", "language": null, "element": "subject", "qualifier": "other", "schema": "dc"}, {"key": "dc.subject.other", "value": "one-class support vector machine", "language": null, "element": "subject", "qualifier": "other", "schema": "dc"}, {"key": "dc.subject.other", "value": "wavelet packet decomposition", "language": null, "element": "subject", "qualifier": "other", "schema": "dc"}, {"key": "dc.title", "value": "Anomaly detection using one-class SVM with wavelet packet decomposition", "language": null, "element": "title", "qualifier": null, "schema": "dc"}, {"key": "dc.type", "value": "master thesis", "language": null, "element": "type", "qualifier": null, "schema": "dc"}, {"key": "dc.identifier.urn", "value": "URN:NBN:fi:jyu-201202291321", "language": null, "element": "identifier", "qualifier": "urn", "schema": "dc"}, {"key": "dc.type.dcmitype", "value": "Text", "language": "en", "element": "type", "qualifier": "dcmitype", "schema": "dc"}, {"key": "dc.type.ontasot", "value": "Pro gradu -tutkielma", "language": "fi", "element": "type", "qualifier": "ontasot", "schema": "dc"}, {"key": "dc.type.ontasot", "value": "Master\u2019s thesis", "language": "en", "element": "type", "qualifier": "ontasot", "schema": "dc"}, {"key": "dc.contributor.faculty", "value": "Informaatioteknologian tiedekunta", "language": "fi", "element": "contributor", "qualifier": "faculty", "schema": "dc"}, {"key": "dc.contributor.faculty", "value": "Faculty of Information Technology", "language": "en", "element": "contributor", "qualifier": "faculty", "schema": "dc"}, {"key": "dc.contributor.department", "value": "Tietotekniikan laitos", "language": "fi", "element": "contributor", "qualifier": "department", "schema": "dc"}, {"key": "dc.contributor.department", "value": "Department of Mathematical Information Technology", "language": "en", "element": "contributor", "qualifier": "department", "schema": "dc"}, {"key": "dc.contributor.organization", "value": "University of Jyv\u00e4skyl\u00e4", "language": "en", "element": "contributor", "qualifier": "organization", "schema": "dc"}, {"key": "dc.contributor.organization", "value": "Jyv\u00e4skyl\u00e4n yliopisto", "language": "fi", "element": "contributor", "qualifier": "organization", "schema": "dc"}, {"key": "dc.subject.discipline", "value": "Tietotekniikka", "language": "fi", "element": "subject", "qualifier": "discipline", "schema": "dc"}, {"key": "dc.subject.discipline", "value": "Mathematical Information Technology", "language": "en", "element": "subject", "qualifier": "discipline", "schema": "dc"}, {"key": "dc.date.updated", "value": "2012-02-29T17:01:23Z", "language": "", "element": "date", "qualifier": "updated", "schema": "dc"}, {"key": "dc.type.coar", "value": "http://purl.org/coar/resource_type/c_bdcc", "language": null, "element": "type", "qualifier": "coar", 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