Behavior Classification based on Sensor Data - Classifying time series using low-dimensional manifold representations

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dc.contributor.authorRosén, John
dc.contributor.departmentChalmers tekniska högskola / Institutionen för tillÀmpad mekaniksv
dc.contributor.departmentChalmers University of Technology / Department of Applied Mechanicsen
dc.date.accessioned2019-07-03T13:43:05Z
dc.date.available2019-07-03T13:43:05Z
dc.date.issued2015
dc.description.abstractThis masterÂŽs thesis focuses on developing and testing methods that can automatically classify a given time series as having a certain behavior, chosen from a set of pre-specified, known behaviors. The first part of the thesis focused on finding statistical values where the empirical cumulative distribution of these values could be used for classification. The inverse of the cumulative distributions where then sampled at equally distanced sampling points and the resulting vector of sample values were treated as points in a high-dimensional Euclidean space. These points were then dimensionally reduced using projections onto a 2-dimensional manifold, where the manifold was warped in the high-dimensional Euclidean space using the elastic map and Kohonen Self-Organizing Map methodologies. The outputs from the manifold projections were then clustered using a 𝑘-nearest-neighbor algorithm. Both methodologies gave fairly good classification result for the two behaviors under consideration (86.5% / 80.3%, class đ¶1 / đ¶2 for elastic map, 83.6% / 78.3%, class đ¶1 / đ¶2 for Kohonen SOM). It was also shown that there truly were convergence in distribution for the behaviors under consideration.
dc.identifier.urihttps://hdl.handle.net/20.500.12380/219225
dc.language.isoeng
dc.relation.ispartofseriesDiploma work - Department of Applied Mechanics, Chalmers University of Technology, Göteborg, Sweden : 2015:51
dc.setspec.uppsokTechnology
dc.subjectTransport
dc.subjectGrundlÀggande vetenskaper
dc.subjectHĂ„llbar utveckling
dc.subjectInnovation och entreprenörskap (nyttiggörande)
dc.subjectFarkostteknik
dc.subjectTransport
dc.subjectBasic Sciences
dc.subjectSustainable Development
dc.subjectInnovation & Entrepreneurship
dc.subjectVehicle Engineering
dc.titleBehavior Classification based on Sensor Data - Classifying time series using low-dimensional manifold representations
dc.type.degreeExamensarbete för masterexamensv
dc.type.degreeMaster Thesisen
dc.type.uppsokH
local.programmeEngineering mathematics and computational science (MPENM), MSc
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