In the last decade unsupervised pattern discovery in time series, i.e. the problem of finding recurrent similar subsequences in long multivariate time series without the need of querying subsequences, has earned more and more attention in research and industry. Pattern discovery was already successfully applied to various areas like seismology, medicine, robotics or music. Until now an application to automotive time series has not been investigated. This dissertation fills this desideratum by studying the special ...
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In the last decade unsupervised pattern discovery in time series, i.e. the problem of finding recurrent similar subsequences in long multivariate time series without the need of querying subsequences, has earned more and more attention in research and industry. Pattern discovery was already successfully applied to various areas like seismology, medicine, robotics or music. Until now an application to automotive time series has not been investigated. This dissertation fills this desideratum by studying the special characteristics of vehicle sensor logs and proposing an appropriate approach for pattern discovery. To prove the benefit of pattern discovery methods in automotive applications, the algorithm is applied to construct representative driving cycles.
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Add this copy of Unsupervised Pattern Discovery in Automotive Time to cart. $93.93, new condition, Sold by Ingram Customer Returns Center rated 5.0 out of 5 stars, ships from NV, USA, published 2022 by Springer Vieweg.
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New. Print on demand Contains: Illustrations, black & white, Illustrations, color. AutoUni - Schriftenreihe . XXI, 148 p. 56 illus., 19 illus. in color. Intended for professional and scholarly audience.
Add this copy of Unsupervised Pattern Discovery in Automotive Time to cart. $118.84, new condition, Sold by Ria Christie Books rated 4.0 out of 5 stars, ships from Uxbridge, MIDDLESEX, UNITED KINGDOM, published 2022 by Springer Vieweg.
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New. Contains: Illustrations, black & white, Illustrations, color. AutoUni - Schriftenreihe . XXI, 148 p. 56 illus., 19 illus. in color. Intended for professional and scholarly audience.
