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Noering, Fabian Kai Dietrich 1991- VerfasserIn (DE-588)1266450351 (DE-627)1815229888 aut, Unsupervised pattern discovery in automotive time series pattern-based construction of representative driving cycles Fabian Kai Dietrich Noering, Wiesbaden [Heidelberg] Springer Vieweg [2022], © 2022, xxi, 148 Seiten Illustrationen, Diagramme 21 cm x 14.8 cm, Text txt rdacontent, ohne Hilfsmittel zu benutzen n rdamedia, Band nc rdacarrier, AutoUni – Schriftenreihe volume 159, Dissertation Technische Universität Carolo-Wilhelmina zu Braunschweig 2022, 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. About the author Fabian Kai Dietrich Noering is currently working in the technical development of Volkswagen AG as data scientist with a special interest in the analysis of time series regarding e.g. product optimization., Zusammenfassung in deutscher und englischer Sprache, Archivierung/Langzeitarchivierung gewährleistet DISS pdager DE-84, Archivierung/Langzeitarchivierung gewährleistet PEBW pdager DE-31, Hochschulschrift (DE-588)4113937-9 (DE-627)105825778 (DE-576)209480580 gnd-content, s (DE-588)4073757-3 (DE-627)106091581 (DE-576)209191449 Kraftfahrzeug gnd, s (DE-588)4243688-6 (DE-627)104839767 (DE-576)21045668X Fahrtest gnd, s (DE-588)4127298-5 (DE-627)105725757 (DE-576)209592486 Zeitreihe gnd, s (DE-588)4040936-3 (DE-627)104360054 (DE-576)209042761 Mustererkennung gnd, s (DE-588)4580265-8 (DE-627)325212341 (DE-576)21392000X Unüberwachtes Lernen gnd, DE-101, s (DE-588)4032690-1 (DE-627)106260642 (DE-576)208998233 Kraftfahrzeugindustrie gnd, s (DE-588)7544630-3 (DE-627)516684183 (DE-576)257738959 Fahrzyklus gnd, (DE-627), Klawonn, Frank 1964- AkademischeR BetreuerIn (DE-588)113214030 (DE-627)502583207 (DE-576)170210472 dgs, Deserno, Thomas M. 1966- AkademischeR BetreuerIn (DE-588)140954120 (DE-627)623699559 (DE-576)280581084 dgs, Technische Universität Braunschweig Grad-verleihende Institution (DE-588)36227-X (DE-627)100834183 (DE-576)19034511X dgg, Springer Fachmedien Wiesbaden Verlag (DE-588)1043386068 (DE-627)770495842 (DE-576)394755561 pbl, Braunschweig (DE-588)4008065-1 (DE-627)106370030 (DE-576)208874119 uvp, 9783658363369, Erscheint auch als Online-Ausgabe Noering, Fabian Kai Dietrich, 1991 - Unsupervised Pattern Discovery in Automotive Time Series 1st ed. 2022. Wiesbaden : Springer Fachmedien Wiesbaden, 2022 1 Online-Ressource(XXI, 148 p. 56 illus., 19 illus. in color.) (DE-627)179688412X 9783658363369, Auto-Uni Wolfsburg AutoUni-Schriftenreihe volume 159 159 (DE-627)58322198X (DE-576)308436229 (DE-600)2458732-1 1867-3635 ns, http://deposit.dnb.de/cgi-bin/dokserv?id=76ca65aacdec497288eded88cfc9035c&prov=M&dok_var=1&dok_ext=htm X:MVB text/html 2021-11-17 Verlag Inhaltstext, https://www.gbv.de/dms/tib-ub-hannover/177788599x.pdf V:DE-601 B:DE-89 pdf/application Inhaltsverzeichnis |
| spellingShingle |
Noering, Fabian Kai Dietrich, Unsupervised pattern discovery in automotive time series: pattern-based construction of representative driving cycles, Auto-Uni Wolfsburg, AutoUni-Schriftenreihe, volume 159, 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. About the author Fabian Kai Dietrich Noering is currently working in the technical development of Volkswagen AG as data scientist with a special interest in the analysis of time series regarding e.g. product optimization., Hochschulschrift, Kraftfahrzeug, Fahrtest, Zeitreihe, Mustererkennung, Unüberwachtes Lernen, Kraftfahrzeugindustrie, Fahrzyklus |
| title |
Unsupervised pattern discovery in automotive time series: pattern-based construction of representative driving cycles |
| title_auth |
Unsupervised pattern discovery in automotive time series pattern-based construction of representative driving cycles |
| title_full |
Unsupervised pattern discovery in automotive time series pattern-based construction of representative driving cycles Fabian Kai Dietrich Noering |
| title_fullStr |
Unsupervised pattern discovery in automotive time series pattern-based construction of representative driving cycles Fabian Kai Dietrich Noering |
| title_full_unstemmed |
Unsupervised pattern discovery in automotive time series pattern-based construction of representative driving cycles Fabian Kai Dietrich Noering |
| title_in_hierarchy |
volume 159. Unsupervised pattern discovery in automotive time series: pattern-based construction of representative driving cycles ([2022]) |
| title_short |
Unsupervised pattern discovery in automotive time series |
| title_sort |
unsupervised pattern discovery in automotive time series pattern-based construction of representative driving cycles |
| title_sub |
pattern-based construction of representative driving cycles |
| title_unstemmed |
Unsupervised pattern discovery in automotive time series: pattern-based construction of representative driving cycles |
| topic |
Hochschulschrift, Kraftfahrzeug, Fahrtest, Zeitreihe, Mustererkennung, Unüberwachtes Lernen, Kraftfahrzeugindustrie, Fahrzyklus |
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