Unsupervised pattern discovery in automotive time series : pattern-based construction of representative driving cycles

Bibliographische Detailangaben

Titel
Unsupervised pattern discovery in automotive time series pattern-based construction of representative driving cycles
verantwortlich
Noering, Fabian Kai Dietrich (VerfasserIn); Klawonn, Frank (AkademischeR BetreuerIn); Deserno, Thomas M. (AkademischeR BetreuerIn); Technische Universität Braunschweig (Grad-verleihende Institution); Springer Fachmedien Wiesbaden (Verlag)
Schriftenreihe
AutoUni – Schriftenreihe ; volume 159
Hochschulschriftenvermerk
Dissertation, Technische Universität Carolo-Wilhelmina zu Braunschweig, 2022
veröffentlicht
Wiesbaden, [Heidelberg]: Springer Vieweg, [2022]
© 2022
Erscheinungsjahr
2022
Teil von
Auto-Uni Wolfsburg: AutoUni-Schriftenreihe ; volume 159
Erscheint auch als
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.)
Andere Ausgaben
Unsupervised Pattern Discovery in Automotive Time Series: Pattern-based Construction of Representative Driving Cycles
Medientyp
Buch Hochschulschrift
Datenquelle
K10plus Verbundkatalog
Tags
Tag hinzufügen
Zusammenfassung
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.
Anmerkungen
Zusammenfassung in deutscher und englischer Sprache
Umfang
xxi, 148 Seiten; Illustrationen, Diagramme; 21 cm x 14.8 cm
Sprache
Englisch
Schlagworte
Kraftfahrzeugindustrie > Fahrzyklus > Mustererkennung > Zeitreihe
BK-Notation
55.20 Straßenfahrzeugtechnik
54.74 Maschinelles Sehen
DDC-Notation
629.2824028564 ; 620
ISBN
9783658363352
3658363355