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Introduction -- RelatedWork -- Development of Pattern Discovery Algorithms for Automotive Time Series -- Pattern-based Representative Cycles -- Evaluation -- Conclusion., 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. |
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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 by Fabian Kai Dietrich Noering, 1st ed. 2022., Wiesbaden Springer Fachmedien Wiesbaden 2022., Wiesbaden Imprint: Springer Vieweg 2022., 1 Online-Ressource(XXI, 148 p. 56 illus., 19 illus. in color.), Text txt rdacontent, Computermedien c rdamedia, Online-Ressource cr rdacarrier, AutoUni – Schriftenreihe 159, Springer eBook Collection, Introduction -- RelatedWork -- Development of Pattern Discovery Algorithms for Automotive Time Series -- Pattern-based Representative Cycles -- Evaluation -- Conclusion., 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., Automotive engineering., Image processing—Digital techniques., Computer vision., Pattern recognition systems., Computer science., Hochschulschrift (DE-588)4113937-9 (DE-627)105825778 (DE-576)209480580 gnd-content, 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, s (DE-588)4040936-3 (DE-627)104360054 (DE-576)209042761 Mustererkennung gnd, s (DE-588)4127298-5 (DE-627)105725757 (DE-576)209592486 Zeitreihe gnd, (DE-627), 9783658363352, 9783658363376, Erscheint auch als Druck-Ausgabe 9783658363376, Erscheint auch als Druck-Ausgabe Noering, Fabian Kai Dietrich, 1991 - Unsupervised pattern discovery in automotive time series Wiesbaden : Springer Vieweg, 2022 xxi, 148 Seiten (DE-627)177788599X 9783658363352 3658363355, https://doi.org/10.1007/978-3-658-36336-9 X:SPRINGER Resolving-System lizenzpflichtig |
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Noering, Fabian Kai Dietrich, Unsupervised Pattern Discovery in Automotive Time Series: Pattern-based Construction of Representative Driving Cycles, Introduction -- RelatedWork -- Development of Pattern Discovery Algorithms for Automotive Time Series -- Pattern-based Representative Cycles -- Evaluation -- Conclusion., 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., Automotive engineering., Image processing—Digital techniques., Computer vision., Pattern recognition systems., Computer science., Hochschulschrift, Kraftfahrzeugindustrie, Fahrzyklus, Mustererkennung, Zeitreihe |
| 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 by Fabian Kai Dietrich Noering |
| title_fullStr |
Unsupervised Pattern Discovery in Automotive Time Series Pattern-based Construction of Representative Driving Cycles by Fabian Kai Dietrich Noering |
| title_full_unstemmed |
Unsupervised Pattern Discovery in Automotive Time Series Pattern-based Construction of Representative Driving Cycles by Fabian Kai Dietrich Noering |
| 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 |
Automotive engineering., Image processing—Digital techniques., Computer vision., Pattern recognition systems., Computer science., Hochschulschrift, Kraftfahrzeugindustrie, Fahrzyklus, Mustererkennung, Zeitreihe |
| topic_facet |
Automotive engineering., Image processing—Digital techniques., Computer vision., Pattern recognition systems., Computer science., Hochschulschrift, Kraftfahrzeugindustrie, Fahrzyklus, Mustererkennung, Zeitreihe |
| url |
https://doi.org/10.1007/978-3-658-36336-9 |
| work_keys_str_mv |
AT noeringfabiankaidietrich unsupervisedpatterndiscoveryinautomotivetimeseriespatternbasedconstructionofrepresentativedrivingcycles |