Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 172 pp. Englisch. Codice articolo 9783658363352

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Unsupervised Pattern Discovery in Automotive Time Series: Pattern-based Construction of Representative Driving Cycles (AutoUni - Schriftenreihe)

Noering, Fabian Kai Dietrich

Editore: Springer Vieweg, 2022

ISBN 10: 3658363355 ISBN 13: 9783658363352

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Unsupervised Pattern Discovery in Automotive Time Series : Pattern-based Construction of Representative Driving Cycles

Fabian Kai Dietrich Noering

Editore: Springer Fachmedien Wiesbaden, 2022

ISBN 10: 3658363355 ISBN 13: 9783658363352

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Taschenbuch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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. Codice articolo 9783658363352

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Unsupervised Pattern Discovery in Automotive Time Series

Noering, Fabian Kai Dietrich

Editore: Springer, Berlin|Springer Fachmedien Wiesbaden|Springer Vieweg, 2022

ISBN 10: 3658363355 ISBN 13: 9783658363352

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Kartoniert / Broschiert. Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. In the last decade unsupervised pattern discovery in time series, i.e. To prove the benefit of pattern discovery methods in automotive applications, the algorithm is applied to construct representative driving cycles.In the last decade unsupervised p. Codice articolo 525696019

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