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Unsupervised Pattern Discovery in Automotive Time Series: Pattern-based Construction of Representative Driving Cycles: 159 (AutoUni – Schriftenreihe) - Tapa blanda
Sinopsis
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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Acerca del autor
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.
De la contraportada
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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Unsupervised Pattern Discovery in Automotive Time Series (eng)
Librería: Brook Bookstore, Milano, MI, Italia
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Unsupervised Pattern Discovery in Automotive Time Series
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Springer Fachmedien Wiesbaden, 2022
ISBN 10: 3658363355
ISBN 13: 9783658363352
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Librería: moluna, Greven, Alemania
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Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. 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.In the last decade u. Nº de ref. del artículo: 525696019
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Unsupervised Pattern Discovery in Automotive Time Series: Pattern-based Construction of Representative Driving Cycles
Impresión bajo demandaLibrería: Revaluation Books, Exeter, Reino Unido
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Paperback. Condición: Brand New. 169 pages. 8.26x5.82x0.37 inches. In Stock. This item is printed on demand. Nº de ref. del artículo: __3658363355
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Unsupervised Pattern Discovery in Automotive Time Series
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Springer Fachmedien Wiesbaden Mrz 2022, 2022
ISBN 10: 3658363355
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Taschenbuch. Condición: 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. Nº de ref. del artículo: 9783658363352
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Unsupervised Pattern Discovery in Automotive Time Series : Pattern-based Construction of Representative Driving Cycles
Publicado por
Springer Fachmedien Wiesbaden, 2022
ISBN 10: 3658363355
ISBN 13: 9783658363352
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Librería: AHA-BUCH GmbH, Einbeck, Alemania
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Taschenbuch. Condición: 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. Nº de ref. del artículo: 9783658363352
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Unsupervised Pattern Discovery in Automotive Time Series: Pattern-based Construction of Representative Driving Cycles (AutoUni - Schriftenreihe)
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Unsupervised Pattern Discovery in Automotive Time Series: Pattern-based Construction of Representative Driving Cycles (AutoUni - Schriftenreihe)
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Unsupervised Pattern Discovery in Automotive Time Series : Pattern-based Construction of Representative Driving Cycles
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Unsupervised Pattern Discovery in Automotive Time Series (eng)
Librería: Brook Bookstore On Demand, Napoli, NA, Italia
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Unsupervised Pattern Discovery in Automotive Time Series
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Springer Fachmedien Wiesbaden, Springer Fachmedien Wiesbaden Mär 2022, 2022
ISBN 10: 3658363355
ISBN 13: 9783658363352
Nuevo
Taschenbuch
Librería: buchversandmimpf2000, Emtmannsberg, BAYE, Alemania
Calificación del vendedor: 5 de 5 estrellas
Taschenbuch. Condición: Neu. 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.Springer Vieweg in Springer Science + Business Media, Abraham-Lincoln-Straße 46, 65189 Wiesbaden 172 pp. Englisch. Nº de ref. del artículo: 9783658363352
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