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.
"Sinopsis" puede pertenecer a otra edición de este libro.
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.
"Sobre este título" puede pertenecer a otra edición de este libro.
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Unsupervised Pattern Discovery in Automotive Time Series (eng)
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Unsupervised Pattern Discovery in Automotive Time Series (Paperback)
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Paperback. Condición: new. Paperback. 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. 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. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Nº de ref. del artículo: 9783658363352
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Unsupervised Pattern Discovery in Automotive Time Series (eng)
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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: Pattern-based Construction of Representative Driving Cycles (AutoUni - Schriftenreihe)
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Unsupervised Pattern Discovery in Automotive Time Series
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ISBN 10: 3658363355
ISBN 13: 9783658363352
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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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