Unsupervised Pattern Discovery in Automotive Time Series : Pattern-based Construction of Representative Driving Cycles
Noering, Fabian Kai-dietrich
ISBN 10: 3658363355 ISBN 13: 9783658363352
Published by Springer Vieweg, 2022
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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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Title: Unsupervised Pattern Discovery in Automotive...
Publisher: Springer Vieweg
Publication Date: 2022
Binding: Soft cover
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Condition: 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. Seller Inventory # 525696019
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Paperback. Condition: 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. Seller Inventory # 9783658363352
Unsupervised Pattern Discovery in Automotive Time Series | Pattern-based Construction of Representative Driving Cycles
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Taschenbuch. Condition: Neu. Unsupervised Pattern Discovery in Automotive Time Series | Pattern-based Construction of Representative Driving Cycles | Fabian Kai Dietrich Noering | Taschenbuch | xxi | Englisch | 2022 | Springer Gabler | EAN 9783658363352 | Verantwortliche Person für die EU: Springer Vieweg in Springer Science + Business Media, Abraham-Lincoln-Str. 46, 65189 Wiesbaden, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu. Seller Inventory # 120791031
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