Articles liés à Unsupervised Pattern Discovery in Automotive Time Series:...

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

Noering, Fabian Kai Dietrich

 
9783658363352: Unsupervised Pattern Discovery in Automotive Time Series: Pattern-based Construction of Representative Driving Cycles
Couverture souple
ISBN 10 : 3658363355 ISBN 13 : 9783658363352
Editeur : Springer Vieweg, 2022

Synopsis

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.

 

Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.

À propos de l'auteur

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.

Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.

Éditeur
Springer Vieweg
Date d'édition
2022
Langue
anglais
ISBN 10 
3658363355
ISBN 13 
9783658363352
Reliure
Broché
Numéro d'édition
1
Nombre de pages
172
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