Téléchargez le livre : Unsupervised Pattern Discovery in Automotive Time Series

Unsupervised Pattern Discovery in Automotive Time Series

Pattern-based Construction of Representative Driving Cycles

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Éditeur :

Springer Vieweg


Paru le : 2022-03-23



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Description

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.
 
Pages
148 pages
Collection
n.c
Parution
2022-03-23
Marque
Springer Vieweg
EAN papier
9783658363352
EAN PDF
9783658363369
Informations sur l'ebook
Nombre pages copiables
1
Nombre pages imprimables
14
Taille du fichier
5540 Ko
Ce livre est accessible aux handicaps Voir les informations sur l'accessibilité
Prix
94,94 €
EAN EPUB
9783658363369
Informations sur l'ebook
Nombre pages copiables
1
Nombre pages imprimables
14
Taille du fichier
25580 Ko
Ce livre est accessible aux handicaps Voir les informations sur l'accessibilité
Prix
94,94 €
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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