Unsupervised Pattern Discovery in Automotive Time Series - Pattern-based Construction of Representative Driving Cycles (Paperback, 1st ed. 2022)

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.

General

Imprint:	Springer Vieweg
Country of origin:	Germany
Series:	AutoUni - Schriftenreihe, 159
Release date:	March 2022
First published:	2022
Authors:	Fabian Kai Dietrich Noering
Dimensions:	210 x 148mm (L x W)
Format:	Paperback
Pages:	148
Edition:	1st ed. 2022
ISBN-13:	978-3-658-36335-2
Categories:	Books > Computing & IT > General theory of computing > General Books > Computing & IT > Applications of computing > Pattern recognition Books > Professional & Technical > Transport technology > Automotive technology > General Books > Computing & IT > Applications of computing > Artificial intelligence > Computer vision
LSN:	3-658-36335-5
Barcode:	9783658363352

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