Übersicht			Auf mobile öffnen

Lieferstatus:	i.d.R. innert 5-10 Tagen versandfertig
Veröffentlichung:	März 2022
Genre:	Naturwissensch., Medizin, Technik
	Automotive / Bildverarbeitung / Fahrzeugbau / motifdiscovery / PatternDiscovery / representativecycles / Theoretische Informatik / timeseries / unsupervised mehr...
ISBN:	9783658363352
EAN-Code:	9783658363352
Verlag:	Springer Gabler
Einband:	Kartoniert
Sprache:	English
Dimensionen:	H 210 mm / B 148 mm / D 10 mm
Gewicht:	231 gr
Seiten:	172
Bewertung:	Titel bewerten / Meinung schreiben

Inhalt:

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.