Unsupervised Pattern Discovery in Automotive Time Series
Produktnummer:
1876ca65aacdec497288eded88cfc9035c
| Autor: | Noering, Fabian Kai Dietrich |
|---|---|
| Themengebiete: | automotive motif discovery pattern discovery representative cycles time series unsupervised |
| Veröffentlichungsdatum: | 24.03.2022 |
| EAN: | 9783658363352 |
| Sprache: | Englisch |
| Seitenzahl: | 148 |
| Produktart: | Kartoniert / Broschiert |
| Verlag: | Springer Fachmedien Wiesbaden GmbH |
| Untertitel: | Pattern-based Construction of Representative Driving Cycles |
Produktinformationen "Unsupervised Pattern Discovery in Automotive Time Series"
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.
Sie möchten lieber vor Ort einkaufen?
Sie haben Fragen zu diesem oder anderen Produkten oder möchten einfach gerne analog im Laden stöbern? Wir sind gerne für Sie da und beraten Sie auch telefonisch.
Juristische Fachbuchhandlung
Georg Blendl
Parcellistraße 5 (Maxburg)
8033 München
Montag - Freitag: 8:15 -18 Uhr
Samstags geschlossen