• Anglický jazyk

Unsupervised Pattern Discovery in Automotive Time Series

Autor: Fabian Kai Dietrich Noering

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.... Viac o knihe

Na objednávku, dodanie 2-4 týždne

89.09 €

bežná cena: 98.99 €

O knihe

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.


  • Vydavateľstvo: Springer Fachmedien Wiesbaden
  • Rok vydania: 2022
  • Formát: Paperback
  • Rozmer: 210 x 148 mm
  • Jazyk: Anglický jazyk
  • ISBN: 9783658363352

Generuje redakčný systém BUXUS CMS spoločnosti ui42.