Fabian Kai Dietrich Noering 
Unsupervised Pattern Discovery in Automotive Time Series [PDF ebook] 
Pattern-based Construction of Representative Driving Cycles

Sokongan

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.

 

€96.29
cara bayaran

Jadual kandungan

Introduction.- Related Work.- Development of Pattern Discovery Algorithms for Automotive Time Series.- Pattern-based Representative Cycles.- Evaluation.- Conclusion.

Mengenai Pengarang

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.

Beli ebook ini dan dapatkan 1 lagi PERCUMA!
Bahasa Inggeris ● Format PDF ● Halaman-halaman 148 ● ISBN 9783658363369 ● Saiz fail 5.7 MB ● Penerbit Springer Fachmedien Wiesbaden ● Bandar raya Wiesbaden ● Negara DE ● Diterbitkan 2022 ● Muat turun 24 bulan ● Mata wang EUR ● ID 8339107 ● Salin perlindungan Social DRM

Lebih banyak ebook daripada pengarang yang sama / Penyunting

16,400 Ebooks dalam kategori ini