Enhanced Machine Learning and Data Mining Methods for Analysing Large Hybrid Electric Vehicle Fleets based on Load Spectrum Data (Record no. 76092)

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fixed length control field 03126nam a22005175i 4500
001 - CONTROL NUMBER
control field 978-3-658-20367-2
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20220801214221.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 171201s2018 gw | s |||| 0|eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
ISBN 9783658203672
-- 978-3-658-20367-2
082 04 - CLASSIFICATION NUMBER
Call Number 629.2
100 1# - AUTHOR NAME
Author Bergmeir, Philipp.
245 10 - TITLE STATEMENT
Title Enhanced Machine Learning and Data Mining Methods for Analysing Large Hybrid Electric Vehicle Fleets based on Load Spectrum Data
250 ## - EDITION STATEMENT
Edition statement 1st ed. 2018.
300 ## - PHYSICAL DESCRIPTION
Number of Pages XXXII, 166 p. 34 illus., 11 illus. in color.
490 1# - SERIES STATEMENT
Series statement Wissenschaftliche Reihe Fahrzeugtechnik Universität Stuttgart,
520 ## - SUMMARY, ETC.
Summary, etc Philipp Bergmeir works on the development and enhancement of data mining and machine learning methods with the aim of analysing automatically huge amounts of load spectrum data that are recorded for large hybrid electric vehicle fleets. In particular, he presents new approaches for uncovering and describing stress and usage patterns that are related to failures of selected components of the hybrid power-train. Contents Classifying Component Failures of a Vehicle Fleet Visualising Different Kinds of Vehicle Stress and Usage Identifying Usage and Stress Patterns in a Vehicle Fleet Target Groups  Students and scientists in the field of automotive engineering and data science Engineers in the automotive industry About the Author Philipp Bergmeir did a PhD in the doctoral program “Promotionskolleg HYBRID” at the Institute for Internal Combustion Engines and Automotive Engineering, University of Stuttgart, in cooperation with the Esslingen University of Applied Sciences and a well-known vehicle manufacturer. Currently, he is working as a data scientist in the automotive industry.
856 40 - ELECTRONIC LOCATION AND ACCESS
Uniform Resource Identifier https://doi.org/10.1007/978-3-658-20367-2
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Koha item type eBooks
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-- Wiesbaden :
-- Springer Fachmedien Wiesbaden :
-- Imprint: Springer Vieweg,
-- 2018.
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-- online resource
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-- text file
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650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Automotive engineering.
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Data mining.
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Pattern recognition systems.
650 14 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Automotive Engineering.
650 24 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Data Mining and Knowledge Discovery.
650 24 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Automated Pattern Recognition.
830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE
-- 2567-0352
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