Language:
English
繁體中文
Help
Login
Back
Switch To:
Labeled
|
MARC Mode
|
ISBD
Unsupervised Pattern Discovery in Automotive Time Series = Pattern-based Construction of Representative Driving Cycles /
Record Type:
Language materials, printed : Monograph/item
Title/Author:
Unsupervised Pattern Discovery in Automotive Time Series/ by Fabian Kai Dietrich Noering.
Reminder of title:
Pattern-based Construction of Representative Driving Cycles /
Author:
Noering, Fabian Kai Dietrich.
Description:
XXI, 148 p. 56 illus., 19 illus. in color.online resource. :
Contained By:
Springer Nature eBook
Subject:
Theory and Algorithms for Application Domains. -
Online resource:
https://doi.org/10.1007/978-3-658-36336-9
ISBN:
9783658363369
Unsupervised Pattern Discovery in Automotive Time Series = Pattern-based Construction of Representative Driving Cycles /
Noering, Fabian Kai Dietrich.
Unsupervised Pattern Discovery in Automotive Time Series
Pattern-based Construction of Representative Driving Cycles /[electronic resource] :by Fabian Kai Dietrich Noering. - 1st ed. 2022. - XXI, 148 p. 56 illus., 19 illus. in color.online resource. - AutoUni – Schriftenreihe,1592512-1154 ;. - AutoUni – Schriftenreihe,90.
Introduction -- RelatedWork -- Development of Pattern Discovery Algorithms for Automotive Time Series -- Pattern-based Representative Cycles -- Evaluation -- Conclusion.
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. About the author 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.
ISBN: 9783658363369
Standard No.: 10.1007/978-3-658-36336-9doiSubjects--Topical Terms:
1388553
Theory and Algorithms for Application Domains.
LC Class. No.: TL1-483
Dewey Class. No.: 629.2
Unsupervised Pattern Discovery in Automotive Time Series = Pattern-based Construction of Representative Driving Cycles /
LDR
:02585nam a22003975i 4500
001
1091123
003
DE-He213
005
20220323113416.0
007
cr nn 008mamaa
008
221228s2022 gw | s |||| 0|eng d
020
$a
9783658363369
$9
978-3-658-36336-9
024
7
$a
10.1007/978-3-658-36336-9
$2
doi
035
$a
978-3-658-36336-9
050
4
$a
TL1-483
072
7
$a
TRC
$2
bicssc
072
7
$a
TEC009090
$2
bisacsh
072
7
$a
TRC
$2
thema
082
0 4
$a
629.2
$2
23
100
1
$a
Noering, Fabian Kai Dietrich.
$e
author.
$4
aut
$4
http://id.loc.gov/vocabulary/relators/aut
$3
1398658
245
1 0
$a
Unsupervised Pattern Discovery in Automotive Time Series
$h
[electronic resource] :
$b
Pattern-based Construction of Representative Driving Cycles /
$c
by Fabian Kai Dietrich Noering.
250
$a
1st ed. 2022.
264
1
$a
Wiesbaden :
$b
Springer Fachmedien Wiesbaden :
$b
Imprint: Springer Vieweg,
$c
2022.
300
$a
XXI, 148 p. 56 illus., 19 illus. in color.
$b
online resource.
336
$a
text
$b
txt
$2
rdacontent
337
$a
computer
$b
c
$2
rdamedia
338
$a
online resource
$b
cr
$2
rdacarrier
347
$a
text file
$b
PDF
$2
rda
490
1
$a
AutoUni – Schriftenreihe,
$x
2512-1154 ;
$v
159
505
0
$a
Introduction -- RelatedWork -- Development of Pattern Discovery Algorithms for Automotive Time Series -- Pattern-based Representative Cycles -- Evaluation -- Conclusion.
520
$a
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. About the author 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.
650
2 4
$a
Theory and Algorithms for Application Domains.
$3
1388553
650
2 4
$a
Automated Pattern Recognition.
$3
1365734
650
2 4
$a
Computer Imaging, Vision, Pattern Recognition and Graphics.
$3
671334
650
1 4
$a
Automotive Engineering.
$3
683772
650
0
$a
Computer science.
$3
573171
650
0
$a
Pattern recognition systems.
$3
557384
650
0
$a
Computer vision.
$3
561800
650
0
$a
Image processing—Digital techniques.
$3
1365735
650
0
$a
Automotive engineering.
$3
1104081
710
2
$a
SpringerLink (Online service)
$3
593884
773
0
$t
Springer Nature eBook
776
0 8
$i
Printed edition:
$z
9783658363352
776
0 8
$i
Printed edition:
$z
9783658363376
830
0
$a
AutoUni – Schriftenreihe,
$x
1867-3635 ;
$v
90
$3
1267366
856
4 0
$u
https://doi.org/10.1007/978-3-658-36336-9
912
$a
ZDB-2-ENG
912
$a
ZDB-2-SXE
950
$a
Engineering (SpringerNature-11647)
950
$a
Engineering (R0) (SpringerNature-43712)
based on 0 review(s)
Multimedia
Reviews
Add a review
and share your thoughts with other readers
Export
pickup library
Processing
...
Change password
Login