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001445402 001__ 1445402
001445402 003__ OCoLC
001445402 005__ 20230310003829.0
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001445402 020__ $$a9783658363369$$q(electronic bk.)
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001445402 020__ $$z9783658363352
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001445402 0247_ $$a10.1007/978-3-658-36336-9$$2doi
001445402 035__ $$aSP(OCoLC)1305912568
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001445402 049__ $$aISEA
001445402 050_4 $$aTL152.5$$b.N64 2022
001445402 08204 $$a629.28/3$$223
001445402 1001_ $$aNoering, Fabian Kai Dietrich,$$eauthor.
001445402 24510 $$aUnsupervised pattern discovery in automotive time series :$$bpattern-based construction of representative driving cycles /$$cFabrian Kai Dietrich Noering.
001445402 264_1 $$aWiesbaden :$$bSpringer Vieweg,$$c[2022]
001445402 264_4 $$c©2022
001445402 300__ $$a1 online resource :$$billustrations (some color).
001445402 336__ $$atext$$btxt$$2rdacontent
001445402 337__ $$acomputer$$bc$$2rdamedia
001445402 338__ $$aonline resource$$bcr$$2rdacarrier
001445402 4901_ $$aAutoUni - Schriftenreihe,$$x2512-1154 ;$$vvolume 159
001445402 504__ $$aIncludes bibliographical references.
001445402 5050_ $$aIntroduction -- RelatedWork -- Development of Pattern Discovery Algorithms for Automotive Time Series -- Pattern-based Representative Cycles -- Evaluation -- Conclusion.
001445402 506__ $$aAccess limited to authorized users.
001445402 520__ $$aIn 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.
001445402 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed April 6, 2022).
001445402 650_0 $$aMotor vehicle driving$$xMathematical models.
001445402 650_0 $$aTime-series analysis.
001445402 650_6 $$aConduite automobile$$xModèles mathématiques.
001445402 650_6 $$aSérie chronologique.
001445402 655_0 $$aElectronic books.
001445402 77608 $$iPrint version: $$z3658363355$$z9783658363352$$w(OCoLC)1285164038
001445402 830_0 $$aAutoUni-Schriftenreihe ;$$vBand 159.$$x2512-1154
001445402 852__ $$bebk
001445402 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-658-36336-9$$zOnline Access$$91397441.1
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001445402 980__ $$aBIB
001445402 980__ $$aEBOOK
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001445402 983__ $$aOnline
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