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dc.contributor.authorBotticelli, Massimiliano
dc.date.accessioned2023-07-10T10:22:50Z
dc.date.available2023-07-10T10:22:50Z
dc.date.issued2023
dc.identifier.urihttps://library.oapen.org/handle/20.500.12657/63852
dc.description.abstractIn this work, a novel knowledge discovery framework able to analyze data produced in the Gasoline Direct Injection (GDI) context through machine learning is presented and validated. This approach is able to explore and exploit the investigated design spaces based on a limited number of observations, discovering and visualizing connections and correlations in complex phenomena. The extracted knowledge is then validated with domain expertise, revealing potential and limitations of this method.en_US
dc.languageEnglishen_US
dc.relation.ispartofseriesReihe Informationsmanagement im Engineering Karlsruheen_US
dc.subject.classificationthema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TG Mechanical engineering and materialsen_US
dc.subject.otherGasoline Direct Injection; Data-Driven Development; Machine Learning Application; Datengetriebene Entwicklung; Anwendung des Maschinellen Lernens; Knowledge Discovery; Benzin-Direkteinspritzungen_US
dc.titleDevelopment of a modular Knowledge-Discovery Framework based on Machine Learningen_US
dc.title.alternativefor the interdisciplinary analysis of complex phenomena in the context of GDI combustion processesen_US
dc.typebook
oapen.identifier.doi10.5445/KSP/1000158016en_US
oapen.relation.isPublishedBy44e29711-8d53-496b-85cc-3d10c9469be9en_US
oapen.series.number2en_US
oapen.pages210en_US
peerreview.anonymityAll identities known
peerreview.id51a542ec-eaeb-47c2-861d-6022e981a97a
peerreview.open.reviewNo
peerreview.publish.responsibilityBooks or series editor
peerreview.review.stagePre-publication
peerreview.review.typeFull text
peerreview.reviewer.typeEditorial board member
peerreview.reviewer.typeExternal peer reviewer
peerreview.titleDissertations in Series (Dissertationen in Schriftenreihe)


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