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oapen-20.500.12657-63852
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dspace
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oapen-20.500.12657-638522023-07-11T02:40:17Z Development of a modular Knowledge-Discovery Framework based on Machine Learning Botticelli, Massimiliano Gasoline Direct Injection; Data-Driven Development; Machine Learning Application; Datengetriebene Entwicklung; Anwendung des Maschinellen Lernens; Knowledge Discovery; Benzin-Direkteinspritzung bic Book Industry Communication::T Technology, engineering, agriculture::TG Mechanical engineering & materials In 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. 2023-07-10T10:22:50Z 2023-07-10T10:22:50Z 2023 book https://library.oapen.org/handle/20.500.12657/63852 eng Reihe Informationsmanagement im Engineering Karlsruhe application/pdf Attribution-ShareAlike 4.0 International development-of-a-modular-knowledge-discovery-framework-based-on-machine-learning-for-the-interdisciplinary-analysis-of-complex-phenomena-in-the-context-of-gdi-combustion-processes.pdf https://doi.org/10.5445/KSP/1000158016 KIT Scientific Publishing 10.5445/KSP/1000158016 10.5445/KSP/1000158016 44e29711-8d53-496b-85cc-3d10c9469be9 2 210 open access
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| institution |
OAPEN
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DSpace
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| language |
English
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| description |
In 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.
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| title |
development-of-a-modular-knowledge-discovery-framework-based-on-machine-learning-for-the-interdisciplinary-analysis-of-complex-phenomena-in-the-context-of-gdi-combustion-processes.pdf
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| spellingShingle |
development-of-a-modular-knowledge-discovery-framework-based-on-machine-learning-for-the-interdisciplinary-analysis-of-complex-phenomena-in-the-context-of-gdi-combustion-processes.pdf
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| title_short |
development-of-a-modular-knowledge-discovery-framework-based-on-machine-learning-for-the-interdisciplinary-analysis-of-complex-phenomena-in-the-context-of-gdi-combustion-processes.pdf
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| title_full |
development-of-a-modular-knowledge-discovery-framework-based-on-machine-learning-for-the-interdisciplinary-analysis-of-complex-phenomena-in-the-context-of-gdi-combustion-processes.pdf
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| title_fullStr |
development-of-a-modular-knowledge-discovery-framework-based-on-machine-learning-for-the-interdisciplinary-analysis-of-complex-phenomena-in-the-context-of-gdi-combustion-processes.pdf
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| title_full_unstemmed |
development-of-a-modular-knowledge-discovery-framework-based-on-machine-learning-for-the-interdisciplinary-analysis-of-complex-phenomena-in-the-context-of-gdi-combustion-processes.pdf
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| title_sort |
development-of-a-modular-knowledge-discovery-framework-based-on-machine-learning-for-the-interdisciplinary-analysis-of-complex-phenomena-in-the-context-of-gdi-combustion-processes.pdf
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| publisher |
KIT Scientific Publishing
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| publishDate |
2023
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| url |
https://doi.org/10.5445/KSP/1000158016
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| _version_ |
1799945276172009472
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