Development of a modular Knowledge-Discovery Framework based on Machine Learning
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,...
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| Médium: | Online |
| Jazyk: | angličtina |
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KIT Scientific Publishing
2023
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| Témata: | |
| On-line přístup: | https://library.oapen.org/handle/20.500.12657/63852 |
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| _version_ | 1869515598758674432 |
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| author | Botticelli, Massimiliano |
| author_browse | Botticelli, Massimiliano |
| author_facet | Botticelli, Massimiliano |
| author_sort | Botticelli, Massimiliano |
| collection | Directory of Open Access Books |
| 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. |
| format | Online |
| id | doab-20.500.12854ir-101677 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2023 |
| publishDateRange | 2023 |
| publishDateSort | 2023 |
| publisher | KIT Scientific Publishing |
| publisherStr | KIT Scientific Publishing |
| record_format | ojs |
| spelling | doab-20.500.12854ir-1016772025-05-27T07:21:38Z 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 thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TG Mechanical engineering and 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. 2023-07-19T09:24:53Z 2023-07-19T09:24:53Z 2023-07-10T10:22:50Z 2023 book https://library.oapen.org/handle/20.500.12657/63852 9783731512950 https://directory.doabooks.org/handle/20.500.12854/101677 eng Reihe Informationsmanagement im Engineering Karlsruhe open access image/jpeg image/jpeg image/jpeg image/jpeg Attribution-ShareAlike 4.0 International Attribution-ShareAlike 4.0 International Attribution-ShareAlike 4.0 International Attribution-ShareAlike 4.0 International https://library.oapen.org/bitstream/20.500.12657/63852/1/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://library.oapen.org/bitstream/20.500.12657/63852/1/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://library.oapen.org/bitstream/20.500.12657/63852/1/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://library.oapen.org/bitstream/20.500.12657/63852/1/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 KIT Scientific Publishing 10.5445/KSP/1000158016 10.5445/KSP/1000158016 68fffc18-8f7b-44fa-ac7e-0b7d7d979bd2 9783731512950 AG Universitätsverlage 210 open access |
| spellingShingle | Gasoline Direct Injection; Data-Driven Development; Machine Learning Application; Datengetriebene Entwicklung; Anwendung des Maschinellen Lernens; Knowledge Discovery; Benzin-Direkteinspritzung thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TG Mechanical engineering and materials Botticelli, Massimiliano Development of a modular Knowledge-Discovery Framework based on Machine Learning |
| title | Development of a modular Knowledge-Discovery Framework based on Machine Learning |
| title_full | Development of a modular Knowledge-Discovery Framework based on Machine Learning |
| title_fullStr | Development of a modular Knowledge-Discovery Framework based on Machine Learning |
| title_full_unstemmed | Development of a modular Knowledge-Discovery Framework based on Machine Learning |
| title_short | Development of a modular Knowledge-Discovery Framework based on Machine Learning |
| title_sort | development of a modular knowledge discovery framework based on machine learning |
| topic | Gasoline Direct Injection; Data-Driven Development; Machine Learning Application; Datengetriebene Entwicklung; Anwendung des Maschinellen Lernens; Knowledge Discovery; Benzin-Direkteinspritzung thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TG Mechanical engineering and materials |
| topic_facet | Gasoline Direct Injection; Data-Driven Development; Machine Learning Application; Datengetriebene Entwicklung; Anwendung des Maschinellen Lernens; Knowledge Discovery; Benzin-Direkteinspritzung thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TG Mechanical engineering and materials |
| url | https://library.oapen.org/handle/20.500.12657/63852 |
| work_keys_str_mv | AT botticellimassimiliano developmentofamodularknowledgediscoveryframeworkbasedonmachinelearning |