Zobrazeno 1 - 9
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pro vyhledávání: '"Imsland, Lars S."'
In many industrial processes, an apparent lack of data limits the development of data-driven soft sensors. There are, however, often opportunities to learn stronger models by being more data-efficient. To achieve this, one can leverage knowledge abou
Externí odkaz:
http://arxiv.org/abs/2407.13310
Recent literature has explored various ways to improve soft sensors by utilizing learning algorithms with transferability. A performance gain is generally attained when knowledge is transferred among strongly related soft sensor learning tasks. A par
Externí odkaz:
http://arxiv.org/abs/2309.15828
Publikováno v:
Applied Soft Computing, Volume 112, 2021
Recent works have presented promising results from the application of machine learning (ML) to the modeling of flow rates in oil and gas wells. Encouraging results and advantageous properties of ML models, such as computationally cheap evaluation and
Externí odkaz:
http://arxiv.org/abs/2102.01391
Publikováno v:
In Expert Systems With Applications 30 December 2022 210
Publikováno v:
2014 European Control Conference (ECC); 2014, p1242-1248, 7p
Autor:
Baugstø, Sondre Wangenstein
Denne masteroppgaven undersøker bruken av Bayesianske nevrale nett for prediktiv modellering i oppstrøms olje- og gassproduksjon. Hypotesen er at Bayesianske nevrale nett kan brukes til å lage prediktive modeller som har kredibilitetsintervall til
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od_______537::e2271275836739bed8878a5d4fa62837
https://hdl.handle.net/11250/2625658
https://hdl.handle.net/11250/2625658