Bayesian optimized deep ensemble for uncertainty quantification of deep neural networks: a system safety case study on sodium fast reactor thermal stratification modeling

Autor: Abulawi, Zaid, Hu, Rui, Balaprakash, Prasanna, Liu, Yang

Accurate predictions and uncertainty quantification (UQ) are essential for decision-making in risk-sensitive fields such as system safety modeling. Deep ensembles (DEs) are efficient and scalable methods for UQ in Deep Neural Networks (DNNs); however

Externí odkaz: http://arxiv.org/abs/2412.08776

The Open Autonomy Safety Case Framework

Autor: Wagner, Michael, Carlan, Carmen

Publikováno v: Safety-Critical Systems eJournal, Vol. 3 No. 1 (2024)

A system safety case is a compelling, comprehensible, and valid argument about the satisfaction of the safety goals of a given system operating in a given environment supported by convincing evidence. Since the publication of UL 4600 in 2020, safety

Externí odkaz: http://arxiv.org/abs/2404.05444

Generic kinetic and kinetic‐dynamic modelling in human subgroups of the population and animal species to support transparency in food and feed safety: Case studies.

Autor: Beaudoin, Rémy¹ (AUTHOR) mese@efsa.europa.eu, Benfenati, Emilio² (AUTHOR), Billat, Pierre‐André¹ (AUTHOR), Buratti, Franca Maria³ (AUTHOR), Dall'Asta, Chiara⁴ (AUTHOR), Darney, Keyvin⁵ (AUTHOR), Galaverna, Gianni² (AUTHOR), Dellafiora, Luca⁴ (AUTHOR), Pedroni, Lorenzo⁴ (AUTHOR), Hoogenboom, Ron⁶ (AUTHOR), Lautz, Leonie⁵ (AUTHOR), Louisse, Jochem⁶ (AUTHOR), Roncaglioni, Alessandra² (AUTHOR), Testai, Emanuela³ (AUTHOR), Tebby, Cleo¹ (AUTHOR), Thépaut, Élisa¹ (AUTHOR), Vichi, Susanna³ (AUTHOR), Zeman, Florence³ (AUTHOR)

Publikováno v: EFSA Supporting Publications. Dec2024, Vol. 21 Issue 12, p1-138. 138p.

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Engineered barrier systems (EBS) in the safety case [electronic resource] : design confirmation and demonstration : workshop proceedings, Tokyo, Japan, 12-15 September 2006.

Externí odkaz: Kolekce e-knih KNAV Registrovani uzivatele: plny text online 5 minut, dalsi pristup na vyzadani. Registered users: full text online 5 minutes, further access on request.