Zobrazeno 1 - 10
of 619
pro vyhledávání: '"Black-box model"'
Publikováno v:
foresight, 2024, Vol. 26, Issue 5, pp. 921-947.
Externí odkaz:
http://www.emeraldinsight.com/doi/10.1108/FS-01-2023-0014
Autor:
Chibuzo Cosmas Nwanwe, Ugochukwu Ilozurike Duru, Charley Iyke C. Anyadiegwu, Azunna I. B. Ekejuba, Stanley I. Onwukwe, Angela N. Nwachukwu, Boniface U. Okonkwo
Publikováno v:
Journal of Engineering and Applied Science, Vol 71, Iss 1, Pp 1-35 (2024)
Abstract Slug liquid holdup (SLH) is a critical requirement for accurate pressure drop prediction during multiphase pipe flows and by extension optimal gas lift design and production optimization in wellbores. Existing empirical correlations provide
Externí odkaz:
https://doaj.org/article/9842c397fa7c445d9f27e1aefd0ea24f
Publikováno v:
Machine Learning and Knowledge Extraction, Vol 6, Iss 3, Pp 1969-1996 (2024)
Standard ML relies on ample data, but limited availability poses challenges. Transfer learning offers a solution by leveraging pre-existing knowledge. Yet many methods require access to the model’s internal aspects, limiting applicability to white
Externí odkaz:
https://doaj.org/article/b2295baaa64047558cc4e747bc277129
Autor:
Zenghui Liu, Jing Lin
Publikováno v:
Journal of Asian Architecture and Building Engineering, Vol 0, Iss 0, Pp 1-12 (2024)
This study focuses on addressing housing gaps in populous southern countries by emphasizing the importance of accurate early cost estimation in construction projects. It compares individual cost prediction models (Decision Tree, BP Neural Network, an
Externí odkaz:
https://doaj.org/article/81fcca090a404f8c9e0b3acb40695f0d
Publikováno v:
Applied Computing and Geosciences, Vol 23, Iss , Pp 100191- (2024)
Due to the nature of black-box machine learning (ML) models used in the spatial modelling field of environmental and natural hazards, the interpretation of predictive model outputs is necessary. For this purpose, we applied four interpretation techni
Externí odkaz:
https://doaj.org/article/5832f345fa634d4189ceab006782af83
Publikováno v:
Energies, Vol 17, Iss 23, p 5941 (2024)
Understanding energy demands and costs is important for policy makers and the energy sector, especially in the context of residential heating and cooling systems. To estimate the thermal demand of a residential house, a grey-box modelling method with
Externí odkaz:
https://doaj.org/article/64c91bf1236b42e8be3b3a727a062bbd
Publikováno v:
Journal of Marine Science and Engineering, Vol 12, Iss 9, p 1661 (2024)
Maneuvering motion prediction is central to the control and operation of ships, and the application of machine learning algorithms in this field is increasingly prevalent. However, challenges such as extensive training time, complex parameter tuning
Externí odkaz:
https://doaj.org/article/73050a5aa26343b083e136619f022dd5
Publikováno v:
Energies, Vol 17, Iss 16, p 4180 (2024)
To combat climate change successfully, enhancing existing processes is imperative alongside exploring new regenerative technologies. For this purpose, new components must be considered to improve the efficiency of thermodynamic processes. A promising
Externí odkaz:
https://doaj.org/article/84100b86b4674c63acaf6c3d941125f3
Autor:
Armando Rojas Vargas, Liudmila Pérez García, Crispin Sánchez Guillen, Forat Yasir AlJaberi, Ali Dawood Salman, Saja Mohsen Alardhi, Phuoc-Cuong Le
Publikováno v:
Heliyon, Vol 9, Iss 11, Pp e21345- (2023)
The lateritic ore drying in the Cuban nickel producing industry is realized within flighted rotary dryers. In this investigation, performance indicators in regards to transfer of momentum, heat and mass were evaluated. The dryers operate in a concurr
Externí odkaz:
https://doaj.org/article/8e457639188c46a8ad406a5345b3df10
Publikováno v:
IEEE Access, Vol 11, Pp 48158-48168 (2023)
Many applications involving physical systems, such as system control or fault detection, call for a behavioral, black-box, or digital twin of the real system. By observing input-output pairs, a nonlinear system’s black-box twinning model can be bui
Externí odkaz:
https://doaj.org/article/e0c41cba4fae42aba20bed57ff637bd6