Zobrazeno 1 - 10
of 1 610
pro vyhledávání: '"Monte-Carlo sampling"'
Autor:
Charles Onyutha
Publikováno v:
Hydrology Research, Vol 55, Iss 3, Pp 319-335 (2024)
Although hydrological model forecasts aid water management decisions, they normally have predictive uncertainties. Generalized likelihood uncertainty estimation (GLUE) is popular for constructing predictive uncertainty bounds (PUBs). GLUE is based on
Externí odkaz:
https://doaj.org/article/f308aa3ea8494b3090382e85f79d17fb
Publikováno v:
Underground Space, Vol 14, Iss , Pp 338-356 (2024)
Three-dimensional (3D) roughness of discontinuity affects the quality of the rock mass, but 3D roughness is hard to be measured due to that the discontinuity is invisible in the engineering. Two-dimensional (2D) roughness can be calculated from the v
Externí odkaz:
https://doaj.org/article/5dbebf29549b46bb995568c62a8ef8dd
Publikováno v:
Photochem, Vol 4, Iss 1, Pp 57-110 (2024)
Ultrafast pump–probe spectroscopic studies allow for deep insights into the mechanisms and timescales of photophysical and photochemical processes. Extracting valuable information from these studies, such as reactive intermediates’ lifetimes and
Externí odkaz:
https://doaj.org/article/01885d03d536474ea27c1b6a1716eaa7
A new Monte Carlo sampling method based on Gaussian Mixture Model for imbalanced data classification
Publikováno v:
Mathematical Biosciences and Engineering, Vol 20, Iss 10, Pp 17866-17885 (2023)
Imbalanced data classification has been a major topic in the machine learning community. Different approaches can be taken to solve the issue in recent years, and researchers have given a lot of attention to data level techniques and algorithm level.
Externí odkaz:
https://doaj.org/article/30567356444e4e7a930c03c3a3a1cfe7
Publikováno v:
Engineering Reports, Vol 6, Iss 1, Pp n/a-n/a (2024)
CGH is considered as the key technology to integrate synthetic 3D imaging to get best photo‐realistic rendering quality and optimum viewing experience. We present a partial Monte Carlo sampling algorithm that uses a random subset of rays for the ca
Externí odkaz:
https://doaj.org/article/7420ff63b03f43fdb127a8adaaa93e96
Publikováno v:
Comptes Rendus. Géoscience, Vol , Iss , Pp 1-26 (2022)
Groundwater flow depends on subsurface heterogeneity, which often calls for categorical fields to represent different geological facies. The knowledge about subsurface is however limited and often provided indirectly by state variables, such as hydra
Externí odkaz:
https://doaj.org/article/42977eb8771c49eea2ca884271710a53
Publikováno v:
Yuanzineng kexue jishu, Vol 56, Iss 10, Pp 2078-2084 (2022)
The risk-informed safety margin is a new safety concept of nuclear power industry in recent ten years. The calculation framework and the quantification technology under the Monte Carlo method of the riskinformed safety margin were studied. The ris
Externí odkaz:
https://doaj.org/article/eea22bd793804b1e88c2f788ddfa38bc
Publikováno v:
Mechanical Engineering Journal, Vol 10, Iss 4, Pp 23-00051-23-00051 (2023)
The significance of probabilistic risk assessments (PRAs) of nuclear power plants against external events was re-recognized after the Fukushima Daiichi Nuclear Power Plant accident. Regarding the seismic PRA, handling correlated failures of systems,
Externí odkaz:
https://doaj.org/article/907621725d7c4e538053dc050a665c20
Autor:
Marschall Manuel, Demeyer Séverine, Petit Sébastien, Wübbeler Gerd, Fischer Nicolas, Elster Clemens
Publikováno v:
International Journal of Metrology and Quality Engineering, Vol 15, p 14 (2024)
Type A uncertainty evaluation can significantly benefit from incorporating prior knowledge about the precision of an employed measurement device, which allows for reliable uncertainty assessments with limited observations. The Bayesian framework, emp
Externí odkaz:
https://doaj.org/article/d80021fbf6034995b1e892019ee13206
Uncertainty quantification methodology for model parameters in sub-channel codes using MCMC sampling
Publikováno v:
He jishu, Vol 46, Iss 12, Pp 120602-120602 (2023)
BackgroundTraditional safety analysis methods rely on expert advice and user self-evaluation, lacking the ability to quantify output uncertainty. In contrast, the best estimation plus uncertainty (BEPU) methodology can quantify the uncertainty of the
Externí odkaz:
https://doaj.org/article/f9f75d4e106541eda9a6bcabcdf8e125