Results 11 to 20 of about 3,292,975 (323)
Uncertainty Analysis for Data-Driven Chance-Constrained Optimization [PDF]
, 2020 In this contribution our developed framework for data-driven chance-constrained optimization is extended with an uncertainty analysis module. The module quantifies uncertainty in output variables of rigorous simulations.
Esche, Erik +3 more
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Remote Sensing, 2020 Field calibration is a feasible way to evaluate space-borne optical sensor observations via natural or artificial sites on Earth’s surface with the aid of synchronous surface and atmospheric characteristic data. Since field calibration is affected by the
Caixia Gao +11 more
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Model parameter estimation and uncertainty analysis: a report of the ISPOR-SMDM modeling good research practices task force working group - 6 [PDF]
, 2012 A model’s purpose is to inform medical decisions and health care resource allocation. Modelers employ quantitative methods to structure the clinical, epidemiological, and economic evidence base and gain qualitative insight to assist decision makers in ...
Briggs, A.H. +5 more
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Selecting the Best Image Pairs to Measure Slope Deformation
Sensors, 2020 Optical remote sensing images can be used to monitor slope deformation in mountain regions. Abundant optical sensors onboard various platforms were designed to provide increasingly high spatial–temporal resolution images at low cost; however, finding the
Wentao Yang
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Machine Learning and Knowledge Extraction, 2020 A Long Short Term Memory (LSTM) based sales model has been developed to forecast the global sales of hotel business of Travel Boutique Online Holidays (TBO Holidays). The LSTM model is a multivariate model; input to the model includes several independent
Shakti Goel, Rahul Bajpai
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Towards Best Practice Framing of Uncertainty in Scientific Publications: A Review of Water Resources Research Abstracts [PDF]
, 2017 Uncertainty is recognized as a key issue in water resources research, amongst other sciences. Discussions of uncertainty typically focus on tools and techniques applied within an analysis, e.g. uncertainty quantification and model validation.
Elsawah, Sondoss +4 more
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Uncertainty analysis of health damage of coal mine dust using the Monte Carlo method
矿业科学学报, 2017 In order to provide an accurate insight into the occupational health situation of coal miners, a health damage assessment model of coal mine dust was established applying the methods of health risk assessment and exposure parameters.The dust health risks
Tong Ruipeng +4 more
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Frontiers in Ecology and Evolution, 2022 Pollen accumulation rates (PAR, grains cm–2 year–1) have been shown to be a reliable but methodologically complex bioproxy for quantitative reconstruction of past tree abundance.
Clarke A. Knight +6 more
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Uncertainty Analysis of Neutron Diffusion Eigenvalue Problem Based on Reduced-order Model
Yuanzineng kexue jishu, 2023 In order to improve the efficiency of core physical uncertainty analysis based on sampling statistics, the proper orthogonal decomposition (POD) and Galerkin projection method were combined to study the application feasibility of reduced-order model ...
In order to improve the efficiency of core physical uncertainty analysis based on sampling statistics, the proper orthogonal decomposition (POD) and Galerkin projection method were combined to study the application feasibility of reduced-order model based on POD-Galerkin method in core physical uncertainty analysis. The two-dimensional two group TWIGL benchmark question was taken as the research object, the key variation characteristics of the core flux distribution were extracted under the finite perturbation of the group constants of each material region, and the full-order neutron diffusion problem was projected on the variation characteristics to establish a reduced-order neutron diffusion model. The reduced-order model was used to replace the full-order model to carry out the uncertainty analysis of the group constants of the material region. The results show that the bias of the mathematical expectation of keff calculated by reduced-order and full-order models is close to 1 pcm. In addition, compared with the calculation time required for uncertainty analysis of full-order model, the analysis time of reduced-order model (including the calculation time of the full-order model required for the construction of reduced-order model) is only 11.48%, which greatly improves the efficiency of uncertainty analysis. The biases of mathematical expectation of keff calculated by reduced-order and full-order models based on Latin hypercube sampling and simple random sampling are less than 8 pcm, and under the same sample size, the bias from the Latin hypercube sampling result is smaller. From the TWIGL benchmark test results, under the same sample size, Latin hypercube sampling method is more recommended for POD-Galerkin reduced-order model.
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Remote Sensing, 2014 This paper aims to retrieve temporal high-resolution LAI derived by fusing MOD15 products (1 km resolution), field-measured LAI and ASTER reflectance (15-m resolution).
Yonghua Qu, Wenchao Han, Mingguo Ma
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