Uncertainty Analysis.
Lili E. Ehrlich (740686), Justin S. G. Feig (740687), Scott N. Schiffres (740688) et al. · Figshare · 2015
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About 10,000 papers for “Uncertainty analysis”
Uncertainty Analysis.
Ian Vernon, Michael Goldstein, Richard G. Bower et al. · 2011
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Uncertainty Analysis
2007
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Uncertainty analysis
Katherine von Stackelberg · Elsevier eBooks · 2023
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Uncertainty analysis
Frøydis Gillund A, Kamilla A. Kjølberg B, Martin Krayer Von Krauss C et al. · 2008
available at www.sciencedirect.com www.elsevier.com/locate/scitotenv Do uncertainty analyses reveal uncertainties? Using the introduction of DNA vaccines to aquaculture as a case
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Uncertainty Analysis
Douglas G. Martinson · 2018
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Uncertainty Analysis
Y. Z. · 2019
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Uncertainty analysis
Weiguang Cai, Yanhui Yu, Kairui You et al. · Figshare · 2024
Results of Monte Carlo uncertainty analysis for each year's data.
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Uncertainty analysis
Cai, Weiguang, Yu, Yanhui, You, Kairui et al. · Figshare · 2024
Results of Monte Carlo uncertainty analysis for each year's data.
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Uncertainty analysis
Atte Moilanen A, Brendan A. Wintle B, Edge Effect · 2005
available at www.sciencedirect.com journal homepage: www.elsevier.com/locate/biocon Short communication Uncertainty analysis favours selection of spatially aggregated reserve networks
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Uncertainty Analysis
Josephine Mauskopf, Stephanie Earnshaw · 2017
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Uncertainty Analysis
2014
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Uncertainty analysis
R. J. Moffat · 2020
This chapter deals with uncertainty analysis applied to single sample experiments. Planning experiments with uncertainty analysis in mind will focus one’s attention on the quality of their instruments and the care with which they run their experiments. An error has traditionally been classified as “fixed” or “random” depending on how it would behave on repeated trials if a series of measurement were made. The identifying feature of a “fixed error” is that the error is the same each time the same nominal value is observed. A fixed error may be different at different points across the range of the experiment, but if it is repeatable, it is a fixed error. A random error can be expressed as a percentage of full scale or a percentage of reading, or in absolute units.
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Uncertainty analysis
R. Edward Thomas · OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) · 1982
An evaluation is made of the suitability of analytical and statistical sampling methods for making uncertainty analyses. The adjoint method is found to be well-suited for obtaining sensitivity coefficients for computer programs involving large numbers of equations and input parameters. For this purpose the Latin Hypercube Sampling method is found to be inferior to conventional experimental designs. The Latin hypercube method can be used to estimate output probability density functions, but requires supplementary rank transformations followed by stepwise regression to obtain uncertainty information on individual input parameters. A simple Cork and Bottle problem is used to illustrate the efficiency of the adjoint method relative to certain statistical sampling methods. For linear models of the form Ax=b it is shown that a complete adjoint sensitivity analysis can be made without formulating and solving the adjoint problem. This can be done either by using a special type of statistical sampling or by reformulating the primal problem and using suitable linear programming software.
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Uncertainty analysis
Tim Bedford, Roger Cooke · Cambridge University Press eBooks · 2001
This chapter gives a brief introduction to the relatively new and expanding field of uncertainty analysis. Fundamental concepts are introduced, but theorems will not be proved here. Since uncertainty analysis is effectively dependent on computer support, the models used in uncertainty analysis are discussed in relation to simulation methods. A good elementary introduction to simulation is found in the book of Ross [Ross, 1990].
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Uncertainty Analysis
Adam Rose, Fynnwin Prager, Zhenhua Chen et al. · Integrated disaster risk management · 2017
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UNCERTAINTY ANALYSIS,
Powers,Stephen G. · Defense Technical Information Center (DTIC) · 1967
The total constraint of a dynamic system is analyzed in terms of the cause-effect relationship between parts of the system. The cause-effect relationships are represented qualitatively on Ashby diagrams of immediate effects (DIE) (Introduction to Cybernetics) and on diagrams of K effects for systems over k time intervals. (Construction of the DIE requires either knowledge of the mapping of every variable of the system and the mapping joining them, or the ability to force the system into all possible system states). Systems of 2, 3, and n variables are considered. Preliminarily, techniques are presented that facilitate analysis by allowing selection or creation of information relations of the desired form from a modest catalog of the most general and useful relations. Proofs are given for the reformulations.
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Uncertainty Analysis
2021
Measurement of key aircraft parameters must be bounded by the uncertainty (or error) associated with the measurements. This chapter details the standard statistical approaches used to determine a quantitative estimate of the uncertainty in a given measurement. The concepts of systematic (or bias) error and random (or precision) error are introduced, with an overview of how these error sources are combined and how the uncertainties in measurands propagate to an overall uncertainty. This chapter concludes with a discussion of typical error sources experienced in flight testing, along with an example of uncertainty associated with airspeed measurement.
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uncertainty: Uncertainty Estimation and Contribution Analysis
Hugo Gasca-Aragon · 2018
Implements the Gaussian method of first and second order, the Kragten numerical method and the Monte Carlo simulation method for uncertainty estimation and analysis.Depends graphics (>= 3.4.0),stats (>= 3.4.0),mvtnorm (>= 0.9), triangle (>= 0.7), R (>= 3.4.0)
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Analysis of uncertainty.
Chen Yang (207381), Tong Yao (4471843), Dong Shiming (21101251) et al. · Figshare · 2025
<div> Within the context of advancing global sustainable development goals, universities are recognized as leaders in energy conservation and emissions reduction within the education sector. Universities should actively engage in the accounting and analysis of carbon emissions. This study uses Sichuan University Jinjiang College(Hereafter referred to as J University) in Sichuan, China, as a case study, where the campus’s carbon emissions for the year 2023 were calculated using the Emission Factor Method and the Delphi Method. The uncertainty associated with these emissions was further explored using Monte Carlo simulation. The results indicate that the net carbon emissions of J University amounted to 44,584.33 tons of CO<sub>2</sub> equivalent (tCO<sub>2</sub>e), with per capita emissions of 1.89 tCO<sub>2</sub>e. The primary sources of campus carbon emissions, in descending order, include electricity (18879.94tCO<sub>2</sub>e), natural gas (8647.25tCO<sub>2</sub>e), business travel (5224.55tCO<sub>2</sub>e), campus commuting (3852.33tCO<sub>2</sub>e), food (3444.67tCO<sub>2</sub>e), and thermal energy (2566.63tCO<sub>2</sub>e). Among these sources, the carbon emissions from electricity, natural gas, and thermal energy were closely correlated with seasonal and regional factors. The uncertainties related to commuting and business travel had the most significant impact on the overall carbon emissions accounting for the campus. The study presents a framework for campus carbon emission accounting, providing a concrete case study for future researchers in this field. In particular, an in-depth exploration of statistical uncertainties is conducted, offering a scientific basis for the accurate calculation of carbon emissions in future studies. </div>
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