Improved linear least squares estimation using bounded data uncertainty
Ballal, Tarig
2015-04-01
This paper addresses the problemof linear least squares (LS) estimation of a vector x from linearly related observations. In spite of being unbiased, the original LS estimator suffers from high mean squared error, especially at low signal-to-noise ratios. The mean squared error (MSE) of the LS estimator can be improved by introducing some form of regularization based on certain constraints. We propose an improved LS (ILS) estimator that approximately minimizes the MSE, without imposing any constraints. To achieve this, we allow for perturbation in the measurement matrix. Then we utilize a bounded data uncertainty (BDU) framework to derive a simple iterative procedure to estimate the regularization parameter. Numerical results demonstrate that the proposed BDU-ILS estimator is superior to the original LS estimator, and it converges to the best linear estimator, the linear-minimum-mean-squared error estimator (LMMSE), when the elements of x are statistically white.
Improved linear least squares estimation using bounded data uncertainty
Ballal, Tarig; Al-Naffouri, Tareq Y.
2015-01-01
This paper addresses the problemof linear least squares (LS) estimation of a vector x from linearly related observations. In spite of being unbiased, the original LS estimator suffers from high mean squared error, especially at low signal-to-noise ratios. The mean squared error (MSE) of the LS estimator can be improved by introducing some form of regularization based on certain constraints. We propose an improved LS (ILS) estimator that approximately minimizes the MSE, without imposing any constraints. To achieve this, we allow for perturbation in the measurement matrix. Then we utilize a bounded data uncertainty (BDU) framework to derive a simple iterative procedure to estimate the regularization parameter. Numerical results demonstrate that the proposed BDU-ILS estimator is superior to the original LS estimator, and it converges to the best linear estimator, the linear-minimum-mean-squared error estimator (LMMSE), when the elements of x are statistically white.
Physical Uncertainty Bounds (PUB)
Energy Technology Data Exchange (ETDEWEB)
Vaughan, Diane Elizabeth [Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Preston, Dean L. [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
2015-03-19
This paper introduces and motivates the need for a new methodology for determining upper bounds on the uncertainties in simulations of engineered systems due to limited fidelity in the composite continuum-level physics models needed to simulate the systems. We show that traditional uncertainty quantification methods provide, at best, a lower bound on this uncertainty. We propose to obtain bounds on the simulation uncertainties by first determining bounds on the physical quantities or processes relevant to system performance. By bounding these physics processes, as opposed to carrying out statistical analyses of the parameter sets of specific physics models or simply switching out the available physics models, one can obtain upper bounds on the uncertainties in simulated quantities of interest.
International Nuclear Information System (INIS)
Eappen, K.P.; Mayya, Y.S.; Patnaik, R.L.; Kushwaha, H.S.
2006-01-01
For the assessment of inhalation doses due to radon and its progeny to uranium mine workers, it is necessary to have information on the time integrated gas concentrations and equilibrium factors. Passive single cup dosimeters using solid state nuclear track detectors (SSNTD) are best suited for this purpose. These generally contain two SSNTDs, one placed inside the cup to measure only the radon gas concentration and other outside the cup for recording tracks due to both radon gas and the progeny species. However, since one obtains only two numbers by this method whereas information on four quantities is required for an unambiguous estimation of dose, there is a need for developing an optimal methodology for extracting information on the equilibrium factors. Several techniques proposed earlier have essentially been based on deterministic approaches, which do not fully take into account all the possible uncertainties in the environmental parameters. Keeping this in view, a simple 'mean of bounds' methodology is proposed to extract equilibrium factors based on their absolute bounds and the associated uncertainties as obtained from general arguments of radon progeny disequilibrium. This may be considered as reasonable estimates of the equilibrium factors in the absence of a knowledge of fluctuation in the environmental variables. The results are compared with those from direct measurements both in the laboratory and in real field situations. In view of the good agreement found between these, it is proposed that the simple mean of bounds estimate may be useful for practical applications in inhalation dosimetry of mine workers
International Nuclear Information System (INIS)
Charonko, John J; Vlachos, Pavlos P
2013-01-01
Numerous studies have established firmly that particle image velocimetry (PIV) is a robust method for non-invasive, quantitative measurements of fluid velocity, and that when carefully conducted, typical measurements can accurately detect displacements in digital images with a resolution well below a single pixel (in some cases well below a hundredth of a pixel). However, to date, these estimates have only been able to provide guidance on the expected error for an average measurement under specific image quality and flow conditions. This paper demonstrates a new method for estimating the uncertainty bounds to within a given confidence interval for a specific, individual measurement. Here, cross-correlation peak ratio, the ratio of primary to secondary peak height, is shown to correlate strongly with the range of observed error values for a given measurement, regardless of flow condition or image quality. This relationship is significantly stronger for phase-only generalized cross-correlation PIV processing, while the standard correlation approach showed weaker performance. Using an analytical model of the relationship derived from synthetic data sets, the uncertainty bounds at a 95% confidence interval are then computed for several artificial and experimental flow fields, and the resulting errors are shown to match closely to the predicted uncertainties. While this method stops short of being able to predict the true error for a given measurement, knowledge of the uncertainty level for a PIV experiment should provide great benefits when applying the results of PIV analysis to engineering design studies and computational fluid dynamics validation efforts. Moreover, this approach is exceptionally simple to implement and requires negligible additional computational cost. (paper)
Low-sampling-rate ultra-wideband channel estimation using a bounded-data-uncertainty approach
Ballal, Tarig
2014-01-01
This paper proposes a low-sampling-rate scheme for ultra-wideband channel estimation. In the proposed scheme, P pulses are transmitted to produce P observations. These observations are exploited to produce channel impulse response estimates at a desired sampling rate, while the ADC operates at a rate that is P times less. To avoid loss of fidelity, the interpulse interval, given in units of sampling periods of the desired rate, is restricted to be co-prime with P. This condition is affected when clock drift is present and the transmitted pulse locations change. To handle this situation and to achieve good performance without using prior information, we derive an improved estimator based on the bounded data uncertainty (BDU) model. This estimator is shown to be related to the Bayesian linear minimum mean squared error (LMMSE) estimator. The performance of the proposed sub-sampling scheme was tested in conjunction with the new estimator. It is shown that high reduction in sampling rate can be achieved. The proposed estimator outperforms the least squares estimator in most cases; while in the high SNR regime, it also outperforms the LMMSE estimator. © 2014 IEEE.
Barth, Timothy J.
2014-01-01
This workshop presentation discusses the design and implementation of numerical methods for the quantification of statistical uncertainty, including a-posteriori error bounds, for output quantities computed using CFD methods. Hydrodynamic realizations often contain numerical error arising from finite-dimensional approximation (e.g. numerical methods using grids, basis functions, particles) and statistical uncertainty arising from incomplete information and/or statistical characterization of model parameters and random fields. The first task at hand is to derive formal error bounds for statistics given realizations containing finite-dimensional numerical error [1]. The error in computed output statistics contains contributions from both realization error and the error resulting from the calculation of statistics integrals using a numerical method. A second task is to devise computable a-posteriori error bounds by numerically approximating all terms arising in the error bound estimates. For the same reason that CFD calculations including error bounds but omitting uncertainty modeling are only of limited value, CFD calculations including uncertainty modeling but omitting error bounds are only of limited value. To gain maximum value from CFD calculations, a general software package for uncertainty quantification with quantified error bounds has been developed at NASA. The package provides implementations for a suite of numerical methods used in uncertainty quantification: Dense tensorization basis methods [3] and a subscale recovery variant [1] for non-smooth data, Sparse tensorization methods[2] utilizing node-nested hierarchies, Sampling methods[4] for high-dimensional random variable spaces.
Estimation of uncertainty bounds for the future performance of a power plant
DEFF Research Database (Denmark)
Odgaard, Peter Fogh; Stoustrup, Jakob
2009-01-01
on recent data and the other is based on operating points as well. The third proposed scheme uses dynamical models of the prediction uncertainties, like in H-infinity-control. The proposed schemes are subsequently applied to experimental data from a coal-fired power plant. {Two sets of data from an actual......} the future performance of these plants is that available models of the plants are uncertain. In this paper three schemes for predicting uncertain dynamical systems are presented. The schemes estimate upper and lower bounds on the system performance. Two of the schemes are statistically based, one only based......Prediction of the future performance of large-scale power plants can be very relevant for the operators of these plants, as the predictions can indicate possible problems or failures due to current operating conditions and/or future possible operating conditions. {A problem in predicting...
Barth, Timothy J.
2014-01-01
Simulation codes often utilize finite-dimensional approximation resulting in numerical error. Some examples include, numerical methods utilizing grids and finite-dimensional basis functions, particle methods using a finite number of particles. These same simulation codes also often contain sources of uncertainty, for example, uncertain parameters and fields associated with the imposition of initial and boundary data,uncertain physical model parameters such as chemical reaction rates, mixture model parameters, material property parameters, etc.
The uncertainties in estimating measurement uncertainties
International Nuclear Information System (INIS)
Clark, J.P.; Shull, A.H.
1994-01-01
All measurements include some error. Whether measurements are used for accountability, environmental programs or process support, they are of little value unless accompanied by an estimate of the measurements uncertainty. This fact is often overlooked by the individuals who need measurements to make decisions. This paper will discuss the concepts of measurement, measurements errors (accuracy or bias and precision or random error), physical and error models, measurement control programs, examples of measurement uncertainty, and uncertainty as related to measurement quality. Measurements are comparisons of unknowns to knowns, estimates of some true value plus uncertainty; and are no better than the standards to which they are compared. Direct comparisons of unknowns that match the composition of known standards will normally have small uncertainties. In the real world, measurements usually involve indirect comparisons of significantly different materials (e.g., measuring a physical property of a chemical element in a sample having a matrix that is significantly different from calibration standards matrix). Consequently, there are many sources of error involved in measurement processes that can affect the quality of a measurement and its associated uncertainty. How the uncertainty estimates are determined and what they mean is as important as the measurement. The process of calculating the uncertainty of a measurement itself has uncertainties that must be handled correctly. Examples of chemistry laboratory measurement will be reviewed in this report and recommendations made for improving measurement uncertainties
Upper bounds on quantum uncertainty products and complexity measures
Energy Technology Data Exchange (ETDEWEB)
Guerrero, Angel; Sanchez-Moreno, Pablo; Dehesa, Jesus S. [Department of Atomic, Molecular and Nuclear Physics, University of Granada, Granada (Spain); Department of Applied Mathematics, University of Granada, Granada (Spain) and Institute Carlos I for Computational and Theoretical Physics, University of Granada, Granada (Spain); Department of Atomic, Molecular and Nuclear Physics, University of Granada, Granada (Spain); Institute Carlos I for Computational and Theoretical Physics, University of Granada, Granada (Spain)
2011-10-15
The position-momentum Shannon and Renyi uncertainty products of general quantum systems are shown to be bounded not only from below (through the known uncertainty relations), but also from above in terms of the Heisenberg-Kennard product . Moreover, the Cramer-Rao, Fisher-Shannon, and Lopez-Ruiz, Mancini, and Calbet shape measures of complexity (whose lower bounds have been recently found) are also bounded from above. The improvement of these bounds for systems subject to spherically symmetric potentials is also explicitly given. Finally, applications to hydrogenic and oscillator-like systems are done.
Jacquin, A. P.
2012-04-01
This study analyses the effect of precipitation spatial distribution uncertainty on the uncertainty bounds of a snowmelt runoff model's discharge estimates. Prediction uncertainty bounds are derived using the Generalized Likelihood Uncertainty Estimation (GLUE) methodology. The model analysed is a conceptual watershed model operating at a monthly time step. The model divides the catchment into five elevation zones, where the fifth zone corresponds to the catchment glaciers. Precipitation amounts at each elevation zone i are estimated as the product between observed precipitation (at a single station within the catchment) and a precipitation factor FPi. Thus, these factors provide a simplified representation of the spatial variation of precipitation, specifically the shape of the functional relationship between precipitation and height. In the absence of information about appropriate values of the precipitation factors FPi, these are estimated through standard calibration procedures. The catchment case study is Aconcagua River at Chacabuquito, located in the Andean region of Central Chile. Monte Carlo samples of the model output are obtained by randomly varying the model parameters within their feasible ranges. In the first experiment, the precipitation factors FPi are considered unknown and thus included in the sampling process. The total number of unknown parameters in this case is 16. In the second experiment, precipitation factors FPi are estimated a priori, by means of a long term water balance between observed discharge at the catchment outlet, evapotranspiration estimates and observed precipitation. In this case, the number of unknown parameters reduces to 11. The feasible ranges assigned to the precipitation factors in the first experiment are slightly wider than the range of fixed precipitation factors used in the second experiment. The mean squared error of the Box-Cox transformed discharge during the calibration period is used for the evaluation of the
Bound entangled states violate a nonsymmetric local uncertainty relation
International Nuclear Information System (INIS)
Hofmann, Holger F.
2003-01-01
As a consequence of having a positive partial transpose, bound entangled states lack many of the properties otherwise associated with entanglement. It is therefore interesting to identify properties that distinguish bound entangled states from separable states. In this paper, it is shown that some bound entangled states violate a nonsymmetric class of local uncertainty relations [H. F. Hofmann and S. Takeuchi, Phys. Rev. A 68, 032103 (2003)]. This result indicates that the asymmetry of nonclassical correlations may be a characteristic feature of bound entanglement
Uncertainty Estimates in Cold Critical Eigenvalue Predictions
International Nuclear Information System (INIS)
Karve, Atul A.; Moore, Brian R.; Mills, Vernon W.; Marrotte, Gary N.
2005-01-01
A recent cycle of a General Electric boiling water reactor performed two beginning-of-cycle local cold criticals. The eigenvalues estimated by the core simulator were 0.99826 and 1.00610. The large spread in them (= 0.00784) is a source of concern, and it is studied here. An analysis process is developed using statistical techniques, where first a transfer function relating the core observable Y (eigenvalue) to various factors (X's) is established. Engineering judgment is used to recognize the best candidates for X's. They are identified as power-weighted assembly k ∞ 's of selected assemblies around the withdrawn rods. These are a small subset of many X's that could potentially influence Y. However, the intention here is not to do a comprehensive study by accounting for all the X's. Rather, the scope is to demonstrate that the process developed is reasonable and to show its applicability to performing detailed studies. Variability in X's is obtained by perturbing nodal k ∞ 's since they directly influence the buckling term in the quasi-two-group diffusion equation model of the core simulator. Any perturbations introduced in them are bounded by standard well-established uncertainties. The resulting perturbations in the X's may not necessarily be directly correlated to physical attributes, but they encompass numerous biases and uncertainties credited to input and modeling uncertainties. The 'vital few' from the 'unimportant many' X's are determined, and then they are subgrouped according to assembly type, location, exposure, and control rod insertion. The goal is to study how the subgroups influence Y in order to have a better understanding of the variability observed in it
Rivera, Diego; Rivas, Yessica; Godoy, Alex
2015-02-01
Hydrological models are simplified representations of natural processes and subject to errors. Uncertainty bounds are a commonly used way to assess the impact of an input or model architecture uncertainty in model outputs. Different sets of parameters could have equally robust goodness-of-fit indicators, which is known as Equifinality. We assessed the outputs from a lumped conceptual hydrological model to an agricultural watershed in central Chile under strong interannual variability (coefficient of variability of 25%) by using the Equifinality concept and uncertainty bounds. The simulation period ran from January 1999 to December 2006. Equifinality and uncertainty bounds from GLUE methodology (Generalized Likelihood Uncertainty Estimation) were used to identify parameter sets as potential representations of the system. The aim of this paper is to exploit the use of uncertainty bounds to differentiate behavioural parameter sets in a simple hydrological model. Then, we analyze the presence of equifinality in order to improve the identification of relevant hydrological processes. The water balance model for Chillan River exhibits, at a first stage, equifinality. However, it was possible to narrow the range for the parameters and eventually identify a set of parameters representing the behaviour of the watershed (a behavioural model) in agreement with observational and soft data (calculation of areal precipitation over the watershed using an isohyetal map). The mean width of the uncertainty bound around the predicted runoff for the simulation period decreased from 50 to 20 m3s-1 after fixing the parameter controlling the areal precipitation over the watershed. This decrement is equivalent to decreasing the ratio between simulated and observed discharge from 5.2 to 2.5. Despite the criticisms against the GLUE methodology, such as the lack of statistical formality, it is identified as a useful tool assisting the modeller with the identification of critical parameters.
Estimates of Uncertainty around the RBA's Forecasts
Peter Tulip; Stephanie Wallace
2012-01-01
We use past forecast errors to construct confidence intervals and other estimates of uncertainty around the Reserve Bank of Australia's forecasts of key macroeconomic variables. Our estimates suggest that uncertainty about forecasts is high. We find that the RBA's forecasts have substantial explanatory power for the inflation rate but not for GDP growth.
Paynter, Ian; Genest, Daniel; Peri, Francesco; Schaaf, Crystal
2018-04-06
Volumetric models with known biases are shown to provide bounds for the uncertainty in estimations of volume for ecologically interesting objects, observed with a terrestrial laser scanner (TLS) instrument. Bounding cuboids, three-dimensional convex hull polygons, voxels, the Outer Hull Model and Square Based Columns (SBCs) are considered for their ability to estimate the volume of temperate and tropical trees, as well as geomorphological features such as bluffs and saltmarsh creeks. For temperate trees, supplementary geometric models are evaluated for their ability to bound the uncertainty in cylinder-based reconstructions, finding that coarser volumetric methods do not currently constrain volume meaningfully, but may be helpful with further refinement, or in hybridized models. Three-dimensional convex hull polygons consistently overestimate object volume, and SBCs consistently underestimate volume. Voxel estimations vary in their bias, due to the point density of the TLS data, and occlusion, particularly in trees. The response of the models to parametrization is analysed, observing unexpected trends in the SBC estimates for the drumlin dataset. Establishing that this result is due to the resolution of the TLS observations being insufficient to support the resolution of the geometric model, it is suggested that geometric models with predictable outcomes can also highlight data quality issues when they produce illogical results.
Estimating uncertainty in resolution tests
CSIR Research Space (South Africa)
Goncalves, DP
2006-05-01
Full Text Available frequencies yields a biased estimate, and we provide an improved estimator. An application illustrates how the results derived can be incorporated into a larger un- certainty analysis. ? 2006 Society of Photo-Optical Instrumentation Engineers. H20851DOI: 10....1117/1.2202914H20852 Subject terms: resolution testing; USAF 1951 test target; resolution uncertainity. Paper 050404R received May 20, 2005; revised manuscript received Sep. 2, 2005; accepted for publication Sep. 9, 2005; published online May 10, 2006. 1...
Estimating uncertainty of data limited stock assessments
DEFF Research Database (Denmark)
Kokkalis, Alexandros; Eikeset, Anne Maria; Thygesen, Uffe Høgsbro
2017-01-01
-limited. Particular emphasis is put on providing uncertainty estimates of the data-limited assessment. We assess four cod stocks in the North-East Atlantic and compare our estimates of stock status (F/Fmsy) with the official assessments. The estimated stock status of all four cod stocks followed the established stock...
Bounded Perturbation Regularization for Linear Least Squares Estimation
Ballal, Tarig; Suliman, Mohamed Abdalla Elhag; Al-Naffouri, Tareq Y.
2017-01-01
This paper addresses the problem of selecting the regularization parameter for linear least-squares estimation. We propose a new technique called bounded perturbation regularization (BPR). In the proposed BPR method, a perturbation with a bounded
The wave function and minimum uncertainty function of the bound quadratic Hamiltonian system
Yeon, Kyu Hwang; Um, Chung IN; George, T. F.
1994-01-01
The bound quadratic Hamiltonian system is analyzed explicitly on the basis of quantum mechanics. We have derived the invariant quantity with an auxiliary equation as the classical equation of motion. With the use of this invariant it can be determined whether or not the system is bound. In bound system we have evaluated the exact eigenfunction and minimum uncertainty function through unitary transformation.
Shao, Xingling; Wang, Honglun
2015-01-01
This paper investigates a novel compound control scheme combined with the advantages of trajectory linearization control (TLC) and alternative active disturbance rejection control (ADRC) for hypersonic reentry vehicle (HRV) attitude tracking system with bounded uncertainties. Firstly, in order to overcome actuator saturation problem, nonlinear tracking differentiator (TD) is applied in the attitude loop to achieve fewer control consumption. Then, linear extended state observers (LESO) are constructed to estimate the uncertainties acting on the LTV system in the attitude and angular rate loop. In addition, feedback linearization (FL) based controllers are designed using estimates of uncertainties generated by LESO in each loop, which enable the tracking error for closed-loop system in the presence of large uncertainties to converge to the residual set of the origin asymptotically. Finally, the compound controllers are derived by integrating with the nominal controller for open-loop nonlinear system and FL based controller. Also, comparisons and simulation results are presented to illustrate the effectiveness of the control strategy. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.
Uncertainty Measures of Regional Flood Frequency Estimators
DEFF Research Database (Denmark)
Rosbjerg, Dan; Madsen, Henrik
1995-01-01
Regional flood frequency models have different assumptions regarding homogeneity and inter-site independence. Thus, uncertainty measures of T-year event estimators are not directly comparable. However, having chosen a particular method, the reliability of the estimate should always be stated, e...
Lower Bounds to the Reliabilities of Factor Score Estimators.
Hessen, David J
2016-10-06
Under the general common factor model, the reliabilities of factor score estimators might be of more interest than the reliability of the total score (the unweighted sum of item scores). In this paper, lower bounds to the reliabilities of Thurstone's factor score estimators, Bartlett's factor score estimators, and McDonald's factor score estimators are derived and conditions are given under which these lower bounds are equal. The relative performance of the derived lower bounds is studied using classic example data sets. The results show that estimates of the lower bounds to the reliabilities of Thurstone's factor score estimators are greater than or equal to the estimates of the lower bounds to the reliabilities of Bartlett's and McDonald's factor score estimators.
Automatic bounding estimation in modified NLMS algorithm
International Nuclear Information System (INIS)
Shahtalebi, K.; Doost-Hoseini, A.M.
2002-01-01
Modified Normalized Least Mean Square algorithm, which is a sign form of Nlm based on set-membership (S M) theory in the class of optimal bounding ellipsoid (OBE) algorithms, requires a priori knowledge of error bounds that is unknown in most applications. In a special but popular case of measurement noise, a simple algorithm has been proposed. With some simulation examples the performance of algorithm is compared with Modified Normalized Least Mean Square
Uncertainty estimation of ultrasonic thickness measurement
International Nuclear Information System (INIS)
Yassir Yassen, Abdul Razak Daud; Mohammad Pauzi Ismail; Abdul Aziz Jemain
2009-01-01
The most important factor that should be taken into consideration when selecting ultrasonic thickness measurement technique is its reliability. Only when the uncertainty of a measurement results is known, it may be judged if the result is adequate for intended purpose. The objective of this study is to model the ultrasonic thickness measurement function, to identify the most contributing input uncertainty components, and to estimate the uncertainty of the ultrasonic thickness measurement results. We assumed that there are five error sources significantly contribute to the final error, these sources are calibration velocity, transit time, zero offset, measurement repeatability and resolution, by applying the propagation of uncertainty law to the model function, a combined uncertainty of the ultrasonic thickness measurement was obtained. In this study the modeling function of ultrasonic thickness measurement was derived. By using this model the estimation of the uncertainty of the final output result was found to be reliable. It was also found that the most contributing input uncertainty components are calibration velocity, transit time linearity and zero offset. (author)
Rigorous Statistical Bounds in Uncertainty Quantification for One-Layer Turbulent Geophysical Flows
Qi, Di; Majda, Andrew J.
2018-04-01
Statistical bounds controlling the total fluctuations in mean and variance about a basic steady-state solution are developed for the truncated barotropic flow over topography. Statistical ensemble prediction is an important topic in weather and climate research. Here, the evolution of an ensemble of trajectories is considered using statistical instability analysis and is compared and contrasted with the classical deterministic instability for the growth of perturbations in one pointwise trajectory. The maximum growth of the total statistics in fluctuations is derived relying on the statistical conservation principle of the pseudo-energy. The saturation bound of the statistical mean fluctuation and variance in the unstable regimes with non-positive-definite pseudo-energy is achieved by linking with a class of stable reference states and minimizing the stable statistical energy. Two cases with dependence on initial statistical uncertainty and on external forcing and dissipation are compared and unified under a consistent statistical stability framework. The flow structures and statistical stability bounds are illustrated and verified by numerical simulations among a wide range of dynamical regimes, where subtle transient statistical instability exists in general with positive short-time exponential growth in the covariance even when the pseudo-energy is positive-definite. Among the various scenarios in this paper, there exist strong forward and backward energy exchanges between different scales which are estimated by the rigorous statistical bounds.
Experimental uncertainty estimation and statistics for data having interval uncertainty.
Energy Technology Data Exchange (ETDEWEB)
Kreinovich, Vladik (Applied Biomathematics, Setauket, New York); Oberkampf, William Louis (Applied Biomathematics, Setauket, New York); Ginzburg, Lev (Applied Biomathematics, Setauket, New York); Ferson, Scott (Applied Biomathematics, Setauket, New York); Hajagos, Janos (Applied Biomathematics, Setauket, New York)
2007-05-01
This report addresses the characterization of measurements that include epistemic uncertainties in the form of intervals. It reviews the application of basic descriptive statistics to data sets which contain intervals rather than exclusively point estimates. It describes algorithms to compute various means, the median and other percentiles, variance, interquartile range, moments, confidence limits, and other important statistics and summarizes the computability of these statistics as a function of sample size and characteristics of the intervals in the data (degree of overlap, size and regularity of widths, etc.). It also reviews the prospects for analyzing such data sets with the methods of inferential statistics such as outlier detection and regressions. The report explores the tradeoff between measurement precision and sample size in statistical results that are sensitive to both. It also argues that an approach based on interval statistics could be a reasonable alternative to current standard methods for evaluating, expressing and propagating measurement uncertainties.
Estimating Coastal Digital Elevation Model (DEM) Uncertainty
Amante, C.; Mesick, S.
2017-12-01
Integrated bathymetric-topographic digital elevation models (DEMs) are representations of the Earth's solid surface and are fundamental to the modeling of coastal processes, including tsunami, storm surge, and sea-level rise inundation. Deviations in elevation values from the actual seabed or land surface constitute errors in DEMs, which originate from numerous sources, including: (i) the source elevation measurements (e.g., multibeam sonar, lidar), (ii) the interpolative gridding technique (e.g., spline, kriging) used to estimate elevations in areas unconstrained by source measurements, and (iii) the datum transformation used to convert bathymetric and topographic data to common vertical reference systems. The magnitude and spatial distribution of the errors from these sources are typically unknown, and the lack of knowledge regarding these errors represents the vertical uncertainty in the DEM. The National Oceanic and Atmospheric Administration (NOAA) National Centers for Environmental Information (NCEI) has developed DEMs for more than 200 coastal communities. This study presents a methodology developed at NOAA NCEI to derive accompanying uncertainty surfaces that estimate DEM errors at the individual cell-level. The development of high-resolution (1/9th arc-second), integrated bathymetric-topographic DEMs along the southwest coast of Florida serves as the case study for deriving uncertainty surfaces. The estimated uncertainty can then be propagated into the modeling of coastal processes that utilize DEMs. Incorporating the uncertainty produces more reliable modeling results, and in turn, better-informed coastal management decisions.
Uncertainties in Organ Burdens Estimated from PAS
International Nuclear Information System (INIS)
La Bone, T.R.
2004-01-01
To calculate committed effective dose equivalent, one needs to know the quantity of the radionuclide in all significantly irradiated organs (the organ burden) as a function of time following the intake. There are two major sources of uncertainty in an organ burden estimated from personal air sampling (PAS) data: (1) The uncertainty in going from the exposure measured with the PAS to the quantity of aerosol inhaled by the individual, and (2) The uncertainty in going from the intake to the organ burdens at any given time, taking into consideration the biological variability of the biokinetic models from person to person (interperson variability) and in one person over time (intra-person variability). We have been using biokinetic modeling methods developed by researchers at the University of Florida to explore the impact of inter-person variability on the uncertainty of organ burdens estimated from PAS data. These initial studies suggest that the uncertainties are so large that PAS might be considered to be a qualitative (rather than quantitative) technique. These results indicate that more studies should be performed to properly classify the reliability and usefulness of using PAS monitoring data to estimate organ burdens, organ dose, and ultimately CEDE
Estimating uncertainty of inference for validation
Energy Technology Data Exchange (ETDEWEB)
Booker, Jane M [Los Alamos National Laboratory; Langenbrunner, James R [Los Alamos National Laboratory; Hemez, Francois M [Los Alamos National Laboratory; Ross, Timothy J [UNM
2010-09-30
We present a validation process based upon the concept that validation is an inference-making activity. This has always been true, but the association has not been as important before as it is now. Previously, theory had been confirmed by more data, and predictions were possible based on data. The process today is to infer from theory to code and from code to prediction, making the role of prediction somewhat automatic, and a machine function. Validation is defined as determining the degree to which a model and code is an accurate representation of experimental test data. Imbedded in validation is the intention to use the computer code to predict. To predict is to accept the conclusion that an observable final state will manifest; therefore, prediction is an inference whose goodness relies on the validity of the code. Quantifying the uncertainty of a prediction amounts to quantifying the uncertainty of validation, and this involves the characterization of uncertainties inherent in theory/models/codes and the corresponding data. An introduction to inference making and its associated uncertainty is provided as a foundation for the validation problem. A mathematical construction for estimating the uncertainty in the validation inference is then presented, including a possibility distribution constructed to represent the inference uncertainty for validation under uncertainty. The estimation of inference uncertainty for validation is illustrated using data and calculations from Inertial Confinement Fusion (ICF). The ICF measurements of neutron yield and ion temperature were obtained for direct-drive inertial fusion capsules at the Omega laser facility. The glass capsules, containing the fusion gas, were systematically selected with the intent of establishing a reproducible baseline of high-yield 10{sup 13}-10{sup 14} neutron output. The deuterium-tritium ratio in these experiments was varied to study its influence upon yield. This paper on validation inference is the
Uncertainty estimation of shape and roughness measurement
Morel, M.A.A.
2006-01-01
One of the most common techniques to measure a surface or form is mechanical probing. Although used since the early 30s of the 20th century, a method to calculate a task specific uncertainty budget was not yet devised. Guidelines and statistical estimates are common in certain cases but an
Bounds and estimates for the linearly perturbed eigenvalue problem
International Nuclear Information System (INIS)
Raddatz, W.D.
1983-01-01
This thesis considers the problem of bounding and estimating the discrete portion of the spectrum of a linearly perturbed self-adjoint operator, M(x). It is supposed that one knows an incomplete set of data consisting in the first few coefficients of the Taylor series expansions of one or more of the eigenvalues of M(x) about x = 0. The foundations of the variational study of eigen-values are first presented. These are then used to construct the best possible upper bounds and estimates using various sets of given information. Lower bounds are obtained by estimating the error in the upper bounds. The extension of these bounds and estimates to the eigenvalues of the doubly-perturbed operator M(x,y) is discussed. The results presented have numerous practical application in the physical sciences, including problems in atomic physics and the theory of vibrations of acoustical and mechanical systems
Uncertainty relations for approximation and estimation
Energy Technology Data Exchange (ETDEWEB)
Lee, Jaeha, E-mail: jlee@post.kek.jp [Department of Physics, University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033 (Japan); Tsutsui, Izumi, E-mail: izumi.tsutsui@kek.jp [Department of Physics, University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033 (Japan); Theory Center, Institute of Particle and Nuclear Studies, High Energy Accelerator Research Organization (KEK), 1-1 Oho, Tsukuba, Ibaraki 305-0801 (Japan)
2016-05-27
We present a versatile inequality of uncertainty relations which are useful when one approximates an observable and/or estimates a physical parameter based on the measurement of another observable. It is shown that the optimal choice for proxy functions used for the approximation is given by Aharonov's weak value, which also determines the classical Fisher information in parameter estimation, turning our inequality into the genuine Cramér–Rao inequality. Since the standard form of the uncertainty relation arises as a special case of our inequality, and since the parameter estimation is available as well, our inequality can treat both the position–momentum and the time–energy relations in one framework albeit handled differently. - Highlights: • Several inequalities interpreted as uncertainty relations for approximation/estimation are derived from a single ‘versatile inequality’. • The ‘versatile inequality’ sets a limit on the approximation of an observable and/or the estimation of a parameter by another observable. • The ‘versatile inequality’ turns into an elaboration of the Robertson–Kennard (Schrödinger) inequality and the Cramér–Rao inequality. • Both the position–momentum and the time–energy relation are treated in one framework. • In every case, Aharonov's weak value arises as a key geometrical ingredient, deciding the optimal choice for the proxy functions.
Uncertainty relations for approximation and estimation
International Nuclear Information System (INIS)
Lee, Jaeha; Tsutsui, Izumi
2016-01-01
We present a versatile inequality of uncertainty relations which are useful when one approximates an observable and/or estimates a physical parameter based on the measurement of another observable. It is shown that the optimal choice for proxy functions used for the approximation is given by Aharonov's weak value, which also determines the classical Fisher information in parameter estimation, turning our inequality into the genuine Cramér–Rao inequality. Since the standard form of the uncertainty relation arises as a special case of our inequality, and since the parameter estimation is available as well, our inequality can treat both the position–momentum and the time–energy relations in one framework albeit handled differently. - Highlights: • Several inequalities interpreted as uncertainty relations for approximation/estimation are derived from a single ‘versatile inequality’. • The ‘versatile inequality’ sets a limit on the approximation of an observable and/or the estimation of a parameter by another observable. • The ‘versatile inequality’ turns into an elaboration of the Robertson–Kennard (Schrödinger) inequality and the Cramér–Rao inequality. • Both the position–momentum and the time–energy relation are treated in one framework. • In every case, Aharonov's weak value arises as a key geometrical ingredient, deciding the optimal choice for the proxy functions.
Uncertainty Evaluation of Best Estimate Calculation Results
International Nuclear Information System (INIS)
Glaeser, H.
2006-01-01
Efforts are underway in Germany to perform analysis using best estimate computer codes and to include uncertainty evaluation in licensing. The German Reactor Safety Commission (RSK) issued a recommendation to perform uncertainty analysis in loss of coolant accident safety analyses (LOCA), recently. A more general requirement is included in a draft revision of the German Nuclear Regulation which is an activity of the German Ministry of Environment and Reactor Safety (BMU). According to the recommendation of the German RSK to perform safety analyses for LOCA in licensing the following deterministic requirements have still to be applied: Most unfavourable single failure, Unavailability due to preventive maintenance, Break location, Break size and break type, Double ended break, 100 percent through 200 percent, Large, medium and small break, Loss of off-site power, Core power (at accident initiation the most unfavourable conditions and values have to be assumed which may occur under normal operation taking into account the set-points of integral power and power density control. Measurement and calibration errors can be considered statistically), Time of fuel cycle. Analysis using best estimate codes with evaluation of uncertainties is the only way to quantify conservatisms with regard to code models and uncertainties of plant, fuel parameters and decay heat. This is especially the case for approaching licensing limits, e.g. due to power up-rates, higher burn-up and higher enrichment. Broader use of best estimate analysis is therefore envisaged in the future. Since some deterministic unfavourable assumptions regarding availability of NPP systems are still used, some conservatism in best-estimate analyses remains. Methods of uncertainty analyses have been developed and applied by the vendor Framatome ANP as well as by GRS in Germany. The GRS development was sponsored by the German Ministry of Economy and Labour (BMWA). (author)
Estimation of the uncertainties considered in NPP PSA level 2
International Nuclear Information System (INIS)
Kalchev, B.; Hristova, R.
2005-01-01
The main approaches of the uncertainties analysis are presented. The sources of uncertainties which should be considered in PSA level 2 for WWER reactor such as: uncertainties propagated from level 1 PSA; uncertainties in input parameters; uncertainties related to the modelling of physical phenomena during the accident progression and uncertainties related to the estimation of source terms are defined. The methods for estimation of the uncertainties are also discussed in this paper
Uncertainty and validation. Effect of user interpretation on uncertainty estimates
International Nuclear Information System (INIS)
Kirchner, G.; Peterson, R.
1996-11-01
Uncertainty in predictions of environmental transfer models arises from, among other sources, the adequacy of the conceptual model, the approximations made in coding the conceptual model, the quality of the input data, the uncertainty in parameter values, and the assumptions made by the user. In recent years efforts to quantify the confidence that can be placed in predictions have been increasing, but have concentrated on a statistical propagation of the influence of parameter uncertainties on the calculational results. The primary objective of this Working Group of BIOMOVS II was to test user's influence on model predictions on a more systematic basis than has been done before. The main goals were as follows: To compare differences between predictions from different people all using the same model and the same scenario description with the statistical uncertainties calculated by the model. To investigate the main reasons for different interpretations by users. To create a better awareness of the potential influence of the user on the modeling results. Terrestrial food chain models driven by deposition of radionuclides from the atmosphere were used. Three codes were obtained and run with three scenarios by a maximum of 10 users. A number of conclusions can be drawn some of which are general and independent of the type of models and processes studied, while others are restricted to the few processes that were addressed directly: For any set of predictions, the variation in best estimates was greater than one order of magnitude. Often the range increased from deposition to pasture to milk probably due to additional transfer processes. The 95% confidence intervals about the predictions calculated from the parameter distributions prepared by the participants did not always overlap the observations; similarly, sometimes the confidence intervals on the predictions did not overlap. Often the 95% confidence intervals of individual predictions were smaller than the
Uncertainty and validation. Effect of user interpretation on uncertainty estimates
Energy Technology Data Exchange (ETDEWEB)
Kirchner, G. [Univ. of Bremen (Germany); Peterson, R. [AECL, Chalk River, ON (Canada)] [and others
1996-11-01
Uncertainty in predictions of environmental transfer models arises from, among other sources, the adequacy of the conceptual model, the approximations made in coding the conceptual model, the quality of the input data, the uncertainty in parameter values, and the assumptions made by the user. In recent years efforts to quantify the confidence that can be placed in predictions have been increasing, but have concentrated on a statistical propagation of the influence of parameter uncertainties on the calculational results. The primary objective of this Working Group of BIOMOVS II was to test user's influence on model predictions on a more systematic basis than has been done before. The main goals were as follows: To compare differences between predictions from different people all using the same model and the same scenario description with the statistical uncertainties calculated by the model. To investigate the main reasons for different interpretations by users. To create a better awareness of the potential influence of the user on the modeling results. Terrestrial food chain models driven by deposition of radionuclides from the atmosphere were used. Three codes were obtained and run with three scenarios by a maximum of 10 users. A number of conclusions can be drawn some of which are general and independent of the type of models and processes studied, while others are restricted to the few processes that were addressed directly: For any set of predictions, the variation in best estimates was greater than one order of magnitude. Often the range increased from deposition to pasture to milk probably due to additional transfer processes. The 95% confidence intervals about the predictions calculated from the parameter distributions prepared by the participants did not always overlap the observations; similarly, sometimes the confidence intervals on the predictions did not overlap. Often the 95% confidence intervals of individual predictions were smaller than the
Epistemic uncertainties when estimating component failure rate
International Nuclear Information System (INIS)
Jordan Cizelj, R.; Mavko, B.; Kljenak, I.
2000-01-01
A method for specific estimation of a component failure rate, based on specific quantitative and qualitative data other than component failures, was developed and is described in the proposed paper. The basis of the method is the Bayesian updating procedure. A prior distribution is selected from a generic database, whereas likelihood is built using fuzzy logic theory. With the proposed method, the component failure rate estimation is based on a much larger quantity of information compared to the presently used classical methods. Consequently, epistemic uncertainties, which are caused by lack of knowledge about a component or phenomenon are reduced. (author)
Estimating uncertainty in multivariate responses to selection.
Stinchcombe, John R; Simonsen, Anna K; Blows, Mark W
2014-04-01
Predicting the responses to natural selection is one of the key goals of evolutionary biology. Two of the challenges in fulfilling this goal have been the realization that many estimates of natural selection might be highly biased by environmentally induced covariances between traits and fitness, and that many estimated responses to selection do not incorporate or report uncertainty in the estimates. Here we describe the application of a framework that blends the merits of the Robertson-Price Identity approach and the multivariate breeder's equation to address these challenges. The approach allows genetic covariance matrices, selection differentials, selection gradients, and responses to selection to be estimated without environmentally induced bias, direct and indirect selection and responses to selection to be distinguished, and if implemented in a Bayesian-MCMC framework, statistically robust estimates of uncertainty on all of these parameters to be made. We illustrate our approach with a worked example of previously published data. More generally, we suggest that applying both the Robertson-Price Identity and the multivariate breeder's equation will facilitate hypothesis testing about natural selection, genetic constraints, and evolutionary responses. © 2013 The Author(s). Evolution © 2013 The Society for the Study of Evolution.
Quantifying Uncertainty in Soil Volume Estimates
International Nuclear Information System (INIS)
Roos, A.D.; Hays, D.C.; Johnson, R.L.; Durham, L.A.; Winters, M.
2009-01-01
Proper planning and design for remediating contaminated environmental media require an adequate understanding of the types of contaminants and the lateral and vertical extent of contamination. In the case of contaminated soils, this generally takes the form of volume estimates that are prepared as part of a Feasibility Study for Comprehensive Environmental Response, Compensation, and Liability Act (CERCLA) sites and/or as part of the remedial design. These estimates are typically single values representing what is believed to be the most likely volume of contaminated soil present at the site. These single-value estimates, however, do not convey the level of confidence associated with the estimates. Unfortunately, the experience has been that pre-remediation soil volume estimates often significantly underestimate the actual volume of contaminated soils that are encountered during the course of remediation. This underestimation has significant implications, both technically (e.g., inappropriate remedial designs) and programmatically (e.g., establishing technically defensible budget and schedule baselines). Argonne National Laboratory (Argonne) has developed a joint Bayesian/geostatistical methodology for estimating contaminated soil volumes based on sampling results, that also provides upper and lower probabilistic bounds on those volumes. This paper evaluates the performance of this method in a retrospective study that compares volume estimates derived using this technique with actual excavated soil volumes for select Formerly Utilized Sites Remedial Action Program (FUSRAP) Maywood properties that have completed remedial action by the U.S. Army Corps of Engineers (USACE) New York District. (authors)
Uncertainty estimation in nuclear material weighing
Energy Technology Data Exchange (ETDEWEB)
Thaure, Bernard [Institut de Radioprotection et de Surete Nucleaire, Fontenay aux Roses, (France)
2011-12-15
The assessment of nuclear material quantities located in nuclear plants requires knowledge of additions and subtractions of amounts of different types of materials. Most generally, the quantity of nuclear material held is deduced from 3 parameters: a mass (or a volume of product); a concentration of nuclear material in the product considered; and an isotopic composition. Global uncertainties associated with nuclear material quantities depend upon the confidence level of results obtained in the measurement of every different parameter. Uncertainties are generally estimated by considering five influencing parameters (ISHIKAWA's rule): the material itself; the measurement system; the applied method; the environmental conditions; and the operator. A good practice guide, to be used to deal with weighing errors and problems encountered, is presented in the paper.
Bounding estimate of DWPF mercury emissions
International Nuclear Information System (INIS)
Jacobs, R.A.
1992-01-01
Purges required for H2 flammability control and verification of elevated Formic Acid Vent Condenser (FAVC) exit temperatures due to NO x reactions have lead to significant changes in Chemical Process Cell (CPC) operating conditions. Accordingly, mercury emissions estimates have been updated based upon the new operating requirements, IDMS (Integrated DWPF Melter System) experience, and development of an NO x /FAVC model which predicts FAVC exit temperatures. Using very conservative assumptions and maximum purge rates, the maximum calculated Hg emissions is approximately 130 lbs/yr. A range of 100 to 120 lbs/yr is conservatively predicted for other operating conditions. Defense Waste Processing Facility (DWPF) permitted Hg emissions are 175 lbs/yr (0.02 lbs/hr annual average)
Bounding estimate of DWPF mercury emissions
International Nuclear Information System (INIS)
Jacobs, R.A.
1993-01-01
Two factors which have substantial impact on predicted Mercury emissions are the air flows in the Chemical Process Cell (CPC) and the exit temperature of the Formic Acid Vent Condenser (FAVC). The discovery in the IDMS (Integrated DWPF Melter System) of H 2 generation by noble metal catalyzed formic acid decomposition and the resultant required dilution air flow has increased the expected instantaneous CPC air flow by as much as a factor of four. In addition, IDMS has experienced higher than design (10 degrees C) FAVC exit temperatures during certain portions of the operating cycle. These temperatures were subsequently attributed to the exothermic reaction of NO to NO 2 . Moreover, evaluation of the DWPF FAVC indicated it was undersized and unless modified or replaced, routine exit temperatures would be in excess of design. Purges required for H 2 flammability control and verification of elevated FAVC exit temperatures due to NO x reactions have lead to significant changes in CPC operating conditions. Accordingly, mercury emissions estimates have been updated based upon the new operating requirements, IDMS experience, and development of an NO x /FAVC model which predicts FAVC exit temperatures. Using very conservative assumptions and maximum purge rates, the maximum calculated Hg emissions is approximately 130 lbs/yr. A range of 100 to 120 lbs/yr is conservatively predicted for other operating conditions. The peak emission rate calculated is 0.027 lbs/hr. The estimated DWPF Hg emissions for the construction permit are 175 lbs/yr (0.02 lbs/hr annual average)
Directory of Open Access Journals (Sweden)
Vicari Kristin J
2012-04-01
Full Text Available Abstract Background Cost-effective production of lignocellulosic biofuels remains a major financial and technical challenge at the industrial scale. A critical tool in biofuels process development is the techno-economic (TE model, which calculates biofuel production costs using a process model and an economic model. The process model solves mass and energy balances for each unit, and the economic model estimates capital and operating costs from the process model based on economic assumptions. The process model inputs include experimental data on the feedstock composition and intermediate product yields for each unit. These experimental yield data are calculated from primary measurements. Uncertainty in these primary measurements is propagated to the calculated yields, to the process model, and ultimately to the economic model. Thus, outputs of the TE model have a minimum uncertainty associated with the uncertainty in the primary measurements. Results We calculate the uncertainty in the Minimum Ethanol Selling Price (MESP estimate for lignocellulosic ethanol production via a biochemical conversion process: dilute sulfuric acid pretreatment of corn stover followed by enzymatic hydrolysis and co-fermentation of the resulting sugars to ethanol. We perform a sensitivity analysis on the TE model and identify the feedstock composition and conversion yields from three unit operations (xylose from pretreatment, glucose from enzymatic hydrolysis, and ethanol from fermentation as the most important variables. The uncertainty in the pretreatment xylose yield arises from multiple measurements, whereas the glucose and ethanol yields from enzymatic hydrolysis and fermentation, respectively, are dominated by a single measurement: the fraction of insoluble solids (fIS in the biomass slurries. Conclusions We calculate a $0.15/gal uncertainty in MESP from the TE model due to uncertainties in primary measurements. This result sets a lower bound on the error bars of
Uncertainties Involved in the Iopospheric Conductivity Estimation
Directory of Open Access Journals (Sweden)
Young-Sil Kwak
2002-12-01
Full Text Available Various uncertainties involved in ionospheric conductivity estimation utilizing the electron density profile obtained from the Sondrestrom incoherent scatter radar are examined. First, we compare the conductivity which is based on raw electron density and the one based on corrected electron density that takes into account the effects of the difference between the electron and ion temperatures and the Debye length. The corrected electron density yields higher Pedersen and Hall conductivities than the raw electron density does. Second, the dependence of collision frequency model on the conductivity estimation is examined. Below 110 km conductivity does not depend significantly on collision frequency models. Above 110 km, however, the collision models affect the conductivity estimation. Third, the influence of the electron and ion temperatures on the conductivity estimation is examined. Electron and ion temperatures carrying an error of about 10% do not seem to affect significantly the conductivity estimation. Fourth, also examined is the effect of the choice of the altitude range of integration in calculating the height-integrated conductivity, conductance. It has been demonstrated that the lower and upper boundaries of the integration are quite sensitive to the estimation of the Hall and Pedersen conductances, respectively.
A linear programming approach to characterizing norm bounded uncertainty from experimental data
Scheid, R. E.; Bayard, D. S.; Yam, Y.
1991-01-01
The linear programming spectral overbounding and factorization (LPSOF) algorithm, an algorithm for finding a minimum phase transfer function of specified order whose magnitude tightly overbounds a specified nonparametric function of frequency, is introduced. This method has direct application to transforming nonparametric uncertainty bounds (available from system identification experiments) into parametric representations required for modern robust control design software (i.e., a minimum-phase transfer function multiplied by a norm-bounded perturbation).
Lower bounds to the reliabilities of factor score estimators
Hessen, D.J.
2017-01-01
Under the general common factor model, the reliabilities of factor score estimators might be of more interest than the reliability of the total score (the unweighted sum of item scores). In this paper, lower bounds to the reliabilities of Thurstone’s factor score estimators, Bartlett’s factor score
Uncertainty Quantification of Turbulence Model Closure Coefficients for Transonic Wall-Bounded Flows
Schaefer, John; West, Thomas; Hosder, Serhat; Rumsey, Christopher; Carlson, Jan-Renee; Kleb, William
2015-01-01
The goal of this work was to quantify the uncertainty and sensitivity of commonly used turbulence models in Reynolds-Averaged Navier-Stokes codes due to uncertainty in the values of closure coefficients for transonic, wall-bounded flows and to rank the contribution of each coefficient to uncertainty in various output flow quantities of interest. Specifically, uncertainty quantification of turbulence model closure coefficients was performed for transonic flow over an axisymmetric bump at zero degrees angle of attack and the RAE 2822 transonic airfoil at a lift coefficient of 0.744. Three turbulence models were considered: the Spalart-Allmaras Model, Wilcox (2006) k-w Model, and the Menter Shear-Stress Trans- port Model. The FUN3D code developed by NASA Langley Research Center was used as the flow solver. The uncertainty quantification analysis employed stochastic expansions based on non-intrusive polynomial chaos as an efficient means of uncertainty propagation. Several integrated and point-quantities are considered as uncertain outputs for both CFD problems. All closure coefficients were treated as epistemic uncertain variables represented with intervals. Sobol indices were used to rank the relative contributions of each closure coefficient to the total uncertainty in the output quantities of interest. This study identified a number of closure coefficients for each turbulence model for which more information will reduce the amount of uncertainty in the output significantly for transonic, wall-bounded flows.
Uncertainty Estimates: A New Editorial Standard
International Nuclear Information System (INIS)
Drake, Gordon W.F.
2014-01-01
Full text: The objective of achieving higher standards for uncertainty estimates in the publication of theoretical data for atoms and molecules requires a concerted effort by both the authors of papers and the editors who send them out for peer review. In April, 2011, the editors of Physical Review A published an Editorial announcing a new standard that uncertainty estimates would be required whenever practicable, and in particular in the following circumstances: 1. If the authors claim high accuracy, or improvements on the accuracy of previous work. 2. If the primary motivation for the paper is to make comparisons with present or future high precision experimental measurements. 3. If the primary motivation is to provide interpolations or extrapolations of known experimental measurements. The new policy means that papers that do not meet these standards are not sent out for peer review until they have been suitably revised, and the authors are so notified immediately upon receipt. The policy has now been in effect for three years. (author
van der Schaft, Arjan
1995-01-01
The approach to robust stabilization of linear systems using normalized left coprime factorizations with H∞ bounded uncertainty is generalized to nonlinear systems. A nonlinear perturbation model is derived, based on the concept of a stable kernel representation of nonlinear systems. The robust
Energy Technology Data Exchange (ETDEWEB)
Chatterjee, Samrat; Tipireddy, Ramakrishna; Oster, Matthew R.; Halappanavar, Mahantesh
2016-09-16
Securing cyber-systems on a continual basis against a multitude of adverse events is a challenging undertaking. Game-theoretic approaches, that model actions of strategic decision-makers, are increasingly being applied to address cybersecurity resource allocation challenges. Such game-based models account for multiple player actions and represent cyber attacker payoffs mostly as point utility estimates. Since a cyber-attacker’s payoff generation mechanism is largely unknown, appropriate representation and propagation of uncertainty is a critical task. In this paper we expand on prior work and focus on operationalizing the probabilistic uncertainty quantification framework, for a notional cyber system, through: 1) representation of uncertain attacker and system-related modeling variables as probability distributions and mathematical intervals, and 2) exploration of uncertainty propagation techniques including two-phase Monte Carlo sampling and probability bounds analysis.
Uncertainty estimation of uranium determination in urine by fluorometry
International Nuclear Information System (INIS)
Shakhashiro, A.; Al-Khateeb, S.
2003-11-01
In this study an applicable mathematical model is proposed for the estimation of uncertainty in uranium determination by fluorometry in urine sample. The study based on EURACHEM guide for uncertainty estimation. This model was tested on a sample containing 0.02 μg/ml uranium, where calculated uncertainty was 0.007 μg/ml. The sources of uncertainty were shown on fish-bone plane as the following: In addition, the weight of each uncertainty parameter was shown in a histogram: Finally, it was found that the estimated uncertainty by the proposed model was 3 to 4 time more that the usually reported standard deviation. (author)
Eigenvalue estimates for submanifolds with bounded f-mean curvature
Indian Academy of Sciences (India)
GUANGYUE HUANG
1College of Mathematics and Information Science, Henan Normal University,. Xinxiang 453007 ... submanifolds in a hyperbolic space with the norm of their mean curvature vector bounded above by a constant. ..... [2] Batista M, Cavalcante M P and Pyo J, Some isoperimetric inequalities and eigenvalue estimates in ...
International Nuclear Information System (INIS)
Wilson, G.E.; Boyack, B.E.; Duffey, R.B.; Griffith, P.; Katsma, K.R.; Lellouche, G.S.; Rohatgi, U.S.; Wulff, W.; Zuber, N.
1988-01-01
Issue of a revised rule for loss of coolant accident/emergency core cooling system (LOCA/ECCS) analysis of light water reactors will allow the use of best estimate (BE) computer codes in safety analysis, with uncertainty analysis. This paper describes a systematic methodology, CSAU (Code Scaling, Applicability and Uncertainty), which will provide uncertainty bounds in a cost effective, auditable, rational and practical manner. 8 figs., 2 tabs
Uncertainty of Volatility Estimates from Heston Greeks
Directory of Open Access Journals (Sweden)
Oliver Pfante
2018-01-01
Full Text Available Volatility is a widely recognized measure of market risk. As volatility is not observed it has to be estimated from market prices, i.e., as the implied volatility from option prices. The volatility index VIX making volatility a tradeable asset in its own right is computed from near- and next-term put and call options on the S&P 500 with more than 23 days and less than 37 days to expiration and non-vanishing bid. In the present paper we quantify the information content of the constituents of the VIX about the volatility of the S&P 500 in terms of the Fisher information matrix. Assuming that observed option prices are centered on the theoretical price provided by Heston's model perturbed by additive Gaussian noise we relate their Fisher information matrix to the Greeks in the Heston model. We find that the prices of options contained in the VIX basket allow for reliable estimates of the volatility of the S&P 500 with negligible uncertainty as long as volatility is large enough. Interestingly, if volatility drops below a critical value of roughly 3%, inferences from option prices become imprecise because Vega, the derivative of a European option w.r.t. volatility, and thereby the Fisher information nearly vanishes.
Uncertainty and validation. Effect of model complexity on uncertainty estimates
International Nuclear Information System (INIS)
Elert, M.
1996-09-01
In the Model Complexity subgroup of BIOMOVS II, models of varying complexity have been applied to the problem of downward transport of radionuclides in soils. A scenario describing a case of surface contamination of a pasture soil was defined. Three different radionuclides with different environmental behavior and radioactive half-lives were considered: Cs-137, Sr-90 and I-129. The intention was to give a detailed specification of the parameters required by different kinds of model, together with reasonable values for the parameter uncertainty. A total of seven modelling teams participated in the study using 13 different models. Four of the modelling groups performed uncertainty calculations using nine different modelling approaches. The models used range in complexity from analytical solutions of a 2-box model using annual average data to numerical models coupling hydrology and transport using data varying on a daily basis. The complex models needed to consider all aspects of radionuclide transport in a soil with a variable hydrology are often impractical to use in safety assessments. Instead simpler models, often box models, are preferred. The comparison of predictions made with the complex models and the simple models for this scenario show that the predictions in many cases are very similar, e g in the predictions of the evolution of the root zone concentration. However, in other cases differences of many orders of magnitude can appear. One example is the prediction of the flux to the groundwater of radionuclides being transported through the soil column. Some issues that have come to focus in this study: There are large differences in the predicted soil hydrology and as a consequence also in the radionuclide transport, which suggests that there are large uncertainties in the calculation of effective precipitation and evapotranspiration. The approach used for modelling the water transport in the root zone has an impact on the predictions of the decline in root
Uncertainty and validation. Effect of model complexity on uncertainty estimates
Energy Technology Data Exchange (ETDEWEB)
Elert, M. [Kemakta Konsult AB, Stockholm (Sweden)] [ed.
1996-09-01
In the Model Complexity subgroup of BIOMOVS II, models of varying complexity have been applied to the problem of downward transport of radionuclides in soils. A scenario describing a case of surface contamination of a pasture soil was defined. Three different radionuclides with different environmental behavior and radioactive half-lives were considered: Cs-137, Sr-90 and I-129. The intention was to give a detailed specification of the parameters required by different kinds of model, together with reasonable values for the parameter uncertainty. A total of seven modelling teams participated in the study using 13 different models. Four of the modelling groups performed uncertainty calculations using nine different modelling approaches. The models used range in complexity from analytical solutions of a 2-box model using annual average data to numerical models coupling hydrology and transport using data varying on a daily basis. The complex models needed to consider all aspects of radionuclide transport in a soil with a variable hydrology are often impractical to use in safety assessments. Instead simpler models, often box models, are preferred. The comparison of predictions made with the complex models and the simple models for this scenario show that the predictions in many cases are very similar, e g in the predictions of the evolution of the root zone concentration. However, in other cases differences of many orders of magnitude can appear. One example is the prediction of the flux to the groundwater of radionuclides being transported through the soil column. Some issues that have come to focus in this study: There are large differences in the predicted soil hydrology and as a consequence also in the radionuclide transport, which suggests that there are large uncertainties in the calculation of effective precipitation and evapotranspiration. The approach used for modelling the water transport in the root zone has an impact on the predictions of the decline in root
H∞ Loop Shaping Control of Input Saturated Systems with Norm-Bounded Parametric Uncertainty
Directory of Open Access Journals (Sweden)
Renan Lima Pereira
2015-01-01
Full Text Available This paper proposes a gain-scheduling control design strategy for a class of linear systems with the presence of both input saturation constraints and norm-bounded parametric uncertainty. LMI conditions are derived in order to obtain a gain-scheduled controller that ensures the robust stability and performance of the closed loop system. The main steps to obtain such a controller are given. Differently from other gain-scheduled approaches in the literature, this one focuses on the problem of H∞ loop shaping control design with input saturation nonlinearity and norm-bounded uncertainty to reduce the effect of the disturbance input on the controlled outputs. Here, the design problem has been formulated in the four-block H∞ synthesis framework, in which it is possible to describe the parametric uncertainty and the input saturation nonlinearity as perturbations to normalized coprime factors of the shaped plant. As a result, the shaped plant is represented as a linear parameter-varying (LPV system while the norm-bounded uncertainty and input saturation are incorporated. This procedure yields a linear parameter-varying structure for the controller that ensures the stability of the polytopic LPV shaped plant from the vertex property. Finally, the effectiveness of the method is illustrated through application to a physical system: a VTOL “vertical taking-off landing” helicopter.
Huang, Hening
2018-01-01
This paper is the second (Part II) in a series of two papers (Part I and Part II). Part I has quantitatively discussed the fundamental limitations of the t-interval method for uncertainty estimation with a small number of measurements. This paper (Part II) reveals that the t-interval is an ‘exact’ answer to a wrong question; it is actually misused in uncertainty estimation. This paper proposes a redefinition of uncertainty, based on the classical theory of errors and the theory of point estimation, and a modification of the conventional approach to estimating measurement uncertainty. It also presents an asymptotic procedure for estimating the z-interval. The proposed modification is to replace the t-based uncertainty with an uncertainty estimator (mean- or median-unbiased). The uncertainty estimator method is an approximate answer to the right question to uncertainty estimation. The modified approach provides realistic estimates of uncertainty, regardless of whether the population standard deviation is known or unknown, or if the sample size is small or large. As an application example of the modified approach, this paper presents a resolution to the Du-Yang paradox (i.e. Paradox 2), one of the three paradoxes caused by the misuse of the t-interval in uncertainty estimation.
Neglect Of Parameter Estimation Uncertainty Can Significantly Overestimate Structural Reliability
Directory of Open Access Journals (Sweden)
Rózsás Árpád
2015-12-01
Full Text Available Parameter estimation uncertainty is often neglected in reliability studies, i.e. point estimates of distribution parameters are used for representative fractiles, and in probabilistic models. A numerical example examines the effect of this uncertainty on structural reliability using Bayesian statistics. The study reveals that the neglect of parameter estimation uncertainty might lead to an order of magnitude underestimation of failure probability.
The Uncertainty estimation of Alanine/ESR dosimetry
International Nuclear Information System (INIS)
Kim, Bo Rum; An, Jin Hee; Choi, Hoon; Kim, Young Ki
2008-01-01
Machinery, tools and cable etc are in the nuclear power plant which environment is very severe. By measuring actual dose, it needs for extending life expectancy of the machinery and tools and the cable. Therefore, we estimated on dose (gamma ray) of Wolsong nuclear power division 1 by dose estimation technology for three years. The dose estimation technology was secured by ESR(Electron Spin Resonance) dose estimation using regression analysis. We estimate uncertainty for secure a reliability of results. The uncertainty estimation will be able to judge the reliability of measurement results. The estimation of uncertainty referred the international unified guide in order; GUM(Guide to the Expression of Uncertainty in Measurement). It was published by International Standardization for Organization (ISO) in 1993. In this study the uncertainty of e-scan and EMX those are ESR equipment were evaluated and compared. Base on these results, it will improve the reliability of measurement
Estimating real-time predictive hydrological uncertainty
Verkade, J.S.
2015-01-01
Flood early warning systems provide a potentially highly effective flood risk reduction measure. The effectiveness of early warning, however, is affected by forecasting uncertainty: the impossibility of knowing, in advance, the exact future state of hydrological systems. Early warning systems
Bound on the estimation grid size for sparse reconstruction in direction of arrival estimation
Coutiño Minguez, M.A.; Pribic, R; Leus, G.J.T.
2016-01-01
A bound for sparse reconstruction involving both the signal-to-noise ratio (SNR) and the estimation grid size is presented. The bound is illustrated for the case of a uniform linear array (ULA). By reducing the number of possible sparse vectors present in the feasible set of a constrained ℓ1-norm
Bounded Perturbation Regularization for Linear Least Squares Estimation
Ballal, Tarig
2017-10-18
This paper addresses the problem of selecting the regularization parameter for linear least-squares estimation. We propose a new technique called bounded perturbation regularization (BPR). In the proposed BPR method, a perturbation with a bounded norm is allowed into the linear transformation matrix to improve the singular-value structure. Following this, the problem is formulated as a min-max optimization problem. Next, the min-max problem is converted to an equivalent minimization problem to estimate the unknown vector quantity. The solution of the minimization problem is shown to converge to that of the ℓ2 -regularized least squares problem, with the unknown regularizer related to the norm bound of the introduced perturbation through a nonlinear constraint. A procedure is proposed that combines the constraint equation with the mean squared error (MSE) criterion to develop an approximately optimal regularization parameter selection algorithm. Both direct and indirect applications of the proposed method are considered. Comparisons with different Tikhonov regularization parameter selection methods, as well as with other relevant methods, are carried out. Numerical results demonstrate that the proposed method provides significant improvement over state-of-the-art methods.
Risk, unexpected uncertainty, and estimation uncertainty: Bayesian learning in unstable settings.
Directory of Open Access Journals (Sweden)
Elise Payzan-LeNestour
Full Text Available Recently, evidence has emerged that humans approach learning using Bayesian updating rather than (model-free reinforcement algorithms in a six-arm restless bandit problem. Here, we investigate what this implies for human appreciation of uncertainty. In our task, a Bayesian learner distinguishes three equally salient levels of uncertainty. First, the Bayesian perceives irreducible uncertainty or risk: even knowing the payoff probabilities of a given arm, the outcome remains uncertain. Second, there is (parameter estimation uncertainty or ambiguity: payoff probabilities are unknown and need to be estimated. Third, the outcome probabilities of the arms change: the sudden jumps are referred to as unexpected uncertainty. We document how the three levels of uncertainty evolved during the course of our experiment and how it affected the learning rate. We then zoom in on estimation uncertainty, which has been suggested to be a driving force in exploration, in spite of evidence of widespread aversion to ambiguity. Our data corroborate the latter. We discuss neural evidence that foreshadowed the ability of humans to distinguish between the three levels of uncertainty. Finally, we investigate the boundaries of human capacity to implement Bayesian learning. We repeat the experiment with different instructions, reflecting varying levels of structural uncertainty. Under this fourth notion of uncertainty, choices were no better explained by Bayesian updating than by (model-free reinforcement learning. Exit questionnaires revealed that participants remained unaware of the presence of unexpected uncertainty and failed to acquire the right model with which to implement Bayesian updating.
Development of Property Models with Uncertainty Estimate for Process Design under Uncertainty
DEFF Research Database (Denmark)
Hukkerikar, Amol; Sarup, Bent; Abildskov, Jens
more reliable predictions with a new and improved set of model parameters for GC (group contribution) based and CI (atom connectivity index) based models and to quantify the uncertainties in the estimated property values from a process design point-of-view. This includes: (i) parameter estimation using....... The comparison of model prediction uncertainties with reported range of measurement uncertainties is presented for the properties with related available data. The application of the developed methodology to quantify the effect of these uncertainties on the design of different unit operations (distillation column......, the developed methodology can be used to quantify the sensitivity of process design to uncertainties in property estimates; obtain rationally the risk/safety factors in process design; and identify additional experimentation needs in order to reduce most critical uncertainties....
Assessing concentration uncertainty estimates from passive microwave sea ice products
Meier, W.; Brucker, L.; Miller, J. A.
2017-12-01
Sea ice concentration is an essential climate variable and passive microwave derived estimates of concentration are one of the longest satellite-derived climate records. However, until recently uncertainty estimates were not provided. Numerous validation studies provided insight into general error characteristics, but the studies have found that concentration error varied greatly depending on sea ice conditions. Thus, an uncertainty estimate from each observation is desired, particularly for initialization, assimilation, and validation of models. Here we investigate three sea ice products that include an uncertainty for each concentration estimate: the NASA Team 2 algorithm product, the EUMETSAT Ocean and Sea Ice Satellite Application Facility (OSI-SAF) product, and the NOAA/NSIDC Climate Data Record (CDR) product. Each product estimates uncertainty with a completely different approach. The NASA Team 2 product derives uncertainty internally from the algorithm method itself. The OSI-SAF uses atmospheric reanalysis fields and a radiative transfer model. The CDR uses spatial variability from two algorithms. Each approach has merits and limitations. Here we evaluate the uncertainty estimates by comparing the passive microwave concentration products with fields derived from the NOAA VIIRS sensor. The results show that the relationship between the product uncertainty estimates and the concentration error (relative to VIIRS) is complex. This may be due to the sea ice conditions, the uncertainty methods, as well as the spatial and temporal variability of the passive microwave and VIIRS products.
Jay, Sylvain; Guillaume, Mireille; Chami, Malik; Minghelli, Audrey; Deville, Yannick; Lafrance, Bruno; Serfaty, Véronique
2018-01-22
We present an analytical approach based on Cramer-Rao Bounds (CRBs) to investigate the uncertainties in estimated ocean color parameters resulting from the propagation of uncertainties in the bio-optical reflectance modeling through the inversion process. Based on given bio-optical and noise probabilistic models, CRBs can be computed efficiently for any set of ocean color parameters and any sensor configuration, directly providing the minimum estimation variance that can be possibly attained by any unbiased estimator of any targeted parameter. Here, CRBs are explicitly developed using (1) two water reflectance models corresponding to deep and shallow waters, resp., and (2) four probabilistic models describing the environmental noises observed within four Sentinel-2 MSI, HICO, Sentinel-3 OLCI and MODIS images, resp. For both deep and shallow waters, CRBs are shown to be consistent with the experimental estimation variances obtained using two published remote-sensing methods, while not requiring one to perform any inversion. CRBs are also used to investigate to what extent perfect a priori knowledge on one or several geophysical parameters can improve the estimation of remaining unknown parameters. For example, using pre-existing knowledge of bathymetry (e.g., derived from LiDAR) within the inversion is shown to greatly improve the retrieval of bottom cover for shallow waters. Finally, CRBs are shown to provide valuable information on the best estimation performances that may be achieved with the MSI, HICO, OLCI and MODIS configurations for a variety of oceanic, coastal and inland waters. CRBs are thus demonstrated to be an informative and efficient tool to characterize minimum uncertainties in inverted ocean color geophysical parameters.
Estimation of uncertainty in TLD calibration
International Nuclear Information System (INIS)
Hasabelrasoul, H. A.
2013-07-01
In this study thermoluminescence dosimeter TLD was use of individual control devices to make sure the quality assurance and quality control in individual monitoring. The uncertainty measured in reader calibration coefficients for tow reader and uncertainty in radiation dose after irradiate in SSDL laboratory. Fifty sample was selected for the study was placed in the oven at a temperature of 400 for an hour to get zero or background and took zero count by or background and took zero count by reader (1) and reader (2) and then irradiate in SSDL by cesium-137 at a dose of 5 mGy and laid back in the oven at degrees 100 and degrees 10 minutes, to 10 chips for calibration and readout count by reader one and reader two. The RCF was found for each reader above 1.47 and 1.11, respectively, and found the uncertainty RCF was found for each reader above 1.47 and 1.11, respectively, and found the uncertainly RCF 0.430629 and 0.431973. Radiation dose was measured for fifty samples irradiate to dose of 5 mGy and read the count by reader 1 and reader 2 the uncertainty was found for each reader 0.490446 and 0.587602.(Author)
International Nuclear Information System (INIS)
Strigini, Lorenzo; Wright, David
2014-01-01
When deciding whether to accept into service a new safety-critical system, or choosing between alternative systems, uncertainty about the parameters that affect future failure probability may be a major problem. This uncertainty can be extreme if there is the possibility of unknown design errors (e.g. in software), or wide variation between nominally equivalent components. We study the effect of parameter uncertainty on future reliability (survival probability), for systems required to have low risk of even only one failure or accident over the long term (e.g. their whole operational lifetime) and characterised by a single reliability parameter (e.g. probability of failure per demand – pfd). A complete mathematical treatment requires stating a probability distribution for any parameter with uncertain value. This is hard, so calculations are often performed using point estimates, like the expected value. We investigate conditions under which such simplified descriptions yield reliability values that are sure to be pessimistic (or optimistic) bounds for a prediction based on the true distribution. Two important observations are (i) using the expected value of the reliability parameter as its true value guarantees a pessimistic estimate of reliability, a useful property in most safety-related decisions; (ii) with a given expected pfd, broader distributions (in a formally defined meaning of “broader”), that is, systems that are a priori “less predictable”, lower the risk of failures or accidents. Result (i) justifies the simplification of using a mean in reliability modelling; we discuss within which scope this justification applies, and explore related scenarios, e.g. how things improve if we can test the system before operation. Result (ii) not only offers more flexible ways of bounding reliability predictions, but also has important, often counter-intuitive implications for decision making in various areas, like selection of components, project management
Uncertainty in Forest Net Present Value Estimations
Directory of Open Access Journals (Sweden)
Ilona Pietilä
2010-09-01
Full Text Available Uncertainty related to inventory data, growth models and timber price fluctuation was investigated in the assessment of forest property net present value (NPV. The degree of uncertainty associated with inventory data was obtained from previous area-based airborne laser scanning (ALS inventory studies. The study was performed, applying the Monte Carlo simulation, using stand-level growth and yield projection models and three alternative rates of interest (3, 4 and 5%. Timber price fluctuation was portrayed with geometric mean-reverting (GMR price models. The analysis was conducted for four alternative forest properties having varying compartment structures: (A a property having an even development class distribution, (B sapling stands, (C young thinning stands, and (D mature stands. Simulations resulted in predicted yield value (predicted NPV distributions at both stand and property levels. Our results showed that ALS inventory errors were the most prominent source of uncertainty, leading to a 5.1–7.5% relative deviation of property-level NPV when an interest rate of 3% was applied. Interestingly, ALS inventory led to significant biases at the property level, ranging from 8.9% to 14.1% (3% interest rate. ALS inventory-based bias was the most significant in mature stand properties. Errors related to the growth predictions led to a relative standard deviation in NPV, varying from 1.5% to 4.1%. Growth model-related uncertainty was most significant in sapling stand properties. Timber price fluctuation caused the relative standard deviations ranged from 3.4% to 6.4% (3% interest rate. The combined relative variation caused by inventory errors, growth model errors and timber price fluctuation varied, depending on the property type and applied rates of interest, from 6.4% to 12.6%. By applying the methodology described here, one may take into account the effects of various uncertainty factors in the prediction of forest yield value and to supply the
Taming Chaos by Linear Regulation with Bound Estimation
Directory of Open Access Journals (Sweden)
Jiqiang Wang
2015-01-01
Full Text Available Chaos control has become an important area of research and consequently many approaches have been proposed to control chaos. This paper proposes a linear regulation method. Different from the existing approaches is that it can provide region of attraction while estimating the bounding behaviour of the norm of the states. The proposed method also possesses design flexibility and can be easily used to cater for special requirement such that control signal should be generated via single input, single state, static feedback and so forth. The applications to the Tigan system, the Genesio chaotic system, the novel chaotic system, and the Lorenz chaotic system justify the above claims.
Traceability and uncertainty estimation in coordinate metrology
DEFF Research Database (Denmark)
Hansen, Hans Nørgaard; Savio, Enrico; De Chiffre, Leonardo
2001-01-01
National and international standards have defined performance verification procedures for coordinate measuring machines (CMMs) that typically involve their ability to measure calibrated lengths and to a certain extent form. It is recognised that, without further analysis or testing, these results...... are required. Depending on the requirements for uncertainty level, different approaches may be adopted to achieve traceability. Especially in the case of complex measurement situations and workpieces the procedures are not trivial. This paper discusses the establishment of traceability in coordinate metrology...
Uncertainty estimates for theoretical atomic and molecular data
International Nuclear Information System (INIS)
Chung, H-K; Braams, B J; Bartschat, K; Császár, A G; Drake, G W F; Kirchner, T; Kokoouline, V; Tennyson, J
2016-01-01
Sources of uncertainty are reviewed for calculated atomic and molecular data that are important for plasma modeling: atomic and molecular structures and cross sections for electron-atom, electron-molecule, and heavy particle collisions. We concentrate on model uncertainties due to approximations to the fundamental many-body quantum mechanical equations and we aim to provide guidelines to estimate uncertainties as a routine part of computations of data for structure and scattering. (topical review)
Energy Technology Data Exchange (ETDEWEB)
Stephens, T. S. [Argonne National Lab. (ANL), Argonne, IL (United States); Gonder, Jeff [National Renewable Energy Lab. (NREL), Golden, CO (United States); Chen, Yuche [National Renewable Energy Lab. (NREL), Golden, CO (United States); Lin, Z. [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Liu, C. [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Gohlke, D. [US Dept. of Energy, Washington, DC (United States)
2016-11-01
This report details a study of the potential effects of connected and automated vehicle (CAV) technologies on vehicle miles traveled (VMT), vehicle fuel efficiency, and consumer costs. Related analyses focused on a range of light-duty CAV technologies in conventional powertrain vehicles -- from partial automation to full automation, with and without ridesharing -- compared to today's base-case scenario. Analysis results revealed widely disparate upper- and lower-bound estimates for fuel use and VMT, ranging from a tripling of fuel use to decreasing light-duty fuel use to below 40% of today's level. This wide range reflects uncertainties in the ways that CAV technologies can influence vehicle efficiency and use through changes in vehicle designs, driving habits, and travel behavior. The report further identifies the most significant potential impacting factors, the largest areas of uncertainty, and where further research is particularly needed.
Energy Technology Data Exchange (ETDEWEB)
Nielsen, Joseph, E-mail: joseph.nielsen@inl.gov [Idaho National Laboratory, 1955 N. Fremont Avenue, P.O. Box 1625, Idaho Falls, ID 83402 (United States); University of Idaho, Department of Mechanical Engineering and Nuclear Engineering Program, 1776 Science Center Drive, Idaho Falls, ID 83402-1575 (United States); Tokuhiro, Akira [University of Idaho, Department of Mechanical Engineering and Nuclear Engineering Program, 1776 Science Center Drive, Idaho Falls, ID 83402-1575 (United States); Hiromoto, Robert [University of Idaho, Department of Computer Science, 1776 Science Center Drive, Idaho Falls, ID 83402-1575 (United States); Tu, Lei [University of Idaho, Department of Mechanical Engineering and Nuclear Engineering Program, 1776 Science Center Drive, Idaho Falls, ID 83402-1575 (United States)
2015-12-15
Highlights: • Dynamic Event Tree solutions have been optimized using the Branch-and-Bound algorithm. • A 60% efficiency in optimization has been achieved. • Modeling uncertainty within a risk-informed framework is evaluated. - Abstract: Evaluation of the impacts of uncertainty and sensitivity in modeling presents a significant set of challenges in particular to high fidelity modeling. Computational costs and validation of models creates a need for cost effective decision making with regards to experiment design. Experiments designed to validate computation models can be used to reduce uncertainty in the physical model. In some cases, large uncertainty in a particular aspect of the model may or may not have a large impact on the final results. For example, modeling of a relief valve may result in large uncertainty, however, the actual effects on final peak clad temperature in a reactor transient may be small and the large uncertainty with respect to valve modeling may be considered acceptable. Additionally, the ability to determine the adequacy of a model and the validation supporting it should be considered within a risk informed framework. Low fidelity modeling with large uncertainty may be considered adequate if the uncertainty is considered acceptable with respect to risk. In other words, models that are used to evaluate the probability of failure should be evaluated more rigorously with the intent of increasing safety margin. Probabilistic risk assessment (PRA) techniques have traditionally been used to identify accident conditions and transients. Traditional classical event tree methods utilize analysts’ knowledge and experience to identify the important timing of events in coordination with thermal-hydraulic modeling. These methods lack the capability to evaluate complex dynamic systems. In these systems, time and energy scales associated with transient events may vary as a function of transition times and energies to arrive at a different physical
International Nuclear Information System (INIS)
Nielsen, Joseph; Tokuhiro, Akira; Hiromoto, Robert; Tu, Lei
2015-01-01
Highlights: • Dynamic Event Tree solutions have been optimized using the Branch-and-Bound algorithm. • A 60% efficiency in optimization has been achieved. • Modeling uncertainty within a risk-informed framework is evaluated. - Abstract: Evaluation of the impacts of uncertainty and sensitivity in modeling presents a significant set of challenges in particular to high fidelity modeling. Computational costs and validation of models creates a need for cost effective decision making with regards to experiment design. Experiments designed to validate computation models can be used to reduce uncertainty in the physical model. In some cases, large uncertainty in a particular aspect of the model may or may not have a large impact on the final results. For example, modeling of a relief valve may result in large uncertainty, however, the actual effects on final peak clad temperature in a reactor transient may be small and the large uncertainty with respect to valve modeling may be considered acceptable. Additionally, the ability to determine the adequacy of a model and the validation supporting it should be considered within a risk informed framework. Low fidelity modeling with large uncertainty may be considered adequate if the uncertainty is considered acceptable with respect to risk. In other words, models that are used to evaluate the probability of failure should be evaluated more rigorously with the intent of increasing safety margin. Probabilistic risk assessment (PRA) techniques have traditionally been used to identify accident conditions and transients. Traditional classical event tree methods utilize analysts’ knowledge and experience to identify the important timing of events in coordination with thermal-hydraulic modeling. These methods lack the capability to evaluate complex dynamic systems. In these systems, time and energy scales associated with transient events may vary as a function of transition times and energies to arrive at a different physical
Uncertainties in the estimation of Mmax
Indian Academy of Sciences (India)
local site conditions and expanded for a region, ... A case study of estimation of Mmax for ... Delhi, enters the city from north and flows south- ... not been considered for developing relationships ..... denotes the probability of seismic network to.
Estimating risk using bounding calculations and limited data
International Nuclear Information System (INIS)
COWLEY, W.L.
1999-01-01
This paper describes a methodology for estimating the potential risk to workers and the public from igniting organic solvents in any of the 177 underground waste storage tanks at the Hanford Site in southeastern Washington state. The Hanford Site is one of the U.S. Department of Energy's former production facilities for nuclear materials. The tanks contain mixed radioactive wastes. Risk is measured by calculating toxicological and radiological accident consequences and frequencies and comparing the results to established regulatory guidelines. Available sample data is insufficient to adequately characterize the waste and solvent, so a model that maximizes releases from the tanks (bounding case) is used. Maximizing releases (and thus consequences) is a standard technique used in safety analysis to compensate for lack of information. The model predicts bounding values of fire duration, the time at which the fire extinguishes because of lack of oxygen, and a pressure history of a fire in a tank. The model output is used to calculate mass and volume release rates of material from the tanks. The mass and volume release rates permit calculation of radiological and toxicological consequences. The resulting consequence calculations demonstrate that risk from an organic solvent fire in the tanks is within regulatory guidelines
Incorporation of various uncertainties in dependent failure-probability estimation
International Nuclear Information System (INIS)
Samanta, P.K.; Mitra, S.P.
1982-01-01
This paper describes an approach that allows the incorporation of various types of uncertainties in the estimation of dependent failure (common mode failure) probability. The types of uncertainties considered are attributable to data, modeling and coupling. The method developed is applied to a class of dependent failures, i.e., multiple human failures during testing, maintenance and calibration. Estimation of these failures is critical as they have been shown to be significant contributors to core melt probability in pressurized water reactors
Uncertainty Estimation Cheat Sheet for Probabilistic Risk Assessment
Britton, Paul T.; Al Hassan, Mohammad; Ring, Robert W.
2017-01-01
"Uncertainty analysis itself is uncertain, therefore, you cannot evaluate it exactly," Source Uncertain Quantitative results for aerospace engineering problems are influenced by many sources of uncertainty. Uncertainty analysis aims to make a technical contribution to decision-making through the quantification of uncertainties in the relevant variables as well as through the propagation of these uncertainties up to the result. Uncertainty can be thought of as a measure of the 'goodness' of a result and is typically represented as statistical dispersion. This paper will explain common measures of centrality and dispersion; and-with examples-will provide guidelines for how they may be estimated to ensure effective technical contributions to decision-making.
Estimation and uncertainty of reversible Markov models.
Trendelkamp-Schroer, Benjamin; Wu, Hao; Paul, Fabian; Noé, Frank
2015-11-07
Reversibility is a key concept in Markov models and master-equation models of molecular kinetics. The analysis and interpretation of the transition matrix encoding the kinetic properties of the model rely heavily on the reversibility property. The estimation of a reversible transition matrix from simulation data is, therefore, crucial to the successful application of the previously developed theory. In this work, we discuss methods for the maximum likelihood estimation of transition matrices from finite simulation data and present a new algorithm for the estimation if reversibility with respect to a given stationary vector is desired. We also develop new methods for the Bayesian posterior inference of reversible transition matrices with and without given stationary vector taking into account the need for a suitable prior distribution preserving the meta-stable features of the observed process during posterior inference. All algorithms here are implemented in the PyEMMA software--http://pyemma.org--as of version 2.0.
Uncertainties in estimating working level months
International Nuclear Information System (INIS)
Johnson, J.R.
1978-11-01
A statistical procedure is presented that can be used to estimate the number of Working Level (WL) measurements that are required to calculate the average WL to any required precision, at given confidence levels. The procedure assumes that the WL measurements have a normal distribution. WL measurement from Canadian Uranium mines are used to illustrate a procedure of insuring that estimated Working Level Months can be calculated to the required precision. An addendum reports the results of tests of normality of the WL data using the W-test and the Kolmagornov-Smirnov test. (author)
Triangular and Trapezoidal Fuzzy State Estimation with Uncertainty on Measurements
Directory of Open Access Journals (Sweden)
Mohammad Sadeghi Sarcheshmah
2012-01-01
Full Text Available In this paper, a new method for uncertainty analysis in fuzzy state estimation is proposed. The uncertainty is expressed in measurements. Uncertainties in measurements are modelled with different fuzzy membership functions (triangular and trapezoidal. To find the fuzzy distribution of any state variable, the problem is formulated as a constrained linear programming (LP optimization. The viability of the proposed method would be verified with the ones obtained from the weighted least squares (WLS and the fuzzy state estimation (FSE in the 6-bus system and in the IEEE-14 and 30 bus system.
Addressing Uncertainties in Cost Estimates for Decommissioning Nuclear Facilities
International Nuclear Information System (INIS)
Benjamin, Serge; Descures, Sylvain; Du Pasquier, Louis; Francois, Patrice; Buonarotti, Stefano; Mariotti, Giovanni; Tarakonov, Jurij; Daniska, Vladimir; Bergh, Niklas; Carroll, Simon; AaSTRoeM, Annika; Cato, Anna; De La Gardie, Fredrik; Haenggi, Hannes; Rodriguez, Jose; Laird, Alastair; Ridpath, Andy; La Guardia, Thomas; O'Sullivan, Patrick; ); Weber, Inge; )
2017-01-01
The cost estimation process of decommissioning nuclear facilities has continued to evolve in recent years, with a general trend towards demonstrating greater levels of detail in the estimate and more explicit consideration of uncertainties, the latter of which may have an impact on decommissioning project costs. The 2012 report on the International Structure for Decommissioning Costing (ISDC) of Nuclear Installations, a joint recommendation by the Nuclear Energy Agency (NEA), the International Atomic Energy Agency (IAEA) and the European Commission, proposes a standardised structure of cost items for decommissioning projects that can be used either directly for the production of cost estimates or for mapping of cost items for benchmarking purposes. The ISDC, however, provides only limited guidance on the treatment of uncertainty when preparing cost estimates. Addressing Uncertainties in Cost Estimates for Decommissioning Nuclear Facilities, prepared jointly by the NEA and IAEA, is intended to complement the ISDC, assisting cost estimators and reviewers in systematically addressing uncertainties in decommissioning cost estimates. Based on experiences gained in participating countries and projects, the report describes how uncertainty and risks can be analysed and incorporated in decommissioning cost estimates, while presenting the outcomes in a transparent manner
Adult head CT scans: the uncertainties of effective dose estimates
International Nuclear Information System (INIS)
Gregory, Kent J.; Bibbo, Giovanni; Pattison, John E.
2008-01-01
Full Text: CT scanning is a high dose imaging modality. Effective dose estimates from CT scans can provide important information to patients and medical professionals. For example, medical practitioners can use the dose to estimate the risk to the patient, and judge whether this risk is outweighed by the benefits of the CT examination, while radiographers can gauge the effect of different scanning protocols on the patient effective dose, and take this into consideration when establishing routine scan settings. Dose estimates also form an important part of epidemiological studies examining the health effects of medical radiation exposures on the wider population. Medical physicists have been devoting significant effort towards estimating patient radiation doses from diagnostic CT scans for some years. The question arises: How accurate are these effective dose estimates? The need for a greater understanding and improvement of the uncertainties in CT dose estimates is now gaining recognition as an important issue (BEIR VII 2006). This study is an attempt to analyse and quantify the uncertainty components relating to effective dose estimates from adult head CT examinations that are calculated with four commonly used methods. The dose estimation methods analysed are the Nagel method, the ImpaCT method, the Wellhoefer method and the Dose-Length Product (DLP) method. The analysis of the uncertainties was performed in accordance with the International Standards Organisation's Guide to the Expression of Uncertainty in Measurement as discussed in Gregory et al (Australas. Phys. Eng. Sci. Med., 28: 131-139, 2005). The uncertainty components vary, depending on the method used to derive the effective dose estimate. Uncertainty components in this study include the statistical and other errors from Monte Carlo simulations, uncertainties in the CT settings and positions of patients in the CT gantry, calibration errors from pencil ionization chambers, the variations in the organ
Estimates of bias and uncertainty in recorded external dose
International Nuclear Information System (INIS)
Fix, J.J.; Gilbert, E.S.; Baumgartner, W.V.
1994-10-01
A study is underway to develop an approach to quantify bias and uncertainty in recorded dose estimates for workers at the Hanford Site based on personnel dosimeter results. This paper focuses on selected experimental studies conducted to better define response characteristics of Hanford dosimeters. The study is more extensive than the experimental studies presented in this paper and includes detailed consideration and evaluation of other sources of bias and uncertainty. Hanford worker dose estimates are used in epidemiologic studies of nuclear workers. A major objective of these studies is to provide a direct assessment of the carcinogenic risk of exposure to ionizing radiation at low doses and dose rates. Considerations of bias and uncertainty in the recorded dose estimates are important in the conduct of this work. The method developed for use with Hanford workers can be considered an elaboration of the approach used to quantify bias and uncertainty in estimated doses for personnel exposed to radiation as a result of atmospheric testing of nuclear weapons between 1945 and 1962. This approach was first developed by a National Research Council (NRC) committee examining uncertainty in recorded film badge doses during atmospheric tests (NRC 1989). It involved quantifying both bias and uncertainty from three sources (i.e., laboratory, radiological, and environmental) and then combining them to obtain an overall assessment. Sources of uncertainty have been evaluated for each of three specific Hanford dosimetry systems (i.e., the Hanford two-element film dosimeter, 1944-1956; the Hanford multi-element film dosimeter, 1957-1971; and the Hanford multi-element TLD, 1972-1993) used to estimate personnel dose throughout the history of Hanford operations. Laboratory, radiological, and environmental sources of bias and uncertainty have been estimated based on historical documentation and, for angular response, on selected laboratory measurements
Thomsen, N. I.; Troldborg, M.; McKnight, U. S.; Binning, P. J.; Bjerg, P. L.
2012-04-01
Mass discharge estimates are increasingly being used in the management of contaminated sites. Such estimates have proven useful for supporting decisions related to the prioritization of contaminated sites in a groundwater catchment. Potential management options can be categorised as follows: (1) leave as is, (2) clean up, or (3) further investigation needed. However, mass discharge estimates are often very uncertain, which may hamper the management decisions. If option 1 is incorrectly chosen soil and water quality will decrease, threatening or destroying drinking water resources. The risk of choosing option 2 is to spend money on remediating a site that does not pose a problem. Choosing option 3 will often be safest, but may not be the optimal economic solution. Quantification of the uncertainty in mass discharge estimates can therefore greatly improve the foundation for selecting the appropriate management option. The uncertainty of mass discharge estimates depends greatly on the extent of the site characterization. A good approach for uncertainty estimation will be flexible with respect to the investigation level, and account for both parameter and conceptual model uncertainty. We propose a method for quantifying the uncertainty of dynamic mass discharge estimates from contaminant point sources on the local scale. The method considers both parameter and conceptual uncertainty through a multi-model approach. The multi-model approach evaluates multiple conceptual models for the same site. The different conceptual models consider different source characterizations and hydrogeological descriptions. The idea is to include a set of essentially different conceptual models where each model is believed to be realistic representation of the given site, based on the current level of information. Parameter uncertainty is quantified using Monte Carlo simulations. For each conceptual model we calculate a transient mass discharge estimate with uncertainty bounds resulting from
Huang, Yan-Jun; Zeng, Fan; Zhang, Bing; Chen, Chao-Feng; Qin, Hong-Juan; Wu, Lian-Sheng; Guo, Gui-Yin; Yang, Li-Tao; Shang-Guan, Zhi-Hong
2014-08-01
The analytical method for organically bound tritium (OBT) was developed in our laboratory. The optimized operating conditions and parameters were established for sample drying, special combustion, distillation, and measurement on a liquid scintillation spectrometer (LSC). Selected types of OBT samples such as rice, corn, rapeseed, fresh lettuce and pork were analyzed for method validation of recovery rate reproducibility, the minimum detection concentration, and the uncertainty for typical low level environmental sample was evaluated. The combustion water recovery rate of different dried environmental sample was kept at about 80%, the minimum detection concentration of OBT ranged from 0.61 to 0.89 Bq/kg (dry weight), depending on the hydrogen content. It showed that this method is suitable for OBT analysis of environmental sample with stable recovery rate, and the combustion water yield of a sample with weight about 40 g would provide sufficient quantity for measurement on LSC. Copyright © 2014 Elsevier Ltd. All rights reserved.
Estimating the measurement uncertainty in forensic blood alcohol analysis.
Gullberg, Rod G
2012-04-01
For many reasons, forensic toxicologists are being asked to determine and report their measurement uncertainty in blood alcohol analysis. While understood conceptually, the elements and computations involved in determining measurement uncertainty are generally foreign to most forensic toxicologists. Several established and well-documented methods are available to determine and report the uncertainty in blood alcohol measurement. A straightforward bottom-up approach is presented that includes: (1) specifying the measurand, (2) identifying the major components of uncertainty, (3) quantifying the components, (4) statistically combining the components and (5) reporting the results. A hypothetical example is presented that employs reasonable estimates for forensic blood alcohol analysis assuming headspace gas chromatography. These computations are easily employed in spreadsheet programs as well. Determining and reporting measurement uncertainty is an important element in establishing fitness-for-purpose. Indeed, the demand for such computations and information from the forensic toxicologist will continue to increase.
Bayesian analysis for uncertainty estimation of a canopy transpiration model
Samanta, S.; Mackay, D. S.; Clayton, M. K.; Kruger, E. L.; Ewers, B. E.
2007-04-01
A Bayesian approach was used to fit a conceptual transpiration model to half-hourly transpiration rates for a sugar maple (Acer saccharum) stand collected over a 5-month period and probabilistically estimate its parameter and prediction uncertainties. The model used the Penman-Monteith equation with the Jarvis model for canopy conductance. This deterministic model was extended by adding a normally distributed error term. This extension enabled using Markov chain Monte Carlo simulations to sample the posterior parameter distributions. The residuals revealed approximate conformance to the assumption of normally distributed errors. However, minor systematic structures in the residuals at fine timescales suggested model changes that would potentially improve the modeling of transpiration. Results also indicated considerable uncertainties in the parameter and transpiration estimates. This simple methodology of uncertainty analysis would facilitate the deductive step during the development cycle of deterministic conceptual models by accounting for these uncertainties while drawing inferences from data.
Calibration and Measurement Uncertainty Estimation of Radiometric Data: Preprint
Energy Technology Data Exchange (ETDEWEB)
Habte, A.; Sengupta, M.; Reda, I.; Andreas, A.; Konings, J.
2014-11-01
Evaluating the performance of photovoltaic cells, modules, and arrays that form large solar deployments relies on accurate measurements of the available solar resource. Therefore, determining the accuracy of these solar radiation measurements provides a better understanding of investment risks. This paper provides guidelines and recommended procedures for estimating the uncertainty in calibrations and measurements by radiometers using methods that follow the International Bureau of Weights and Measures Guide to the Expression of Uncertainty (GUM). Standardized analysis based on these procedures ensures that the uncertainty quoted is well documented.
Uncertainty estimation and risk prediction in air quality
International Nuclear Information System (INIS)
Garaud, Damien
2011-01-01
This work is about uncertainty estimation and risk prediction in air quality. Firstly, we build a multi-model ensemble of air quality simulations which can take into account all uncertainty sources related to air quality modeling. Ensembles of photochemical simulations at continental and regional scales are automatically generated. Then, these ensemble are calibrated with a combinatorial optimization method. It selects a sub-ensemble which is representative of uncertainty or shows good resolution and reliability for probabilistic forecasting. This work shows that it is possible to estimate and forecast uncertainty fields related to ozone and nitrogen dioxide concentrations or to improve the reliability of threshold exceedance predictions. The approach is compared with Monte Carlo simulations, calibrated or not. The Monte Carlo approach appears to be less representative of the uncertainties than the multi-model approach. Finally, we quantify the observational error, the representativeness error and the modeling errors. The work is applied to the impact of thermal power plants, in order to quantify the uncertainty on the impact estimates. (author) [fr
Czech Academy of Sciences Publication Activity Database
Pavelková, Lenka
2011-01-01
Roč. 47, č. 3 (2011), s. 370-384 ISSN 0023-5954 R&D Projects: GA MŠk 1M0572 Institutional research plan: CEZ:AV0Z10750506 Keywords : non-linear state space model * bounded uncertainty * missing measurements * state filtering * vehicle position estimation Subject RIV: BC - Control Systems Theory Impact factor: 0.454, year: 2011 http://library.utia.cas.cz/separaty/2011/AS/pavelkova-0360239.pdf
Uncertainties in fatal cancer risk estimates used in radiation protection
International Nuclear Information System (INIS)
Kai, Michiaki
1999-01-01
Although ICRP and NCRP had not described the details of uncertainties in cancer risk estimates in radiation protection, NCRP, in 1997, firstly reported the results of uncertainty analysis (NCRP No.126) and which is summarized in this paper. The NCRP report pointed out that there are following five factors which uncertainty possessing: uncertainty in epidemiological studies, in dose assessment, in transforming the estimates to risk assessment, in risk prediction and in extrapolation to the low dose/dose rate. These individual factors were analyzed statistically to obtain the relationship between the probability of cancer death in the US population and life time risk coefficient (% per Sv), which showed that, for the latter, the mean value was 3.99 x 10 -2 /Sv, median, 3.38 x 10 -2 /Sv, GSD (geometrical standard deviation), 1.83 x 10 -2 /Sv and 95% confidential limit, 1.2-8.84 x 10 -2 /Sv. The mean value was smaller than that of ICRP recommendation (5 x 10 -2 /Sv), indicating that the value has the uncertainty factor of 2.5-3. Moreover, the most important factor was shown to be the uncertainty in DDREF (dose/dose rate reduction factor). (K.H.)
A Note on the W-S Lower Bound of the MEE Estimation
Directory of Open Access Journals (Sweden)
Badong Chen
2014-02-01
Full Text Available The minimum error entropy (MEE estimation is concerned with the estimation of a certain random variable (unknown variable based on another random variable (observation, so that the entropy of the estimation error is minimized. This estimation method may outperform the well-known minimum mean square error (MMSE estimation especially for non-Gaussian situations. There is an important performance bound on the MEE estimation, namely the W-S lower bound, which is computed as the conditional entropy of the unknown variable given observation. Though it has been known in the literature for a considerable time, up to now there is little study on this performance bound. In this paper, we reexamine the W-S lower bound. Some basic properties of the W-S lower bound are presented, and the characterization of Gaussian distribution using the W-S lower bound is investigated.
Quantifying phenomenological importance in best-estimate plus uncertainty analyses
International Nuclear Information System (INIS)
Martin, Robert P.
2009-01-01
This paper describes a general methodology for quantifying the importance of specific phenomenological elements to analysis measures evaluated from non-parametric best-estimate plus uncertainty evaluation methodologies. The principal objective of an importance analysis is to reveal those uncertainty contributors having the greatest influence on key analysis measures. This characterization supports the credibility of the uncertainty analysis, the applicability of the analytical tools, and even the generic evaluation methodology through the validation of the engineering judgments that guided the evaluation methodology development. A demonstration of the importance analysis is provided using data from a sample problem considered in the development of AREVA's Realistic LBLOCA methodology. The results are presented against the original large-break LOCA Phenomena Identification and Ranking Table developed by the Technical Program Group responsible for authoring the Code Scaling, Applicability and Uncertainty methodology. (author)
Uncertainty Estimation using Bootstrapped Kriging Predictions for Precipitation Isoscapes
Ma, C.; Bowen, G. J.; Vander Zanden, H.; Wunder, M.
2017-12-01
Isoscapes are spatial models representing the distribution of stable isotope values across landscapes. Isoscapes of hydrogen and oxygen in precipitation are now widely used in a diversity of fields, including geology, biology, hydrology, and atmospheric science. To generate isoscapes, geostatistical methods are typically applied to extend predictions from limited data measurements. Kriging is a popular method in isoscape modeling, but quantifying the uncertainty associated with the resulting isoscapes is challenging. Applications that use precipitation isoscapes to determine sample origin require estimation of uncertainty. Here we present a simple bootstrap method (SBM) to estimate the mean and uncertainty of the krigged isoscape and compare these results with a generalized bootstrap method (GBM) applied in previous studies. We used hydrogen isotopic data from IsoMAP to explore these two approaches for estimating uncertainty. We conducted 10 simulations for each bootstrap method and found that SBM results in more kriging predictions (9/10) compared to GBM (4/10). Prediction from SBM was closer to the original prediction generated without bootstrapping and had less variance than GBM. SBM was tested on different datasets from IsoMAP with different numbers of observation sites. We determined that predictions from the datasets with fewer than 40 observation sites using SBM were more variable than the original prediction. The approaches we used for estimating uncertainty will be compiled in an R package that is under development. We expect that these robust estimates of precipitation isoscape uncertainty can be applied in diagnosing the origin of samples ranging from various type of waters to migratory animals, food products, and humans.
Estimation of a multivariate mean under model selection uncertainty
Directory of Open Access Journals (Sweden)
Georges Nguefack-Tsague
2014-05-01
Full Text Available Model selection uncertainty would occur if we selected a model based on one data set and subsequently applied it for statistical inferences, because the "correct" model would not be selected with certainty. When the selection and inference are based on the same dataset, some additional problems arise due to the correlation of the two stages (selection and inference. In this paper model selection uncertainty is considered and model averaging is proposed. The proposal is related to the theory of James and Stein of estimating more than three parameters from independent normal observations. We suggest that a model averaging scheme taking into account the selection procedure could be more appropriate than model selection alone. Some properties of this model averaging estimator are investigated; in particular we show using Stein's results that it is a minimax estimator and can outperform Stein-type estimators.
Uncertainty estimation in nuclear power plant probabilistic safety assessment
International Nuclear Information System (INIS)
Guarro, S.B.; Cummings, G.E.
1989-01-01
Probabilistic Risk Assessment (PRA) was introduced in the nuclear industry and the nuclear regulatory process in 1975 with the publication of the Reactor Safety Study by the U.S. Nuclear Regulatory Commission. Almost fifteen years later, the state-of-the-art in this field has been expanded and sharpened in many areas, and about thirty-five plant-specific PRAs (Probabilistic Risk Assessments) have been performed by the nuclear utility companies or by the U.S. Nuclear Regulatory commission. Among the areas where the most evident progress has been made in PRA and PSA (Probabilistic Safety Assessment, as these studies are more commonly referred to in the international community outside the U.S.) is the development of a consistent framework for the identification of sources of uncertainty and the estimation of their magnitude as it impacts various risk measures. Techniques to propagate uncertainty in reliability data through the risk models and display its effect on the top level risk estimates were developed in the early PRAs. The Seismic Safety Margin Research Program (SSMRP) study was the first major risk study to develop an approach to deal explicitly with uncertainty in risk estimates introduced not only by uncertainty in component reliability data, but by the incomplete state of knowledge of the assessor(s) with regard to basic phenomena that may trigger and drive a severe accident. More recently NUREG-1150, another major study of reactor risk sponsored by the NRC, has expanded risk uncertainty estimation and analysis into the realm of model uncertainty related to the relatively poorly known post-core-melt phenomena which determine the behavior of the molten core and of the rector containment structures
Institute of Scientific and Technical Information of China (English)
XIA Yuanqing; HAN Jingqing
2005-01-01
This paper concerns robust Kalman filtering for systems under norm bounded uncertainties in all the system matrices and error covariance constraints. Sufficient conditions are given for the existence of such filters in terms of Riccati equations. The solutions to the conditions can be used to design the filters. Finally, an illustrative example is given to demonstrate the effectiveness of the proposed design procedure.
Estimation of Model Uncertainties in Closed-loop Systems
DEFF Research Database (Denmark)
Niemann, Hans Henrik; Poulsen, Niels Kjølstad
2008-01-01
This paper describe a method for estimation of parameters or uncertainties in closed-loop systems. The method is based on an application of the dual YJBK (after Youla, Jabr, Bongiorno and Kucera) parameterization of all systems stabilized by a given controller. The dual YJBK transfer function...
Estimating annual bole biomass production using uncertainty analysis
Travis J. Woolley; Mark E. Harmon; Kari B. O' Connell
2007-01-01
Two common sampling methodologies coupled with a simple statistical model were evaluated to determine the accuracy and precision of annual bole biomass production (BBP) and inter-annual variability estimates using this type of approach. We performed an uncertainty analysis using Monte Carlo methods in conjunction with radial growth core data from trees in three Douglas...
Uncertainty Estimate in Resources Assessment: A Geostatistical Contribution
International Nuclear Information System (INIS)
Souza, Luis Eduardo de; Costa, Joao Felipe C. L.; Koppe, Jair C.
2004-01-01
For many decades the mining industry regarded resources/reserves estimation and classification as a mere calculation requiring basic mathematical and geological knowledge. Most methods were based on geometrical procedures and spatial data distribution. Therefore, uncertainty associated with tonnages and grades either were ignored or mishandled, although various mining codes require a measure of confidence in the values reported. Traditional methods fail in reporting the level of confidence in the quantities and grades. Conversely, kriging is known to provide the best estimate and its associated variance. Among kriging methods, Ordinary Kriging (OK) probably is the most widely used one for mineral resource/reserve estimation, mainly because of its robustness and its facility in uncertainty assessment by using the kriging variance. It also is known that OK variance is unable to recognize local data variability, an important issue when heterogeneous mineral deposits with higher and poorer grade zones are being evaluated. Alternatively, stochastic simulation are used to build local or global uncertainty about a geological attribute respecting its statistical moments. This study investigates methods capable of incorporating uncertainty to the estimates of resources and reserves via OK and sequential gaussian and sequential indicator simulation The results showed that for the type of mineralization studied all methods classified the tonnages similarly. The methods are illustrated using an exploration drill hole data sets from a large Brazilian coal deposit
Sensitivity of Process Design due to Uncertainties in Property Estimates
DEFF Research Database (Denmark)
Hukkerikar, Amol; Jones, Mark Nicholas; Sarup, Bent
2012-01-01
The objective of this paper is to present a systematic methodology for performing analysis of sensitivity of process design due to uncertainties in property estimates. The methodology provides the following results: a) list of properties with critical importance on design; b) acceptable levels of...... in chemical processes. Among others vapour pressure accuracy for azeotropic mixtures is critical and needs to be measured or estimated with a ±0.25% accuracy to satisfy acceptable safety levels in design....
International Nuclear Information System (INIS)
Van Berkel, M; Hogeweij, G M D; Van den Brand, H; De Baar, M R; Zwart, H J; Vandersteen, G
2014-01-01
In this paper, the estimation of the thermal diffusivity from perturbative experiments in fusion plasmas is discussed. The measurements used to estimate the thermal diffusivity suffer from stochastic noise. Accurate estimation of the thermal diffusivity should take this into account. It will be shown that formulas found in the literature often result in a thermal diffusivity that has a bias (a difference between the estimated value and the actual value that remains even if more measurements are added) or have an unnecessarily large uncertainty. This will be shown by modeling a plasma using only diffusion as heat transport mechanism and measurement noise based on ASDEX Upgrade measurements. The Fourier coefficients of a temperature perturbation will exhibit noise from the circular complex normal distribution (CCND). Based on Fourier coefficients distributed according to a CCND, it is shown that the resulting probability density function of the thermal diffusivity is an inverse non-central chi-squared distribution. The thermal diffusivity that is found by sampling this distribution will always be biased, and averaging of multiple estimated diffusivities will not necessarily improve the estimation. Confidence bounds are constructed to illustrate the uncertainty in the diffusivity using several formulas that are equivalent in the noiseless case. Finally, a different method of averaging, that reduces the uncertainty significantly, is suggested. The methodology is also extended to the case where damping is included, and it is explained how to include the cylindrical geometry. (paper)
Impact of dose-distribution uncertainties on rectal ntcp modeling I: Uncertainty estimates
International Nuclear Information System (INIS)
Fenwick, John D.; Nahum, Alan E.
2001-01-01
A trial of nonescalated conformal versus conventional radiotherapy treatment of prostate cancer has been carried out at the Royal Marsden NHS Trust (RMH) and Institute of Cancer Research (ICR), demonstrating a significant reduction in the rate of rectal bleeding reported for patients treated using the conformal technique. The relationship between planned rectal dose-distributions and incidences of bleeding has been analyzed, showing that the rate of bleeding falls significantly as the extent of the rectal wall receiving a planned dose-level of more than 57 Gy is reduced. Dose-distributions delivered to the rectal wall over the course of radiotherapy treatment inevitably differ from planned distributions, due to sources of uncertainty such as patient setup error, rectal wall movement and variation in the absolute rectal wall surface area. In this paper estimates of the differences between planned and treated rectal dose-distribution parameters are obtained for the RMH/ICR nonescalated conformal technique, working from a distribution of setup errors observed during the RMH/ICR trial, movement data supplied by Lebesque and colleagues derived from repeat CT scans, and estimates of rectal circumference variations extracted from the literature. Setup errors and wall movement are found to cause only limited systematic differences between mean treated and planned rectal dose-distribution parameter values, but introduce considerable uncertainties into the treated values of some dose-distribution parameters: setup errors lead to 22% and 9% relative uncertainties in the highly dosed fraction of the rectal wall and the wall average dose, respectively, with wall movement leading to 21% and 9% relative uncertainties. Estimates obtained from the literature of the uncertainty in the absolute surface area of the distensible rectal wall are of the order of 13%-18%. In a subsequent paper the impact of these uncertainties on analyses of the relationship between incidences of bleeding
Estimating the Effective Lower Bound for the Czech National Bank's Policy Rate
Kolcunova, Dominika; Havranek, Tomas
2018-01-01
The paper focuses on the estimation of the effective lower bound for the Czech National Bank's policy rate. The effective lower bound is determined by the value below which holding and using cash would be more convenient than deposits with negative yields. This bound is approximated based on storage, the insurance and transportation costs of cash and the costs associated with the loss of the convenience of cashless payments and complemented with the estimate based on interest charges, which p...
Uncertainty related to Environmental Data and Estimated Extreme Events
DEFF Research Database (Denmark)
Burcharth, H. F.
The design loads on rubble mound breakwaters are almost entirely determined by the environmental conditions, i.e. sea state, water levels, sea bed characteristics, etc. It is the objective of sub-group B to identify the most important environmental parameters and evaluate the related uncertainties...... including those corresponding to extreme estimates typically used for design purposes. Basically a design condition is made up of a set of parameter values stemming from several environmental parameters. To be able to evaluate the uncertainty related to design states one must know the corresponding joint....... Consequently this report deals mainly with each parameter separately. Multi parameter problems are briefly discussed in section 9. It is important to notice that the quantified uncertainties reported in section 7.7 represent what might be regarded as typical figures to be used only when no more qualified...
Gaussian Process Interpolation for Uncertainty Estimation in Image Registration
Wachinger, Christian; Golland, Polina; Reuter, Martin; Wells, William
2014-01-01
Intensity-based image registration requires resampling images on a common grid to evaluate the similarity function. The uncertainty of interpolation varies across the image, depending on the location of resampled points relative to the base grid. We propose to perform Bayesian inference with Gaussian processes, where the covariance matrix of the Gaussian process posterior distribution estimates the uncertainty in interpolation. The Gaussian process replaces a single image with a distribution over images that we integrate into a generative model for registration. Marginalization over resampled images leads to a new similarity measure that includes the uncertainty of the interpolation. We demonstrate that our approach increases the registration accuracy and propose an efficient approximation scheme that enables seamless integration with existing registration methods. PMID:25333127
REDD+ emissions estimation and reporting: dealing with uncertainty
International Nuclear Information System (INIS)
Pelletier, Johanne; Potvin, Catherine; Martin, Davy
2013-01-01
The United Nations Framework Convention on Climate Change (UNFCCC) defined the technical and financial modalities of policy approaches and incentives to reduce emissions from deforestation and forest degradation in developing countries (REDD+). Substantial technical challenges hinder precise and accurate estimation of forest-related emissions and removals, as well as the setting and assessment of reference levels. These challenges could limit country participation in REDD+, especially if REDD+ emission reductions were to meet quality standards required to serve as compliance grade offsets for developed countries’ emissions. Using Panama as a case study, we tested the matrix approach proposed by Bucki et al (2012 Environ. Res. Lett. 7 024005) to perform sensitivity and uncertainty analysis distinguishing between ‘modelling sources’ of uncertainty, which refers to model-specific parameters and assumptions, and ‘recurring sources’ of uncertainty, which refers to random and systematic errors in emission factors and activity data. The sensitivity analysis estimated differences in the resulting fluxes ranging from 4.2% to 262.2% of the reference emission level. The classification of fallows and the carbon stock increment or carbon accumulation of intact forest lands were the two key parameters showing the largest sensitivity. The highest error propagated using Monte Carlo simulations was caused by modelling sources of uncertainty, which calls for special attention to ensure consistency in REDD+ reporting which is essential for securing environmental integrity. Due to the role of these modelling sources of uncertainty, the adoption of strict rules for estimation and reporting would favour comparability of emission reductions between countries. We believe that a reduction of the bias in emission factors will arise, among other things, from a globally concerted effort to improve allometric equations for tropical forests. Public access to datasets and methodology
REDD+ emissions estimation and reporting: dealing with uncertainty
Pelletier, Johanne; Martin, Davy; Potvin, Catherine
2013-09-01
The United Nations Framework Convention on Climate Change (UNFCCC) defined the technical and financial modalities of policy approaches and incentives to reduce emissions from deforestation and forest degradation in developing countries (REDD+). Substantial technical challenges hinder precise and accurate estimation of forest-related emissions and removals, as well as the setting and assessment of reference levels. These challenges could limit country participation in REDD+, especially if REDD+ emission reductions were to meet quality standards required to serve as compliance grade offsets for developed countries’ emissions. Using Panama as a case study, we tested the matrix approach proposed by Bucki et al (2012 Environ. Res. Lett. 7 024005) to perform sensitivity and uncertainty analysis distinguishing between ‘modelling sources’ of uncertainty, which refers to model-specific parameters and assumptions, and ‘recurring sources’ of uncertainty, which refers to random and systematic errors in emission factors and activity data. The sensitivity analysis estimated differences in the resulting fluxes ranging from 4.2% to 262.2% of the reference emission level. The classification of fallows and the carbon stock increment or carbon accumulation of intact forest lands were the two key parameters showing the largest sensitivity. The highest error propagated using Monte Carlo simulations was caused by modelling sources of uncertainty, which calls for special attention to ensure consistency in REDD+ reporting which is essential for securing environmental integrity. Due to the role of these modelling sources of uncertainty, the adoption of strict rules for estimation and reporting would favour comparability of emission reductions between countries. We believe that a reduction of the bias in emission factors will arise, among other things, from a globally concerted effort to improve allometric equations for tropical forests. Public access to datasets and methodology
Eigenspace perturbations for structural uncertainty estimation of turbulence closure models
Jofre, Lluis; Mishra, Aashwin; Iaccarino, Gianluca
2017-11-01
With the present state of computational resources, a purely numerical resolution of turbulent flows encountered in engineering applications is not viable. Consequently, investigations into turbulence rely on various degrees of modeling. Archetypal amongst these variable resolution approaches would be RANS models in two-equation closures, and subgrid-scale models in LES. However, owing to the simplifications introduced during model formulation, the fidelity of all such models is limited, and therefore the explicit quantification of the predictive uncertainty is essential. In such scenario, the ideal uncertainty estimation procedure must be agnostic to modeling resolution, methodology, and the nature or level of the model filter. The procedure should be able to give reliable prediction intervals for different Quantities of Interest, over varied flows and flow conditions, and at diametric levels of modeling resolution. In this talk, we present and substantiate the Eigenspace perturbation framework as an uncertainty estimation paradigm that meets these criteria. Commencing from a broad overview, we outline the details of this framework at different modeling resolution. Thence, using benchmark flows, along with engineering problems, the efficacy of this procedure is established. This research was partially supported by NNSA under the Predictive Science Academic Alliance Program (PSAAP) II, and by DARPA under the Enabling Quantification of Uncertainty in Physical Systems (EQUiPS) project (technical monitor: Dr Fariba Fahroo).
Energy Technology Data Exchange (ETDEWEB)
Berryman, J. G.
2012-03-01
While the well-known Voigt and Reuss (VR) bounds, and the Voigt-Reuss-Hill (VRH) elastic constant estimators for random polycrystals are all straightforwardly calculated once the elastic constants of anisotropic crystals are known, the Hashin-Shtrikman (HS) bounds and related self-consistent (SC) estimators for the same constants are, by comparison, more difficult to compute. Recent work has shown how to simplify (to some extent) these harder to compute HS bounds and SC estimators. An overview and analysis of a subsampling of these results is presented here with the main point being to show whether or not this extra work (i.e., in calculating both the HS bounds and the SC estimates) does provide added value since, in particular, the VRH estimators often do not fall within the HS bounds, while the SC estimators (for good reasons) have always been found to do so. The quantitative differences between the SC and the VRH estimators in the eight cases considered are often quite small however, being on the order of ±1%. These quantitative results hold true even though these polycrystal Voigt-Reuss-Hill estimators more typically (but not always) fall outside the Hashin-Shtrikman bounds, while the self-consistent estimators always fall inside (or on the boundaries of) these same bounds.
Estimation of sedimentary proxy records together with associated uncertainty
Goswami, B.; Heitzig, J.; Rehfeld, K.; Marwan, N.; Anoop, A.; Prasad, S.; Kurths, J.
2014-01-01
Sedimentary proxy records constitute a significant portion of the recorded evidence that allows us to investigate paleoclimatic conditions and variability. However, uncertainties in the dating of proxy archives limit our ability to fix the timing of past events and interpret proxy record intercomparisons. While there are various age-modeling approaches to improve the estimation of the age–depth relations of archives, relatively little focus has been placed on the propagation...
Sediment Curve Uncertainty Estimation Using GLUE and Bootstrap Methods
Directory of Open Access Journals (Sweden)
aboalhasan fathabadi
2017-02-01
Full Text Available Introduction: In order to implement watershed practices to decrease soil erosion effects it needs to estimate output sediment of watershed. Sediment rating curve is used as the most conventional tool to estimate sediment. Regarding to sampling errors and short data, there are some uncertainties in estimating sediment using sediment curve. In this research, bootstrap and the Generalized Likelihood Uncertainty Estimation (GLUE resampling techniques were used to calculate suspended sediment loads by using sediment rating curves. Materials and Methods: The total drainage area of the Sefidrood watershed is about 560000 km2. In this study uncertainty in suspended sediment rating curves was estimated in four stations including Motorkhane, Miyane Tonel Shomare 7, Stor and Glinak constructed on Ayghdamosh, Ghrangho, GHezelOzan and Shahrod rivers, respectively. Data were randomly divided into a training data set (80 percent and a test set (20 percent by Latin hypercube random sampling.Different suspended sediment rating curves equations were fitted to log-transformed values of sediment concentration and discharge and the best fit models were selected based on the lowest root mean square error (RMSE and the highest correlation of coefficient (R2. In the GLUE methodology, different parameter sets were sampled randomly from priori probability distribution. For each station using sampled parameter sets and selected suspended sediment rating curves equation suspended sediment concentration values were estimated several times (100000 to 400000 times. With respect to likelihood function and certain subjective threshold, parameter sets were divided into behavioral and non-behavioral parameter sets. Finally using behavioral parameter sets the 95% confidence intervals for suspended sediment concentration due to parameter uncertainty were estimated. In bootstrap methodology observed suspended sediment and discharge vectors were resampled with replacement B (set to
Hydrological model uncertainty due to spatial evapotranspiration estimation methods
Yu, Xuan; Lamačová, Anna; Duffy, Christopher; Krám, Pavel; Hruška, Jakub
2016-05-01
Evapotranspiration (ET) continues to be a difficult process to estimate in seasonal and long-term water balances in catchment models. Approaches to estimate ET typically use vegetation parameters (e.g., leaf area index [LAI], interception capacity) obtained from field observation, remote sensing data, national or global land cover products, and/or simulated by ecosystem models. In this study we attempt to quantify the uncertainty that spatial evapotranspiration estimation introduces into hydrological simulations when the age of the forest is not precisely known. The Penn State Integrated Hydrologic Model (PIHM) was implemented for the Lysina headwater catchment, located 50°03‧N, 12°40‧E in the western part of the Czech Republic. The spatial forest patterns were digitized from forest age maps made available by the Czech Forest Administration. Two ET methods were implemented in the catchment model: the Biome-BGC forest growth sub-model (1-way coupled to PIHM) and with the fixed-seasonal LAI method. From these two approaches simulation scenarios were developed. We combined the estimated spatial forest age maps and two ET estimation methods to drive PIHM. A set of spatial hydrologic regime and streamflow regime indices were calculated from the modeling results for each method. Intercomparison of the hydrological responses to the spatial vegetation patterns suggested considerable variation in soil moisture and recharge and a small uncertainty in the groundwater table elevation and streamflow. The hydrologic modeling with ET estimated by Biome-BGC generated less uncertainty due to the plant physiology-based method. The implication of this research is that overall hydrologic variability induced by uncertain management practices was reduced by implementing vegetation models in the catchment models.
Estimation of the uncertainty in wind power forecasting
International Nuclear Information System (INIS)
Pinson, P.
2006-03-01
WIND POWER experiences a tremendous development of its installed capacities in Europe. Though, the intermittence of wind generation causes difficulties in the management of power systems. Also, in the context of the deregulation of electricity markets, wind energy is penalized by its intermittent nature. It is recognized today that the forecasting of wind power for horizons up to 2/3-day ahead eases the integration of wind generation. Wind power forecasts are traditionally provided in the form of point predictions, which correspond to the most-likely power production for a given horizon. That sole information is not sufficient for developing optimal management or trading strategies. Therefore, we investigate on possible ways for estimating the uncertainty of wind power forecasts. The characteristics of the prediction uncertainty are described by a thorough study of the performance of some of the state-of-the-art approaches, and by underlining the influence of some variables e.g. level of predicted power on distributions of prediction errors. Then, a generic method for the estimation of prediction intervals is introduced. This statistical method is non-parametric and utilizes fuzzy logic concepts for integrating expertise on the prediction uncertainty characteristics. By estimating several prediction intervals at once, one obtains predictive distributions of wind power output. The proposed method is evaluated in terms of its reliability, sharpness and resolution. In parallel, we explore the potential use of ensemble predictions for skill forecasting. Wind power ensemble forecasts are obtained either by converting meteorological ensembles (from ECMWF and NCEP) to power or by applying a poor man's temporal approach. A proposal for the definition of prediction risk indices is given, reflecting the disagreement between ensemble members over a set of successive look-ahead times. Such prediction risk indices may comprise a more comprehensive signal on the expected level
Yan, Zheng; Wang, Jun
2014-03-01
This paper presents a neural network approach to robust model predictive control (MPC) for constrained discrete-time nonlinear systems with unmodeled dynamics affected by bounded uncertainties. The exact nonlinear model of underlying process is not precisely known, but a partially known nominal model is available. This partially known nonlinear model is first decomposed to an affine term plus an unknown high-order term via Jacobian linearization. The linearization residue combined with unmodeled dynamics is then modeled using an extreme learning machine via supervised learning. The minimax methodology is exploited to deal with bounded uncertainties. The minimax optimization problem is reformulated as a convex minimization problem and is iteratively solved by a two-layer recurrent neural network. The proposed neurodynamic approach to nonlinear MPC improves the computational efficiency and sheds a light for real-time implementability of MPC technology. Simulation results are provided to substantiate the effectiveness and characteristics of the proposed approach.
Bounds and Estimates for Transport Coefficients of Random and Porous Media with High Contrasts
International Nuclear Information System (INIS)
Berryman, J G
2004-01-01
Bounds on transport coefficients of random polycrystals of laminates are presented, including the well-known Hashin-Shtrikman bounds and some newly formulated bounds involving two formation factors for a two-component porous medium. Some new types of self-consistent estimates are then formulated based on the observed analytical structure both of these bounds and also of earlier self-consistent estimates (of the CPA or coherent potential approximation type). A numerical study is made, assuming first that the internal structure (i.e., the laminated grain structure) is not known, and then that it is known. The purpose of this aspect of the study is to attempt to quantify the differences in the predictions of properties of a system being modeled when such organized internal structure is present in the medium but detailed spatial correlation information may or (more commonly) may not be available. Some methods of estimating formation factors from data are also presented and then applied to a high-contrast fluid-permeability data set. Hashin-Shtrikman bounds are found to be very accurate estimates for low contrast heterogeneous media. But formation factor lower bounds are superior estimates for high contrast situations. The new self-consistent estimators also tend to agree better with data than either the bounds or the CPA estimates, which themselves tend to overestimate values for high contrast conducting composites
Error Estimation and Uncertainty Propagation in Computational Fluid Mechanics
Zhu, J. Z.; He, Guowei; Bushnell, Dennis M. (Technical Monitor)
2002-01-01
Numerical simulation has now become an integral part of engineering design process. Critical design decisions are routinely made based on the simulation results and conclusions. Verification and validation of the reliability of the numerical simulation is therefore vitally important in the engineering design processes. We propose to develop theories and methodologies that can automatically provide quantitative information about the reliability of the numerical simulation by estimating numerical approximation error, computational model induced errors and the uncertainties contained in the mathematical models so that the reliability of the numerical simulation can be verified and validated. We also propose to develop and implement methodologies and techniques that can control the error and uncertainty during the numerical simulation so that the reliability of the numerical simulation can be improved.
Estimation variance bounds of importance sampling simulations in digital communication systems
Lu, D.; Yao, K.
1991-01-01
In practical applications of importance sampling (IS) simulation, two basic problems are encountered, that of determining the estimation variance and that of evaluating the proper IS parameters needed in the simulations. The authors derive new upper and lower bounds on the estimation variance which are applicable to IS techniques. The upper bound is simple to evaluate and may be minimized by the proper selection of the IS parameter. Thus, lower and upper bounds on the improvement ratio of various IS techniques relative to the direct Monte Carlo simulation are also available. These bounds are shown to be useful and computationally simple to obtain. Based on the proposed technique, one can readily find practical suboptimum IS parameters. Numerical results indicate that these bounding techniques are useful for IS simulations of linear and nonlinear communication systems with intersymbol interference in which bit error rate and IS estimation variances cannot be obtained readily using prior techniques.
Residual Wage Differences by Gender: Bounding the Estimates.
Sakellariou, Chris N.; Patrinos, Harry A.
1996-01-01
Uses data from the 1986 Canadian labor market activity survey file to derive estimates of residual gender wage gap differences. Investigates these estimates' dependence on experimental design and on assumptions about discrimination-free wage structures. Residual differences persist, even after restricting the sample to a group of highly motivated,…
Quantifying uncertainty in NDSHA estimates due to earthquake catalogue
Magrin, Andrea; Peresan, Antonella; Vaccari, Franco; Panza, Giuliano
2014-05-01
The procedure for the neo-deterministic seismic zoning, NDSHA, is based on the calculation of synthetic seismograms by the modal summation technique. This approach makes use of information about the space distribution of large magnitude earthquakes, which can be defined based on seismic history and seismotectonics, as well as incorporating information from a wide set of geological and geophysical data (e.g., morphostructural features and ongoing deformation processes identified by earth observations). Hence the method does not make use of attenuation models (GMPE), which may be unable to account for the complexity of the product between seismic source tensor and medium Green function and are often poorly constrained by the available observations. NDSHA defines the hazard from the envelope of the values of ground motion parameters determined considering a wide set of scenario earthquakes; accordingly, the simplest outcome of this method is a map where the maximum of a given seismic parameter is associated to each site. In NDSHA uncertainties are not statistically treated as in PSHA, where aleatory uncertainty is traditionally handled with probability density functions (e.g., for magnitude and distance random variables) and epistemic uncertainty is considered by applying logic trees that allow the use of alternative models and alternative parameter values of each model, but the treatment of uncertainties is performed by sensitivity analyses for key modelling parameters. To fix the uncertainty related to a particular input parameter is an important component of the procedure. The input parameters must account for the uncertainty in the prediction of fault radiation and in the use of Green functions for a given medium. A key parameter is the magnitude of sources used in the simulation that is based on catalogue informations, seismogenic zones and seismogenic nodes. Because the largest part of the existing catalogues is based on macroseismic intensity, a rough estimate
DEFF Research Database (Denmark)
Nielsen, Jesper Ellerbæk; Thorndahl, Søren Liedtke; Rasmussen, Michael R.
2011-01-01
Distributed weather radar precipitation measurements are used as rainfall input for an urban drainage model, to simulate the runoff from a small catchment of Denmark. It is demonstrated how the Generalized Likelihood Uncertainty Estimation (GLUE) methodology can be implemented and used to estimate...
Linear minimax estimation for random vectors with parametric uncertainty
Bitar, E
2010-06-01
In this paper, we take a minimax approach to the problem of computing a worst-case linear mean squared error (MSE) estimate of X given Y , where X and Y are jointly distributed random vectors with parametric uncertainty in their distribution. We consider two uncertainty models, PA and PB. Model PA represents X and Y as jointly Gaussian whose covariance matrix Λ belongs to the convex hull of a set of m known covariance matrices. Model PB characterizes X and Y as jointly distributed according to a Gaussian mixture model with m known zero-mean components, but unknown component weights. We show: (a) the linear minimax estimator computed under model PA is identical to that computed under model PB when the vertices of the uncertain covariance set in PA are the same as the component covariances in model PB, and (b) the problem of computing the linear minimax estimator under either model reduces to a semidefinite program (SDP). We also consider the dynamic situation where x(t) and y(t) evolve according to a discrete-time LTI state space model driven by white noise, the statistics of which is modeled by PA and PB as before. We derive a recursive linear minimax filter for x(t) given y(t).
ON THE ESTIMATION OF RANDOM UNCERTAINTIES OF STAR FORMATION HISTORIES
Energy Technology Data Exchange (ETDEWEB)
Dolphin, Andrew E., E-mail: adolphin@raytheon.com [Raytheon Company, Tucson, AZ, 85734 (United States)
2013-09-20
The standard technique for measurement of random uncertainties of star formation histories (SFHs) is the bootstrap Monte Carlo, in which the color-magnitude diagram (CMD) is repeatedly resampled. The variation in SFHs measured from the resampled CMDs is assumed to represent the random uncertainty in the SFH measured from the original data. However, this technique systematically and significantly underestimates the uncertainties for times in which the measured star formation rate is low or zero, leading to overly (and incorrectly) high confidence in that measurement. This study proposes an alternative technique, the Markov Chain Monte Carlo (MCMC), which samples the probability distribution of the parameters used in the original solution to directly estimate confidence intervals. While the most commonly used MCMC algorithms are incapable of adequately sampling a probability distribution that can involve thousands of highly correlated dimensions, the Hybrid Monte Carlo algorithm is shown to be extremely effective and efficient for this particular task. Several implementation details, such as the handling of implicit priors created by parameterization of the SFH, are discussed in detail.
ON THE ESTIMATION OF RANDOM UNCERTAINTIES OF STAR FORMATION HISTORIES
International Nuclear Information System (INIS)
Dolphin, Andrew E.
2013-01-01
The standard technique for measurement of random uncertainties of star formation histories (SFHs) is the bootstrap Monte Carlo, in which the color-magnitude diagram (CMD) is repeatedly resampled. The variation in SFHs measured from the resampled CMDs is assumed to represent the random uncertainty in the SFH measured from the original data. However, this technique systematically and significantly underestimates the uncertainties for times in which the measured star formation rate is low or zero, leading to overly (and incorrectly) high confidence in that measurement. This study proposes an alternative technique, the Markov Chain Monte Carlo (MCMC), which samples the probability distribution of the parameters used in the original solution to directly estimate confidence intervals. While the most commonly used MCMC algorithms are incapable of adequately sampling a probability distribution that can involve thousands of highly correlated dimensions, the Hybrid Monte Carlo algorithm is shown to be extremely effective and efficient for this particular task. Several implementation details, such as the handling of implicit priors created by parameterization of the SFH, are discussed in detail
Uncertainty Model for Total Solar Irradiance Estimation on Australian Rooftops
Al-Saadi, Hassan; Zivanovic, Rastko; Al-Sarawi, Said
2017-11-01
The installations of solar panels on Australian rooftops have been in rise for the last few years, especially in the urban areas. This motivates academic researchers, distribution network operators and engineers to accurately address the level of uncertainty resulting from grid-connected solar panels. The main source of uncertainty is the intermittent nature of radiation, therefore, this paper presents a new model to estimate the total radiation incident on a tilted solar panel. Where a probability distribution factorizes clearness index, the model is driven upon clearness index with special attention being paid for Australia with the utilization of best-fit-correlation for diffuse fraction. The assessment of the model validity is achieved with the adoption of four goodness-of-fit techniques. In addition, the Quasi Monte Carlo and sparse grid methods are used as sampling and uncertainty computation tools, respectively. High resolution data resolution of solar irradiations for Adelaide city were used for this assessment, with an outcome indicating a satisfactory agreement between actual data variation and model.
Expanded uncertainty estimation methodology in determining the sandy soils filtration coefficient
Rusanova, A. D.; Malaja, L. D.; Ivanov, R. N.; Gruzin, A. V.; Shalaj, V. V.
2018-04-01
The combined standard uncertainty estimation methodology in determining the sandy soils filtration coefficient has been developed. The laboratory researches were carried out which resulted in filtration coefficient determination and combined uncertainty estimation obtaining.
Data-Driven Model Uncertainty Estimation in Hydrologic Data Assimilation
Pathiraja, S.; Moradkhani, H.; Marshall, L.; Sharma, A.; Geenens, G.
2018-02-01
The increasing availability of earth observations necessitates mathematical methods to optimally combine such data with hydrologic models. Several algorithms exist for such purposes, under the umbrella of data assimilation (DA). However, DA methods are often applied in a suboptimal fashion for complex real-world problems, due largely to several practical implementation issues. One such issue is error characterization, which is known to be critical for a successful assimilation. Mischaracterized errors lead to suboptimal forecasts, and in the worst case, to degraded estimates even compared to the no assimilation case. Model uncertainty characterization has received little attention relative to other aspects of DA science. Traditional methods rely on subjective, ad hoc tuning factors or parametric distribution assumptions that may not always be applicable. We propose a novel data-driven approach (named SDMU) to model uncertainty characterization for DA studies where (1) the system states are partially observed and (2) minimal prior knowledge of the model error processes is available, except that the errors display state dependence. It includes an approach for estimating the uncertainty in hidden model states, with the end goal of improving predictions of observed variables. The SDMU is therefore suited to DA studies where the observed variables are of primary interest. Its efficacy is demonstrated through a synthetic case study with low-dimensional chaotic dynamics and a real hydrologic experiment for one-day-ahead streamflow forecasting. In both experiments, the proposed method leads to substantial improvements in the hidden states and observed system outputs over a standard method involving perturbation with Gaussian noise.
A precise error bound for quantum phase estimation.
Directory of Open Access Journals (Sweden)
James M Chappell
Full Text Available Quantum phase estimation is one of the key algorithms in the field of quantum computing, but up until now, only approximate expressions have been derived for the probability of error. We revisit these derivations, and find that by ensuring symmetry in the error definitions, an exact formula can be found. This new approach may also have value in solving other related problems in quantum computing, where an expected error is calculated. Expressions for two special cases of the formula are also developed, in the limit as the number of qubits in the quantum computer approaches infinity and in the limit as the extra added qubits to improve reliability goes to infinity. It is found that this formula is useful in validating computer simulations of the phase estimation procedure and in avoiding the overestimation of the number of qubits required in order to achieve a given reliability. This formula thus brings improved precision in the design of quantum computers.
Directory of Open Access Journals (Sweden)
Adilson Aderito da Silva
2012-06-01
Full Text Available The levels of uncertainty perceived by managers as having the perspective of the theoretical support of the Information Uncertainty, focusing on the multidimensional approach proposed by Milliken (1987, which supports the existence of three types of uncertainty: uncertainty of state, uncertainty effect and response uncertainty. The levels of rationality of managers were estimated to construct a second order from the uncertainties of effect and response uncertainty, with the theoretical support in the definitions of the concept of bounded rationality proposed by Simon (1957. The data collected from the 118 employees of the banking sector in the State of São Paulo were analyzed using Structural Equation Modeling with the Software Smart PLS. The results indicated a significant influence of the state uncertainty on the level of rationality of managers and bring important methodological and conceptual contributions to the advancement of studies on the subject of uncertainty in decision makingEsta pesquisa foi desenvolvida com o objetivo de avaliar o impacto da incerteza percebida no ambiente sobre os níveis de racionalidade dos gestores do setor financeiro. Para tal, foram estimados os níveis de incerteza percebidos pelos gestores tendo como suporte teórico a perspectiva da Incerteza da Informação, com foco na abordagem multidimensional proposta por Milliken (1987, que defende a existência de três tipos de incerteza: incerteza de estado, incerteza de efeito e incerteza de resposta. Os níveis de racionalidade dos gestores foram estimados como um construto de segunda ordem a partir das incertezas de efeito e da incerteza de resposta, com o suporte teórico nas definições do conceito de racionalidade limitada propostas por Simon (1957. Os dados coletados junto aos 118 funcionários do setor bancário no Estado de São Paulo foram analisados por meio de Modelagem por Equações Estruturais com o Software Smart PLS. Os resultados
Estimation of spatial uncertainties of tomographic velocity models
Energy Technology Data Exchange (ETDEWEB)
Jordan, M.; Du, Z.; Querendez, E. [SINTEF Petroleum Research, Trondheim (Norway)
2012-12-15
This research project aims to evaluate the possibility of assessing the spatial uncertainties in tomographic velocity model building in a quantitative way. The project is intended to serve as a test of whether accurate and specific uncertainty estimates (e.g., in meters) can be obtained. The project is based on Monte Carlo-type perturbations of the velocity model as obtained from the tomographic inversion guided by diagonal and off-diagonal elements of the resolution and the covariance matrices. The implementation and testing of this method was based on the SINTEF in-house stereotomography code, using small synthetic 2D data sets. To test the method the calculation and output of the covariance and resolution matrices was implemented, and software to perform the error estimation was created. The work included the creation of 2D synthetic data sets, the implementation and testing of the software to conduct the tests (output of the covariance and resolution matrices which are not implicitly provided by stereotomography), application to synthetic data sets, analysis of the test results, and creating the final report. The results show that this method can be used to estimate the spatial errors in tomographic images quantitatively. The results agree with' the known errors for our synthetic models. However, the method can only be applied to structures in the model, where the change of seismic velocity is larger than the predicted error of the velocity parameter amplitudes. In addition, the analysis is dependent on the tomographic method, e.g., regularization and parameterization. The conducted tests were very successful and we believe that this method could be developed further to be applied to third party tomographic images.
Directory of Open Access Journals (Sweden)
Douglas A. Fynan
2016-06-01
Full Text Available The Gaussian process model (GPM is a flexible surrogate model that can be used for nonparametric regression for multivariate problems. A unique feature of the GPM is that a prediction variance is automatically provided with the regression function. In this paper, we estimate the safety margin of a nuclear power plant by performing regression on the output of best-estimate simulations of a large-break loss-of-coolant accident with sampling of safety system configuration, sequence timing, technical specifications, and thermal hydraulic parameter uncertainties. The key aspect of our approach is that the GPM regression is only performed on the dominant input variables, the safety injection flow rate and the delay time for AC powered pumps to start representing sequence timing uncertainty, providing a predictive model for the peak clad temperature during a reflood phase. Other uncertainties are interpreted as contributors to the measurement noise of the code output and are implicitly treated in the GPM in the noise variance term, providing local uncertainty bounds for the peak clad temperature. We discuss the applicability of the foregoing method to reduce the use of conservative assumptions in best estimate plus uncertainty (BEPU and Level 1 probabilistic safety assessment (PSA success criteria definitions while dealing with a large number of uncertainties.
Estimation of uncertainties from missing higher orders in perturbative calculations
International Nuclear Information System (INIS)
Bagnaschi, E.
2015-05-01
In this proceeding we present the results of our recent study (hep-ph/1409.5036) of the statistical performances of two different approaches, Scale Variation (SV) and the Bayesian model of Cacciari and Houdeau (CH)(hep-ph/1105.5152) (which we also extend to observables with initial state hadrons), to the estimation of Missing Higher-Order Uncertainties (MHOUs)(hep-ph/1307.1843) in perturbation theory. The behavior of the models is determined by analyzing, on a wide set of observables, how the MHOU intervals they produce are successful in predicting the next orders. We observe that the Bayesian model behaves consistently, producing intervals at 68% Degree of Belief (DoB) comparable with the scale variation intervals with a rescaling factor r larger than 2 and closer to 4. Concerning SV, our analysis allows the derivation of a heuristic Confidence Level (CL) for the intervals. We find that assigning a CL of 68% to the intervals obtained with the conventional choice of varying the scales within a factor of two with respect to the central scale could potentially lead to an underestimation of the uncertainties in the case of observables with initial state hadrons.
State Estimation for Sensor Monitoring System with Uncertainty and Disturbance
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Jianhong Sun
2014-10-01
Full Text Available This paper considers the state estimation problem for the sensor monitoring system which contains system uncertainty and nonlinear disturbance. In the sensor monitoring system, states of each inner sensor node usually contains system uncertainty, and external noise often works as nonlinear item. Besides, information transmission in the system is also time consuming. All mentioned above may arouse in unstable of the monitoring system. In this case, states of sensors could be wrongly sampled. Under this circumstance, a proper mathematical model is proposed and by the use of Lipschitz condition, the nonlinear item is transformed to linear one. In addition, we suppose that all sensor nodes are distributed arranged, no interface occurs with each other. By establishing proper Lyapunov– Krasovskii functional, sufficient conditions are acquired by solving linear matrix inequality to make the error augmented system stable, and the gains of observers are also derived. Finally, an illustrated example is given to show that system observed value tracks system states well, which fully demonstrate the effectiveness of our result.
Interlaboratory analytical performance studies; a way to estimate measurement uncertainty
Directory of Open Access Journals (Sweden)
El¿bieta £ysiak-Pastuszak
2004-09-01
Full Text Available Comparability of data collected within collaborative programmes became the key challenge of analytical chemistry in the 1990s, including monitoring of the marine environment. To obtain relevant and reliable data, the analytical process has to proceed under a well-established Quality Assurance (QA system with external analytical proficiency tests as an inherent component. A programme called Quality Assurance in Marine Monitoring in Europe (QUASIMEME was established in 1993 and evolved over the years as the major provider of QA proficiency tests for nutrients, trace metals and chlorinated organic compounds in marine environment studies. The article presents an evaluation of results obtained in QUASIMEME Laboratory Performance Studies by the monitoring laboratory of the Institute of Meteorology and Water Management (Gdynia, Poland in exercises on nutrient determination in seawater. The measurement uncertainty estimated from routine internal quality control measurements and from results of analytical performance exercises is also presented in the paper.
Estimation of lower-bound KJc on pressure vessel steels from invalid data
International Nuclear Information System (INIS)
McCable, D.E.; Merkle, J.G.
1996-01-01
Statistical methods are currently being introduced into the transition temperature characterization of ferritic steels. Objective is to replace imprecise correlations between empirical impact test methods and universal K Ic or K Ia lower-bound curves with direct use of material-specific fracture mechanics data. This paper introduces a computational procedure that couples order statistics, weakest-link statistical theory, and a constraint model to arrive at estimates of lower-bound K Jc values. All of the above concepts have been used before to meet various objectives. In the present case, scheme is to make a best estimate of lower-bound fracture toughness when resource K Jc data are too few to use conventional statistical analyses. Utility of the procedure is of greatest value in the middle-to-high toughness part of the transition range where specimen constraint loss and elevated lower-bound toughness interfere with conventional statistical analysis methods
Best-estimate analysis and decision making under uncertainty
International Nuclear Information System (INIS)
Orechwa, Y.
2004-01-01
In many engineering analyses of system safety the traditional reliance on conservative evaluation model calculations is being replaced with so called best-estimate analysis. These best-estimate analyses differentiate themselves from the traditional conservative analyses through two ingredients, namely realistic models and an account of the residual uncertainty associated with the model calculations. Best-estimate analysis, in the context of this paper, refers to the numerical evaluation of system properties of interest in situations where direct confirmatory measurements are not feasible. A decision with regard to the safety of the system is then made based on the computed numerical values of the system properties of interest. These situations generally arise in the design of systems that require computed and generally nontrivial extrapolations from the available data. In the case of nuclear reactors, examples are criticality of spent fuel pools, neutronic parameters of new advanced designs where insufficient material is available for mockup critical experiments and, the large break loss of coolant accident (LOCA). In this paper the case of LOCA, is taken to discuss the best-estimate analysis and decision making. Central to decision making is information. Thus, of interest is the source, quantity and quality of the information obtained in a best-estimate analysis, and used to define the acceptance criteria and to formulate a decision rule. This in effect expands the problem from the calculation of a conservative margin to a predefined acceptance criterion, to the formulation of a consistent decision rule and the computation of a test statistic for application of the decision rule. The latter view is a necessary condition for developing risk informed decision rules, and, thus, the relation between design basis analysis criteria and probabilistic risk assessment criteria is key. The discussion is in the context of making a decision under uncertainty for a reactor
Bounded estimation of flux-at-a-point for one or more detectors
International Nuclear Information System (INIS)
Steinberg, H.A.
1974-01-01
The procedure for biasing the collision density of Monte Carlo particles, so that the estimation of flux at a point detector is bounded, is described. The theory and program design of the solution of the problem of obtaining estimates for several detectors during a single Monte Carlo calculation is presented. 4 references. (U.S.)
Distortion-Rate Bounds for Distributed Estimation Using Wireless Sensor Networks
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Nihar Jindal
2008-03-01
Full Text Available We deal with centralized and distributed rate-constrained estimation of random signal vectors performed using a network of wireless sensors (encoders communicating with a fusion center (decoder. For this context, we determine lower and upper bounds on the corresponding distortion-rate (D-R function. The nonachievable lower bound is obtained by considering centralized estimation with a single-sensor which has all observation data available, and by determining the associated D-R function in closed-form. Interestingly, this D-R function can be achieved using an estimate first compress afterwards (EC approach, where the sensor (i forms the minimum mean-square error (MMSE estimate for the signal of interest; and (ii optimally (in the MSE sense compresses and transmits it to the FC that reconstructs it. We further derive a novel alternating scheme to numerically determine an achievable upper bound of the D-R function for general distributed estimation using multiple sensors. The proposed algorithm tackles an analytically intractable minimization problem, while it accounts for sensor data correlations. The obtained upper bound is tighter than the one determined by having each sensor performing MSE optimal encoding independently of the others. Numerical examples indicate that the algorithm performs well and yields D-R upper bounds which are relatively tight with respect to analytical alternatives obtained without taking into account the cross-correlations among sensor data.
DEFF Research Database (Denmark)
Blasone, Roberta-Serena; Vrugt, Jasper A.; Madsen, Henrik
2008-01-01
propose an alternative strategy to determine the value of the cutoff threshold based on the appropriate coverage of the resulting uncertainty bounds. We demonstrate the superiority of this revised GLUE method with three different conceptual watershed models of increasing complexity, using both synthetic......In the last few decades hydrologists have made tremendous progress in using dynamic simulation models for the analysis and understanding of hydrologic systems. However, predictions with these models are often deterministic and as such they focus on the most probable forecast, without an explicit...... of applications. However, the MC based sampling strategy of the prior parameter space typically utilized in GLUE is not particularly efficient in finding behavioral simulations. This becomes especially problematic for high-dimensional parameter estimation problems, and in the case of complex simulation models...
The estimation of uncertainty of radioactivity measurement on gamma counters in radiopharmacy
International Nuclear Information System (INIS)
Jovanovic, M.S.; Orlic, M.; Vranjes, S.; Stamenkovic, Lj. . E-mail address of corresponding author: nikijov@vin.bg.ac.yu; Jovanovic, M.S.)
2005-01-01
In this paper the estimation of uncertainty of measurement of radioactivity on gamma counter in Laboratory for radioisotopes is presented. The uncertainty components, which are important for these measurements, are identified and taken into account while estimating the uncertainty of measurement.(author)
Uncertainty in estimating and mitigating industrial related GHG emissions
International Nuclear Information System (INIS)
El-Fadel, M.; Zeinati, M.; Ghaddar, N.; Mezher, T.
2001-01-01
Global climate change has been one of the challenging environmental concerns facing policy makers in the past decade. The characterization of the wide range of greenhouse gas emissions sources and sinks as well as their behavior in the atmosphere remains an on-going activity in many countries. Lebanon, being a signatory to the Framework Convention on Climate Change, is required to submit and regularly update a national inventory of greenhouse gas emissions sources and removals. Accordingly, an inventory of greenhouse gases from various sectors was conducted following the guidelines set by the United Nations Intergovernmental Panel on Climate Change (IPCC). The inventory indicated that the industrial sector contributes about 29% to the total greenhouse gas emissions divided between industrial processes and energy requirements at 12 and 17%, respectively. This paper describes major mitigation scenarios to reduce emissions from this sector based on associated technical, economic, environmental, and social characteristics. Economic ranking of these scenarios was conducted and uncertainty in emission factors used in the estimation process was emphasized. For this purpose, theoretical and experimental emission factors were used as alternatives to default factors recommended by the IPCC and the significance of resulting deviations in emission estimation is presented. (author)
International Nuclear Information System (INIS)
Yu Watanabe; Masahito Ueda
2012-01-01
Full text: When we try to obtain information about a quantum system, we need to perform measurement on the system. The measurement process causes unavoidable state change. Heisenberg discussed a thought experiment of the position measurement of a particle by using a gamma-ray microscope, and found a trade-off relation between the error of the measured position and the disturbance in the momentum caused by the measurement process. The trade-off relation epitomizes the complementarity in quantum measurements: we cannot perform a measurement of an observable without causing disturbance in its canonically conjugate observable. However, at the time Heisenberg found the complementarity, quantum measurement theory was not established yet, and Kennard and Robertson's inequality erroneously interpreted as a mathematical formulation of the complementarity. Kennard and Robertson's inequality actually implies the indeterminacy of the quantum state: non-commuting observables cannot have definite values simultaneously. However, Kennard and Robertson's inequality reflects the inherent nature of a quantum state alone, and does not concern any trade-off relation between the error and disturbance in the measurement process. In this talk, we report a resolution to the complementarity in quantum measurements. First, we find that it is necessary to involve the estimation process from the outcome of the measurement for quantifying the error and disturbance in the quantum measurement. We clarify the implicitly involved estimation process in Heisenberg's gamma-ray microscope and other measurement schemes, and formulate the error and disturbance for an arbitrary quantum measurement by using quantum estimation theory. The error and disturbance are defined in terms of the Fisher information, which gives the upper bound of the accuracy of the estimation. Second, we obtain uncertainty relations between the measurement errors of two observables [1], and between the error and disturbance in the
Sturm, J.-E.; de Haan, J.
2005-01-01
Two important problems exist in cross-country growth studies: outliers and model uncertainty. Employing Sala-i-Martin's (1997a,b) data set, we first use robust estimation and analyze to what extent outliers influence OLS regressions. We then use both OLS and robust estimation techniques in applying
Frisbee, Joseph H., Jr.
2015-01-01
Upper bounds on high speed satellite collision probability, P (sub c), have been investigated. Previous methods assume an individual position error covariance matrix is available for each object. The two matrices being combined into a single, relative position error covariance matrix. Components of the combined error covariance are then varied to obtain a maximum P (sub c). If error covariance information for only one of the two objects was available, either some default shape has been used or nothing could be done. An alternative is presented that uses the known covariance information along with a critical value of the missing covariance to obtain an approximate but useful P (sub c) upper bound. There are various avenues along which an upper bound on the high speed satellite collision probability has been pursued. Typically, for the collision plane representation of the high speed collision probability problem, the predicted miss position in the collision plane is assumed fixed. Then the shape (aspect ratio of ellipse), the size (scaling of standard deviations) or the orientation (rotation of ellipse principal axes) of the combined position error ellipse is varied to obtain a maximum P (sub c). Regardless as to the exact details of the approach, previously presented methods all assume that an individual position error covariance matrix is available for each object and the two are combined into a single, relative position error covariance matrix. This combined position error covariance matrix is then modified according to the chosen scheme to arrive at a maximum P (sub c). But what if error covariance information for one of the two objects is not available? When error covariance information for one of the objects is not available the analyst has commonly defaulted to the situation in which only the relative miss position and velocity are known without any corresponding state error covariance information. The various usual methods of finding a maximum P (sub c) do
USE OF BOUNDING ANALYSES TO ESTIMATE THE PREFORMANCE OF A SEISMICALLY ISOLATED STRUCTURE
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Gökhan ÖZDEMİR
2017-03-01
Full Text Available Current design approach for seismic isolated structures is to perform bounding analyses. These analyses provide an envelope for the response of the seismic isolated structure rather than focusing on the actual performance. In this study, the success of bounding analyses to estimate performance of a seismic isolated structure, in which the isolation is provided by means of lead rubber bearings (LRBs, is evaluated in a comparative manner. For this purpose, nonlinear response history analyses were performed under the effect of bidirectional ground motion excitations. In bounding analyses, non-deteriorating hysteretic representations were used to model the hysteretic behavior of LRBs. On the other hand, to estimate the actual performance of both the superstructure and isolator units, deteriorating hysteretic idealizations were employed. The deterioration in strength of LRBs was defined as a function of temperature rise in the lead core. The analyzed structure is an existing seismically isolated hospital building and analytically modeled in accordance with its reported design properties for both isolation units and superstructure. Results obtained from analyses where LRBs are idealized by both deteriorating and non-deteriorating hysteretic representations are used in the comparisons. The response quantities used in the comparisons are maximum isolator displacement, maximum isolator force, maximum absolute floor acceleration, and maximum relative story displacements. In an average sense, bounding analyses is found to provide conservative estimates for the selected response quantities and fulfills its intended purpose. However, it is revealed that there may be individual cases where bounding analyses fails to provide a safe envelope.
Evaluation and uncertainty estimates of Charpy-impact data
International Nuclear Information System (INIS)
Stallman, F.W.
1982-01-01
Shifts in transition temperature and upper-shelf energy from Charpy tests are used to determine the extent of radiation embrittlement in steels. In order to determine these parameters reliably and to obtain uncertainty estimates, curve fitting procedures need to be used. The hyperbolic tangent or similar models have been proposed to fit the temperature-impact-energy curve. These models are not based on the actual fracture mechanics and are indeed poorly suited in many applications. The results may be falsified by forcing an inflexible curve through too many data points. The nonlinearity of the fit poses additional problems. In this paper, a simple linear fit is proposed. By eliminating data which are irrelevant for the determination of a given parameter, better reliability and accuracy can be achieved. Additional input parameters like fluence and irradiation temperature can be included. This is important if there is a large variation of fluence and temperature in different test specimens. The method has been tested with Charpy specimens from the NRC-HSST experiments
Evaluating Prognostics Performance for Algorithms Incorporating Uncertainty Estimates
National Aeronautics and Space Administration — Uncertainty Representation and Management (URM) are an integral part of the prognostic system development.1As capabilities of prediction algorithms evolve, research...
International Nuclear Information System (INIS)
Perez, J.F.; Coutinho, F.A.B.; Malta, C.P.
1985-01-01
It is shown that critical long distance behaviour for a two-body potential, defining the finiteness or infinitude of the number of negative eigenvalues of Schrodinger operators in ν-dimensions, are given by v sub(k) (r) = - [ν-2/2r] 2 - 1/(2rlnr) 2 + ... - 1/(2rlnr.lnlnr...ln sub(k)r) 2 where k=0,1... for ν not=2 and k=1,2... if ν=2. This result is a consequence of logarithmic corrections to an inequality known as Uncertainty Principle. If the continuum threshold in the N-body problem is defined by a two-cluster break up our results generate corrections to the existing sufficient conditions for the existence of infinitely many bound states. (Author) [pt
An error bound estimate and convergence of the Nodal-LTS N solution in a rectangle
International Nuclear Information System (INIS)
Hauser, Eliete Biasotto; Pazos, Ruben Panta; Tullio de Vilhena, Marco
2005-01-01
In this work, we report the mathematical analysis concerning error bound estimate and convergence of the Nodal-LTS N solution in a rectangle. For such we present an efficient algorithm, called LTS N 2D-Diag solution for Cartesian geometry
Rothman, Jessica M; Chapman, Colin A; Pell, Alice N
2008-07-01
Protein is essential for living organisms, but digestibility of crude protein is poorly understood and difficult to predict. Nitrogen is used to estimate protein content because nitrogen is a component of the amino acids that comprise protein, but a substantial portion of the nitrogen in plants may be bound to fiber in an indigestible form. To estimate the amount of crude protein that is unavailable in the diets of mountain gorillas (Gorilla beringei) in Bwindi Impenetrable National Park, Uganda, foods routinely eaten were analyzed to determine the amount of nitrogen bound to the acid-detergent fiber residue. The amount of fiber-bound nitrogen varied among plant parts: herbaceous leaves 14.5+/-8.9% (reported as a percentage of crude protein on a dry matter (DM) basis), tree leaves (16.1+/-6.7% DM), pith/herbaceous peel (26.2+/-8.9% DM), fruit (34.7+/-17.8% DM), bark (43.8+/-15.6% DM), and decaying wood (85.2+/-14.6% DM). When crude protein and available protein intake of adult gorillas was estimated over a year, 15.1% of the dietary crude protein was indigestible. These results indicate that the proportion of fiber-bound protein in primate diets should be considered when estimating protein intake, food selection, and food/habitat quality.
Strong Convergence Bound of the Pareto Index Estimator under Right Censoring
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Peng Zuoxiang
2010-01-01
Full Text Available Let be a sequence of positive independent and identically distributed random variables with common Pareto-type distribution function as , where represents a slowly varying function at infinity. In this note we study the strong convergence bound of a kind of right censored Pareto index estimator under second-order regularly varying conditions.
Uncertainty of Modal Parameters Estimated by ARMA Models
DEFF Research Database (Denmark)
Jensen, Jakob Laigaard; Brincker, Rune; Rytter, Anders
In this paper the uncertainties of identified modal parameters such as eigenfrequencies and damping ratios are assessed. From the measured response of dynamic excited structures the modal parameters may be identified and provide important structural knowledge. However the uncertainty of the param...
International Nuclear Information System (INIS)
Bosko, A.; Croft, St.; Gulbransen, E.
2009-01-01
General purpose gamma scanners are often used to assay unknown drums that differ from those used to create the default calibration. This introduces a potential source of bias into the matrix correction when the correction is based on the estimation of the mean density of the drum contents from a weigh scale measurement. In this paper we evaluate the magnitude of this bias that may be introduced by performing assay measurements with a system whose matrix correction algorithm was calibrated with a set of standard drums but applied to a population of drums whose tare weight may be different. The matrix correction factors are perturbed in such cases because the unknown difference in tare weight gets reflected as a bias in the derived matrix density. This would be the only impact if the difference in tare weight was due solely to the weight of the lid or base, say. But in reality the reason for the difference may be because the steel wall of the drum is of a different thickness. Thus, there is an opposing interplay at work which tends to compensate. The purpose of this work is to evaluate and bound the magnitude of the resulting assay uncertainty introduced by tare weight variation. We compare the results obtained using simple analytical models and the 3-D ray tracing with ISOCS software to illustrate and quantify the problem. The numerical results allow a contribution to the Total Measurement Uncertainty (TMU) to be propagated into the final assay result. (authors)
Directory of Open Access Journals (Sweden)
G. R. Pasha
2006-07-01
Full Text Available In this paper, we present that how much the variances of the classical estimators, namely, maximum likelihood estimator and moment estimator deviate from the minimum variance bound while estimating for the Maxwell distribution. We also sketch this difference for the negative integer moment estimator. We note the poor performance of the negative integer moment estimator in the said consideration while maximum likelihood estimator attains minimum variance bound and becomes an attractive choice.
Uncertainty of Modal Parameters Estimated by ARMA Models
DEFF Research Database (Denmark)
Jensen, Jacob Laigaard; Brincker, Rune; Rytter, Anders
1990-01-01
In this paper the uncertainties of identified modal parameters such as eidenfrequencies and damping ratios are assed. From the measured response of dynamic excited structures the modal parameters may be identified and provide important structural knowledge. However the uncertainty of the parameters...... by simulation study of a lightly damped single degree of freedom system. Identification by ARMA models has been choosen as system identification method. It is concluded that both the sampling interval and number of sampled points may play a significant role with respect to the statistical errors. Furthermore......, it is shown that the model errors may also contribute significantly to the uncertainty....
Energy Technology Data Exchange (ETDEWEB)
Bruschewski, Martin; Schiffer, Heinz-Peter [Technische Universitaet Darmstadt, Institute of Gas Turbines and Aerospace Propulsion, Darmstadt (Germany); Freudenhammer, Daniel [Technische Universitaet Darmstadt, Institute of Fluid Mechanics and Aerodynamics, Center of Smart Interfaces, Darmstadt (Germany); Buchenberg, Waltraud B. [University Medical Center Freiburg, Medical Physics, Department of Radiology, Freiburg (Germany); Grundmann, Sven [University of Rostock, Institute of Fluid Mechanics, Rostock (Germany)
2016-05-15
Velocity measurements with magnetic resonance velocimetry offer outstanding possibilities for experimental fluid mechanics. The purpose of this study was to provide practical guidelines for the estimation of the measurement uncertainty in such experiments. Based on various test cases, it is shown that the uncertainty estimate can vary substantially depending on how the uncertainty is obtained. The conventional approach to estimate the uncertainty from the noise in the artifact-free background can lead to wrong results. A deviation of up to -75% is observed with the presented experiments. In addition, a similarly high deviation is demonstrated with the data from other studies. As a more accurate approach, the uncertainty is estimated directly from the image region with the flow sample. Two possible estimation methods are presented. (orig.)
Bruschewski, Martin; Freudenhammer, Daniel; Buchenberg, Waltraud B.; Schiffer, Heinz-Peter; Grundmann, Sven
2016-05-01
Velocity measurements with magnetic resonance velocimetry offer outstanding possibilities for experimental fluid mechanics. The purpose of this study was to provide practical guidelines for the estimation of the measurement uncertainty in such experiments. Based on various test cases, it is shown that the uncertainty estimate can vary substantially depending on how the uncertainty is obtained. The conventional approach to estimate the uncertainty from the noise in the artifact-free background can lead to wrong results. A deviation of up to -75 % is observed with the presented experiments. In addition, a similarly high deviation is demonstrated with the data from other studies. As a more accurate approach, the uncertainty is estimated directly from the image region with the flow sample. Two possible estimation methods are presented.
Estimation of absorbed dose in cell nuclei due to DNA-bound /sup 3/H
Energy Technology Data Exchange (ETDEWEB)
Saito, M; Ishida, M R; Streffer, C; Molls, M
1985-04-01
The average absorbed dose due to DNA-bound /sup 3/H in a cell nucleus was estimated by a Monte Carlo simulation for a model nucleus which was assumed to be spheroidal. The volume of the cell nucleus was the major dose-determining factor for cell nuclei which have the same DNA content and the same specific activity of DNA. This result was applied to estimating the accumulated dose in the cell nuclei of organs of young mice born from mother mice which ingested /sup 3/H-thymidine with drinking water during pregnancy. The values of dose-modifying factors for the accumulated dose due to DNA-bound /sup 3/H compared to the dose due to an assumed homogenous distribution of /sup 3/H in organ were found to be between about 2 and 6 for the various organs.
Strong Convergence Bound of the Pareto Index Estimator under Right Censoring
Directory of Open Access Journals (Sweden)
Bao Tao
2010-01-01
Full Text Available Let {Xn,n≥1} be a sequence of positive independent and identically distributed random variables with common Pareto-type distribution function F(x=1−x−1/γlF(x as γ>0, where lF(x represents a slowly varying function at infinity. In this note we study the strong convergence bound of a kind of right censored Pareto index estimator under second-order regularly varying conditions.
Linear minimax estimation for random vectors with parametric uncertainty
Bitar, E; Baeyens, E; Packard, A; Poolla, K
2010-01-01
consider two uncertainty models, PA and PB. Model PA represents X and Y as jointly Gaussian whose covariance matrix Λ belongs to the convex hull of a set of m known covariance matrices. Model PB characterizes X and Y as jointly distributed according to a
Hydrological model uncertainty due to spatial evapotranspiration estimation methods
Czech Academy of Sciences Publication Activity Database
Yu, X.; Lamačová, Anna; Duffy, Ch.; Krám, P.; Hruška, Jakub
2016-01-01
Roč. 90, part B (2016), s. 90-101 ISSN 0098-3004 R&D Projects: GA MŠk(CZ) LO1415 Institutional support: RVO:67179843 Keywords : Uncertainty * Evapotranspiration * Forest management * PIHM * Biome-BGC Subject RIV: DA - Hydrology ; Limnology OBOR OECD: Hydrology Impact factor: 2.533, year: 2016
Quantum tomography via compressed sensing: error bounds, sample complexity and efficient estimators
International Nuclear Information System (INIS)
Flammia, Steven T; Gross, David; Liu, Yi-Kai; Eisert, Jens
2012-01-01
Intuitively, if a density operator has small rank, then it should be easier to estimate from experimental data, since in this case only a few eigenvectors need to be learned. We prove two complementary results that confirm this intuition. Firstly, we show that a low-rank density matrix can be estimated using fewer copies of the state, i.e. the sample complexity of tomography decreases with the rank. Secondly, we show that unknown low-rank states can be reconstructed from an incomplete set of measurements, using techniques from compressed sensing and matrix completion. These techniques use simple Pauli measurements, and their output can be certified without making any assumptions about the unknown state. In this paper, we present a new theoretical analysis of compressed tomography, based on the restricted isometry property for low-rank matrices. Using these tools, we obtain near-optimal error bounds for the realistic situation where the data contain noise due to finite statistics, and the density matrix is full-rank with decaying eigenvalues. We also obtain upper bounds on the sample complexity of compressed tomography, and almost-matching lower bounds on the sample complexity of any procedure using adaptive sequences of Pauli measurements. Using numerical simulations, we compare the performance of two compressed sensing estimators—the matrix Dantzig selector and the matrix Lasso—with standard maximum-likelihood estimation (MLE). We find that, given comparable experimental resources, the compressed sensing estimators consistently produce higher fidelity state reconstructions than MLE. In addition, the use of an incomplete set of measurements leads to faster classical processing with no loss of accuracy. Finally, we show how to certify the accuracy of a low-rank estimate using direct fidelity estimation, and describe a method for compressed quantum process tomography that works for processes with small Kraus rank and requires only Pauli eigenstate preparations
Uncertainty estimation of core safety parameters using cross-correlations of covariance matrix
International Nuclear Information System (INIS)
Yamamoto, A.; Yasue, Y.; Endo, T.; Kodama, Y.; Ohoka, Y.; Tatsumi, M.
2012-01-01
An uncertainty estimation method for core safety parameters, for which measurement values are not obtained, is proposed. We empirically recognize the correlations among the prediction errors among core safety parameters, e.g., a correlation between the control rod worth and assembly relative power of corresponding position. Correlations of uncertainties among core safety parameters are theoretically estimated using the covariance of cross sections and sensitivity coefficients for core parameters. The estimated correlations among core safety parameters are verified through the direct Monte-Carlo sampling method. Once the correlation of uncertainties among core safety parameters is known, we can estimate the uncertainty of a safety parameter for which measurement value is not obtained. Furthermore, the correlations can be also used for the reduction of uncertainties of core safety parameters. (authors)
Kato, Seiji; Loeb, Norman G.; Rutan, David A.; Rose, Fred G.; Sun-Mack, Sunny; Miller, Walter F.; Chen, Yan
2012-07-01
Differences of modeled surface upward and downward longwave and shortwave irradiances are calculated using modeled irradiance computed with active sensor-derived and passive sensor-derived cloud and aerosol properties. The irradiance differences are calculated for various temporal and spatial scales, monthly gridded, monthly zonal, monthly global, and annual global. Using the irradiance differences, the uncertainty of surface irradiances is estimated. The uncertainty (1σ) of the annual global surface downward longwave and shortwave is, respectively, 7 W m-2 (out of 345 W m-2) and 4 W m-2 (out of 192 W m-2), after known bias errors are removed. Similarly, the uncertainty of the annual global surface upward longwave and shortwave is, respectively, 3 W m-2 (out of 398 W m-2) and 3 W m-2 (out of 23 W m-2). The uncertainty is for modeled irradiances computed using cloud properties derived from imagers on a sun-synchronous orbit that covers the globe every day (e.g., moderate-resolution imaging spectrometer) or modeled irradiances computed for nadir view only active sensors on a sun-synchronous orbit such as Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation and CloudSat. If we assume that longwave and shortwave uncertainties are independent of each other, but up- and downward components are correlated with each other, the uncertainty in global annual mean net surface irradiance is 12 W m-2. One-sigma uncertainty bounds of the satellite-based net surface irradiance are 106 W m-2 and 130 W m-2.
DEFF Research Database (Denmark)
Morace, Renata Erica; Hansen, Hans Nørgaard; De Chiffre, Leonardo
2005-01-01
This paper deals with the uncertainty estimation of measurements performed on optical coordinate measuring machines (CMMs). Two different methods were used to assess the uncertainty of circle diameter measurements using an optical CMM: the sensitivity analysis developing an uncertainty budget...
MacIntosh, C. R.; Merchant, C. J.; von Schuckmann, K.
2017-01-01
This article presents a review of current practice in estimating steric sea level change, focussed on the treatment of uncertainty. Steric sea level change is the contribution to the change in sea level arising from the dependence of density on temperature and salinity. It is a significant component of sea level rise and a reflection of changing ocean heat content. However, tracking these steric changes still remains a significant challenge for the scientific community. We review the importance of understanding the uncertainty in estimates of steric sea level change. Relevant concepts of uncertainty are discussed and illustrated with the example of observational uncertainty propagation from a single profile of temperature and salinity measurements to steric height. We summarise and discuss the recent literature on methodologies and techniques used to estimate steric sea level in the context of the treatment of uncertainty. Our conclusions are that progress in quantifying steric sea level uncertainty will benefit from: greater clarity and transparency in published discussions of uncertainty, including exploitation of international standards for quantifying and expressing uncertainty in measurement; and the development of community "recipes" for quantifying the error covariances in observations and from sparse sampling and for estimating and propagating uncertainty across spatio-temporal scales.
Directory of Open Access Journals (Sweden)
Jaewook Lee
2015-06-01
Full Text Available This paper presents an efficient method for estimating capacity-fade uncertainty in lithium-ion batteries (LIBs in order to integrate them into the battery-management system (BMS of electric vehicles, which requires simple and inexpensive computation for successful application. The study uses the pseudo-two-dimensional (P2D electrochemical model, which simulates the battery state by solving a system of coupled nonlinear partial differential equations (PDEs. The model parameters that are responsible for electrode degradation are identified and estimated, based on battery data obtained from the charge cycles. The Bayesian approach, with parameters estimated by probability distributions, is employed to account for uncertainties arising in the model and battery data. The Markov Chain Monte Carlo (MCMC technique is used to draw samples from the distributions. The complex computations that solve a PDE system for each sample are avoided by employing a polynomial-based metamodel. As a result, the computational cost is reduced from 5.5 h to a few seconds, enabling the integration of the method into the vehicle BMS. Using this approach, the conservative bound of capacity fade can be determined for the vehicle in service, which represents the safety margin reflecting the uncertainty.
Rigo-Bonnin, Raül; Blanco-Font, Aurora; Canalias, Francesca
2018-05-08
Values of mass concentration of tacrolimus in whole blood are commonly used by the clinicians for monitoring the status of a transplant patient and for checking whether the administered dose of tacrolimus is effective. So, clinical laboratories must provide results as accurately as possible. Measurement uncertainty can allow ensuring reliability of these results. The aim of this study was to estimate measurement uncertainty of whole blood mass concentration tacrolimus values obtained by UHPLC-MS/MS using two top-down approaches: the single laboratory validation approach and the proficiency testing approach. For the single laboratory validation approach, we estimated the uncertainties associated to the intermediate imprecision (using long-term internal quality control data) and the bias (utilizing a certified reference material). Next, we combined them together with the uncertainties related to the calibrators-assigned values to obtain a combined uncertainty for, finally, to calculate the expanded uncertainty. For the proficiency testing approach, the uncertainty was estimated in a similar way that the single laboratory validation approach but considering data from internal and external quality control schemes to estimate the uncertainty related to the bias. The estimated expanded uncertainty for single laboratory validation, proficiency testing using internal and external quality control schemes were 11.8%, 13.2%, and 13.0%, respectively. After performing the two top-down approaches, we observed that their uncertainty results were quite similar. This fact would confirm that either two approaches could be used to estimate the measurement uncertainty of whole blood mass concentration tacrolimus values in clinical laboratories. Copyright © 2018 The Canadian Society of Clinical Chemists. Published by Elsevier Inc. All rights reserved.
Di Vittorio, A. V.; Mao, J.; Shi, X.; Chini, L.; Hurtt, G.; Collins, W. D.
2018-01-01
Previous studies have examined land use change as a driver of global change, but the translation of land use change into land cover conversion has been largely unconstrained. Here we quantify the effects of land cover conversion uncertainty on the global carbon and climate system using the integrated Earth System Model. Our experiments use identical land use change data and vary land cover conversions to quantify associated uncertainty in carbon and climate estimates. Land cover conversion uncertainty is large, constitutes a 5 ppmv range in estimated atmospheric CO2 in 2004, and generates carbon uncertainty that is equivalent to 80% of the net effects of CO2 and climate and 124% of the effects of nitrogen deposition during 1850-2004. Additionally, land cover uncertainty generates differences in local surface temperature of over 1°C. We conclude that future studies addressing land use, carbon, and climate need to constrain and reduce land cover conversion uncertainties.
Hughes, J. D.; Metz, P. A.
2014-12-01
Most watershed studies include observation-based water budget analyses to develop first-order estimates of significant flow terms. Surface-water/groundwater (SWGW) exchange is typically assumed to be equal to the residual of the sum of inflows and outflows in a watershed. These estimates of SWGW exchange, however, are highly uncertain as a result of the propagation of uncertainty inherent in the calculation or processing of the other terms of the water budget, such as stage-area-volume relations, and uncertainties associated with land-cover based evapotranspiration (ET) rate estimates. Furthermore, the uncertainty of estimated SWGW exchanges can be magnified in large wetland systems that transition from dry to wet during wet periods. Although it is well understood that observation-based estimates of SWGW exchange are uncertain it is uncommon for the uncertainty of these estimates to be directly quantified. High-level programming languages like Python can greatly reduce the effort required to (1) quantify the uncertainty of estimated SWGW exchange in large wetland systems and (2) evaluate how different approaches for partitioning land-cover data in a watershed may affect the water-budget uncertainty. We have used Python with the Numpy, Scipy.stats, and pyDOE packages to implement an unconstrained Monte Carlo approach with Latin Hypercube sampling to quantify the uncertainty of monthly estimates of SWGW exchange in the Floral City watershed of the Tsala Apopka wetland system in west-central Florida, USA. Possible sources of uncertainty in the water budget analysis include rainfall, ET, canal discharge, and land/bathymetric surface elevations. Each of these input variables was assigned a probability distribution based on observation error or spanning the range of probable values. The Monte Carlo integration process exposes the uncertainties in land-cover based ET rate estimates as the dominant contributor to the uncertainty in SWGW exchange estimates. We will discuss
Comparison between bottom-up and top-down approaches in the estimation of measurement uncertainty.
Lee, Jun Hyung; Choi, Jee-Hye; Youn, Jae Saeng; Cha, Young Joo; Song, Woonheung; Park, Ae Ja
2015-06-01
Measurement uncertainty is a metrological concept to quantify the variability of measurement results. There are two approaches to estimate measurement uncertainty. In this study, we sought to provide practical and detailed examples of the two approaches and compare the bottom-up and top-down approaches to estimating measurement uncertainty. We estimated measurement uncertainty of the concentration of glucose according to CLSI EP29-A guideline. Two different approaches were used. First, we performed a bottom-up approach. We identified the sources of uncertainty and made an uncertainty budget and assessed the measurement functions. We determined the uncertainties of each element and combined them. Second, we performed a top-down approach using internal quality control (IQC) data for 6 months. Then, we estimated and corrected systematic bias using certified reference material of glucose (NIST SRM 965b). The expanded uncertainties at the low glucose concentration (5.57 mmol/L) by the bottom-up approach and top-down approaches were ±0.18 mmol/L and ±0.17 mmol/L, respectively (all k=2). Those at the high glucose concentration (12.77 mmol/L) by the bottom-up and top-down approaches were ±0.34 mmol/L and ±0.36 mmol/L, respectively (all k=2). We presented practical and detailed examples for estimating measurement uncertainty by the two approaches. The uncertainties by the bottom-up approach were quite similar to those by the top-down approach. Thus, we demonstrated that the two approaches were approximately equivalent and interchangeable and concluded that clinical laboratories could determine measurement uncertainty by the simpler top-down approach.
DEFF Research Database (Denmark)
Thomsen, Nanna Isbak; Troldborg, Mads; McKnight, Ursula S.
2012-01-01
site. The different conceptual models consider different source characterizations and hydrogeological descriptions. The idea is to include a set of essentially different conceptual models where each model is believed to be realistic representation of the given site, based on the current level...... the appropriate management option. The uncertainty of mass discharge estimates depends greatly on the extent of the site characterization. A good approach for uncertainty estimation will be flexible with respect to the investigation level, and account for both parameter and conceptual model uncertainty. We...... propose a method for quantifying the uncertainty of dynamic mass discharge estimates from contaminant point sources on the local scale. The method considers both parameter and conceptual uncertainty through a multi-model approach. The multi-model approach evaluates multiple conceptual models for the same...
International Nuclear Information System (INIS)
Shimada, Yoko; Morisawa, Shinsuke
1998-01-01
Most of model estimation of the environmental contamination includes some uncertainty associated with the parameter uncertainty in the model. In this study, the uncertainty was analyzed in a model for evaluating the ingestion of radionuclide caused by the long-term global low-level radioactive contamination by using various uncertainty analysis methods: the percentile estimate, the robustness analysis and the fuzzy estimate. The model is mainly composed of five sub-models, which include their own uncertainty; we also analyzed the uncertainty. The major findings obtained in this study include that the possibility of the discrepancy between predicted value by the model simulation and the observed data is less than 10%; the uncertainty of the predicted value is higher before 1950 and after 1980; the uncertainty of the predicted value can be reduced by decreasing the uncertainty of some environmental parameters in the model; the reliability of the model can definitively depend on the following environmental factors: direct foliar absorption coefficient, transfer factor of radionuclide from stratosphere down to troposphere, residual rate by food processing and cooking, transfer factor of radionuclide in ocean and sedimentation in ocean. (author)
Asphere cross testing: an exercise in uncertainty estimation
Murphy, Paul E.
2017-10-01
Aspheric surfaces can provide substantial improvements to optical designs, but they can also be difficult to manufacture cost-effectively. Asphere metrology contributes significantly to this difficulty, especially for high-precision aspheric surfaces. With the advent of computer-controlled fabrication machinery, optical surface quality is chiefly limited by the ability to measure it. Consequently, understanding the uncertainty of surface measurements is of great importance for determining what optical surface quality can be achieved. We measured sample aspheres using multiple techniques: profilometry, null interferometry, and subaperture stitching. We also obtained repeatability and reproducibility (R&R) measurement data by retesting the same aspheres under various conditions. We highlight some of the details associated with the different measurement techniques, especially efforts to reduce bias in the null tests via calibration. We compare and contrast the measurement results, and obtain an empirical view of the measurement uncertainty of the different techniques. We found fair agreement in overall surface form among the methods, but meaningful differences in reproducibility and mid-spatial frequency performance.
Using interpolation to estimate system uncertainty in gene expression experiments.
Directory of Open Access Journals (Sweden)
Lee J Falin
Full Text Available The widespread use of high-throughput experimental assays designed to measure the entire complement of a cell's genes or gene products has led to vast stores of data that are extremely plentiful in terms of the number of items they can measure in a single sample, yet often sparse in the number of samples per experiment due to their high cost. This often leads to datasets where the number of treatment levels or time points sampled is limited, or where there are very small numbers of technical and/or biological replicates. Here we introduce a novel algorithm to quantify the uncertainty in the unmeasured intervals between biological measurements taken across a set of quantitative treatments. The algorithm provides a probabilistic distribution of possible gene expression values within unmeasured intervals, based on a plausible biological constraint. We show how quantification of this uncertainty can be used to guide researchers in further data collection by identifying which samples would likely add the most information to the system under study. Although the context for developing the algorithm was gene expression measurements taken over a time series, the approach can be readily applied to any set of quantitative systems biology measurements taken following quantitative (i.e. non-categorical treatments. In principle, the method could also be applied to combinations of treatments, in which case it could greatly simplify the task of exploring the large combinatorial space of future possible measurements.
Uncertainties in effective dose estimates of adult CT head scans: The effect of head size
International Nuclear Information System (INIS)
Gregory, Kent J.; Bibbo, Giovanni; Pattison, John E.
2009-01-01
Purpose: This study is an extension of a previous study where the uncertainties in effective dose estimates from adult CT head scans were calculated using four CT effective dose estimation methods, three of which were computer programs (CT-EXPO, CTDOSIMETRY, and IMPACTDOSE) and one that involved the dose length product (DLP). However, that study did not include the uncertainty contribution due to variations in head sizes. Methods: The uncertainties due to head size variations were estimated by first using the computer program data to calculate doses to small and large heads. These doses were then compared with doses calculated for the phantom heads used by the computer programs. An uncertainty was then assigned based on the difference between the small and large head doses and the doses of the phantom heads. Results: The uncertainties due to head size variations alone were found to be between 4% and 26% depending on the method used and the patient gender. When these uncertainties were included with the results of the previous study, the overall uncertainties in effective dose estimates (stated at the 95% confidence interval) were 20%-31% (CT-EXPO), 15%-30% (CTDOSIMETRY), 20%-36% (IMPACTDOSE), and 31%-40% (DLP). Conclusions: For the computer programs, the lower overall uncertainties were still achieved when measured values of CT dose index were used rather than tabulated values. For DLP dose estimates, head size variations made the largest (for males) and second largest (for females) contributions to effective dose uncertainty. An improvement in the uncertainty of the DLP method dose estimates will be achieved if head size variation can be taken into account.
Uncertainties in effective dose estimates of adult CT head scans: The effect of head size
Energy Technology Data Exchange (ETDEWEB)
Gregory, Kent J.; Bibbo, Giovanni; Pattison, John E. [Department of Medical Physics, Royal Adelaide Hospital, Adelaide, South Australia 5000 (Australia) and School of Electrical and Information Engineering (Applied Physics), University of South Australia, Mawson Lakes, South Australia 5095 (Australia); Division of Medical Imaging, Women' s and Children' s Hospital, North Adelaide, South Australia 5006 (Australia) and School of Electrical and Information Engineering (Applied Physics), University of South Australia, Mawson Lakes, South Australia 5095 (Australia); School of Electrical and Information Engineering (Applied Physics), University of South Australia, Mawson Lakes, South Australia 5095 (Australia)
2009-09-15
Purpose: This study is an extension of a previous study where the uncertainties in effective dose estimates from adult CT head scans were calculated using four CT effective dose estimation methods, three of which were computer programs (CT-EXPO, CTDOSIMETRY, and IMPACTDOSE) and one that involved the dose length product (DLP). However, that study did not include the uncertainty contribution due to variations in head sizes. Methods: The uncertainties due to head size variations were estimated by first using the computer program data to calculate doses to small and large heads. These doses were then compared with doses calculated for the phantom heads used by the computer programs. An uncertainty was then assigned based on the difference between the small and large head doses and the doses of the phantom heads. Results: The uncertainties due to head size variations alone were found to be between 4% and 26% depending on the method used and the patient gender. When these uncertainties were included with the results of the previous study, the overall uncertainties in effective dose estimates (stated at the 95% confidence interval) were 20%-31% (CT-EXPO), 15%-30% (CTDOSIMETRY), 20%-36% (IMPACTDOSE), and 31%-40% (DLP). Conclusions: For the computer programs, the lower overall uncertainties were still achieved when measured values of CT dose index were used rather than tabulated values. For DLP dose estimates, head size variations made the largest (for males) and second largest (for females) contributions to effective dose uncertainty. An improvement in the uncertainty of the DLP method dose estimates will be achieved if head size variation can be taken into account.
On the uncertainties in effective dose estimates of adult CT head scans
International Nuclear Information System (INIS)
Gregory, Kent J.; Bibbo, Giovanni; Pattison, John E.
2008-01-01
Estimates of the effective dose to adult patients from computed tomography (CT) head scanning can be calculated using a number of different methods. These estimates can be used for a variety of purposes, such as improving scanning protocols, comparing different CT imaging centers, and weighing the benefits of the scan against the risk of radiation-induced cancer. The question arises: What is the uncertainty in these effective dose estimates? This study calculates the uncertainty of effective dose estimates produced by three computer programs (CT-EXPO, CTDosimetry, and ImpactDose) and one method that makes use of dose-length product (DLP) values. Uncertainties were calculated in accordance with an internationally recognized uncertainty analysis guide. For each of the four methods, the smallest and largest overall uncertainties (stated at the 95% confidence interval) were: 20%-31% (CT-EXPO), 15%-28% (CTDosimetry), 20%-36% (ImpactDose), and 22%-32% (DLP), respectively. The overall uncertainties for each method vary due to differences in the uncertainties of factors used in each method. The smallest uncertainties apply when the CT dose index for the scanner has been measured using a calibrated pencil ionization chamber
Leaf area index uncertainty estimates for model-data fusion applications
Andrew D. Richardson; D. Bryan Dail; D.Y. Hollinger
2011-01-01
Estimates of data uncertainties are required to integrate different observational data streams as model constraints using model-data fusion. We describe an approach with which random and systematic uncertainties in optical measurements of leaf area index [LAI] can be quantified. We use data from a measurement campaign at the spruce-dominated Howland Forest AmeriFlux...
International Nuclear Information System (INIS)
1989-10-01
Studies of underground miners provide the principal basis for assessing the risk from radon daughter exposure. An important problem in all epidemiological studies of underground miners is the reliability of the estimates of the miners' exposures. This study examines the various sources of uncertainty in exposure estimation for the principal epidemiologic studies reported in the literature including the temporal and spatial variability of radon sources and, with the passage of time, changes to both mining methods and ventilation conditions. Uncertainties about work histories and the role of other hard rock mining experience are also discussed. The report also describes two statistical approaches, both based on Bayesian methods, by which the effects on the estimated risk coefficient of uncertainty in exposure (WLM) can be examined. One approach requires only an estimate of the cumulative WLM exposure of a group of miners, an estimate of the number of (excess) lung cancers potentially attributable to that exposure, and a specification of the uncertainty about the cumulative exposure of the group. The second approach is based on a linear regression model which incorporates errors (uncertainty) in the independent variable (WLM) and allows the dependent variable (cases) to be Poisson distributed. The method permits the calculation of marginal probability distributions for either slope (risk coefficient) or intercept. The regression model approach is applied to several published data sets from epidemiological studies of miners. Specific results are provided for each data set and apparent differences in risk coefficients are discussed. The studies of U.S. uranium miners, Ontario uranium miners and Czechoslovakian uranium miners are argued to provide the best basis for risk estimation at this time. In general terms, none of the analyses performed are inconsistent with a linear exposure-effect relation. Based on analyses of the overall miner groups, the most likely ranges
Andrade, João Rodrigo; Martins, Ramon Silva; Thompson, Roney Leon; Mompean, Gilmar; da Silveira Neto, Aristeu
2018-04-01
The present paper provides an analysis of the statistical uncertainties associated with direct numerical simulation (DNS) results and experimental data for turbulent channel and pipe flows, showing a new physically based quantification of these errors, to improve the determination of the statistical deviations between DNSs and experiments. The analysis is carried out using a recently proposed criterion by Thompson et al. ["A methodology to evaluate statistical errors in DNS data of plane channel flows," Comput. Fluids 130, 1-7 (2016)] for fully turbulent plane channel flows, where the mean velocity error is estimated by considering the Reynolds stress tensor, and using the balance of the mean force equation. It also presents how the residual error evolves in time for a DNS of a plane channel flow, and the influence of the Reynolds number on its convergence rate. The root mean square of the residual error is shown in order to capture a single quantitative value of the error associated with the dimensionless averaging time. The evolution in time of the error norm is compared with the final error provided by DNS data of similar Reynolds numbers available in the literature. A direct consequence of this approach is that it was possible to compare different numerical results and experimental data, providing an improved understanding of the convergence of the statistical quantities in turbulent wall-bounded flows.
[Dual process in large number estimation under uncertainty].
Matsumuro, Miki; Miwa, Kazuhisa; Terai, Hitoshi; Yamada, Kento
2016-08-01
According to dual process theory, there are two systems in the mind: an intuitive and automatic System 1 and a logical and effortful System 2. While many previous studies about number estimation have focused on simple heuristics and automatic processes, the deliberative System 2 process has not been sufficiently studied. This study focused on the System 2 process for large number estimation. First, we described an estimation process based on participants’ verbal reports. The task, corresponding to the problem-solving process, consisted of creating subgoals, retrieving values, and applying operations. Second, we investigated the influence of such deliberative process by System 2 on intuitive estimation by System 1, using anchoring effects. The results of the experiment showed that the System 2 process could mitigate anchoring effects.
The Balance and Recycle Process Models - Uncertainty, Estimations and Approximations
Czech Academy of Sciences Publication Activity Database
Vrba, Josef
2003-01-01
Roč. 43, č. 5 (2003), s. 601-624 ISSN 0232-9298 Institutional research plan: CEZ:AV0Z4072921 Keywords : balance matrix * recycle process matrix * matrix inverse estimation Subject RIV: CI - Industrial Chemistry, Chemical Engineering
Estimation of Uncertainty in Risk Assessment of Hydrogen Applications
DEFF Research Database (Denmark)
Markert, Frank; Krymsky, V.; Kozine, Igor
2011-01-01
Hydrogen technologies such as hydrogen fuelled vehicles and refuelling stations are being tested in practice in a number of projects (e.g. HyFleet-Cute and Whistler project) giving valuable information on the reliability and maintenance requirements. In order to establish refuelling stations the ...... probability and the NUSAP concept to quantify uncertainties of new not fully qualified hydrogen technologies and implications to risk management.......Hydrogen technologies such as hydrogen fuelled vehicles and refuelling stations are being tested in practice in a number of projects (e.g. HyFleet-Cute and Whistler project) giving valuable information on the reliability and maintenance requirements. In order to establish refuelling stations...... the permitting authorities request qualitative and quantitative risk assessments (QRA) to show the safety and acceptability in terms of failure frequencies and respective consequences. For new technologies not all statistical data might be established or are available in good quality causing assumptions...
CSIR Research Space (South Africa)
Bidgood, Peter M
2017-01-01
Full Text Available The estimation of balance uncertainty using conventional statistical and error propagation methods has been found to be both approximate and laborious to the point of being untenable. Direct Simulation by Monte Carlo (DSMC) has been shown...
Effects of uncertainty in model predictions of individual tree volume on large area volume estimates
Ronald E. McRoberts; James A. Westfall
2014-01-01
Forest inventory estimates of tree volume for large areas are typically calculated by adding model predictions of volumes for individual trees. However, the uncertainty in the model predictions is generally ignored with the result that the precision of the large area volume estimates is overestimated. The primary study objective was to estimate the effects of model...
Indian Academy of Sciences (India)
To reflect this uncertainty in the climate scenarios, the use of AOGCMs that explicitly simulate the carbon cycle and chemistry of all the substances are needed. The Hadley Centre has developed a version of the climate model that allows the effect of climate change on the carbon cycle and its feedback into climate, to be ...
International Nuclear Information System (INIS)
Silva, T.A. da
1988-01-01
The comparison between the uncertainty method recommended by International Atomic Energy Agency (IAEA) and the and the International Weight and Measure Commitee (CIPM) are showed, for the calibration of clinical dosimeters in the secondary standard Dosimetry Laboratory (SSDL). (C.G.C.) [pt
Effect of Uncertainties in Physical Property Estimates on Process Design - Sensitivity Analysis
DEFF Research Database (Denmark)
Hukkerikar, Amol; Jones, Mark Nicholas; Sin, Gürkan
for performing sensitivity of process design subject to uncertainties in the property estimates. To this end, first uncertainty analysis of the property models of pure components and their mixtures was performed in order to obtain the uncertainties in the estimated property values. As a next step, sensitivity......Chemical process design calculations require accurate and reliable physical and thermodynamic property data and property models of pure components and their mixtures in order to obtain reliable design parameters which help to achieve desired specifications. The uncertainties in the property values...... can arise from the experiments itself or from the property models employed. It is important to consider the effect of these uncertainties on the process design in order to assess the quality and reliability of the final design. The main objective of this work is to develop a systematic methodology...
Stream temperature estimated in situ from thermal-infrared images: best estimate and uncertainty
International Nuclear Information System (INIS)
Iezzi, F; Todisco, M T
2015-01-01
The paper aims to show a technique to estimate in situ the stream temperature from thermal-infrared images deepening its best estimate and uncertainty. Stream temperature is an important indicator of water quality and nowadays its assessment is important particularly for thermal pollution monitoring in water bodies. Stream temperature changes are especially due to the anthropogenic heat input from urban wastewater and from water used as a coolant by power plants and industrial manufacturers. The stream temperatures assessment using ordinary techniques (e.g. appropriate thermometers) is limited by sparse sampling in space due to a spatial discretization necessarily punctual. Latest and most advanced techniques assess the stream temperature using thermal-infrared remote sensing based on thermal imagers placed usually on aircrafts or using satellite images. These techniques assess only the surface water temperature and they are suitable to detect the temperature of vast water bodies but do not allow a detailed and precise surface water temperature assessment in limited areas of the water body. The technique shown in this research is based on the assessment of thermal-infrared images obtained in situ via portable thermal imager. As in all thermographic techniques, also in this technique, it is possible to estimate only the surface water temperature. A stream with the presence of a discharge of urban wastewater is proposed as case study to validate the technique and to show its application limits. Since the technique analyzes limited areas in extension of the water body, it allows a detailed and precise assessment of the water temperature. In general, the punctual and average stream temperatures are respectively uncorrected and corrected. An appropriate statistical method that minimizes the errors in the average stream temperature is proposed. The correct measurement of this temperature through the assessment of thermal- infrared images obtained in situ via portable
Directory of Open Access Journals (Sweden)
Eleanor S Devenish Nelson
Full Text Available BACKGROUND: Demographic models are widely used in conservation and management, and their parameterisation often relies on data collected for other purposes. When underlying data lack clear indications of associated uncertainty, modellers often fail to account for that uncertainty in model outputs, such as estimates of population growth. METHODOLOGY/PRINCIPAL FINDINGS: We applied a likelihood approach to infer uncertainty retrospectively from point estimates of vital rates. Combining this with resampling techniques and projection modelling, we show that confidence intervals for population growth estimates are easy to derive. We used similar techniques to examine the effects of sample size on uncertainty. Our approach is illustrated using data on the red fox, Vulpes vulpes, a predator of ecological and cultural importance, and the most widespread extant terrestrial mammal. We show that uncertainty surrounding estimated population growth rates can be high, even for relatively well-studied populations. Halving that uncertainty typically requires a quadrupling of sampling effort. CONCLUSIONS/SIGNIFICANCE: Our results compel caution when comparing demographic trends between populations without accounting for uncertainty. Our methods will be widely applicable to demographic studies of many species.
Debate on Uncertainty in Estimating Bathing Water Quality
DEFF Research Database (Denmark)
Larsen, Torben
1992-01-01
Estimating the bathing water quality along the shore near a planned sewage discharge requires data on the source strength of bacteria, the die-off of bacteria and the actual dilution of the sewage. Together these 3 factors give the actual concentration of bacteria on the interesting spots...
Directory of Open Access Journals (Sweden)
Dengsheng Lu
2012-01-01
Full Text Available Landsat Thematic mapper (TM image has long been the dominate data source, and recently LiDAR has offered an important new structural data stream for forest biomass estimations. On the other hand, forest biomass uncertainty analysis research has only recently obtained sufficient attention due to the difficulty in collecting reference data. This paper provides a brief overview of current forest biomass estimation methods using both TM and LiDAR data. A case study is then presented that demonstrates the forest biomass estimation methods and uncertainty analysis. Results indicate that Landsat TM data can provide adequate biomass estimates for secondary succession but are not suitable for mature forest biomass estimates due to data saturation problems. LiDAR can overcome TM’s shortcoming providing better biomass estimation performance but has not been extensively applied in practice due to data availability constraints. The uncertainty analysis indicates that various sources affect the performance of forest biomass/carbon estimation. With that said, the clear dominate sources of uncertainty are the variation of input sample plot data and data saturation problem related to optical sensors. A possible solution to increasing the confidence in forest biomass estimates is to integrate the strengths of multisensor data.
Entropy Evolution and Uncertainty Estimation with Dynamical Systems
Directory of Open Access Journals (Sweden)
X. San Liang
2014-06-01
Full Text Available This paper presents a comprehensive introduction and systematic derivation of the evolutionary equations for absolute entropy H and relative entropy D, some of which exist sporadically in the literature in different forms under different subjects, within the framework of dynamical systems. In general, both H and D are dissipated, and the dissipation bears a form reminiscent of the Fisher information; in the absence of stochasticity, dH/dt is connected to the rate of phase space expansion, and D stays invariant, i.e., the separation of two probability density functions is always conserved. These formulas are validated with linear systems, and put to application with the Lorenz system and a large-dimensional stochastic quasi-geostrophic flow problem. In the Lorenz case, H falls at a constant rate with time, implying that H will eventually become negative, a situation beyond the capability of the commonly used computational technique like coarse-graining and bin counting. For the stochastic flow problem, it is first reduced to a computationally tractable low-dimensional system, using a reduced model approach, and then handled through ensemble prediction. Both the Lorenz system and the stochastic flow system are examples of self-organization in the light of uncertainty reduction. The latter particularly shows that, sometimes stochasticity may actually enhance the self-organization process.
A determination of parton distributions with faithful uncertainty estimation
International Nuclear Information System (INIS)
Ball, Richard D.; Del Debbio, Luigi; Forte, Stefano; Guffanti, Alberto; Latorre, Jose I.; Piccione, Andrea; Rojo, Juan; Ubiali, Maria
2009-01-01
We present the determination of a set of parton distributions of the nucleon, at next-to-leading order, from a global set of deep-inelastic scattering data: NNPDF1.0. The determination is based on a Monte Carlo approach, with neural networks used as unbiased interpolants. This method, previously discussed by us and applied to a determination of the nonsinglet quark distribution, is designed to provide a faithful and statistically sound representation of the uncertainty on parton distributions. We discuss our dataset, its statistical features, and its Monte Carlo representation. We summarize the technique used to solve the evolution equations and its benchmarking, and the method used to compute physical observables. We discuss the parametrization and fitting of neural networks, and the algorithm used to determine the optimal fit. We finally present our set of parton distributions. We discuss its statistical properties, test for its stability upon various modifications of the fitting procedure, and compare it to other recent parton sets. We use it to compute the benchmark W and Z cross sections at the LHC. We discuss issues of delivery and interfacing to commonly used packages such as LHAPDF
ERROR BOUNDS FOR SURFACE AREA ESTIMATORS BASED ON CROFTON’S FORMULA
Directory of Open Access Journals (Sweden)
Markus Kiderlen
2011-05-01
Full Text Available According to Crofton's formula, the surface area S(A of a sufficiently regular compact set A in Rd is proportional to the mean of all total projections pA (u on a linear hyperplane with normal u, uniformly averaged over all unit vectors u. In applications, pA (u is only measured in k directions and the mean is approximated by a finite weighted sum bS(A of the total projections in these directions. The choice of the weights depends on the selected quadrature rule. We define an associated zonotope Z (depending only on the projection directions and the quadrature rule, and show that the relative error bS (A/S (A is bounded from below by the inradius of Z and from above by the circumradius of Z. Applying a strengthened isoperimetric inequality due to Bonnesen, we show that the rectangular quadrature rule does not give the best possible error bounds for d =2. In addition, we derive asymptotic behavior of the error (with increasing k in the planar case. The paper concludes with applications to surface area estimation in design-based digital stereology where we show that the weights due to Bonnesen's inequality are better than the usual weights based on the rectangular rule and almost optimal in the sense that the relative error of the surface area estimator is very close to the minimal error.
A statistical methodology for quantification of uncertainty in best estimate code physical models
International Nuclear Information System (INIS)
Vinai, Paolo; Macian-Juan, Rafael; Chawla, Rakesh
2007-01-01
A novel uncertainty assessment methodology, based on a statistical non-parametric approach, is presented in this paper. It achieves quantification of code physical model uncertainty by making use of model performance information obtained from studies of appropriate separate-effect tests. Uncertainties are quantified in the form of estimated probability density functions (pdf's), calculated with a newly developed non-parametric estimator. The new estimator objectively predicts the probability distribution of the model's 'error' (its uncertainty) from databases reflecting the model's accuracy on the basis of available experiments. The methodology is completed by applying a novel multi-dimensional clustering technique based on the comparison of model error samples with the Kruskall-Wallis test. This takes into account the fact that a model's uncertainty depends on system conditions, since a best estimate code can give predictions for which the accuracy is affected by the regions of the physical space in which the experiments occur. The final result is an objective, rigorous and accurate manner of assigning uncertainty to coded models, i.e. the input information needed by code uncertainty propagation methodologies used for assessing the accuracy of best estimate codes in nuclear systems analysis. The new methodology has been applied to the quantification of the uncertainty in the RETRAN-3D void model and then used in the analysis of an independent separate-effect experiment. This has clearly demonstrated the basic feasibility of the approach, as well as its advantages in yielding narrower uncertainty bands in quantifying the code's accuracy for void fraction predictions
Indirect methods of tree biomass estimation and their uncertainties ...
African Journals Online (AJOL)
Depending on data availability (dbh only or both dbh and total tree height) either of the models may be applied to generate satisfactory estimates of tree volume needed for planning and decision-making in management of mangrove forests. The study found an overall mean FF value of 0.65 ± 0.03 (SE), 0.56 ± 0.03 (SE) and ...
International Nuclear Information System (INIS)
Pourgol-Mohamad, Mohammad; Modarres, Mohammad; Mosleh, Ali
2013-01-01
This paper discusses an approach called Integrated Methodology for Thermal-Hydraulics Uncertainty Analysis (IMTHUA) to characterize and integrate a wide range of uncertainties associated with the best estimate models and complex system codes used for nuclear power plant safety analyses. Examples of applications include complex thermal hydraulic and fire analysis codes. In identifying and assessing uncertainties, the proposed methodology treats the complex code as a 'white box', thus explicitly treating internal sub-model uncertainties in addition to the uncertainties related to the inputs to the code. The methodology accounts for uncertainties related to experimental data used to develop such sub-models, and efficiently propagates all uncertainties during best estimate calculations. Uncertainties are formally analyzed and probabilistically treated using a Bayesian inference framework. This comprehensive approach presents the results in a form usable in most other safety analyses such as the probabilistic safety assessment. The code output results are further updated through additional Bayesian inference using any available experimental data, for example from thermal hydraulic integral test facilities. The approach includes provisions to account for uncertainties associated with user-specified options, for example for choices among alternative sub-models, or among several different correlations. Complex time-dependent best-estimate calculations are computationally intense. The paper presents approaches to minimize computational intensity during the uncertainty propagation. Finally, the paper will report effectiveness and practicality of the methodology with two applications to a complex thermal-hydraulics system code as well as a complex fire simulation code. In case of multiple alternative models, several techniques, including dynamic model switching, user-controlled model selection, and model mixing, are discussed. (authors)
International Nuclear Information System (INIS)
Miller, C.; Little, C.A.
1982-08-01
The purpose is to summarize estimates based on currently available data of the uncertainty associated with radiological assessment models. The models being examined herein are those recommended previously for use in breeder reactor assessments. Uncertainty estimates are presented for models of atmospheric and hydrologic transport, terrestrial and aquatic food-chain bioaccumulation, and internal and external dosimetry. Both long-term and short-term release conditions are discussed. The uncertainty estimates presented in this report indicate that, for many sites, generic models and representative parameter values may be used to calculate doses from annual average radionuclide releases when these calculated doses are on the order of one-tenth or less of a relevant dose limit. For short-term, accidental releases, especially those from breeder reactors located in sites dominated by complex terrain and/or coastal meteorology, the uncertainty in the dose calculations may be much larger than an order of magnitude. As a result, it may be necessary to incorporate site-specific information into the dose calculation under these circumstances to reduce this uncertainty. However, even using site-specific information, natural variability and the uncertainties in the dose conversion factor will likely result in an overall uncertainty of greater than an order of magnitude for predictions of dose or concentration in environmental media following shortterm releases
International Nuclear Information System (INIS)
Endah Damastuti; Muhayatun; Diah Dwiana L
2009-01-01
Beside to complished the requirements of international standard of ISO/IEC 17025:2005, uncertainty estimation should be done to increase quality and confidence of analysis results and also to establish traceability of the analysis results to SI unit. Neutron activation analysis is a major technique used by Radiometry technique analysis laboratory and is included as scope of accreditation under ISO/IEC 17025:2005, therefore uncertainty estimation of neutron activation analysis is needed to be carried out. Sample and standard preparation as well as, irradiation and measurement using gamma spectrometry were the main activities which could give contribution to uncertainty. The components of uncertainty sources were specifically explained. The result of expanded uncertainty was 4,0 mg/kg with level of confidence 95% (coverage factor=2) and Zn concentration was 25,1 mg/kg. Counting statistic of cuplikan and standard were the major contribution of combined uncertainty. The uncertainty estimation was expected to increase the quality of the analysis results and could be applied further to other kind of samples. (author)
International Nuclear Information System (INIS)
Koch, J.; Peterson, S-R.
1995-10-01
Models used to simulate environmental transfer of radionuclides typically include many parameters, the values of which are uncertain. An estimation of the uncertainty associated with the predictions is therefore essential. Difference methods to quantify the uncertainty in the prediction parameter uncertainties are reviewed. A statistical approach using random sampling techniques is recommended for complex models with many uncertain parameters. In this approach, the probability density function of the model output is obtained from multiple realizations of the model according to a multivariate random sample of the different input parameters. Sampling efficiency can be improved by using a stratified scheme (Latin Hypercube Sampling). Sample size can also be restricted when statistical tolerance limits needs to be estimated. Methods to rank parameters according to their contribution to uncertainty in the model prediction are also reviewed. Recommended are measures of sensitivity, correlation and regression coefficients that can be calculated on values of input and output variables generated during the propagation of uncertainties through the model. A parameter uncertainty analysis is performed for the CHERPAC food chain model which estimates subjective confidence limits and intervals on the predictions at a 95% confidence level. A sensitivity analysis is also carried out using partial rank correlation coefficients. This identified and ranks the parameters which are the main contributors to uncertainty in the predictions, thereby guiding further research efforts. (author). 44 refs., 2 tabs., 4 figs
Energy Technology Data Exchange (ETDEWEB)
Koch, J. [Israel Atomic Energy Commission, Yavne (Israel). Soreq Nuclear Research Center; Peterson, S-R.
1995-10-01
Models used to simulate environmental transfer of radionuclides typically include many parameters, the values of which are uncertain. An estimation of the uncertainty associated with the predictions is therefore essential. Difference methods to quantify the uncertainty in the prediction parameter uncertainties are reviewed. A statistical approach using random sampling techniques is recommended for complex models with many uncertain parameters. In this approach, the probability density function of the model output is obtained from multiple realizations of the model according to a multivariate random sample of the different input parameters. Sampling efficiency can be improved by using a stratified scheme (Latin Hypercube Sampling). Sample size can also be restricted when statistical tolerance limits needs to be estimated. Methods to rank parameters according to their contribution to uncertainty in the model prediction are also reviewed. Recommended are measures of sensitivity, correlation and regression coefficients that can be calculated on values of input and output variables generated during the propagation of uncertainties through the model. A parameter uncertainty analysis is performed for the CHERPAC food chain model which estimates subjective confidence limits and intervals on the predictions at a 95% confidence level. A sensitivity analysis is also carried out using partial rank correlation coefficients. This identified and ranks the parameters which are the main contributors to uncertainty in the predictions, thereby guiding further research efforts. (author). 44 refs., 2 tabs., 4 figs.
International Nuclear Information System (INIS)
Johnson, David R.; Willis, Henry H.; Curtright, Aimee E.; Samaras, Constantine; Skone, Timothy
2011-01-01
Before further investments are made in utilizing biomass as a source of renewable energy, both policy makers and the energy industry need estimates of the net greenhouse gas (GHG) reductions expected from substituting biobased fuels for fossil fuels. Such GHG reductions depend greatly on how the biomass is cultivated, transported, processed, and converted into fuel or electricity. Any policy aiming to reduce GHGs with biomass-based energy must account for uncertainties in emissions at each stage of production, or else it risks yielding marginal reductions, if any, while potentially imposing great costs. This paper provides a framework for incorporating uncertainty analysis specifically into estimates of the life cycle GHG emissions from the production of biomass. We outline the sources of uncertainty, discuss the implications of uncertainty and variability on the limits of life cycle assessment (LCA) models, and provide a guide for practitioners to best practices in modeling these uncertainties. The suite of techniques described herein can be used to improve the understanding and the representation of the uncertainties associated with emissions estimates, thus enabling improved decision making with respect to the use of biomass for energy and fuel production. -- Highlights: → We describe key model, scenario and data uncertainties in LCAs of biobased fuels. → System boundaries and allocation choices should be consistent with study goals. → Scenarios should be designed around policy levers that can be controlled. → We describe a new way to analyze the importance of covariance between inputs.
Estimation of uncertainty in pKa values determined by potentiometric titration.
Koort, Eve; Herodes, Koit; Pihl, Viljar; Leito, Ivo
2004-06-01
A procedure is presented for estimation of uncertainty in measurement of the pK(a) of a weak acid by potentiometric titration. The procedure is based on the ISO GUM. The core of the procedure is a mathematical model that involves 40 input parameters. A novel approach is used for taking into account the purity of the acid, the impurities are not treated as inert compounds only, their possible acidic dissociation is also taken into account. Application to an example of practical pK(a) determination is presented. Altogether 67 different sources of uncertainty are identified and quantified within the example. The relative importance of different uncertainty sources is discussed. The most important source of uncertainty (with the experimental set-up of the example) is the uncertainty of pH measurement followed by the accuracy of the burette and the uncertainty of weighing. The procedure gives uncertainty separately for each point of the titration curve. The uncertainty depends on the amount of titrant added, being lowest in the central part of the titration curve. The possibilities of reducing the uncertainty and interpreting the drift of the pK(a) values obtained from the same curve are discussed.
Performance bounds on micro-Doppler estimation and adaptive waveform design using OFDM signals
Sen, Satyabrata; Barhen, Jacob; Glover, Charles W.
2014-05-01
We analyze the performance of a wideband orthogonal frequency division multiplexing (OFDM) signal in estimating the micro-Doppler frequency of a target having multiple rotating scatterers (e.g., rotor blades of a helicopter, propellers of a submarine). The presence of rotating scatterers introduces Doppler frequency modulation in the received signal by generating sidebands about the transmitted frequencies. This is called the micro-Doppler effects. The use of a frequency-diverse OFDM signal in this context enables us to independently analyze the micro-Doppler characteristics with respect to a set of orthogonal subcarrier frequencies. Therefore, to characterize the accuracy of micro-Doppler frequency estimation, we compute the Craḿer-Rao Bound (CRB) on the angular-velocity estimate of the target while considering the scatterer responses as deterministic but unknown nuisance parameters. Additionally, to improve the accuracy of the estimation procedure, we formulate and solve an optimization problem by minimizing the CRB on the angular-velocity estimate with respect to the transmitting OFDM spectral coefficients. We present several numerical examples to demonstrate the CRB variations at different values of the signal-to-noise ratio (SNR) and the number of OFDM subcarriers. The CRB values not only decrease with the increase in the SNR values, but also reduce as we increase the number of subcarriers implying the significance of frequency-diverse OFDM waveforms. The improvement in estimation accuracy due to the adaptive waveform design is also numerically analyzed. Interestingly, we find that the relative decrease in the CRBs on the angular-velocity estimate is more pronounced for larger number of OFDM subcarriers.
Performance Bounds on Micro-Doppler Estimation and Adaptive Waveform Design Using OFDM Signals
Energy Technology Data Exchange (ETDEWEB)
Sen, Satyabrata [ORNL; Barhen, Jacob [ORNL; Glover, Charles Wayne [ORNL
2014-01-01
We analyze the performance of a wideband orthogonal frequency division multiplexing (OFDM) signal in estimating the micro-Doppler frequency of a target having multiple rotating scatterers (e.g., rotor blades of a helicopter, propellers of a submarine). The presence of rotating scatterers introduces Doppler frequency modulation in the received signal by generating sidebands about the transmitted frequencies. This is called the micro-Doppler effects. The use of a frequency-diverse OFDM signal in this context enables us to independently analyze the micro-Doppler characteristics with respect to a set of orthogonal subcarrier frequencies. Therefore, to characterize the accuracy of micro-Doppler frequency estimation, we compute the Cram er-Rao Bound (CRB) on the angular-velocity estimate of the target while considering the scatterer responses as deterministic but unknown nuisance parameters. Additionally, to improve the accuracy of the estimation procedure, we formulate and solve an optimization problem by minimizing the CRB on the angular-velocity estimate with respect to the transmitting OFDM spectral coefficients. We present several numerical examples to demonstrate the CRB variations at different values of the signal-to-noise ratio (SNR) and the number of OFDM subcarriers. The CRB values not only decrease with the increase in the SNR values, but also reduce as we increase the number of subcarriers implying the significance of frequency-diverse OFDM waveforms. The improvement in estimation accuracy due to the adaptive waveform design is also numerically analyzed. Interestingly, we find that the relative decrease in the CRBs on the angular-velocity estimate is more pronounced for larger number of OFDM subcarriers.
International Nuclear Information System (INIS)
Brechner, R.R.; D'Argenio, D.Z.; Dahalan, R.; Wolf, W.
1986-01-01
Nephrotoxicity remains a major limitation in the use of cisplatin [cis-diamminedichloroplatinum(II)]. Although several strategies are in use to limit this serious side effect, none is fully satisfactory. Classical pharmacokinetic studies of cisplatin have been based on blood and urine samples. As nephrotoxicity plays a significant role in the design of the therapeutic strategy, the kidneys should be considered as a separate state in any model formulated for ultimate control purposes. Previous studies of organ pharmacokinetics have relied on population measurements. The authors have developed an organ compartmental model from individual animal data obtained noninvasively. The eight-compartment model used to represent the distribution of cisplatin considers free and bound platinum in plasma, platinum in the erythrocytes, mobile and bound platinum in the kidneys, mobile and bound platinum in the tissues, and platinum in the urine. Data were collected from experiments with anesthetized female rats, after intravenous administration of [195mPt]cisplatin. Both arterial and bladder samples, and multiple images obtained with an Anger camera interfaced to a microcomputer were used. The model was estimated from individual data obtained after injection of a bolus of cisplatin (six animals). The model was validated by using it to predict data obtained from forcing the system with a different input function, a 0.5-h intravenous infusion (three animals). The results of this work show that it is possible to noninvasively study drug kinetics in organs that are not readily accessible to direct measurements in an individual, rather than relying on invasive measurements performed on a population.(ABSTRACT TRUNCATED AT 250 WORDS)
Applying the conservativeness principle to REDD to deal with the uncertainties of the estimates
International Nuclear Information System (INIS)
Grassi, Giacomo; Monni, Suvi; Achard, Frederic; Mollicone, Danilo; Federici, Sandro
2008-01-01
A common paradigm when the reduction of emissions from deforestations is estimated for the purpose of promoting it as a mitigation option in the context of the United Nations Framework Convention on Climate Change (UNFCCC) is that high uncertainties in input data-i.e., area change and C stock change/area-may seriously undermine the credibility of the estimates and therefore of reduced deforestation as a mitigation option. In this paper, we show how a series of concepts and methodological tools-already existing in UNFCCC decisions and IPCC guidance documents-may greatly help to deal with the uncertainties of the estimates of reduced emissions from deforestation
Applying the conservativeness principle to REDD to deal with the uncertainties of the estimates
Energy Technology Data Exchange (ETDEWEB)
Grassi, Giacomo; Monni, Suvi; Achard, Frederic [Institute for Environment and Sustainability, Joint Research Centre of the European Commission, I-21020 Ispra (Italy); Mollicone, Danilo [Department of Geography, University of Alcala de Henares, Madrid (Spain); Federici, Sandro
2008-07-15
A common paradigm when the reduction of emissions from deforestations is estimated for the purpose of promoting it as a mitigation option in the context of the United Nations Framework Convention on Climate Change (UNFCCC) is that high uncertainties in input data-i.e., area change and C stock change/area-may seriously undermine the credibility of the estimates and therefore of reduced deforestation as a mitigation option. In this paper, we show how a series of concepts and methodological tools-already existing in UNFCCC decisions and IPCC guidance documents-may greatly help to deal with the uncertainties of the estimates of reduced emissions from deforestation.
Lach, Zbigniew T.
2017-08-01
A possibility is shown of a non-disruptive estimation of chromatic dispersion in a fiber of an intensity modulation communication line under work conditions. Uncertainty of the chromatic dispersion estimates is analyzed and quantified with the use of confidence intervals.
Uncertainties in the Item Parameter Estimates and Robust Automated Test Assembly
Veldkamp, Bernard P.; Matteucci, Mariagiulia; de Jong, Martijn G.
2013-01-01
Item response theory parameters have to be estimated, and because of the estimation process, they do have uncertainty in them. In most large-scale testing programs, the parameters are stored in item banks, and automated test assembly algorithms are applied to assemble operational test forms. These algorithms treat item parameters as fixed values,…
Added Value of uncertainty Estimates of SOurce term and Meteorology (AVESOME)
DEFF Research Database (Denmark)
Sørensen, Jens Havskov; Schönfeldt, Fredrik; Sigg, Robert
In the early phase of a nuclear accident, two large sources of uncertainty exist: one related to the source term and one associated with the meteorological data. Operational methods are being developed in AVESOME for quantitative estimation of uncertainties in atmospheric dispersion prediction.......g. at national meteorological services, the proposed methodology is feasible for real-time use, thereby adding value to decision support. In the recent NKS-B projects MUD, FAUNA and MESO, the implications of meteorological uncertainties for nuclear emergency preparedness and management have been studied...... uncertainty in atmospheric dispersion model forecasting stemming from both the source term and the meteorological data is examined. Ways to implement the uncertainties of forecasting in DSSs, and the impacts on real-time emergency management are described. The proposed methodology allows for efficient real...
Estimate of the uncertainty in measurement for the determination of mercury in seafood by TDA AAS.
Torres, Daiane Placido; Olivares, Igor R B; Queiroz, Helena Müller
2015-01-01
An approach for the estimate of the uncertainty in measurement considering the individual sources related to the different steps of the method under evaluation as well as the uncertainties estimated from the validation data for the determination of mercury in seafood by using thermal decomposition/amalgamation atomic absorption spectrometry (TDA AAS) is proposed. The considered method has been fully optimized and validated in an official laboratory of the Ministry of Agriculture, Livestock and Food Supply of Brazil, in order to comply with national and international food regulations and quality assurance. The referred method has been accredited under the ISO/IEC 17025 norm since 2010. The approach of the present work in order to reach the aim of estimating of the uncertainty in measurement was based on six sources of uncertainty for mercury determination in seafood by TDA AAS, following the validation process, which were: Linear least square regression, Repeatability, Intermediate precision, Correction factor of the analytical curve, Sample mass, and Standard reference solution. Those that most influenced the uncertainty in measurement were sample weight, repeatability, intermediate precision and calibration curve. The obtained result for the estimate of uncertainty in measurement in the present work reached a value of 13.39%, which complies with the European Regulation EC 836/2011. This figure represents a very realistic estimate of the routine conditions, since it fairly encompasses the dispersion obtained from the value attributed to the sample and the value measured by the laboratory analysts. From this outcome, it is possible to infer that the validation data (based on calibration curve, recovery and precision), together with the variation on sample mass, can offer a proper estimate of uncertainty in measurement.
Uncertainty estimates for predictions of the impact of breeder-reactor radionuclide releases
International Nuclear Information System (INIS)
Miller, C.W.; Little, C.A.
1982-01-01
This paper summarizes estimates, compiled in a larger report, of the uncertainty associated with models and parameters used to assess the impact on man radionuclide releases to the environment by breeder reactor facilities. These estimates indicate that, for many sites, generic models and representative parameter values may reasonably be used to calculate doses from annual average radionuclide releases when these calculated doses are on the order of one-tenth or less of a relevant dose limit. For short-term, accidental releases, the uncertainty in the dose calculations may be much larger than an order of magnitude. As a result, it may be necessary to incorporate site-specific information into the dose calculation under such circumstances. However, even using site-specific information, inherent natural variability within human receptors, and the uncertainties in the dose conversion factor will likely result in an overall uncertainty of greater than an order of magnitude for predictions of dose following short-term releases
Evaluating uncertainty in 7Be-based soil erosion estimates: an experimental plot approach
Blake, Will; Taylor, Alex; Abdelli, Wahid; Gaspar, Leticia; Barri, Bashar Al; Ryken, Nick; Mabit, Lionel
2014-05-01
Soil erosion remains a major concern for the international community and there is a growing need to improve the sustainability of agriculture to support future food security. High resolution soil erosion data are a fundamental requirement for underpinning soil conservation and management strategies but representative data on soil erosion rates are difficult to achieve by conventional means without interfering with farming practice and hence compromising the representativeness of results. Fallout radionuclide (FRN) tracer technology offers a solution since FRN tracers are delivered to the soil surface by natural processes and, where irreversible binding can be demonstrated, redistributed in association with soil particles. While much work has demonstrated the potential of short-lived 7Be (half-life 53 days), particularly in quantification of short-term inter-rill erosion, less attention has focussed on sources of uncertainty in derived erosion measurements and sampling strategies to minimise these. This poster outlines and discusses potential sources of uncertainty in 7Be-based soil erosion estimates and the experimental design considerations taken to quantify these in the context of a plot-scale validation experiment. Traditionally, gamma counting statistics have been the main element of uncertainty propagated and reported but recent work has shown that other factors may be more important such as: (i) spatial variability in the relaxation mass depth that describes the shape of the 7Be depth distribution for an uneroded point; (ii) spatial variability in fallout (linked to rainfall patterns and shadowing) over both reference site and plot; (iii) particle size sorting effects; (iv) preferential mobility of fallout over active runoff contributing areas. To explore these aspects in more detail, a plot of 4 x 35 m was ploughed and tilled to create a bare, sloped soil surface at the beginning of winter 2013/2014 in southwest UK. The lower edge of the plot was bounded by
Uncertainty analysis for results of thermal hydraulic codes of best-estimate-type
International Nuclear Information System (INIS)
Alva N, J.
2010-01-01
In this thesis, some fundamental knowledge is presented about uncertainty analysis and about diverse methodologies applied in the study of nuclear power plant transient event analysis, particularly related to thermal hydraulics phenomena. These concepts and methodologies mentioned in this work come from a wide bibliographical research in the nuclear power subject. Methodologies for uncertainty analysis have been developed by quite diverse institutions, and they have been widely used worldwide for application to results from best-estimate-type computer codes in nuclear reactor thermal hydraulics and safety analysis. Also, the main uncertainty sources, types of uncertainties, and aspects related to best estimate modeling and methods are introduced. Once the main bases of uncertainty analysis have been set, and some of the known methodologies have been introduced, it is presented in detail the CSAU methodology, which will be applied in the analyses. The main objective of this thesis is to compare the results of an uncertainty and sensibility analysis by using the Response Surface Technique to the application of W ilks formula, apply through a loss coolant experiment and an event of rise in a BWR. Both techniques are options in the part of uncertainty and sensibility analysis of the CSAU methodology, which was developed for the analysis of transients and accidents at nuclear power plants, and it is the base of most of the methodologies used in licensing of nuclear power plants practically everywhere. Finally, the results of applying both techniques are compared and discussed. (Author)
Estimation of sampling error uncertainties in observed surface air temperature change in China
Hua, Wei; Shen, Samuel S. P.; Weithmann, Alexander; Wang, Huijun
2017-08-01
This study examines the sampling error uncertainties in the monthly surface air temperature (SAT) change in China over recent decades, focusing on the uncertainties of gridded data, national averages, and linear trends. Results indicate that large sampling error variances appear at the station-sparse area of northern and western China with the maximum value exceeding 2.0 K2 while small sampling error variances are found at the station-dense area of southern and eastern China with most grid values being less than 0.05 K2. In general, the negative temperature existed in each month prior to the 1980s, and a warming in temperature began thereafter, which accelerated in the early and mid-1990s. The increasing trend in the SAT series was observed for each month of the year with the largest temperature increase and highest uncertainty of 0.51 ± 0.29 K (10 year)-1 occurring in February and the weakest trend and smallest uncertainty of 0.13 ± 0.07 K (10 year)-1 in August. The sampling error uncertainties in the national average annual mean SAT series are not sufficiently large to alter the conclusion of the persistent warming in China. In addition, the sampling error uncertainties in the SAT series show a clear variation compared with other uncertainty estimation methods, which is a plausible reason for the inconsistent variations between our estimate and other studies during this period.
Statistical Methods for Estimating the Uncertainty in the Best Basis Inventories
International Nuclear Information System (INIS)
WILMARTH, S.R.
2000-01-01
This document describes the statistical methods used to determine sample-based uncertainty estimates for the Best Basis Inventory (BBI). For each waste phase, the equation for the inventory of an analyte in a tank is Inventory (Kg or Ci) = Concentration x Density x Waste Volume. the total inventory is the sum of the inventories in the different waste phases. Using tanks sample data: statistical methods are used to obtain estimates of the mean concentration of an analyte the density of the waste, and their standard deviations. The volumes of waste in the different phases, and their standard deviations, are estimated based on other types of data. The three estimates are multiplied to obtain the inventory estimate. The standard deviations are combined to obtain a standard deviation of the inventory. The uncertainty estimate for the Best Basis Inventory (BBI) is the approximate 95% confidence interval on the inventory
Sensitivity and uncertainty analysis for the annual phosphorus loss estimator model.
Bolster, Carl H; Vadas, Peter A
2013-07-01
Models are often used to predict phosphorus (P) loss from agricultural fields. Although it is commonly recognized that model predictions are inherently uncertain, few studies have addressed prediction uncertainties using P loss models. In this study we assessed the effect of model input error on predictions of annual P loss by the Annual P Loss Estimator (APLE) model. Our objectives were (i) to conduct a sensitivity analyses for all APLE input variables to determine which variables the model is most sensitive to, (ii) to determine whether the relatively easy-to-implement first-order approximation (FOA) method provides accurate estimates of model prediction uncertainties by comparing results with the more accurate Monte Carlo simulation (MCS) method, and (iii) to evaluate the performance of the APLE model against measured P loss data when uncertainties in model predictions and measured data are included. Our results showed that for low to moderate uncertainties in APLE input variables, the FOA method yields reasonable estimates of model prediction uncertainties, although for cases where manure solid content is between 14 and 17%, the FOA method may not be as accurate as the MCS method due to a discontinuity in the manure P loss component of APLE at a manure solid content of 15%. The estimated uncertainties in APLE predictions based on assumed errors in the input variables ranged from ±2 to 64% of the predicted value. Results from this study highlight the importance of including reasonable estimates of model uncertainty when using models to predict P loss. Copyright © by the American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America, Inc.
Schwabe, O.; Shehab, E.; Erkoyuncu, J.
2015-08-01
The lack of defensible methods for quantifying cost estimate uncertainty over the whole product life cycle of aerospace innovations such as propulsion systems or airframes poses a significant challenge to the creation of accurate and defensible cost estimates. Based on the axiomatic definition of uncertainty as the actual prediction error of the cost estimate, this paper provides a comprehensive overview of metrics used for the uncertainty quantification of cost estimates based on a literature review, an evaluation of publicly funded projects such as part of the CORDIS or Horizon 2020 programs, and an analysis of established approaches used by organizations such NASA, the U.S. Department of Defence, the ESA, and various commercial companies. The metrics are categorized based on their foundational character (foundations), their use in practice (state-of-practice), their availability for practice (state-of-art) and those suggested for future exploration (state-of-future). Insights gained were that a variety of uncertainty quantification metrics exist whose suitability depends on the volatility of available relevant information, as defined by technical and cost readiness level, and the number of whole product life cycle phases the estimate is intended to be valid for. Information volatility and number of whole product life cycle phases can hereby be considered as defining multi-dimensional probability fields admitting various uncertainty quantification metric families with identifiable thresholds for transitioning between them. The key research gaps identified were the lacking guidance grounded in theory for the selection of uncertainty quantification metrics and lacking practical alternatives to metrics based on the Central Limit Theorem. An innovative uncertainty quantification framework consisting of; a set-theory based typology, a data library, a classification system, and a corresponding input-output model are put forward to address this research gap as the basis
Arnaud, Patrick; Cantet, Philippe; Odry, Jean
2017-11-01
Flood frequency analyses (FFAs) are needed for flood risk management. Many methods exist ranging from classical purely statistical approaches to more complex approaches based on process simulation. The results of these methods are associated with uncertainties that are sometimes difficult to estimate due to the complexity of the approaches or the number of parameters, especially for process simulation. This is the case of the simulation-based FFA approach called SHYREG presented in this paper, in which a rainfall generator is coupled with a simple rainfall-runoff model in an attempt to estimate the uncertainties due to the estimation of the seven parameters needed to estimate flood frequencies. The six parameters of the rainfall generator are mean values, so their theoretical distribution is known and can be used to estimate the generator uncertainties. In contrast, the theoretical distribution of the single hydrological model parameter is unknown; consequently, a bootstrap method is applied to estimate the calibration uncertainties. The propagation of uncertainty from the rainfall generator to the hydrological model is also taken into account. This method is applied to 1112 basins throughout France. Uncertainties coming from the SHYREG method and from purely statistical approaches are compared, and the results are discussed according to the length of the recorded observations, basin size and basin location. Uncertainties of the SHYREG method decrease as the basin size increases or as the length of the recorded flow increases. Moreover, the results show that the confidence intervals of the SHYREG method are relatively small despite the complexity of the method and the number of parameters (seven). This is due to the stability of the parameters and takes into account the dependence of uncertainties due to the rainfall model and the hydrological calibration. Indeed, the uncertainties on the flow quantiles are on the same order of magnitude as those associated with
Review of best estimate plus uncertainty methods of thermal-hydraulic safety analysis
International Nuclear Information System (INIS)
Prosek, A.; Mavko, B.
2003-01-01
In 1988 United States Nuclear Regulatory Commission approved the revised rule on the acceptance of emergency core cooling system (ECCS) performance. Since that there has been significant interest in the development of codes and methodologies for best-estimate loss-of-coolant accident (LOCAs) analyses. Several new best estimate plus uncertainty methods (BEPUs) were developed in the world. The purpose of the paper is to review the developments in the direction of best estimate approaches with uncertainty quantification and to discuss the problems in practical applications of BEPU methods. In general, the licensee methods are following original methods. The study indicated that uncertainty analysis with random sampling of input parameters and the use of order statistics for desired tolerance limits of output parameters is today commonly accepted and mature approach. (author)
Rose, Kevin C.; Winslow, Luke A.; Read, Jordan S.; Read, Emily K.; Solomon, Christopher T.; Adrian, Rita; Hanson, Paul C.
2014-01-01
Diel changes in dissolved oxygen are often used to estimate gross primary production (GPP) and ecosystem respiration (ER) in aquatic ecosystems. Despite the widespread use of this approach to understand ecosystem metabolism, we are only beginning to understand the degree and underlying causes of uncertainty for metabolism model parameter estimates. Here, we present a novel approach to improve the precision and accuracy of ecosystem metabolism estimates by identifying physical metrics that indicate when metabolism estimates are highly uncertain. Using datasets from seventeen instrumented GLEON (Global Lake Ecological Observatory Network) lakes, we discovered that many physical characteristics correlated with uncertainty, including PAR (photosynthetically active radiation, 400-700 nm), daily variance in Schmidt stability, and wind speed. Low PAR was a consistent predictor of high variance in GPP model parameters, but also corresponded with low ER model parameter variance. We identified a threshold (30% of clear sky PAR) below which GPP parameter variance increased rapidly and was significantly greater in nearly all lakes compared with variance on days with PAR levels above this threshold. The relationship between daily variance in Schmidt stability and GPP model parameter variance depended on trophic status, whereas daily variance in Schmidt stability was consistently positively related to ER model parameter variance. Wind speeds in the range of ~0.8-3 m s–1 were consistent predictors of high variance for both GPP and ER model parameters, with greater uncertainty in eutrophic lakes. Our findings can be used to reduce ecosystem metabolism model parameter uncertainty and identify potential sources of that uncertainty.
Uncertainty analysis methods for estimation of reliability of passive system of VHTR
International Nuclear Information System (INIS)
Han, S.J.
2012-01-01
An estimation of reliability of passive system for the probabilistic safety assessment (PSA) of a very high temperature reactor (VHTR) is under development in Korea. The essential approach of this estimation is to measure the uncertainty of the system performance under a specific accident condition. The uncertainty propagation approach according to the simulation of phenomenological models (computer codes) is adopted as a typical method to estimate the uncertainty for this purpose. This presentation introduced the uncertainty propagation and discussed the related issues focusing on the propagation object and its surrogates. To achieve a sufficient level of depth of uncertainty results, the applicability of the propagation should be carefully reviewed. For an example study, Latin-hypercube sampling (LHS) method as a direct propagation was tested for a specific accident sequence of VHTR. The reactor cavity cooling system (RCCS) developed by KAERI was considered for this example study. This is an air-cooled type passive system that has no active components for its operation. The accident sequence is a low pressure conduction cooling (LPCC) accident that is considered as a design basis accident for the safety design of VHTR. This sequence is due to a large failure of the pressure boundary of the reactor system such as a guillotine break of coolant pipe lines. The presentation discussed the obtained insights (benefit and weakness) to apply an estimation of reliability of passive system
Directory of Open Access Journals (Sweden)
K. J. Franz
2011-11-01
Full Text Available The hydrologic community is generally moving towards the use of probabilistic estimates of streamflow, primarily through the implementation of Ensemble Streamflow Prediction (ESP systems, ensemble data assimilation methods, or multi-modeling platforms. However, evaluation of probabilistic outputs has not necessarily kept pace with ensemble generation. Much of the modeling community is still performing model evaluation using standard deterministic measures, such as error, correlation, or bias, typically applied to the ensemble mean or median. Probabilistic forecast verification methods have been well developed, particularly in the atmospheric sciences, yet few have been adopted for evaluating uncertainty estimates in hydrologic model simulations. In the current paper, we overview existing probabilistic forecast verification methods and apply the methods to evaluate and compare model ensembles produced from two different parameter uncertainty estimation methods: the Generalized Uncertainty Likelihood Estimator (GLUE, and the Shuffle Complex Evolution Metropolis (SCEM. Model ensembles are generated for the National Weather Service SACramento Soil Moisture Accounting (SAC-SMA model for 12 forecast basins located in the Southeastern United States. We evaluate the model ensembles using relevant metrics in the following categories: distribution, correlation, accuracy, conditional statistics, and categorical statistics. We show that the presented probabilistic metrics are easily adapted to model simulation ensembles and provide a robust analysis of model performance associated with parameter uncertainty. Application of these methods requires no information in addition to what is already available as part of traditional model validation methodology and considers the entire ensemble or uncertainty range in the approach.
Parameter estimation techniques and uncertainty in ground water flow model predictions
International Nuclear Information System (INIS)
Zimmerman, D.A.; Davis, P.A.
1990-01-01
Quantification of uncertainty in predictions of nuclear waste repository performance is a requirement of Nuclear Regulatory Commission regulations governing the licensing of proposed geologic repositories for high-level radioactive waste disposal. One of the major uncertainties in these predictions is in estimating the ground-water travel time of radionuclides migrating from the repository to the accessible environment. The cause of much of this uncertainty has been attributed to a lack of knowledge about the hydrogeologic properties that control the movement of radionuclides through the aquifers. A major reason for this lack of knowledge is the paucity of data that is typically available for characterizing complex ground-water flow systems. Because of this, considerable effort has been put into developing parameter estimation techniques that infer property values in regions where no measurements exist. Currently, no single technique has been shown to be superior or even consistently conservative with respect to predictions of ground-water travel time. This work was undertaken to compare a number of parameter estimation techniques and to evaluate how differences in the parameter estimates and the estimation errors are reflected in the behavior of the flow model predictions. That is, we wished to determine to what degree uncertainties in flow model predictions may be affected simply by the choice of parameter estimation technique used. 3 refs., 2 figs
Dengsheng Lu; Qi Chen; Guangxing Wang; Emilio Moran; Mateus Batistella; Maozhen Zhang; Gaia Vaglio Laurin; David Saah
2012-01-01
Landsat Thematic mapper (TM) image has long been the dominate data source, and recently LiDAR has offered an important new structural data stream for forest biomass estimations. On the other hand, forest biomass uncertainty analysis research has only recently obtained sufficient attention due to the difficulty in collecting reference data. This paper provides a brief overview of current forest biomass estimation methods using both TM and LiDAR data. A case study is then presented that demonst...
Estimation of uncertainty of measurements of 3D mechanisms after kinematic calibration
International Nuclear Information System (INIS)
Takamasu, K; Sato, O; Shimojima, K; Takahashi, S; Furutani, R
2005-01-01
Calibration methods for 3D mechanisms are necessary to use the mechanisms as coordinate measuring machines. The calibration method of coordinate measuring machine using artifacts, the artifact calibration method, is proposed in taking account of traceability of the mechanism. There are kinematic parameters and form-deviation parameters in geometric parameters for describing the forward kinematic of the mechanism. In this article, the estimation methods of uncertainties using the calibrated coordinate measuring machine after the calibration are formulated. Firstly, the calculation method which takes out the values of kinematic parameters using least squares method is formulated. Secondly, the estimation value of uncertainty of the measuring machine is calculated using the error propagation method
Berryman, James G
2011-04-01
Methods for computing Hashin-Shtrikman bounds and related self-consistent estimates of elastic constants for polycrystals composed of crystals having orthorhombic symmetry have been known for about three decades. However, these methods are underutilized, perhaps because of some perceived difficulties with implementing the necessary computational procedures. Several simplifications of these techniques are introduced, thereby reducing the overall computational burden, as well as the complications inherent in mapping out the Hashin-Shtrikman bounding curves. The self-consistent estimates of the effective elastic constants are very robust, involving a quickly converging iteration procedure. Once these self-consistent values are known, they may then be used to speed up the computations of the Hashin-Shtrikman bounds themselves. It is shown furthermore that the resulting orthorhombic polycrystal code can be used as well to compute both bounds and self-consistent estimates for polycrystals of higher-symmetry tetragonal, hexagonal, and cubic (but not trigonal) materials. The self-consistent results found this way are shown to be the same as those obtained using the earlier methods, specifically those methods designed specially for each individual symmetry type. But the Hashin-Shtrikman bounds found using the orthorhombic code are either the same or (more typically) tighter than those found previously for these special cases (i.e., tetragonal, hexagonal, and cubic). The improvement in the Hashin-Shtrikman bounds is presumably due to the additional degrees of freedom introduced into the available search space.
Energy Technology Data Exchange (ETDEWEB)
Berryman, J. G.
2011-02-01
Methods for computing Hashin-Shtrikman bounds and related self-consistent estimates of elastic constants for polycrystals composed of crystals having orthorhombic symmetry have been known for about three decades. However, these methods are underutilized, perhaps because of some perceived difficulties with implementing the necessary computational procedures. Several simplifications of these techniques are introduced, thereby reducing the overall computational burden, as well as the complications inherent in mapping out the Hashin-Shtrikman bounding curves. The self-consistent estimates of the effective elastic constants are very robust, involving a quickly converging iteration procedure. Once these self-consistent values are known, they may then be used to speed up the computations of the Hashin-Shtrikman bounds themselves. It is shown furthermore that the resulting orthorhombic polycrystal code can be used as well to compute both bounds and self-consistent estimates for polycrystals of higher-symmetry tetragonal, hexagonal, and cubic (but not trigonal) materials. The self-consistent results found this way are shown to be the same as those obtained using the earlier methods, specifically those methods designed specially for each individual symmetry type. But the Hashin-Shtrikman bounds found using the orthorhombic code are either the same or (more typically) tighter than those found previously for these special cases (i.e., tetragonal, hexagonal, and cubic). The improvement in the Hashin-Shtrikman bounds is presumably due to the additional degrees of freedom introduced into the available search space.
Wang, Z.
2015-12-01
For decades, distributed and lumped hydrological models have furthered our understanding of hydrological system. The development of hydrological simulation in large scale and high precision elaborated the spatial descriptions and hydrological behaviors. Meanwhile, the new trend is also followed by the increment of model complexity and number of parameters, which brings new challenges of uncertainty quantification. Generalized Likelihood Uncertainty Estimation (GLUE) has been widely used in uncertainty analysis for hydrological models referring to Monte Carlo method coupled with Bayesian estimation. However, the stochastic sampling method of prior parameters adopted by GLUE appears inefficient, especially in high dimensional parameter space. The heuristic optimization algorithms utilizing iterative evolution show better convergence speed and optimality-searching performance. In light of the features of heuristic optimization algorithms, this study adopted genetic algorithm, differential evolution, shuffled complex evolving algorithm to search the parameter space and obtain the parameter sets of large likelihoods. Based on the multi-algorithm sampling, hydrological model uncertainty analysis is conducted by the typical GLUE framework. To demonstrate the superiority of the new method, two hydrological models of different complexity are examined. The results shows the adaptive method tends to be efficient in sampling and effective in uncertainty analysis, providing an alternative path for uncertainty quantilization.
The use of multiwavelets for uncertainty estimation in seismic surface wave dispersion.
Energy Technology Data Exchange (ETDEWEB)
Poppeliers, Christian [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
2017-12-01
This report describes a new single-station analysis method to estimate the dispersion and uncer- tainty of seismic surface waves using the multiwavelet transform. Typically, when estimating the dispersion of a surface wave using only a single seismic station, the seismogram is decomposed into a series of narrow-band realizations using a bank of narrow-band filters. By then enveloping and normalizing the filtered seismograms and identifying the maximum power as a function of frequency, the group velocity can be estimated if the source-receiver distance is known. However, using the filter bank method, there is no robust way to estimate uncertainty. In this report, I in- troduce a new method of estimating the group velocity that includes an estimate of uncertainty. The method is similar to the conventional filter bank method, but uses a class of functions, called Slepian wavelets, to compute a series of wavelet transforms of the data. Each wavelet transform is mathematically similar to a filter bank, however, the time-frequency tradeoff is optimized. By taking multiple wavelet transforms, I form a population of dispersion estimates from which stan- dard statistical methods can be used to estimate uncertainty. I demonstrate the utility of this new method by applying it to synthetic data as well as ambient-noise surface-wave cross-correlelograms recorded by the University of Nevada Seismic Network.
Statistical characterization of roughness uncertainty and impact on wind resource estimation
Directory of Open Access Journals (Sweden)
M. Kelly
2017-04-01
Full Text Available In this work we relate uncertainty in background roughness length (z0 to uncertainty in wind speeds, where the latter are predicted at a wind farm location based on wind statistics observed at a different site. Sensitivity of predicted winds to roughness is derived analytically for the industry-standard European Wind Atlas method, which is based on the geostrophic drag law. We statistically consider roughness and its corresponding uncertainty, in terms of both z0 derived from measured wind speeds as well as that chosen in practice by wind engineers. We show the combined effect of roughness uncertainty arising from differing wind-observation and turbine-prediction sites; this is done for the case of roughness bias as well as for the general case. For estimation of uncertainty in annual energy production (AEP, we also develop a generalized analytical turbine power curve, from which we derive a relation between mean wind speed and AEP. Following our developments, we provide guidance on approximate roughness uncertainty magnitudes to be expected in industry practice, and we also find that sites with larger background roughness incur relatively larger uncertainties.
DEFF Research Database (Denmark)
Jones, Mark Nicholas; Hukkerikar, Amol; Sin, Gürkan
thermodynamic and thermo-physical models is critical to obtain a feasible and operable process design and many guidelines pertaining to this can be found in the literature. But even if appropriate models have been chosen, the user needs to keep in mind that these models contain uncertainties which may propagate...... through the calculation steps to such an extent that the final design might not be feasible or lead to poor performance. Therefore it is necessary to evaluate the sensitivity of process design to the uncertainties in property estimates obtained from thermo-physical property models. Uncertainty...... of the methodology is illustrated using a case study of extractive distillation in which acetone is separated from methanol using water as a solvent. Among others, the vapour pressure of acetone and water was found to be the most critical and even small uncertainties from -0.25 % to +0.75 % in vapour pressure data...
International Nuclear Information System (INIS)
Sathyabama, N.
2014-01-01
It is now widely recognized that, when all of the known or suspected components of errors have been evaluated and corrected, there still remains an uncertainty, that is, a doubt about how well the result of the measurement represents the value of the quantity being measured. Evaluation of measurement data - Guide to the expression of Uncertainty in Measurement (GUM) is a guidance document, the purpose of which is to promote full information on how uncertainty statements are arrived at and to provide a basis for the international comparison of measurement results. In this paper, uncertainty estimations following GUM guidelines have been made for the measured values of online thoron concentrations using Lucas scintillation cell to prove that the correction for disequilibrium between 220 Rn and 216 Po is significant in online 220 Rn measurements
Uncertainty in Population Estimates for Endangered Animals and Improving the Recovery Process
Directory of Open Access Journals (Sweden)
Janet L. Rachlow
2013-08-01
Full Text Available United States recovery plans contain biological information for a species listed under the Endangered Species Act and specify recovery criteria to provide basis for species recovery. The objective of our study was to evaluate whether recovery plans provide uncertainty (e.g., variance with estimates of population size. We reviewed all finalized recovery plans for listed terrestrial vertebrate species to record the following data: (1 if a current population size was given, (2 if a measure of uncertainty or variance was associated with current estimates of population size and (3 if population size was stipulated for recovery. We found that 59% of completed recovery plans specified a current population size, 14.5% specified a variance for the current population size estimate and 43% specified population size as a recovery criterion. More recent recovery plans reported more estimates of current population size, uncertainty and population size as a recovery criterion. Also, bird and mammal recovery plans reported more estimates of population size and uncertainty compared to reptiles and amphibians. We suggest the use of calculating minimum detectable differences to improve confidence when delisting endangered animals and we identified incentives for individuals to get involved in recovery planning to improve access to quantitative data.
Uncertainty of feedback and state estimation determines the speed of motor adaptation
Directory of Open Access Journals (Sweden)
Kunlin Wei
2010-05-01
Full Text Available Humans can adapt their motor behaviors to deal with ongoing changes. To achieve this, the nervous system needs to estimate central variables for our movement based on past knowledge and new feedback, both of which are uncertain. In the Bayesian framework, rates of adaptation characterize how noisy feedback is in comparison to the uncertainty of the state estimate. The predictions of Bayesian models are intuitive: the nervous system should adapt slower when sensory feedback is more noisy and faster when its state estimate is more uncertain. Here we want to quantitatively understand how uncertainty in these two factors affects motor adaptation. In a hand reaching experiment we measured trial-by-trial adaptation to a randomly changing visual perturbation to characterize the way the nervous system handles uncertainty in state estimation and feedback. We found both qualitative predictions of Bayesian models confirmed. Our study provides evidence that the nervous system represents and uses uncertainty in state estimate and feedback during motor adaptation.
Frisbee, Joseph H., Jr.
2015-01-01
Upper bounds on high speed satellite collision probability, PC †, have been investigated. Previous methods assume an individual position error covariance matrix is available for each object. The two matrices being combined into a single, relative position error covariance matrix. Components of the combined error covariance are then varied to obtain a maximum PC. If error covariance information for only one of the two objects was available, either some default shape has been used or nothing could be done. An alternative is presented that uses the known covariance information along with a critical value of the missing covariance to obtain an approximate but potentially useful Pc upper bound.
International Nuclear Information System (INIS)
Küng, Alain; Meli, Felix; Nicolet, Anaïs; Thalmann, Rudolf
2014-01-01
Tactile ultra-precise coordinate measuring machines (CMMs) are very attractive for accurately measuring optical components with high slopes, such as aspheres. The METAS µ-CMM, which exhibits a single point measurement repeatability of a few nanometres, is routinely used for measurement services of microparts, including optical lenses. However, estimating the measurement uncertainty is very demanding. Because of the many combined influencing factors, an analytic determination of the uncertainty of parameters that are obtained by numerical fitting of the measured surface points is almost impossible. The application of numerical simulation (Monte Carlo methods) using a parametric fitting algorithm coupled with a virtual CMM based on a realistic model of the machine errors offers an ideal solution to this complex problem: to each measurement data point, a simulated measurement variation calculated from the numerical model of the METAS µ-CMM is added. Repeated several hundred times, these virtual measurements deliver the statistical data for calculating the probability density function, and thus the measurement uncertainty for each parameter. Additionally, the eventual cross-correlation between parameters can be analyzed. This method can be applied for the calibration and uncertainty estimation of any parameter of the equation representing a geometric element. In this article, we present the numerical simulation model of the METAS µ-CMM and the application of a Monte Carlo method for the uncertainty estimation of measured asphere parameters. (paper)
Energy Technology Data Exchange (ETDEWEB)
Heath, Garvin [Joint Inst. for Strategic Energy Analysis, Golden, CO (United States); Warner, Ethan [Joint Inst. for Strategic Energy Analysis, Golden, CO (United States); Steinberg, Daniel [Joint Inst. for Strategic Energy Analysis, Golden, CO (United States); Brandt, Adam [Stanford Univ., CA (United States)
2015-08-01
A growing number of studies have raised questions regarding uncertainties in our understanding of methane (CH_{4}) emissions from fugitives and venting along the natural gas (NG) supply chain. In particular, a number of measurement studies have suggested that actual levels of CH_{4} emissions may be higher than estimated by EPA" tm s U.S. GHG Emission Inventory. We reviewed the literature to identify the growing number of studies that have raised questions regarding uncertainties in our understanding of methane (CH_{4}) emissions from fugitives and venting along the natural gas (NG) supply chain.
A Best-Estimate Reactor Core Monitor Using State Feedback Strategies to Reduce Uncertainties
International Nuclear Information System (INIS)
Martin, Robert P.; Edwards, Robert M.
2000-01-01
The development and demonstration of a new algorithm to reduce modeling and state-estimation uncertainty in best-estimate simulation codes has been investigated. Demonstration is given by way of a prototype reactor core monitor. The architecture of this monitor integrates a control-theory-based, distributed-parameter estimation technique into a production-grade best-estimate simulation code. The Kalman Filter-Sequential Least-Squares (KFSLS) parameter estimation algorithm has been extended for application into the computational environment of the best-estimate simulation code RELAP5-3D. In control system terminology, this configuration can be thought of as a 'best-estimate' observer. The application to a distributed-parameter reactor system involves a unique modal model that approximates physical components, such as the reactor, by describing both states and parameters by an orthogonal expansion. The basic KFSLS parameter estimation is used to dynamically refine a spatially varying (distributed) parameter. The application of the distributed-parameter estimator is expected to complement a traditional nonlinear best-estimate simulation code by providing a mechanism for reducing both code input (modeling) and output (state-estimation) uncertainty in complex, distributed-parameter systems
An estimation of uncertainties in containment P/T analysis using CONTEMPT/LT code
International Nuclear Information System (INIS)
Kang, Y.M.; Park, G.C.; Lee, U.C.; Kang, C.S.
1991-01-01
In a nuclear power plant, the containment design pressure and temperature (P/T) have been established based on the unrealistic conservatism with suffering from a drawback in the economics. Thus, it is necessary that the uncertainties of design P/T values have to be well defined through an extensive uncertainty analysis with plant-specific input data and or models used in the computer code. This study is to estimate plant-specific uncertainties of containment design P/T using the Monte Carlo method in Kori-3 reactor. Kori-3 plant parameters and Uchida heat transfer coefficient are selected to be treated statistically after the sensitivity study. The Monte Carlo analysis has performed based on the response surface method with the CONTEMPT/LT code and Latin Hypercube sampling technique. Finally, the design values based on 95 %/95 % probability are compared with worst estimated values to assess the design margin. (author)
Directory of Open Access Journals (Sweden)
Adamczak Stanisław
2014-08-01
Full Text Available The aim of this study was to estimate the measurement uncertainty for a material produced by additive manufacturing. The material investigated was FullCure 720 photocured resin, which was applied to fabricate tensile specimens with a Connex 350 3D printer based on PolyJet technology. The tensile strength of the specimens established through static tensile testing was used to determine the measurement uncertainty. There is a need for extensive research into the performance of model materials obtained via 3D printing as they have not been studied sufficiently like metal alloys or plastics, the most common structural materials. In this analysis, the measurement uncertainty was estimated using a larger number of samples than usual, i.e., thirty instead of typical ten. The results can be very useful to engineers who design models and finished products using this material. The investigations also show how wide the scatter of results is.
Energy Technology Data Exchange (ETDEWEB)
Broquet, G.; Chevallier, F.; Breon, F.M.; Yver, C.; Ciais, P.; Ramonet, M.; Schmidt, M. [Laboratoire des Sciences du Climat et de l' Environnement, CEA-CNRS-UVSQ, UMR8212, IPSL, Gif-sur-Yvette (France); Alemanno, M. [Servizio Meteorologico dell' Aeronautica Militare Italiana, Centro Aeronautica Militare di Montagna, Monte Cimone/Sestola (Italy); Apadula, F. [Research on Energy Systems, RSE, Environment and Sustainable Development Department, Milano (Italy); Hammer, S. [Universitaet Heidelberg, Institut fuer Umweltphysik, Heidelberg (Germany); Haszpra, L. [Hungarian Meteorological Service, Budapest (Hungary); Meinhardt, F. [Federal Environmental Agency, Kirchzarten (Germany); Necki, J. [AGH University of Science and Technology, Krakow (Poland); Piacentino, S. [ENEA, Laboratory for Earth Observations and Analyses, Palermo (Italy); Thompson, R.L. [Max Planck Institute for Biogeochemistry, Jena (Germany); Vermeulen, A.T. [Energy research Centre of the Netherlands ECN, EEE-EA, Petten (Netherlands)
2013-07-01
The Bayesian framework of CO2 flux inversions permits estimates of the retrieved flux uncertainties. Here, the reliability of these theoretical estimates is studied through a comparison against the misfits between the inverted fluxes and independent measurements of the CO2 Net Ecosystem Exchange (NEE) made by the eddy covariance technique at local (few hectares) scale. Regional inversions at 0.5{sup 0} resolution are applied for the western European domain where {approx}50 eddy covariance sites are operated. These inversions are conducted for the period 2002-2007. They use a mesoscale atmospheric transport model, a prior estimate of the NEE from a terrestrial ecosystem model and rely on the variational assimilation of in situ continuous measurements of CO2 atmospheric mole fractions. Averaged over monthly periods and over the whole domain, the misfits are in good agreement with the theoretical uncertainties for prior and inverted NEE, and pass the chi-square test for the variance at the 30% and 5% significance levels respectively, despite the scale mismatch and the independence between the prior (respectively inverted) NEE and the flux measurements. The theoretical uncertainty reduction for the monthly NEE at the measurement sites is 53% while the inversion decreases the standard deviation of the misfits by 38 %. These results build confidence in the NEE estimates at the European/monthly scales and in their theoretical uncertainty from the regional inverse modelling system. However, the uncertainties at the monthly (respectively annual) scale remain larger than the amplitude of the inter-annual variability of monthly (respectively annual) fluxes, so that this study does not engender confidence in the inter-annual variations. The uncertainties at the monthly scale are significantly smaller than the seasonal variations. The seasonal cycle of the inverted fluxes is thus reliable. In particular, the CO2 sink period over the European continent likely ends later than
Balancing uncertainty of context in ERP project estimation: an approach and a case study
Daneva, Maia
2010-01-01
The increasing demand for Enterprise Resource Planning (ERP) solutions as well as the high rates of troubled ERP implementations and outright cancellations calls for developing effort estimation practices to systematically deal with uncertainties in ERP projects. This paper describes an approach -
Reducing uncertainty of Monte Carlo estimated fatigue damage in offshore wind turbines using FORM
DEFF Research Database (Denmark)
H. Horn, Jan-Tore; Jensen, Jørgen Juncher
2016-01-01
Uncertainties related to fatigue damage estimation of non-linear systems are highly dependent on the tail behaviour and extreme values of the stress range distribution. By using a combination of the First Order Reliability Method (FORM) and Monte Carlo simulations (MCS), the accuracy of the fatigue...
Uncertainty in reliability estimation : when do we know everything we know?
Houben, M.J.H.A.; Sonnemans, P.J.M.; Newby, M.J.; Bris, R.; Guedes Soares, C.; Martorell, S.
2009-01-01
In this paperwe demonstrate the use of an adapted GroundedTheory approach through interviews and their analysis to determine explicit uncertainty (known unknowns) for reliability estimation in the early phases of product development.We have applied the adapted Grounded Theory approach in a case
International Nuclear Information System (INIS)
DeMuth, S.
1998-01-01
Enhanced Sludge Washing (ESW) has been selected to reduce the amount of sludge-based underground storage tank (UST) high-level waste at the Hanford site. During the past several years, studies have been conducted to determine the cost savings derived from the implementation of ESW. The tank waste inventory and ESW performance continues to be revised as characterization and development efforts advance. This study provides a new cost savings estimate based upon the most recent inventory and ESW performance revisions, and includes an estimate of the associated cost uncertainty. Whereas the author's previous cost savings estimates for ESW were compared against no sludge washing, this study assumes the baseline to be simple water washing which more accurately reflects the retrieval activity along. The revised ESW cost savings estimate for all UST waste at Hanford is $6.1 B ± $1.3 B within 95% confidence. This is based upon capital and operating cost savings, but does not include development costs. The development costs are assumed negligible since they should be at least an order of magnitude less than the savings. The overall cost savings uncertainty was derived from process performance uncertainties and baseline remediation cost uncertainties, as determined by the author's engineering judgment
Measuring Cross-Section and Estimating Uncertainties with the fissionTPC
Energy Technology Data Exchange (ETDEWEB)
Bowden, N. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Manning, B. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Sangiorgio, S. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Seilhan, B. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
2015-01-30
The purpose of this document is to outline the prescription for measuring fission cross-sections with the NIFFTE fissionTPC and estimating the associated uncertainties. As such it will serve as a work planning guide for NIFFTE collaboration members and facilitate clear communication of the procedures used to the broader community.
Van Uffelen, Lora J; Nosal, Eva-Marie; Howe, Bruce M; Carter, Glenn S; Worcester, Peter F; Dzieciuch, Matthew A; Heaney, Kevin D; Campbell, Richard L; Cross, Patrick S
2013-10-01
Four acoustic Seagliders were deployed in the Philippine Sea November 2010 to April 2011 in the vicinity of an acoustic tomography array. The gliders recorded over 2000 broadband transmissions at ranges up to 700 km from moored acoustic sources as they transited between mooring sites. The precision of glider positioning at the time of acoustic reception is important to resolve the fundamental ambiguity between position and sound speed. The Seagliders utilized GPS at the surface and a kinematic model below for positioning. The gliders were typically underwater for about 6.4 h, diving to depths of 1000 m and traveling on average 3.6 km during a dive. Measured acoustic arrival peaks were unambiguously associated with predicted ray arrivals. Statistics of travel-time offsets between received arrivals and acoustic predictions were used to estimate range uncertainty. Range (travel time) uncertainty between the source and the glider position from the kinematic model is estimated to be 639 m (426 ms) rms. Least-squares solutions for glider position estimated from acoustically derived ranges from 5 sources differed by 914 m rms from modeled positions, with estimated uncertainty of 106 m rms in horizontal position. Error analysis included 70 ms rms of uncertainty due to oceanic sound-speed variability.
Sebacher, B.; Hanea, R.G.; Heemink, A.
2013-01-01
In the past years, many applications of historymatching methods in general and ensemble Kalman filter in particular have been proposed, especially in order to estimate fields that provide uncertainty in the stochastic process defined by the dynamical system of hydrocarbon recovery. Such fields can
A super-resolution approach for uncertainty estimation of PIV measurements
Sciacchitano, A.; Wieneke, B.; Scarano, F.
2012-01-01
A super-resolution approach is proposed for the a posteriori uncertainty estimation of PIV measurements. The measured velocity field is employed to determine the displacement of individual particle images. A disparity set is built from the residual distance between paired particle images of
International Nuclear Information System (INIS)
Burr, T.; Croft, S.; Krieger, T.; Martin, K.; Norman, C.; Walsh, S.
2016-01-01
One example of top-down uncertainty quantification (UQ) involves comparing two or more measurements on each of multiple items. One example of bottom-up UQ expresses a measurement result as a function of one or more input variables that have associated errors, such as a measured count rate, which individually (or collectively) can be evaluated for impact on the uncertainty in the resulting measured value. In practice, it is often found that top-down UQ exhibits larger error variances than bottom-up UQ, because some error sources are present in the fielded assay methods used in top-down UQ that are not present (or not recognized) in the assay studies used in bottom-up UQ. One would like better consistency between the two approaches in order to claim understanding of the measurement process. The purpose of this paper is to refine bottom-up uncertainty estimation by using calibration information so that if there are no unknown error sources, the refined bottom-up uncertainty estimate will agree with the top-down uncertainty estimate to within a specified tolerance. Then, in practice, if the top-down uncertainty estimate is larger than the refined bottom-up uncertainty estimate by more than the specified tolerance, there must be omitted sources of error beyond those predicted from calibration uncertainty. The paper develops a refined bottom-up uncertainty approach for four cases of simple linear calibration: (1) inverse regression with negligible error in predictors, (2) inverse regression with non-negligible error in predictors, (3) classical regression followed by inversion with negligible error in predictors, and (4) classical regression followed by inversion with non-negligible errors in predictors. Our illustrations are of general interest, but are drawn from our experience with nuclear material assay by non-destructive assay. The main example we use is gamma spectroscopy that applies the enrichment meter principle. Previous papers that ignore error in predictors
Directory of Open Access Journals (Sweden)
Robert J. Andres
2014-07-01
Full Text Available Three uncertainty assessments associated with the global total of carbon dioxide emitted from fossil fuel use and cement production are presented. Each assessment has its own strengths and weaknesses and none give a full uncertainty assessment of the emission estimates. This approach grew out of the lack of independent measurements at the spatial and temporal scales of interest. Issues of dependent and independent data are considered as well as the temporal and spatial relationships of the data. The result is a multifaceted examination of the uncertainty associated with fossil fuel carbon dioxide emission estimates. The three assessments collectively give a range that spans from 1.0 to 13% (2 σ. Greatly simplifying the assessments give a global fossil fuel carbon dioxide uncertainty value of 8.4% (2 σ. In the largest context presented, the determination of fossil fuel emission uncertainty is important for a better understanding of the global carbon cycle and its implications for the physical, economic and political world.
Estimating and managing uncertainties in order to detect terrestrial greenhouse gas removals
International Nuclear Information System (INIS)
Rypdal, Kristin; Baritz, Rainer
2002-01-01
Inventories of emissions and removals of greenhouse gases will be used under the United Nations Framework Convention on Climate Change and the Kyoto Protocol to demonstrate compliance with obligations. During the negotiation process of the Kyoto Protocol it has been a concern that uptake of carbon in forest sinks can be difficult to verify. The reason for large uncertainties are high temporal and spatial variability and lack of representative estimation parameters. Additional uncertainties will be a consequence of definitions made in the Kyoto Protocol reporting. In the Nordic countries the national forest inventories will be very useful to estimate changes in carbon stocks. The main uncertainty lies in the conversion from changes in tradable timber to changes in total carbon biomass. The uncertainties in the emissions of the non-CO 2 carbon from forest soils are particularly high. On the other hand the removals reported under the Kyoto Protocol will only be a fraction of the total uptake and are not expected to constitute a high share of the total inventory. It is also expected that the Nordic countries will be able to implement a high tier methodology. As a consequence total uncertainties may not be extremely high. (Author)
Uncertainty estimation of Intensity-Duration-Frequency relationships: A regional analysis
Mélèse, Victor; Blanchet, Juliette; Molinié, Gilles
2018-03-01
We propose in this article a regional study of uncertainties in IDF curves derived from point-rainfall maxima. We develop two generalized extreme value models based on the simple scaling assumption, first in the frequentist framework and second in the Bayesian framework. Within the frequentist framework, uncertainties are obtained i) from the Gaussian density stemming from the asymptotic normality theorem of the maximum likelihood and ii) with a bootstrap procedure. Within the Bayesian framework, uncertainties are obtained from the posterior densities. We confront these two frameworks on the same database covering a large region of 100, 000 km2 in southern France with contrasted rainfall regime, in order to be able to draw conclusion that are not specific to the data. The two frameworks are applied to 405 hourly stations with data back to the 1980's, accumulated in the range 3 h-120 h. We show that i) the Bayesian framework is more robust than the frequentist one to the starting point of the estimation procedure, ii) the posterior and the bootstrap densities are able to better adjust uncertainty estimation to the data than the Gaussian density, and iii) the bootstrap density give unreasonable confidence intervals, in particular for return levels associated to large return period. Therefore our recommendation goes towards the use of the Bayesian framework to compute uncertainty.
Estimation of Uncertainty in Aerosol Concentration Measured by Aerosol Sampling System
Energy Technology Data Exchange (ETDEWEB)
Lee, Jong Chan; Song, Yong Jae; Jung, Woo Young; Lee, Hyun Chul; Kim, Gyu Tae; Lee, Doo Yong [FNC Technology Co., Yongin (Korea, Republic of)
2016-10-15
FNC Technology Co., Ltd has been developed test facilities for the aerosol generation, mixing, sampling and measurement under high pressure and high temperature conditions. The aerosol generation system is connected to the aerosol mixing system which injects SiO{sub 2}/ethanol mixture. In the sampling system, glass fiber membrane filter has been used to measure average mass concentration. Based on the experimental results using main carrier gas of steam and air mixture, the uncertainty estimation of the sampled aerosol concentration was performed by applying Gaussian error propagation law. FNC Technology Co., Ltd. has been developed the experimental facilities for the aerosol measurement under high pressure and high temperature. The purpose of the tests is to develop commercial test module for aerosol generation, mixing and sampling system applicable to environmental industry and safety related system in nuclear power plant. For the uncertainty calculation of aerosol concentration, the value of the sampled aerosol concentration is not measured directly, but must be calculated from other quantities. The uncertainty of the sampled aerosol concentration is a function of flow rates of air and steam, sampled mass, sampling time, condensed steam mass and its absolute errors. These variables propagate to the combination of variables in the function. Using operating parameters and its single errors from the aerosol test cases performed at FNC, the uncertainty of aerosol concentration evaluated by Gaussian error propagation law is less than 1%. The results of uncertainty estimation in the aerosol sampling system will be utilized for the system performance data.
Estimating and managing uncertainties in order to detect terrestrial greenhouse gas removals
Energy Technology Data Exchange (ETDEWEB)
Rypdal, Kristin; Baritz, Rainer
2002-07-01
Inventories of emissions and removals of greenhouse gases will be used under the United Nations Framework Convention on Climate Change and the Kyoto Protocol to demonstrate compliance with obligations. During the negotiation process of the Kyoto Protocol it has been a concern that uptake of carbon in forest sinks can be difficult to verify. The reason for large uncertainties are high temporal and spatial variability and lack of representative estimation parameters. Additional uncertainties will be a consequence of definitions made in the Kyoto Protocol reporting. In the Nordic countries the national forest inventories will be very useful to estimate changes in carbon stocks. The main uncertainty lies in the conversion from changes in tradable timber to changes in total carbon biomass. The uncertainties in the emissions of the non-CO{sub 2} carbon from forest soils are particularly high. On the other hand the removals reported under the Kyoto Protocol will only be a fraction of the total uptake and are not expected to constitute a high share of the total inventory. It is also expected that the Nordic countries will be able to implement a high tier methodology. As a consequence total uncertainties may not be extremely high. (Author)
A Scalable GVT Estimation Algorithm for PDES: Using Lower Bound of Event-Bulk-Time
Directory of Open Access Journals (Sweden)
Yong Peng
2015-01-01
Full Text Available Global Virtual Time computation of Parallel Discrete Event Simulation is crucial for conducting fossil collection and detecting the termination of simulation. The triggering condition of GVT computation in typical approaches is generally based on the wall-clock time or logical time intervals. However, the GVT value depends on the timestamps of events rather than the wall-clock time or logical time intervals. Therefore, it is difficult for the existing approaches to select appropriate time intervals to compute the GVT value. In this study, we propose a scalable GVT estimation algorithm based on Lower Bound of Event-Bulk-Time, which triggers the computation of the GVT value according to the number of processed events. In order to calculate the number of transient messages, our algorithm employs Event-Bulk to record the messages sent and received by Logical Processes. To eliminate the performance bottleneck, we adopt an overlapping computation approach to distribute the workload of GVT computation to all worker-threads. We compare our algorithm with the fast asynchronous GVT algorithm using PHOLD benchmark on the shared memory machine. Experimental results indicate that our algorithm has a light overhead and shows higher speedup and accuracy of GVT computation than the fast asynchronous GVT algorithm.
Uncertainty estimation of the velocity model for stations of the TrigNet GPS network
Hackl, M.; Malservisi, R.; Hugentobler, U.
2010-12-01
Satellite based geodetic techniques - above all GPS - provide an outstanding tool to measure crustal motions. They are widely used to derive geodetic velocity models that are applied in geodynamics to determine rotations of tectonic blocks, to localize active geological features, and to estimate rheological properties of the crust and the underlying asthenosphere. However, it is not a trivial task to derive GPS velocities and their uncertainties from positioning time series. In general time series are assumed to be represented by linear models (sometimes offsets, annual, and semi-annual signals are included) and noise. It has been shown that error models accounting only for white noise tend to underestimate the uncertainties of rates derived from long time series and that different colored noise components (flicker noise, random walk, etc.) need to be considered. However, a thorough error analysis including power spectra analyses and maximum likelihood estimates is computationally expensive and is usually not carried out for every site, but the uncertainties are scaled by latitude dependent factors. Analyses of the South Africa continuous GPS network TrigNet indicate that the scaled uncertainties overestimate the velocity errors. So we applied a method similar to the Allan Variance that is commonly used in the estimation of clock uncertainties and is able to account for time dependent probability density functions (colored noise) to the TrigNet time series. Comparisons with synthetic data show that the noise can be represented quite well by a power law model in combination with a seasonal signal in agreement with previous studies, which allows for a reliable estimation of the velocity error. Finally, we compared these estimates to the results obtained by spectral analyses using CATS. Small differences may originate from non-normal distribution of the noise.
Ward, E. J.; Bell, D. M.; Clark, J. S.; Kim, H.; Oren, R.
2009-12-01
Thermal dissipation probes (TDPs) are a common method for estimating forest transpiration and canopy conductance from sap flux rates in trees, but their implementation is plagued by uncertainties arising from missing data and variability in the diameter and canopy position of trees, as well as sapwood conductivity within individual trees. Uncertainties in estimates of canopy conductance also translate into uncertainties in carbon assimilation in models such as the Canopy Conductance Constrained Carbon Assimilation (4CA) model that combine physiological and environmental data to estimate photosynthetic rates. We developed a method to propagate these uncertainties in the scaling and imputation of TDP data to estimates of canopy transpiration and conductance using a state-space Jarvis-type conductance model in a hierarchical Bayesian framework. This presentation will focus on the impact of these uncertainties on estimates of water and carbon fluxes using 4CA and data from the Duke Free Air Carbon Enrichment (FACE) project, which incorporates both elevated carbon dioxide and soil nitrogen treatments. We will also address the response of canopy conductance to vapor pressure deficit, incident radiation and soil moisture, as well as the effect of treatment-related stand structure differences in scaling TDP measurements. Preliminary results indicate that in 2006, a year of normal precipitation (1127 mm), canopy transpiration increased in elevated carbon dioxide ~8% on a ground area basis. In 2007, a year with a pronounced drought (800 mm precipitation), this increase was only present in the combined carbon dioxide and fertilization treatment. The seasonal dynamics of water and carbon fluxes will be discussed in detail.
Energy Technology Data Exchange (ETDEWEB)
Lee, Kyung Hoon; Park, Ho Jin; Lee, Chung Chan; Cho, Jin Young [Korea Atomic Energy Research Institute, Daejeon (Korea, Republic of)
2015-10-15
The purpose of this paper is to study the effect on output parameters in the lattice physics calculation due to the last input uncertainty such as manufacturing deviations from nominal value for material composition and geometric dimensions. In a nuclear design and analysis, the lattice physics calculations are usually employed to generate lattice parameters for the nodal core simulation and pin power reconstruction. These lattice parameters which consist of homogenized few-group cross-sections, assembly discontinuity factors, and form-functions can be affected by input uncertainties which arise from three different sources: 1) multi-group cross-section uncertainties, 2) the uncertainties associated with methods and modeling approximations utilized in lattice physics codes, and 3) fuel/assembly manufacturing uncertainties. In this paper, data provided by the light water reactor (LWR) uncertainty analysis in modeling (UAM) benchmark has been used as the manufacturing uncertainties. First, the effect of each input parameter has been investigated through sensitivity calculations at the fuel assembly level. Then, uncertainty in prediction of peaking factor due to the most sensitive input parameter has been estimated using the statistical sampling method, often called the brute force method. For our analysis, the two-dimensional transport lattice code DeCART2D and its ENDF/B-VII.1 based 47-group library were used to perform the lattice physics calculation. Sensitivity calculations have been performed in order to study the influence of manufacturing tolerances on the lattice parameters. The manufacturing tolerance that has the largest influence on the k-inf is the fuel density. The second most sensitive parameter is the outer clad diameter.
Uncertainties estimation in surveying measurands: application to lengths, perimeters and areas
Covián, E.; Puente, V.; Casero, M.
2017-10-01
The present paper develops a series of methods for the estimation of uncertainty when measuring certain measurands of interest in surveying practice, such as points elevation given a planimetric position within a triangle mesh, 2D and 3D lengths (including perimeters enclosures), 2D areas (horizontal surfaces) and 3D areas (natural surfaces). The basis for the proposed methodology is the law of propagation of variance-covariance, which, applied to the corresponding model for each measurand, allows calculating the resulting uncertainty from known measurement errors. The methods are tested first in a small example, with a limited number of measurement points, and then in two real-life measurements. In addition, the proposed methods have been incorporated to commercial software used in the field of surveying engineering and focused on the creation of digital terrain models. The aim of this evolution is, firstly, to comply with the guidelines of the BIPM (Bureau International des Poids et Mesures), as the international reference agency in the field of metrology, in relation to the determination and expression of uncertainty; and secondly, to improve the quality of the measurement by indicating the uncertainty associated with a given level of confidence. The conceptual and mathematical developments for the uncertainty estimation in the aforementioned cases were conducted by researchers from the AssIST group at the University of Oviedo, eventually resulting in several different mathematical algorithms implemented in the form of MATLAB code. Based on these prototypes, technicians incorporated the referred functionality to commercial software, developed in C++. As a result of this collaboration, in early 2016 a new version of this commercial software was made available, which will be the first, as far as the authors are aware, that incorporates the possibility of estimating the uncertainty for a given level of confidence when computing the aforementioned surveying
SU-G-BRA-09: Estimation of Motion Tracking Uncertainty for Real-Time Adaptive Imaging
Energy Technology Data Exchange (ETDEWEB)
Yan, H [Capital Medical University, Beijing, Beijing (China); Chen, Z [Yale New Haven Hospital, New Haven, CT (United States); Nath, R; Liu, W [Yale University School of Medicine, New Haven, CT (United States)
2016-06-15
Purpose: kV fluoroscopic imaging combined with MV treatment beam imaging has been investigated for intrafractional motion monitoring and correction. It is, however, subject to additional kV imaging dose to normal tissue. To balance tracking accuracy and imaging dose, we previously proposed an adaptive imaging strategy to dynamically decide future imaging type and moments based on motion tracking uncertainty. kV imaging may be used continuously for maximal accuracy or only when the position uncertainty (probability of out of threshold) is high if a preset imaging dose limit is considered. In this work, we propose more accurate methods to estimate tracking uncertainty through analyzing acquired data in real-time. Methods: We simulated motion tracking process based on a previously developed imaging framework (MV + initial seconds of kV imaging) using real-time breathing data from 42 patients. Motion tracking errors for each time point were collected together with the time point’s corresponding features, such as tumor motion speed and 2D tracking error of previous time points, etc. We tested three methods for error uncertainty estimation based on the features: conditional probability distribution, logistic regression modeling, and support vector machine (SVM) classification to detect errors exceeding a threshold. Results: For conditional probability distribution, polynomial regressions on three features (previous tracking error, prediction quality, and cosine of the angle between the trajectory and the treatment beam) showed strong correlation with the variation (uncertainty) of the mean 3D tracking error and its standard deviation: R-square = 0.94 and 0.90, respectively. The logistic regression and SVM classification successfully identified about 95% of tracking errors exceeding 2.5mm threshold. Conclusion: The proposed methods can reliably estimate the motion tracking uncertainty in real-time, which can be used to guide adaptive additional imaging to confirm the
SU-G-BRA-09: Estimation of Motion Tracking Uncertainty for Real-Time Adaptive Imaging
International Nuclear Information System (INIS)
Yan, H; Chen, Z; Nath, R; Liu, W
2016-01-01
Purpose: kV fluoroscopic imaging combined with MV treatment beam imaging has been investigated for intrafractional motion monitoring and correction. It is, however, subject to additional kV imaging dose to normal tissue. To balance tracking accuracy and imaging dose, we previously proposed an adaptive imaging strategy to dynamically decide future imaging type and moments based on motion tracking uncertainty. kV imaging may be used continuously for maximal accuracy or only when the position uncertainty (probability of out of threshold) is high if a preset imaging dose limit is considered. In this work, we propose more accurate methods to estimate tracking uncertainty through analyzing acquired data in real-time. Methods: We simulated motion tracking process based on a previously developed imaging framework (MV + initial seconds of kV imaging) using real-time breathing data from 42 patients. Motion tracking errors for each time point were collected together with the time point’s corresponding features, such as tumor motion speed and 2D tracking error of previous time points, etc. We tested three methods for error uncertainty estimation based on the features: conditional probability distribution, logistic regression modeling, and support vector machine (SVM) classification to detect errors exceeding a threshold. Results: For conditional probability distribution, polynomial regressions on three features (previous tracking error, prediction quality, and cosine of the angle between the trajectory and the treatment beam) showed strong correlation with the variation (uncertainty) of the mean 3D tracking error and its standard deviation: R-square = 0.94 and 0.90, respectively. The logistic regression and SVM classification successfully identified about 95% of tracking errors exceeding 2.5mm threshold. Conclusion: The proposed methods can reliably estimate the motion tracking uncertainty in real-time, which can be used to guide adaptive additional imaging to confirm the
A comparative experimental evaluation of uncertainty estimation methods for two-component PIV
Boomsma, Aaron; Bhattacharya, Sayantan; Troolin, Dan; Pothos, Stamatios; Vlachos, Pavlos
2016-09-01
Uncertainty quantification in planar particle image velocimetry (PIV) measurement is critical for proper assessment of the quality and significance of reported results. New uncertainty estimation methods have been recently introduced generating interest about their applicability and utility. The present study compares and contrasts current methods, across two separate experiments and three software packages in order to provide a diversified assessment of the methods. We evaluated the performance of four uncertainty estimation methods, primary peak ratio (PPR), mutual information (MI), image matching (IM) and correlation statistics (CS). The PPR method was implemented and tested in two processing codes, using in-house open source PIV processing software (PRANA, Purdue University) and Insight4G (TSI, Inc.). The MI method was evaluated in PRANA, as was the IM method. The CS method was evaluated using DaVis (LaVision, GmbH). Utilizing two PIV systems for high and low-resolution measurements and a laser doppler velocimetry (LDV) system, data were acquired in a total of three cases: a jet flow and a cylinder in cross flow at two Reynolds numbers. LDV measurements were used to establish a point validation against which the high-resolution PIV measurements were validated. Subsequently, the high-resolution PIV measurements were used as a reference against which the low-resolution PIV data were assessed for error and uncertainty. We compared error and uncertainty distributions, spatially varying RMS error and RMS uncertainty, and standard uncertainty coverages. We observed that qualitatively, each method responded to spatially varying error (i.e. higher error regions resulted in higher uncertainty predictions in that region). However, the PPR and MI methods demonstrated reduced uncertainty dynamic range response. In contrast, the IM and CS methods showed better response, but under-predicted the uncertainty ranges. The standard coverages (68% confidence interval) ranged from
A comparative experimental evaluation of uncertainty estimation methods for two-component PIV
International Nuclear Information System (INIS)
Boomsma, Aaron; Troolin, Dan; Pothos, Stamatios; Bhattacharya, Sayantan; Vlachos, Pavlos
2016-01-01
Uncertainty quantification in planar particle image velocimetry (PIV) measurement is critical for proper assessment of the quality and significance of reported results. New uncertainty estimation methods have been recently introduced generating interest about their applicability and utility. The present study compares and contrasts current methods, across two separate experiments and three software packages in order to provide a diversified assessment of the methods. We evaluated the performance of four uncertainty estimation methods, primary peak ratio (PPR), mutual information (MI), image matching (IM) and correlation statistics (CS). The PPR method was implemented and tested in two processing codes, using in-house open source PIV processing software (PRANA, Purdue University) and Insight4G (TSI, Inc.). The MI method was evaluated in PRANA, as was the IM method. The CS method was evaluated using DaVis (LaVision, GmbH). Utilizing two PIV systems for high and low-resolution measurements and a laser doppler velocimetry (LDV) system, data were acquired in a total of three cases: a jet flow and a cylinder in cross flow at two Reynolds numbers. LDV measurements were used to establish a point validation against which the high-resolution PIV measurements were validated. Subsequently, the high-resolution PIV measurements were used as a reference against which the low-resolution PIV data were assessed for error and uncertainty. We compared error and uncertainty distributions, spatially varying RMS error and RMS uncertainty, and standard uncertainty coverages. We observed that qualitatively, each method responded to spatially varying error (i.e. higher error regions resulted in higher uncertainty predictions in that region). However, the PPR and MI methods demonstrated reduced uncertainty dynamic range response. In contrast, the IM and CS methods showed better response, but under-predicted the uncertainty ranges. The standard coverages (68% confidence interval) ranged from
Galvan, Jose Ramon; Saxena, Abhinav; Goebel, Kai Frank
2012-01-01
This article discusses several aspects of uncertainty representation and management for model-based prognostics methodologies based on our experience with Kalman Filters when applied to prognostics for electronics components. In particular, it explores the implications of modeling remaining useful life prediction as a stochastic process, and how it relates to uncertainty representation, management and the role of prognostics in decision-making. A distinction between the interpretations of estimated remaining useful life probability density function is explained and a cautionary argument is provided against mixing interpretations for two while considering prognostics in making critical decisions.
International Nuclear Information System (INIS)
Sousa, C.H.S.; Teixeira, G.J.; Peixoto, J.G.P.
2014-01-01
Activimeters should undergo performance for verifying the functionality tests as technical recommendations. This study estimated the associated expanded uncertainties uncorrelated to the results conducted on three instruments, two detectors with ionization chamber and one with Geiger Mueller tubes. For this we used a standard reference source and screened certified by the National Institute of Technology and Standardization. The methodology of this research was based on the protocols listed in the technical document of the International Atomic Energy Agency. Later two quantities were correlated presenting real correlation and improving expanded uncertainty 3.7%. (author)
Estimation of CO2 emissions from China’s cement production: Methodologies and uncertainties
International Nuclear Information System (INIS)
Ke, Jing; McNeil, Michael; Price, Lynn; Khanna, Nina Zheng; Zhou, Nan
2013-01-01
In 2010, China’s cement output was 1.9 Gt, which accounted for 56% of world cement production. Total carbon dioxide (CO 2 ) emissions from Chinese cement production could therefore exceed 1.2 Gt. The magnitude of emissions from this single industrial sector in one country underscores the need to understand the uncertainty of current estimates of cement emissions in China. This paper compares several methodologies for calculating CO 2 emissions from cement production, including the three main components of emissions: direct emissions from the calcination process for clinker production, direct emissions from fossil fuel combustion and indirect emissions from electricity consumption. This paper examines in detail the differences between common methodologies for each emission component, and considers their effect on total emissions. We then evaluate the overall level of uncertainty implied by the differences among methodologies according to recommendations of the Joint Committee for Guides in Metrology. We find a relative uncertainty in China’s cement-related emissions in the range of 10 to 18%. This result highlights the importance of understanding and refining methods of estimating emissions in this important industrial sector. - Highlights: ► CO 2 emission estimates are critical given China’s cement production scale. ► Methodological differences for emission components are compared. ► Results show relative uncertainty in China’s cement-related emissions of about 10%. ► IPCC Guidelines and CSI Cement CO 2 and Energy Protocol are recommended
Khademian, Amir; Abdollahipour, Hamed; Bagherpour, Raheb; Faramarzi, Lohrasb
2017-10-01
In addition to the numerous planning and executive challenges, underground excavation in urban areas is always followed by certain destructive effects especially on the ground surface; ground settlement is the most important of these effects for which estimation there exist different empirical, analytical and numerical methods. Since geotechnical models are associated with considerable model uncertainty, this study characterized the model uncertainty of settlement estimation models through a systematic comparison between model predictions and past performance data derived from instrumentation. To do so, the amount of surface settlement induced by excavation of the Qom subway tunnel was estimated via empirical (Peck), analytical (Loganathan and Poulos) and numerical (FDM) methods; the resulting maximum settlement value of each model were 1.86, 2.02 and 1.52 cm, respectively. The comparison of these predicted amounts with the actual data from instrumentation was employed to specify the uncertainty of each model. The numerical model outcomes, with a relative error of 3.8%, best matched the reality and the analytical method, with a relative error of 27.8%, yielded the highest level of model uncertainty.
Uncertainties in estimating heart doses from 2D-tangential breast cancer radiotherapy
DEFF Research Database (Denmark)
Laugaard Lorenzen, Ebbe; Brink, Carsten; Taylor, Carolyn W.
2016-01-01
BACKGROUND AND PURPOSE: We evaluated the accuracy of three methods of estimating radiation dose to the heart from two-dimensional tangential radiotherapy for breast cancer, as used in Denmark during 1982-2002. MATERIAL AND METHODS: Three tangential radiotherapy regimens were reconstructed using CT......-based planning scans for 40 patients with left-sided and 10 with right-sided breast cancer. Setup errors and organ motion were simulated using estimated uncertainties. For left-sided patients, mean heart dose was related to maximum heart distance in the medial field. RESULTS: For left-sided breast cancer, mean...... to the uncertainty of estimates based on individual CT-scans. For right-sided breast cancer patients, mean heart dose based on individual CT-scans was always
Uncertainty estimation of core safety parameters using cross-correlations of covariance matrix
International Nuclear Information System (INIS)
Yamamoto, Akio; Yasue, Yoshihiro; Endo, Tomohiro; Kodama, Yasuhiro; Ohoka, Yasunori; Tatsumi, Masahiro
2013-01-01
An uncertainty reduction method for core safety parameters, for which measurement values are not obtained, is proposed. We empirically recognize that there exist some correlations among the prediction errors of core safety parameters, e.g., a correlation between the control rod worth and the assembly relative power at corresponding position. Correlations of errors among core safety parameters are theoretically estimated using the covariance of cross sections and sensitivity coefficients of core parameters. The estimated correlations of errors among core safety parameters are verified through the direct Monte Carlo sampling method. Once the correlation of errors among core safety parameters is known, we can estimate the uncertainty of a safety parameter for which measurement value is not obtained. (author)
International Nuclear Information System (INIS)
Petruzzi, A.; D'Auria, F.; Cacuci, D.G.
2009-01-01
Nuclear Power Plant (NPP) technology has been developed based on the traditional defense in depth philosophy supported by deterministic and overly conservative methods for safety analysis. In the 1970s [1], conservative hypotheses were introduced for safety analyses to address existing uncertainties. Since then, intensive thermal-hydraulic experimental research has resulted in a considerable increase in knowledge and consequently in the development of best-estimate codes able to provide more realistic information about the physical behaviour and to identify the most relevant safety issues allowing the evaluation of the existing actual margins between the results of the calculations and the acceptance criteria. However, the best-estimate calculation results from complex thermal-hydraulic system codes (like Relap5, Cathare, Athlet, Trace, etc..) are affected by unavoidable approximations that are un-predictable without the use of computational tools that account for the various sources of uncertainty. Therefore the use of best-estimate codes (BE) within the reactor technology, either for design or safety purposes, implies understanding and accepting the limitations and the deficiencies of those codes. Taking into consideration the above framework, a comprehensive approach for utilizing quantified uncertainties arising from Integral Test Facilities (ITFs, [2]) and Separate Effect Test Facilities (SETFs, [3]) in the process of calibrating complex computer models for the application to NPP transient scenarios has been developed. The methodology proposed is capable of accommodating multiple SETFs and ITFs to learn as much as possible about uncertain parameters, allowing for the improvement of the computer model predictions based on the available experimental evidences. The proposed methodology constitutes a major step forward with respect to the generally used expert judgment and statistical methods as it permits a) to establish the uncertainties of any parameter
On the evaluation of uncertainties for state estimation with the Kalman filter
International Nuclear Information System (INIS)
Eichstädt, S; Makarava, N; Elster, C
2016-01-01
The Kalman filter is an established tool for the analysis of dynamic systems with normally distributed noise, and it has been successfully applied in numerous areas. It provides sequentially calculated estimates of the system states along with a corresponding covariance matrix. For nonlinear systems, the extended Kalman filter is often used. This is derived from the Kalman filter by linearization around the current estimate. A key issue in metrology is the evaluation of the uncertainty associated with the Kalman filter state estimates. The ‘Guide to the Expression of Uncertainty in Measurement’ (GUM) and its supplements serve as the de facto standard for uncertainty evaluation in metrology. We explore the relationship between the covariance matrix produced by the Kalman filter and a GUM-compliant uncertainty analysis. In addition, the results of a Bayesian analysis are considered. For the case of linear systems with known system matrices, we show that all three approaches are compatible. When the system matrices are not precisely known, however, or when the system is nonlinear, this equivalence breaks down and different results can then be reached. For precisely known nonlinear systems, though, the result of the extended Kalman filter still corresponds to the linearized uncertainty propagation of the GUM. The extended Kalman filter can suffer from linearization and convergence errors. These disadvantages can be avoided to some extent by applying Monte Carlo procedures, and we propose such a method which is GUM-compliant and can also be applied online during the estimation. We illustrate all procedures in terms of a 2D dynamic system and compare the results with those obtained by particle filtering, which has been proposed for the approximate calculation of a Bayesian solution. Finally, we give some recommendations based on our findings. (paper)
Uncertainty estimation of the velocity model for the TrigNet GPS network
Hackl, Matthias; Malservisi, Rocco; Hugentobler, Urs; Wonnacott, Richard
2010-05-01
Satellite based geodetic techniques - above all GPS - provide an outstanding tool to measure crustal motions. They are widely used to derive geodetic velocity models that are applied in geodynamics to determine rotations of tectonic blocks, to localize active geological features, and to estimate rheological properties of the crust and the underlying asthenosphere. However, it is not a trivial task to derive GPS velocities and their uncertainties from positioning time series. In general time series are assumed to be represented by linear models (sometimes offsets, annual, and semi-annual signals are included) and noise. It has been shown that models accounting only for white noise tend to underestimate the uncertainties of rates derived from long time series and that different colored noise components (flicker noise, random walk, etc.) need to be considered. However, a thorough error analysis including power spectra analyses and maximum likelihood estimates is quite demanding and are usually not carried out for every site, but the uncertainties are scaled by latitude dependent factors. Analyses of the South Africa continuous GPS network TrigNet indicate that the scaled uncertainties overestimate the velocity errors. So we applied a method similar to the Allan Variance that is commonly used in the estimation of clock uncertainties and is able to account for time dependent probability density functions (colored noise) to the TrigNet time series. Finally, we compared these estimates to the results obtained by spectral analyses using CATS. Comparisons with synthetic data show that the noise can be represented quite well by a power law model in combination with a seasonal signal in agreement with previous studies.
An error bound estimate and convergence of the Nodal-LTS {sub N} solution in a rectangle
Energy Technology Data Exchange (ETDEWEB)
Hauser, Eliete Biasotto [Faculty of Mathematics, PUCRS Av Ipiranga 6681, Building 15, Porto Alegre - RS 90619-900 (Brazil)]. E-mail: eliete@pucrs.br; Pazos, Ruben Panta [Department of Mathematics, UNISC Av Independencia, 2293, room 1301, Santa Cruz do Sul - RS 96815-900 (Brazil)]. E-mail: rpp@impa.br; Tullio de Vilhena, Marco [Graduate Program in Applied Mathematics, UFRGS Av Bento Goncalves 9500, Building 43-111, Porto Alegre - RS 91509-900 (Brazil)]. E-mail: vilhena@mat.ufrgs.br
2005-07-15
In this work, we report the mathematical analysis concerning error bound estimate and convergence of the Nodal-LTS {sub N} solution in a rectangle. For such we present an efficient algorithm, called LTS {sub N} 2D-Diag solution for Cartesian geometry.
Best-estimate reactor core monitor using state feedback strategies to resolve uncertainties
International Nuclear Information System (INIS)
Martin, R.P.
1997-01-01
The development and demonstration of a new algorithm for quantifying uncertainty in best-estimate simulation codes has been investigated. Demonstration is given by way of a prototype reactor core monitor. The architecture of this monitor integrates a distributed parameter estimation technique and the infrastructure required to support this control theory-based algorithm into a production-grade best-estimate simulation code. The Kalman filter with the sequential least-squares parameter estimation algorithm has been extended for application into the computational environment of a best-estimate simulation code, i.e., RELAP5/DOE. In control system terminology this configuration can be thought of as a best-estimate observer
Wallace, Jack
2010-05-01
While forensic laboratories will soon be required to estimate uncertainties of measurement for those quantitations reported to the end users of the information, the procedures for estimating this have been little discussed in the forensic literature. This article illustrates how proficiency test results provide the basis for estimating uncertainties in three instances: (i) For breath alcohol analyzers the interlaboratory precision is taken as a direct measure of uncertainty. This approach applies when the number of proficiency tests is small. (ii) For blood alcohol, the uncertainty is calculated from the differences between the laboratory's proficiency testing results and the mean quantitations determined by the participants; this approach applies when the laboratory has participated in a large number of tests. (iii) For toxicology, either of these approaches is useful for estimating comparability between laboratories, but not for estimating absolute accuracy. It is seen that data from proficiency tests enable estimates of uncertainty that are empirical, simple, thorough, and applicable to a wide range of concentrations.
Top-down instead of bottom-up estimates of uncertainty in INAA results?
International Nuclear Information System (INIS)
Bode, P.; De Nadai Fernandes, E.A.
2005-01-01
The initial publication of the ISO Guide to the Expression of Uncertainty in Measurement (GUM) and many related documents has resulted in a worldwide awareness of the importance of a realistic estimate of the value reported after the +/- sign. The evaluation of uncertainty in measurement, as introduced by the GUM, is derived from the principles applied in physical measurements. Many testing laboratories have already experienced large problems in applying these principles in e.g. (bio)chemical measurements, resulting in time-consuming evaluations and costly additional experiments. Other, more pragmatic and less costly approaches have been proposed to obtain a realistic estimate of the range in which the true value of the measurement may be found with a certain degree of probability. One of these approaches, the 'top-down method', is based on the standard deviation in the results of intercomparison data. This approach is much easier for tests for which it is either difficult to establish a full measurement equation, or if e.g. matrix-matching reference materials are absent. It has been demonstrated that the GUM 'bottom-up' approach of evaluating uncertainty in measurement can easily be applied in instrumental neutron activation analysis (INAA) as all significant sources of uncertainty can be evaluated. INAA is therefore a valuable technique to test the validity of the top-down approach. In this contribution, examples of the top-down evaluation of uncertainty in INAA derived from participation in intercomparison rounds and proficiency testing schemes will be presented. The results will be compared with the bottom-up evaluation of uncertainty, and ease of applicability, validity and usefullness of both approaches will be discussed.
Assessment of uncertainties of external dose estimation after the Chernobyl accident
International Nuclear Information System (INIS)
Kruk, Julianna
2008-01-01
Full text: In the remote period of time after the Chernobyl accident the estimation of an external exposure with using of direct dose rate measurements or individual monitoring of inhabitants is rationally only for settlements where the preliminary estimation makes the range equal or greater 1.0 mSv per year. For inhabitancies of settlements where the preliminary estimation makes the range less 1.0 mSv per year the external dose is correctly to estimate by calculation. For the last cases the uncertainty should be assessed. The most accessible initial parameter for calculation of a dose of an external exposure is the average ground deposition of Cs-137 for the settlements. The character of density distribution of Cs-137 deposition in an area of one settlement is well enough studied. The best agreement of distribution of this parameter is reached with log-normal distribution practically for all settlements of the investigated territories with factor of a variation 0.3-0.6 and the standard geometrical deviation lying within the limits of 1.4-1.7. The dose factors which correspond to the structure of an available housing of settlement (type of apartment houses: wooden, stone, multi-storey) and age structure of the population are bring the main contribution into uncertainty of the external dose estimation. The situations with a different level of known information have been considered for the estimation of influence of those parameters on the general uncertainty. Thus the estimation of the uncertainty of the external dose was done for two variant: optimistic and pessimistic. In the optimistic case the estimation of external doses will be spent for specific settlement with known structure of housing and according to a known share of the living population in houses of the certain type. In that case, variability value dose factor will be limited to the chosen type of a residential building (for example - the one-storied wooden house), and a share of the living population
Milne, Alice E.; Glendining, Margaret J.; Bellamy, Pat; Misselbrook, Tom; Gilhespy, Sarah; Rivas Casado, Monica; Hulin, Adele; van Oijen, Marcel; Whitmore, Andrew P.
2014-01-01
The UK's greenhouse gas inventory for agriculture uses a model based on the IPCC Tier 1 and Tier 2 methods to estimate the emissions of methane and nitrous oxide from agriculture. The inventory calculations are disaggregated at country level (England, Wales, Scotland and Northern Ireland). Before now, no detailed assessment of the uncertainties in the estimates of emissions had been done. We used Monte Carlo simulation to do such an analysis. We collated information on the uncertainties of each of the model inputs. The uncertainties propagate through the model and result in uncertainties in the estimated emissions. Using a sensitivity analysis, we found that in England and Scotland the uncertainty in the emission factor for emissions from N inputs (EF1) affected uncertainty the most, but that in Wales and Northern Ireland, the emission factor for N leaching and runoff (EF5) had greater influence. We showed that if the uncertainty in any one of these emission factors is reduced by 50%, the uncertainty in emissions of nitrous oxide reduces by 10%. The uncertainty in the estimate for the emissions of methane emission factors for enteric fermentation in cows and sheep most affected the uncertainty in methane emissions. When inventories are disaggregated (as that for the UK is) correlation between separate instances of each emission factor will affect the uncertainty in emissions. As more countries move towards inventory models with disaggregation, it is important that the IPCC give firm guidance on this topic.
International Nuclear Information System (INIS)
Pasanisi, Alberto; Keller, Merlin; Parent, Eric
2012-01-01
In the context of risk analysis under uncertainty, we focus here on the problem of estimating a so-called quantity of interest of an uncertainty analysis problem, i.e. a given feature of the probability distribution function (pdf) of the output of a deterministic model with uncertain inputs. We will stay here in a fully probabilistic setting. A common problem is how to account for epistemic uncertainty tainting the parameter of the probability distribution of the inputs. In the standard practice, this uncertainty is often neglected (plug-in approach). When a specific uncertainty assessment is made, under the basis of the available information (expertise and/or data), a common solution consists in marginalizing the joint distribution of both observable inputs and parameters of the probabilistic model (i.e. computing the predictive pdf of the inputs), then propagating it through the deterministic model. We will reinterpret this approach in the light of Bayesian decision theory, and will put into evidence that this practice leads the analyst to adopt implicitly a specific loss function which may be inappropriate for the problem under investigation, and suboptimal from a decisional perspective. These concepts are illustrated on a simple numerical example, concerning a case of flood risk assessment.
On the influence of uncertainties in estimating risk aversion and working interest
International Nuclear Information System (INIS)
MacKay, J.A.; Lerche, I.
1996-01-01
The influence of uncertainties in costs, value, success probability, risk tolerance and mandated working interest are evaluated for their impact on assessing probable ranges of uncertainty on risk adjusted value, RAV, using different models. The relative importance of different factors in contributing to the uncertainty in RAV is analyzed, as is the influence of different probability distributions for the intrinsic variables entering the RAV model formulae. Numerical illustrations indicate how the RAV probabilities depend not only on the model functions (Cozzolino, hyperbolic tangent) used to provide RAV estimates, but also on the intrinsic shapes of the probability distributions from which are drawn input parameter values for Monte Carlo simulations. In addition, a mandated range of working interest can be addressed as an extra variable contributing to the probabilistic range of RAV; while negative RAV values for high-cost project can be used to assess the probable buy-out amount one should be prepared to pay depending on corporate risk philosophy. Also, the procedures illustrate how the relative contributions of scientific factors influence uncertainty of reserve assessments, allowing one to determine where to concentrate effort to improve the ranges of uncertainty. (Author)
International Nuclear Information System (INIS)
Donald M. McEligot; Hugh M. McIlroy, Jr.; Ryan C. Johnson
2007-01-01
The purpose of the fluid dynamics experiments in the MIR (Matched-Index-of-Refraction) flow system at Idaho National Laboratory (INL) is to develop benchmark databases for the assessment of Computational Fluid Dynamics (CFD) solutions of the momentum equations, scalar mixing, and turbulence models for typical Very High Temperature Reactor (VHTR) plenum geometries in the limiting case of negligible buoyancy and constant fluid properties. The experiments use optical techniques, primarily particle image velocimetry (PIV) in the INL MIR flow system. The benefit of the MIR technique is that it permits optical measurements to determine flow characteristics in passages and around objects to be obtained without locating a disturbing transducer in the flow field and without distortion of the optical paths. The objective of the present report is to develop understanding of the magnitudes of experimental uncertainties in the results to be obtained in such experiments. Unheated MIR experiments are first steps when the geometry is complicated. One does not want to use a computational technique, which will not even handle constant properties properly. This report addresses the general background, requirements for benchmark databases, estimation of experimental uncertainties in mean velocities and turbulence quantities, the MIR experiment, PIV uncertainties, positioning uncertainties, and other contributing measurement uncertainties
Alonso, Ariel; Laenen, Annouschka
2013-05-01
Laenen, Alonso, and Molenberghs (2007) and Laenen, Alonso, Molenberghs, and Vangeneugden (2009) proposed a method to assess the reliability of rating scales in a longitudinal context. The methodology is based on hierarchical linear models, and reliability coefficients are derived from the corresponding covariance matrices. However, finding a good parsimonious model to describe complex longitudinal data is a challenging task. Frequently, several models fit the data equally well, raising the problem of model selection uncertainty. When model uncertainty is high one may resort to model averaging, where inferences are based not on one but on an entire set of models. We explored the use of different model building strategies, including model averaging, in reliability estimation. We found that the approach introduced by Laenen et al. (2007, 2009) combined with some of these strategies may yield meaningful results in the presence of high model selection uncertainty and when all models are misspecified, in so far as some of them manage to capture the most salient features of the data. Nonetheless, when all models omit prominent regularities in the data, misleading results may be obtained. The main ideas are further illustrated on a case study in which the reliability of the Hamilton Anxiety Rating Scale is estimated. Importantly, the ambit of model selection uncertainty and model averaging transcends the specific setting studied in the paper and may be of interest in other areas of psychometrics. © 2012 The British Psychological Society.
Masterlark, Timothy; Donovan, Theodore; Feigl, Kurt L.; Haney, Matt; Thurber, Clifford H.; Tung, Sui
2016-01-01
The eruption cycle of a volcano is controlled in part by the upward migration of magma. The characteristics of the magma flux produce a deformation signature at the Earth's surface. Inverse analyses use geodetic data to estimate strategic controlling parameters that describe the position and pressurization of a magma chamber at depth. The specific distribution of material properties controls how observed surface deformation translates to source parameter estimates. Seismic tomography models describe the spatial distributions of material properties that are necessary for accurate models of volcano deformation. This study investigates how uncertainties in seismic tomography models propagate into variations in the estimates of volcano deformation source parameters inverted from geodetic data. We conduct finite element model-based nonlinear inverse analyses of interferometric synthetic aperture radar (InSAR) data for Okmok volcano, Alaska, as an example. We then analyze the estimated parameters and their uncertainties to characterize the magma chamber. Analyses are performed separately for models simulating a pressurized chamber embedded in a homogeneous domain as well as for a domain having a heterogeneous distribution of material properties according to seismic tomography. The estimated depth of the source is sensitive to the distribution of material properties. The estimated depths for the homogeneous and heterogeneous domains are 2666 ± 42 and 3527 ± 56 m below mean sea level, respectively (99% confidence). A Monte Carlo analysis indicates that uncertainties of the seismic tomography cannot account for this discrepancy at the 99% confidence level. Accounting for the spatial distribution of elastic properties according to seismic tomography significantly improves the fit of the deformation model predictions and significantly influences estimates for parameters that describe the location of a pressurized magma chamber.
A new Method for the Estimation of Initial Condition Uncertainty Structures in Mesoscale Models
Keller, J. D.; Bach, L.; Hense, A.
2012-12-01
The estimation of fast growing error modes of a system is a key interest of ensemble data assimilation when assessing uncertainty in initial conditions. Over the last two decades three methods (and variations of these methods) have evolved for global numerical weather prediction models: ensemble Kalman filter, singular vectors and breeding of growing modes (or now ensemble transform). While the former incorporates a priori model error information and observation error estimates to determine ensemble initial conditions, the latter two techniques directly address the error structures associated with Lyapunov vectors. However, in global models these structures are mainly associated with transient global wave patterns. When assessing initial condition uncertainty in mesoscale limited area models, several problems regarding the aforementioned techniques arise: (a) additional sources of uncertainty on the smaller scales contribute to the error and (b) error structures from the global scale may quickly move through the model domain (depending on the size of the domain). To address the latter problem, perturbation structures from global models are often included in the mesoscale predictions as perturbed boundary conditions. However, the initial perturbations (when used) are often generated with a variant of an ensemble Kalman filter which does not necessarily focus on the large scale error patterns. In the framework of the European regional reanalysis project of the Hans-Ertel-Center for Weather Research we use a mesoscale model with an implemented nudging data assimilation scheme which does not support ensemble data assimilation at all. In preparation of an ensemble-based regional reanalysis and for the estimation of three-dimensional atmospheric covariance structures, we implemented a new method for the assessment of fast growing error modes for mesoscale limited area models. The so-called self-breeding is development based on the breeding of growing modes technique
Inferring uncertainty from interval estimates: Effects of alpha level and numeracy
Directory of Open Access Journals (Sweden)
Luke F. Rinne
2013-05-01
Full Text Available Interval estimates are commonly used to descriptively communicate the degree of uncertainty in numerical values. Conventionally, low alpha levels (e.g., .05 ensure a high probability of capturing the target value between interval endpoints. Here, we test whether alpha levels and individual differences in numeracy influence distributional inferences. In the reported experiment, participants received prediction intervals for fictitious towns' annual rainfall totals (assuming approximately normal distributions. Then, participants estimated probabilities that future totals would be captured within varying margins about the mean, indicating the approximate shapes of their inferred probability distributions. Results showed that low alpha levels (vs. moderate levels; e.g., .25 more frequently led to inferences of over-dispersed approximately normal distributions or approximately uniform distributions, reducing estimate accuracy. Highly numerate participants made more accurate estimates overall, but were more prone to inferring approximately uniform distributions. These findings have important implications for presenting interval estimates to various audiences.
Directory of Open Access Journals (Sweden)
Il Young Song
2015-01-01
Full Text Available This paper focuses on estimation of a nonlinear function of state vector (NFS in discrete-time linear systems with time-delays and model uncertainties. The NFS represents a multivariate nonlinear function of state variables, which can indicate useful information of a target system for control. The optimal nonlinear estimator of an NFS (in mean square sense represents a function of the receding horizon estimate and its error covariance. The proposed receding horizon filter represents the standard Kalman filter with time-delays and special initial horizon conditions described by the Lyapunov-like equations. In general case to calculate an optimal estimator of an NFS we propose using the unscented transformation. Important class of polynomial NFS is considered in detail. In the case of polynomial NFS an optimal estimator has a closed-form computational procedure. The subsequent application of the proposed receding horizon filter and nonlinear estimator to a linear stochastic system with time-delays and uncertainties demonstrates their effectiveness.
Comparison of two perturbation methods to estimate the land surface modeling uncertainty
Su, H.; Houser, P.; Tian, Y.; Kumar, S.; Geiger, J.; Belvedere, D.
2007-12-01
In land surface modeling, it is almost impossible to simulate the land surface processes without any error because the earth system is highly complex and the physics of the land processes has not yet been understood sufficiently. In most cases, people want to know not only the model output but also the uncertainty in the modeling, to estimate how reliable the modeling is. Ensemble perturbation is an effective way to estimate the uncertainty in land surface modeling, since land surface models are highly nonlinear which makes the analytical approach not applicable in this estimation. The ideal perturbation noise is zero mean Gaussian distribution, however, this requirement can't be satisfied if the perturbed variables in land surface model have physical boundaries because part of the perturbation noises has to be removed to feed the land surface models properly. Two different perturbation methods are employed in our study to investigate their impact on quantifying land surface modeling uncertainty base on the Land Information System (LIS) framework developed by NASA/GSFC land team. One perturbation method is the built-in algorithm named "STATIC" in LIS version 5; the other is a new perturbation algorithm which was recently developed to minimize the overall bias in the perturbation by incorporating additional information from the whole time series for the perturbed variable. The statistical properties of the perturbation noise generated by the two different algorithms are investigated thoroughly by using a large ensemble size on a NASA supercomputer and then the corresponding uncertainty estimates based on the two perturbation methods are compared. Their further impacts on data assimilation are also discussed. Finally, an optimal perturbation method is suggested.
Peña, Carlos; Espeland, Marianne
2015-01-01
The species rich butterfly family Nymphalidae has been used to study evolutionary interactions between plants and insects. Theories of insect-hostplant dynamics predict accelerated diversification due to key innovations. In evolutionary biology, analysis of maximum credibility trees in the software MEDUSA (modelling evolutionary diversity using stepwise AIC) is a popular method for estimation of shifts in diversification rates. We investigated whether phylogenetic uncertainty can produce different results by extending the method across a random sample of trees from the posterior distribution of a Bayesian run. Using the MultiMEDUSA approach, we found that phylogenetic uncertainty greatly affects diversification rate estimates. Different trees produced diversification rates ranging from high values to almost zero for the same clade, and both significant rate increase and decrease in some clades. Only four out of 18 significant shifts found on the maximum clade credibility tree were consistent across most of the sampled trees. Among these, we found accelerated diversification for Ithomiini butterflies. We used the binary speciation and extinction model (BiSSE) and found that a hostplant shift to Solanaceae is correlated with increased net diversification rates in Ithomiini, congruent with the diffuse cospeciation hypothesis. Our results show that taking phylogenetic uncertainty into account when estimating net diversification rate shifts is of great importance, as very different results can be obtained when using the maximum clade credibility tree and other trees from the posterior distribution. PMID:25830910
Estimating Uncertainties of Ship Course and Speed in Early Navigations using ICOADS3.0
Chan, D.; Huybers, P. J.
2017-12-01
Information on ship position and its uncertainty is potentially important for mapping out climatologists and changes in SSTs. Using the 2-hourly ship reports from the International Comprehensive Ocean Atmosphere Dataset 3.0 (ICOADS 3.0), we estimate the uncertainties of ship course, ship speed, and latitude/longitude corrections during 1870-1900. After reviewing the techniques used in early navigations, we build forward navigation model that uses dead reckoning technique, celestial latitude corrections, and chronometer longitude corrections. The modeled ship tracks exhibit jumps in longitude and latitude, when a position correction is applied. These jumps are also seen in ICOADS3.0 observations. In this model, position error at the end of each day increases following a 2D random walk; the latitudinal/longitude errors are reset when a latitude/longitude correction is applied.We fit the variance of the magnitude of latitude/longitude corrections in the observation against model outputs, and estimate that the standard deviation of uncertainty is 5.5 degree for ship course, 32% for ship speed, 22km for latitude correction, and 27km for longitude correction. The estimates here are informative priors for Bayesian methods that quantify position errors of individual tracks.
Best estimate analysis of LOFT L2-5 with CATHARE: uncertainty and sensitivity analysis
Energy Technology Data Exchange (ETDEWEB)
JOUCLA, Jerome; PROBST, Pierre [Institute for Radiological Protection and Nuclear Safety, Fontenay-aux-Roses (France); FOUET, Fabrice [APTUS, Versailles (France)
2008-07-01
The revision of the 10 CFR50.46 in 1988 has made possible the use of best-estimate codes. They may be used in safety demonstration and licensing, provided that uncertainties are added to the relevant output parameters before comparing them with the acceptance criteria. In the safety analysis of the large break loss of coolant accident, it was agreed that the 95. percentile estimated with a high degree of confidence should be lower than the acceptance criteria. It appeared necessary to IRSN, technical support of the French Safety Authority, to get more insight into these strategies which are being developed not only in thermal-hydraulics but in other fields such as in neutronics. To estimate the 95. percentile with a high confidence level, we propose to use rank statistics or bootstrap. Toward the objective of assessing uncertainty, it is useful to determine and to classify the main input parameters. We suggest approximating the code by a surrogate model, the Kriging model, which will be used to make a sensitivity analysis with the SOBOL methodology. This paper presents the application of two new methodologies of how to make the uncertainty and sensitivity analysis on the maximum peak cladding temperature of the LOFT L2-5 test with the CATHARE code. (authors)
Directory of Open Access Journals (Sweden)
Carlos Peña
Full Text Available The species rich butterfly family Nymphalidae has been used to study evolutionary interactions between plants and insects. Theories of insect-hostplant dynamics predict accelerated diversification due to key innovations. In evolutionary biology, analysis of maximum credibility trees in the software MEDUSA (modelling evolutionary diversity using stepwise AIC is a popular method for estimation of shifts in diversification rates. We investigated whether phylogenetic uncertainty can produce different results by extending the method across a random sample of trees from the posterior distribution of a Bayesian run. Using the MultiMEDUSA approach, we found that phylogenetic uncertainty greatly affects diversification rate estimates. Different trees produced diversification rates ranging from high values to almost zero for the same clade, and both significant rate increase and decrease in some clades. Only four out of 18 significant shifts found on the maximum clade credibility tree were consistent across most of the sampled trees. Among these, we found accelerated diversification for Ithomiini butterflies. We used the binary speciation and extinction model (BiSSE and found that a hostplant shift to Solanaceae is correlated with increased net diversification rates in Ithomiini, congruent with the diffuse cospeciation hypothesis. Our results show that taking phylogenetic uncertainty into account when estimating net diversification rate shifts is of great importance, as very different results can be obtained when using the maximum clade credibility tree and other trees from the posterior distribution.
Evaluation and uncertainty analysis of regional-scale CLM4.5 net carbon flux estimates
Post, Hanna; Hendricks Franssen, Harrie-Jan; Han, Xujun; Baatz, Roland; Montzka, Carsten; Schmidt, Marius; Vereecken, Harry
2018-01-01
Modeling net ecosystem exchange (NEE) at the regional scale with land surface models (LSMs) is relevant for the estimation of regional carbon balances, but studies on it are very limited. Furthermore, it is essential to better understand and quantify the uncertainty of LSMs in order to improve them. An important key variable in this respect is the prognostic leaf area index (LAI), which is very sensitive to forcing data and strongly affects the modeled NEE. We applied the Community Land Model (CLM4.5-BGC) to the Rur catchment in western Germany and compared estimated and default ecological key parameters for modeling carbon fluxes and LAI. The parameter estimates were previously estimated with the Markov chain Monte Carlo (MCMC) approach DREAM(zs) for four of the most widespread plant functional types in the catchment. It was found that the catchment-scale annual NEE was strongly positive with default parameter values but negative (and closer to observations) with the estimated values. Thus, the estimation of CLM parameters with local NEE observations can be highly relevant when determining regional carbon balances. To obtain a more comprehensive picture of model uncertainty, CLM ensembles were set up with perturbed meteorological input and uncertain initial states in addition to uncertain parameters. C3 grass and C3 crops were particularly sensitive to the perturbed meteorological input, which resulted in a strong increase in the standard deviation of the annual NEE sum (σ ∑ NEE) for the different ensemble members from ˜ 2 to 3 g C m-2 yr-1 (with uncertain parameters) to ˜ 45 g C m-2 yr-1 (C3 grass) and ˜ 75 g C m-2 yr-1 (C3 crops) with perturbed forcings. This increase in uncertainty is related to the impact of the meteorological forcings on leaf onset and senescence, and enhanced/reduced drought stress related to perturbation of precipitation. The NEE uncertainty for the forest plant functional type (PFT) was considerably lower (σ ∑ NEE ˜ 4.0-13.5 g C
Lidar-derived estimate and uncertainty of carbon sink in successional phases of woody encroachment
Sankey, Temuulen; Shrestha, Rupesh; Sankey, Joel B.; Hardgree, Stuart; Strand, Eva
2013-01-01
Woody encroachment is a globally occurring phenomenon that contributes to the global carbon sink. The magnitude of this contribution needs to be estimated at regional and local scales to address uncertainties present in the global- and continental-scale estimates, and guide regional policy and management in balancing restoration activities, including removal of woody plants, with greenhouse gas mitigation goals. The objective of this study was to estimate carbon stored in various successional phases of woody encroachment. Using lidar measurements of individual trees, we present high-resolution estimates of aboveground carbon storage in juniper woodlands. Segmentation analysis of lidar point cloud data identified a total of 60,628 juniper tree crowns across four watersheds. Tree heights, canopy cover, and density derived from lidar were strongly correlated with field measurements of 2613 juniper stems measured in 85 plots (30 × 30 m). Aboveground total biomass of individual trees was estimated using a regression model with lidar-derived height and crown area as predictors (Adj. R2 = 0.76, p 2. Uncertainty in carbon storage estimates was examined with a Monte Carlo approach that addressed major error sources. Ranges predicted with uncertainty analysis in the mean, individual tree, aboveground woody C, and associated standard deviation were 0.35 – 143.6 kg and 0.5 – 1.25 kg, respectively. Later successional phases of woody encroachment had, on average, twice the aboveground carbon relative to earlier phases. Woody encroachment might be more successfully managed and balanced with carbon storage goals by identifying priority areas in earlier phases of encroachment where intensive treatments are most effective.
Lidar-derived estimate and uncertainty of carbon sink in successional phases of woody encroachment
Sankey, Temuulen; Shrestha, Rupesh; Sankey, Joel B.; Hardegree, Stuart; Strand, Eva
2013-07-01
encroachment is a globally occurring phenomenon that contributes to the global carbon sink. The magnitude of this contribution needs to be estimated at regional and local scales to address uncertainties present in the global- and continental-scale estimates, and guide regional policy and management in balancing restoration activities, including removal of woody plants, with greenhouse gas mitigation goals. The objective of this study was to estimate carbon stored in various successional phases of woody encroachment. Using lidar measurements of individual trees, we present high-resolution estimates of aboveground carbon storage in juniper woodlands. Segmentation analysis of lidar point cloud data identified a total of 60,628 juniper tree crowns across four watersheds. Tree heights, canopy cover, and density derived from lidar were strongly correlated with field measurements of 2613 juniper stems measured in 85 plots (30 × 30 m). Aboveground total biomass of individual trees was estimated using a regression model with lidar-derived height and crown area as predictors (Adj. R2 = 0.76, p < 0.001, RMSE = 0.58 kg). The predicted mean aboveground woody carbon storage for the study area was 677 g/m2. Uncertainty in carbon storage estimates was examined with a Monte Carlo approach that addressed major error sources. Ranges predicted with uncertainty analysis in the mean, individual tree, aboveground woody C, and associated standard deviation were 0.35 - 143.6 kg and 0.5 - 1.25 kg, respectively. Later successional phases of woody encroachment had, on average, twice the aboveground carbon relative to earlier phases. Woody encroachment might be more successfully managed and balanced with carbon storage goals by identifying priority areas in earlier phases of encroachment where intensive treatments are most effective.
Modeling Input Errors to Improve Uncertainty Estimates for Sediment Transport Model Predictions
Jung, J. Y.; Niemann, J. D.; Greimann, B. P.
2016-12-01
Bayesian methods using Markov chain Monte Carlo algorithms have recently been applied to sediment transport models to assess the uncertainty in the model predictions due to the parameter values. Unfortunately, the existing approaches can only attribute overall uncertainty to the parameters. This limitation is critical because no model can produce accurate forecasts if forced with inaccurate input data, even if the model is well founded in physical theory. In this research, an existing Bayesian method is modified to consider the potential errors in input data during the uncertainty evaluation process. The input error is modeled using Gaussian distributions, and the means and standard deviations are treated as uncertain parameters. The proposed approach is tested by coupling it to the Sedimentation and River Hydraulics - One Dimension (SRH-1D) model and simulating a 23-km reach of the Tachia River in Taiwan. The Wu equation in SRH-1D is used for computing the transport capacity for a bed material load of non-cohesive material. Three types of input data are considered uncertain: (1) the input flowrate at the upstream boundary, (2) the water surface elevation at the downstream boundary, and (3) the water surface elevation at a hydraulic structure in the middle of the reach. The benefits of modeling the input errors in the uncertainty analysis are evaluated by comparing the accuracy of the most likely forecast and the coverage of the observed data by the credible intervals to those of the existing method. The results indicate that the internal boundary condition has the largest uncertainty among those considered. Overall, the uncertainty estimates from the new method are notably different from those of the existing method for both the calibration and forecast periods.
Kärhä, Petri; Vaskuri, Anna; Mäntynen, Henrik; Mikkonen, Nikke; Ikonen, Erkki
2017-08-01
Spectral irradiance data are often used to calculate colorimetric properties, such as color coordinates and color temperatures of light sources by integration. The spectral data may contain unknown correlations that should be accounted for in the uncertainty estimation. We propose a new method for estimating uncertainties in such cases. The method goes through all possible scenarios of deviations using Monte Carlo analysis. Varying spectral error functions are produced by combining spectral base functions, and the distorted spectra are used to calculate the colorimetric quantities. Standard deviations of the colorimetric quantities at different scenarios give uncertainties assuming no correlations, uncertainties assuming full correlation, and uncertainties for an unfavorable case of unknown correlations, which turn out to be a significant source of uncertainty. With 1% standard uncertainty in spectral irradiance, the expanded uncertainty of the correlated color temperature of a source corresponding to the CIE Standard Illuminant A may reach as high as 37.2 K in unfavorable conditions, when calculations assuming full correlation give zero uncertainty, and calculations assuming no correlations yield the expanded uncertainties of 5.6 K and 12.1 K, with wavelength steps of 1 nm and 5 nm used in spectral integrations, respectively. We also show that there is an absolute limit of 60.2 K in the error of the correlated color temperature for Standard Illuminant A when assuming 1% standard uncertainty in the spectral irradiance. A comparison of our uncorrelated uncertainties with those obtained using analytical methods by other research groups shows good agreement. We re-estimated the uncertainties for the colorimetric properties of our 1 kW photometric standard lamps using the new method. The revised uncertainty of color temperature is a factor of 2.5 higher than the uncertainty assuming no correlations.
DEFF Research Database (Denmark)
Frutiger, Jerome; Marcarie, Camille; Abildskov, Jens
2016-01-01
regression and outlier treatment have been applied to achieve high accuracy. Furthermore, linear error propagation based on covariance matrix of estimated parameters was performed. Therefore, every estimated property value of the flammability-related properties is reported together with its corresponding 95......%-confidence interval of the prediction. Compared to existing models the developed ones have a higher accuracy, are simple to apply and provide uncertainty information on the calculated prediction. The average relative error and correlation coefficient are 11.5% and 0.99 for LFL, 15.9% and 0.91 for UFL, 2...
The impact of a and b value uncertainty on loss estimation in the reinsurance industry
Directory of Open Access Journals (Sweden)
R. Streit
2000-06-01
Full Text Available In the reinsurance industry different probabilistic models are currently used for seismic risk analysis. A credible loss estimation of the insured values depends on seismic hazard analysis and on the vulnerability functions of the given structures. Besides attenuation and local soil amplification, the earthquake occurrence model (often represented by the Gutenberg and Richter relation is a key element in the analysis. However, earthquake catalogues are usually incomplete, the time of observation is too short and the data themselves contain errors. Therefore, a and b values can only be estimated with uncertainties. The knowledge of their variation provides a valuable input for earthquake risk analysis, because they allow the probability distribution of expected losses (expressed by Average Annual Loss (AAL to be modelled. The variations of a and b have a direct effect on the estimated exceeding probability and consequently on the calculated loss level. This effect is best illustrated by exceeding probability versus loss level and AAL versus magnitude graphs. The sensitivity of average annual losses due to different a to b ratios and magnitudes is obvious. The estimation of the variation of a and b and the quantification of the sensitivity of calculated losses are fundamental for optimal earthquake risk management. Ignoring these uncertainties means that risk management decisions neglect possible variations of the earthquake loss estimations.
International Nuclear Information System (INIS)
Wattanapongskorn, Naruemon; Coit, David W.
2007-01-01
In this paper, we model embedded system design and optimization, considering component redundancy and uncertainty in the component reliability estimates. The systems being studied consist of software embedded in associated hardware components. Very often, component reliability values are not known exactly. Therefore, for reliability analysis studies and system optimization, it is meaningful to consider component reliability estimates as random variables with associated estimation uncertainty. In this new research, the system design process is formulated as a multiple-objective optimization problem to maximize an estimate of system reliability, and also, to minimize the variance of the reliability estimate. The two objectives are combined by penalizing the variance for prospective solutions. The two most common fault-tolerant embedded system architectures, N-Version Programming and Recovery Block, are considered as strategies to improve system reliability by providing system redundancy. Four distinct models are presented to demonstrate the proposed optimization techniques with or without redundancy. For many design problems, multiple functionally equivalent software versions have failure correlation even if they have been independently developed. The failure correlation may result from faults in the software specification, faults from a voting algorithm, and/or related faults from any two software versions. Our approach considers this correlation in formulating practical optimization models. Genetic algorithms with a dynamic penalty function are applied in solving this optimization problem, and reasonable and interesting results are obtained and discussed
DEFF Research Database (Denmark)
Frutiger, Jerome; Abildskov, Jens; Sin, Gürkan
Process safety studies and assessments rely on accurate property data. Flammability data like the lower and upper flammability limit (LFL and UFL) play an important role in quantifying the risk of fire and explosion. If experimental values are not available for the safety analysis due to cost...... or time constraints, property prediction models like group contribution (GC) models can estimate flammability data. The estimation needs to be accurate, reliable and as less time consuming as possible. However, GC property prediction methods frequently lack rigorous uncertainty analysis. Hence....... In this study, the MG-GC-factors are estimated using a systematic data and model evaluation methodology in the following way: 1) Data. Experimental flammability data is used from AIChE DIPPR 801 Database. 2) Initialization and sequential parameter estimation. An approximation using linear algebra provides...
Ciriello, V.; Lauriola, I.; Bonvicini, S.; Cozzani, V.; Di Federico, V.; Tartakovsky, Daniel M.
2017-11-01
Ubiquitous hydrogeological uncertainty undermines the veracity of quantitative predictions of soil and groundwater contamination due to accidental hydrocarbon spills from onshore pipelines. Such predictions, therefore, must be accompanied by quantification of predictive uncertainty, especially when they are used for environmental risk assessment. We quantify the impact of parametric uncertainty on quantitative forecasting of temporal evolution of two key risk indices, volumes of unsaturated and saturated soil contaminated by a surface spill of light nonaqueous-phase liquids. This is accomplished by treating the relevant uncertain parameters as random variables and deploying two alternative probabilistic models to estimate their effect on predictive uncertainty. A physics-based model is solved with a stochastic collocation method and is supplemented by a global sensitivity analysis. A second model represents the quantities of interest as polynomials of random inputs and has a virtually negligible computational cost, which enables one to explore any number of risk-related contamination scenarios. For a typical oil-spill scenario, our method can be used to identify key flow and transport parameters affecting the risk indices, to elucidate texture-dependent behavior of different soils, and to evaluate, with a degree of confidence specified by the decision-maker, the extent of contamination and the correspondent remediation costs.
International Nuclear Information System (INIS)
Spadaro, Joseph V.; Rabl, Ari
2008-01-01
Whereas the uncertainty of environmental impacts and damage costs is usually estimated by means of a Monte Carlo calculation, this paper shows that most (and in many cases all) of the uncertainty calculation involves products and/or sums of products and can be accomplished with an analytic solution which is simple and transparent. We present our own assessment of the component uncertainties and calculate the total uncertainty for the impacts and damage costs of the classical air pollutants; results for a Monte Carlo calculation for the dispersion part are also shown. The distribution of the damage costs is approximately lognormal and can be characterized in terms of geometric mean μ g and geometric standard deviation σ g , implying that the confidence interval is multiplicative. We find that for the classical air pollutants σ g is approximately 3 and the 68% confidence interval is [μ g / σ g , μ g σ g ]. Because the lognormal distribution is highly skewed for large σ g , the median is significantly smaller than the mean. We also consider the case where several lognormally distributed damage costs are added, for example to obtain the total damage cost due to all the air pollutants emitted by a power plant, and we find that the relative error of the sum can be significantly smaller than the relative errors of the summands. Even though the distribution for such sums is not exactly lognormal, we present a simple lognormal approximation that is quite adequate for most applications
Sampling of systematic errors to estimate likelihood weights in nuclear data uncertainty propagation
International Nuclear Information System (INIS)
Helgesson, P.; Sjöstrand, H.; Koning, A.J.; Rydén, J.; Rochman, D.; Alhassan, E.; Pomp, S.
2016-01-01
In methodologies for nuclear data (ND) uncertainty assessment and propagation based on random sampling, likelihood weights can be used to infer experimental information into the distributions for the ND. As the included number of correlated experimental points grows large, the computational time for the matrix inversion involved in obtaining the likelihood can become a practical problem. There are also other problems related to the conventional computation of the likelihood, e.g., the assumption that all experimental uncertainties are Gaussian. In this study, a way to estimate the likelihood which avoids matrix inversion is investigated; instead, the experimental correlations are included by sampling of systematic errors. It is shown that the model underlying the sampling methodology (using univariate normal distributions for random and systematic errors) implies a multivariate Gaussian for the experimental points (i.e., the conventional model). It is also shown that the likelihood estimates obtained through sampling of systematic errors approach the likelihood obtained with matrix inversion as the sample size for the systematic errors grows large. In studied practical cases, it is seen that the estimates for the likelihood weights converge impractically slowly with the sample size, compared to matrix inversion. The computational time is estimated to be greater than for matrix inversion in cases with more experimental points, too. Hence, the sampling of systematic errors has little potential to compete with matrix inversion in cases where the latter is applicable. Nevertheless, the underlying model and the likelihood estimates can be easier to intuitively interpret than the conventional model and the likelihood function involving the inverted covariance matrix. Therefore, this work can both have pedagogical value and be used to help motivating the conventional assumption of a multivariate Gaussian for experimental data. The sampling of systematic errors could also
Analysis of uncertainties in a probabilistic seismic hazard estimation, example for France
International Nuclear Information System (INIS)
Beauval, C.
2003-12-01
This thesis proposes a new methodology that allows to pinpoint the key parameters that control probabilistic seismic hazard assessment (PSHA) and at the same time to quantify the impact of these parameters uncertainties on hazard estimates. Cornell-McGuire's method is used here. First, uncertainties on magnitude and location determinations are modeled and quantified: resulting variability on hazard estimates ranges between 5% and 25% (=COV), depending on the site and the return period. An impact study is then performed, in order to determine the hierarchy between the impacts on hazard of the choices of four other parameters: intensity-magnitude correlation, minimum and maximum magnitudes, the truncation of the attenuation relationship. The results at 34 Hz (PGA) indicate that the maximum magnitude is the less influent parameter (from 100 to 10000 years); whereas the intensity-magnitude correlation and the truncation of ground motion predictions (>2σ) are the controlling parameters at all return periods (up to 30% decrease each at 10000 years). An increase in the minimum magnitude contributing to the hazard, from 3.5 to 4.5, can also produce non-negligible impacts at small return periods (up to 20% decrease of hazard results at 475 years). Finally, the overall variability on hazard estimates due to the combined choices of the four parameters can reach up to 30% (COV, at 34 Hz). For lower frequencies (<5 Hz), the overall variability increases and maximum magnitude becomes a controlling parameter. Therefore, variability of estimates due to catalog uncertainties and to the choices of these four parameters must be taken into account in all probabilistic seismic hazard studies in France. To reduce variability in hazard estimates, future research should concentrate on the elaboration of an appropriate intensity- magnitude correlation, as well as on a more realistic way of taking into account ground motion dispersion. (author)
Maboudi Afkham, Heydar; Qiu, Xuanbin; The, Matthew; Käll, Lukas
2017-02-15
Liquid chromatography is frequently used as a means to reduce the complexity of peptide-mixtures in shotgun proteomics. For such systems, the time when a peptide is released from a chromatography column and registered in the mass spectrometer is referred to as the peptide's retention time . Using heuristics or machine learning techniques, previous studies have demonstrated that it is possible to predict the retention time of a peptide from its amino acid sequence. In this paper, we are applying Gaussian Process Regression to the feature representation of a previously described predictor E lude . Using this framework, we demonstrate that it is possible to estimate the uncertainty of the prediction made by the model. Here we show how this uncertainty relates to the actual error of the prediction. In our experiments, we observe a strong correlation between the estimated uncertainty provided by Gaussian Process Regression and the actual prediction error. This relation provides us with new means for assessment of the predictions. We demonstrate how a subset of the peptides can be selected with lower prediction error compared to the whole set. We also demonstrate how such predicted standard deviations can be used for designing adaptive windowing strategies. lukas.kall@scilifelab.se. Our software and the data used in our experiments is publicly available and can be downloaded from https://github.com/statisticalbiotechnology/GPTime . © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com
Uncertainties of estimating average radon and radon decay product concentrations in occupied houses
International Nuclear Information System (INIS)
Ronca-Battista, M.; Magno, P.; Windham, S.
1986-01-01
Radon and radon decay product measurements made in up to 68 Butte, Montana homes over a period of 18 months were used to estimate the uncertainty in estimating long-term average radon and radon decay product concentrations from a short-term measurement. This analysis was performed in support of the development of radon and radon decay product measurement protocols by the Environmental Protection Agency (EPA). The results of six measurement methods were analyzed: continuous radon and working level monitors, radon progeny integrating sampling units, alpha-track detectors, and grab radon and radon decay product techniques. Uncertainties were found to decrease with increasing sampling time and to be smaller when measurements were conducted during the winter months. In general, radon measurements had a smaller uncertainty than radon decay product measurements. As a result of this analysis, the EPA measurements protocols specify that all measurements be made under closed-house (winter) conditions, and that sampling times of at least a 24 hour period be used when the measurement will be the basis for a decision about remedial action or long-term health risks. 13 references, 3 tables
International Nuclear Information System (INIS)
Reventos, F.
2008-01-01
One of the goals of computer code models of Nuclear Power Plants (NPP) is to demonstrate that these are designed to respond safely at postulated accidents. Models and codes are an approximation of the real physical behaviour occurring during a hypothetical transient and the data used to build these models are also known with certain accuracy. Therefore code predictions are uncertain. The BEMUSE programme is focussed on the application of uncertainty methodologies to large break LOCAs. The programme intends to evaluate the practicability, quality and reliability of best-estimate methods including uncertainty evaluations in applications relevant to nuclear reactor safety, to develop common understanding and to promote/facilitate their use by the regulator bodies and the industry. In order to fulfil its objectives BEMUSE is organized in to steps and six phases. The first step is devoted to the complete analysis of a LB-LOCA (L2-5) in an experimental facility (LOFT) while the second step refers to an actual Nuclear Power Plant. Both steps provide results on thermalhydraulic Best Estimate simulation as well as Uncertainty and sensitivity evaluation. At the time this paper is prepared, phases I, II and III are fully completed and the corresponding reports have been issued. Phase IV draft report is by now being reviewed while participants are working on Phase V developments. Phase VI consists in preparing the final status report which will summarizes the most relevant results of the whole programme.
Establishing bounding internal dose estimates for thorium activities at Rocky Flats.
Ulsh, Brant A; Rich, Bryce L; Chew, Melton H; Morris, Robert L; Sharfi, Mutty; Rolfes, Mark R
2008-07-01
As part of an evaluation of a Special Exposure Cohort petition filed on behalf of workers at the Rocky Flats Plant, the National Institute for Occupational Safety and Health (NIOSH) was required to demonstrate that bounding values could be established for radiation doses due to the potential intake of all radionuclides present at the facility. The main radioactive elements of interest at Rocky Flats were plutonium and uranium, but much smaller quantities of several other elements, including thorium, were occasionally handled at the site. Bounding potential doses from thorium has proven challenging at other sites due to the early historical difficulty in detecting this element through urinalysis methods and the relatively high internal dose delivered per unit intake. This paper reports the results of NIOSH's investigation of the uses of thorium at Rocky Flats and provides bounding dose reconstructions for these operations. During this investigation, NIOSH reviewed unclassified reports, unclassified extracts of classified materials, material balance and inventory ledgers, monthly progress reports from various groups, and health physics field logbooks, and conducted interviews with former Rocky Flats workers. Thorium operations included: (1) an experimental metal forming project with 240 kg of thorium in 1960; (2) the use of pre-formed parts in weapons mockups; (3) the removal of Th from U; (4) numerous analytical procedures involving trace quantities of thorium; and (5) the possible experimental use of thorium as a mold coating compound. The thorium handling operations at Rocky Flats were limited in scope, well-monitored and documented, and potential doses can be bounded.
Methods to estimate the between‐study variance and its uncertainty in meta‐analysis†
Jackson, Dan; Viechtbauer, Wolfgang; Bender, Ralf; Bowden, Jack; Knapp, Guido; Kuss, Oliver; Higgins, Julian PT; Langan, Dean; Salanti, Georgia
2015-01-01
Meta‐analyses are typically used to estimate the overall/mean of an outcome of interest. However, inference about between‐study variability, which is typically modelled using a between‐study variance parameter, is usually an additional aim. The DerSimonian and Laird method, currently widely used by default to estimate the between‐study variance, has been long challenged. Our aim is to identify known methods for estimation of the between‐study variance and its corresponding uncertainty, and to summarise the simulation and empirical evidence that compares them. We identified 16 estimators for the between‐study variance, seven methods to calculate confidence intervals, and several comparative studies. Simulation studies suggest that for both dichotomous and continuous data the estimator proposed by Paule and Mandel and for continuous data the restricted maximum likelihood estimator are better alternatives to estimate the between‐study variance. Based on the scenarios and results presented in the published studies, we recommend the Q‐profile method and the alternative approach based on a ‘generalised Cochran between‐study variance statistic’ to compute corresponding confidence intervals around the resulting estimates. Our recommendations are based on a qualitative evaluation of the existing literature and expert consensus. Evidence‐based recommendations require an extensive simulation study where all methods would be compared under the same scenarios. © 2015 The Authors. Research Synthesis Methods published by John Wiley & Sons Ltd. PMID:26332144
Dosso, S. E.; Molnar, S.; Cassidy, J.
2010-12-01
Bayesian inversion of microtremor array dispersion data is applied, with evaluation of data errors and model parameterization, to produce the most-probable shear-wave velocity (VS) profile together with quantitative uncertainty estimates. Generally, the most important property characterizing earthquake site response is the subsurface VS structure. The microtremor array method determines phase velocity dispersion of Rayleigh surface waves from multi-instrument recordings of urban noise. Inversion of dispersion curves for VS structure is a non-unique and nonlinear problem such that meaningful evaluation of confidence intervals is required. Quantitative uncertainty estimation requires not only a nonlinear inversion approach that samples models proportional to their probability, but also rigorous estimation of the data error statistics and an appropriate model parameterization. A Bayesian formulation represents the solution of the inverse problem in terms of the posterior probability density (PPD) of the geophysical model parameters. Markov-chain Monte Carlo methods are used with an efficient implementation of Metropolis-Hastings sampling to provide an unbiased sample from the PPD to compute parameter uncertainties and inter-relationships. Nonparametric estimation of a data error covariance matrix from residual analysis is applied with rigorous a posteriori statistical tests to validate the covariance estimate and the assumption of a Gaussian error distribution. The most appropriate model parameterization is determined using the Bayesian information criterion (BIC), which provides the simplest model consistent with the resolving power of the data. Parameter uncertainties are found to be under-estimated when data error correlations are neglected and when compressional-wave velocity and/or density (nuisance) parameters are fixed in the inversion. Bayesian inversion of microtremor array data is applied at two sites in British Columbia, the area of highest seismic risk in
Uncertainty estimation and multi sensor fusion for kinematic laser tracker measurements
Ulrich, Thomas
2013-08-01
Laser trackers are widely used to measure kinematic tasks such as tracking robot movements. Common methods to evaluate the uncertainty in the kinematic measurement include approximations specified by the manufacturers, various analytical adjustment methods and the Kalman filter. In this paper a new, real-time technique is proposed, which estimates the 4D-path (3D-position + time) uncertainty of an arbitrary path in space. Here a hybrid system estimator is applied in conjunction with the kinematic measurement model. This method can be applied to processes, which include various types of kinematic behaviour, constant velocity, variable acceleration or variable turn rates. The new approach is compared with the Kalman filter and a manufacturer's approximations. The comparison was made using data obtained by tracking an industrial robot's tool centre point with a Leica laser tracker AT901 and a Leica laser tracker LTD500. It shows that the new approach is more appropriate to analysing kinematic processes than the Kalman filter, as it reduces overshoots and decreases the estimated variance. In comparison with the manufacturer's approximations, the new approach takes account of kinematic behaviour with an improved description of the real measurement process and a reduction in estimated variance. This approach is therefore well suited to the analysis of kinematic processes with unknown changes in kinematic behaviour as well as the fusion among laser trackers.
International Nuclear Information System (INIS)
Chen, G.; Ferryman, T.A.; Remund, K.M.
1998-02-01
The exact physical and chemical nature of 55 million gallons of toxic waste held in 177 underground waste tanks at the Hanford Site is not known with sufficient detail to support the safety, retrieval, and immobilization missions presented to Hanford. The Hanford Best Basis team has made point estimates of the inventories in each tank. The purpose of this study is to estimate probability distributions for each of the 71 analytes and 177 tanks that the Hanford Best Basis team has made point estimates for. This will enable uncertainty intervals to be calculated for the Best Basis inventories and should facilitate the safety, retrieval, and immobilization missions. Section 2 of this document describes the overall approach used to estimate tank inventory uncertainties. Three major components are considered in this approach: chemical concentration, density, and waste volume. Section 2 also describes the two different methods used to evaluate the tank wastes in terms of sludges and in terms of supernatant or saltcakes. Sections 3 and 4 describe in detail the methodology to assess the probability distributions for each of the three components, as well as the data sources for implementation. The conclusions are given in Section 5
Developing first time-series of land surface temperature from AATSR with uncertainty estimates
Ghent, Darren; Remedios, John
2013-04-01
Land surface temperature (LST) is the radiative skin temperature of the land, and is one of the key parameters in the physics of land-surface processes on regional and global scales. Earth Observation satellites provide the opportunity to obtain global coverage of LST approximately every 3 days or less. One such source of satellite retrieved LST has been the Advanced Along-Track Scanning Radiometer (AATSR); with LST retrieval being implemented in the AATSR Instrument Processing Facility in March 2004. Here we present first regional and global time-series of LST data from AATSR with estimates of uncertainty. Mean changes in temperature over the last decade will be discussed along with regional patterns. Although time-series across all three ATSR missions have previously been constructed (Kogler et al., 2012), the use of low resolution auxiliary data in the retrieval algorithm and non-optimal cloud masking resulted in time-series artefacts. As such, considerable ESA supported development has been carried out on the AATSR data to address these concerns. This includes the integration of high resolution auxiliary data into the retrieval algorithm and subsequent generation of coefficients and tuning parameters, plus the development of an improved cloud mask based on the simulation of clear sky conditions from radiance transfer modelling (Ghent et al., in prep.). Any inference on this LST record is though of limited value without the accompaniment of an uncertainty estimate; wherein the Joint Committee for Guides in Metrology quote an uncertainty as "a parameter associated with the result of a measurement that characterizes the dispersion of the values that could reasonably be attributed to the measurand that is the value of the particular quantity to be measured". Furthermore, pixel level uncertainty fields are a mandatory requirement in the on-going preparation of the LST product for the upcoming Sea and Land Surface Temperature (SLSTR) instrument on-board Sentinel-3
Cade, Brian S.; Noon, Barry R.; Scherer, Rick D.; Keane, John J.
2017-01-01
Counts of avian fledglings, nestlings, or clutch size that are bounded below by zero and above by some small integer form a discrete random variable distribution that is not approximated well by conventional parametric count distributions such as the Poisson or negative binomial. We developed a logistic quantile regression model to provide estimates of the empirical conditional distribution of a bounded discrete random variable. The logistic quantile regression model requires that counts are randomly jittered to a continuous random variable, logit transformed to bound them between specified lower and upper values, then estimated in conventional linear quantile regression, repeating the 3 steps and averaging estimates. Back-transformation to the original discrete scale relies on the fact that quantiles are equivariant to monotonic transformations. We demonstrate this statistical procedure by modeling 20 years of California Spotted Owl fledgling production (0−3 per territory) on the Lassen National Forest, California, USA, as related to climate, demographic, and landscape habitat characteristics at territories. Spotted Owl fledgling counts increased nonlinearly with decreasing precipitation in the early nesting period, in the winter prior to nesting, and in the prior growing season; with increasing minimum temperatures in the early nesting period; with adult compared to subadult parents; when there was no fledgling production in the prior year; and when percentage of the landscape surrounding nesting sites (202 ha) with trees ≥25 m height increased. Changes in production were primarily driven by changes in the proportion of territories with 2 or 3 fledglings. Average variances of the discrete cumulative distributions of the estimated fledgling counts indicated that temporal changes in climate and parent age class explained 18% of the annual variance in owl fledgling production, which was 34% of the total variance. Prior fledgling production explained as much of
Faris, A M; Wang, H-H; Tarone, A M; Grant, W E
2016-05-31
Estimates of insect age can be informative in death investigations and, when certain assumptions are met, can be useful for estimating the postmortem interval (PMI). Currently, the accuracy and precision of PMI estimates is unknown, as error can arise from sources of variation such as measurement error, environmental variation, or genetic variation. Ecological models are an abstract, mathematical representation of an ecological system that can make predictions about the dynamics of the real system. To quantify the variation associated with the pre-appearance interval (PAI), we developed an ecological model that simulates the colonization of vertebrate remains by Cochliomyia macellaria (Fabricius) (Diptera: Calliphoridae), a primary colonizer in the southern United States. The model is based on a development data set derived from a local population and represents the uncertainty in local temperature variability to address PMI estimates at local sites. After a PMI estimate is calculated for each individual, the model calculates the maximum, minimum, and mean PMI, as well as the range and standard deviation for stadia collected. The model framework presented here is one manner by which errors in PMI estimates can be addressed in court when no empirical data are available for the parameter of interest. We show that PAI is a potential important source of error and that an ecological model is one way to evaluate its impact. Such models can be re-parameterized with any development data set, PAI function, temperature regime, assumption of interest, etc., to estimate PMI and quantify uncertainty that arises from specific prediction systems. © The Authors 2016. Published by Oxford University Press on behalf of Entomological Society of America. All rights reserved. For Permissions, please email: journals.permissions@oup.com.
International Nuclear Information System (INIS)
Boehmer, Bertram
2000-01-01
Results of estimation of the covariance matrix of the neutron spectrum in the WWER-1000 reactor cavity and pressure vessel positions are presented. Two-dimensional calculations with the discrete ordinates transport code DORT in r-theta and r-z-geometry used to determine the neutron group spectrum covariances including gross-correlations between interesting positions. The new Russian ABBN-93 data set and CONSYST code used to supply all transport calculations with group neutron data. All possible sources of uncertainties namely caused by the neutron gross sections, fission sources, geometrical dimensions and material densities considered, whereas the uncertainty of the calculation method was considered negligible in view of the available precision of Monte Carlo simulation used for more precise evaluation of the neutron fluence. (Authors)
UNCERTAINTY ON RADIATION DOSES ESTIMATED BY BIOLOGICAL AND RETROSPECTIVE PHYSICAL METHODS.
Ainsbury, Elizabeth A; Samaga, Daniel; Della Monaca, Sara; Marrale, Maurizio; Bassinet, Celine; Burbidge, Christopher I; Correcher, Virgilio; Discher, Michael; Eakins, Jon; Fattibene, Paola; Güçlü, Inci; Higueras, Manuel; Lund, Eva; Maltar-Strmecki, Nadica; McKeever, Stephen; Rääf, Christopher L; Sholom, Sergey; Veronese, Ivan; Wieser, Albrecht; Woda, Clemens; Trompier, Francois
2018-03-01
Biological and physical retrospective dosimetry are recognised as key techniques to provide individual estimates of dose following unplanned exposures to ionising radiation. Whilst there has been a relatively large amount of recent development in the biological and physical procedures, development of statistical analysis techniques has failed to keep pace. The aim of this paper is to review the current state of the art in uncertainty analysis techniques across the 'EURADOS Working Group 10-Retrospective dosimetry' members, to give concrete examples of implementation of the techniques recommended in the international standards, and to further promote the use of Monte Carlo techniques to support characterisation of uncertainties. It is concluded that sufficient techniques are available and in use by most laboratories for acute, whole body exposures to highly penetrating radiation, but further work will be required to ensure that statistical analysis is always wholly sufficient for the more complex exposure scenarios.
International Nuclear Information System (INIS)
Napier, Bruce A.; Shagina, N B.; Degteva, M O.; Tolstykh, E I.; Vorobiova, M I.; Anspaugh, L R.
2000-01-01
The Mayak Production Association (MPA) was the first facility in the former Soviet Union for the production of plutonium. As a result of failures in the technological processes in the late 1940's and early 1950's, members of the public were exposed via discharge of about 1017 Bq of liquid wastes into the Techa River (1949-1956). Residents of many villages downstream on the Techa River were exposed via a variety of pathways; the more significant included drinking of water from the river and external gamma exposure due to proximity to sediments and shoreline. The specific aim of this project is to enhance the reconstruction of external and internal radiation doses for individuals in the Extended Techa River Cohort. The purpose of this paper is to present the approaches being used to evaluate the uncertainty in the calculated individual doses and to provide example and representative results of the uncertainty analyses. The magnitude of the uncertainties varies depending on location and time of individual exposure, but the results from reference-individual calculations indicate that for external doses, the range of uncertainty is about factors of four to five. For internal doses, the range of uncertainty depends on village of residence, which is actually a surrogate for source of drinking water. For villages with single sources of drinking water (river or well), the ratio of the 97.5th percentile-to 2.5th percentile estimates can be a factor of 20 to 30. For villages with mixed sources of drinking water (river and well), the ratio of the range can be over two orders of magnitude
International Nuclear Information System (INIS)
Kristof, Marian; Kliment, Tomas; Petruzzi, Alessandro; Lipka, Jozef
2009-01-01
Licensing calculations in a majority of countries worldwide still rely on the application of combined approach using best estimate computer code without evaluation of the code models uncertainty and conservative assumptions on initial and boundary, availability of systems and components and additional conservative assumptions. However best estimate plus uncertainty (BEPU) approach representing the state-of-the-art in the area of safety analysis has a clear potential to replace currently used combined approach. There are several applications of BEPU approach in the area of licensing calculations, but some questions are discussed, namely from the regulatory point of view. In order to find a proper solution to these questions and to support the BEPU approach to become a standard approach for licensing calculations, a broad comparison of both approaches for various transients is necessary. Results of one of such comparisons on the example of the VVER-440/213 NPP pressurizer surge line break event are described in this paper. A Kv-scaled simulation based on PH4-SLB experiment from PMK-2 integral test facility applying its volume and power scaling factor is performed for qualitative assessment of the RELAP5 computer code calculation using the VVER-440/213 plant model. Existing hardware differences are identified and explained. The CIAU method is adopted for performing the uncertainty evaluation. Results using combined and BEPU approaches are in agreement with the experimental values in PMK-2 facility. Only minimal difference between combined and BEPU approached has been observed in the evaluation of the safety margins for the peak cladding temperature. Benefits of the CIAU uncertainty method are highlighted.
Varouchakis, Emmanouil; Hristopulos, Dionissios
2015-04-01
Space-time geostatistical approaches can improve the reliability of dynamic groundwater level models in areas with limited spatial and temporal data. Space-time residual Kriging (STRK) is a reliable method for spatiotemporal interpolation that can incorporate auxiliary information. The method usually leads to an underestimation of the prediction uncertainty. The uncertainty of spatiotemporal models is usually estimated by determining the space-time Kriging variance or by means of cross validation analysis. For de-trended data the former is not usually applied when complex spatiotemporal trend functions are assigned. A Bayesian approach based on the bootstrap idea and sequential Gaussian simulation are employed to determine the uncertainty of the spatiotemporal model (trend and covariance) parameters. These stochastic modelling approaches produce multiple realizations, rank the prediction results on the basis of specified criteria and capture the range of the uncertainty. The correlation of the spatiotemporal residuals is modeled using a non-separable space-time variogram based on the Spartan covariance family (Hristopulos and Elogne 2007, Varouchakis and Hristopulos 2013). We apply these simulation methods to investigate the uncertainty of groundwater level variations. The available dataset consists of bi-annual (dry and wet hydrological period) groundwater level measurements in 15 monitoring locations for the time period 1981 to 2010. The space-time trend function is approximated using a physical law that governs the groundwater flow in the aquifer in the presence of pumping. The main objective of this research is to compare the performance of two simulation methods for prediction uncertainty estimation. In addition, we investigate the performance of the Spartan spatiotemporal covariance function for spatiotemporal geostatistical analysis. Hristopulos, D.T. and Elogne, S.N. 2007. Analytic properties and covariance functions for a new class of generalized Gibbs
Sensitivity of Earthquake Loss Estimates to Source Modeling Assumptions and Uncertainty
Reasenberg, Paul A.; Shostak, Nan; Terwilliger, Sharon
2006-01-01
Introduction: This report explores how uncertainty in an earthquake source model may affect estimates of earthquake economic loss. Specifically, it focuses on the earthquake source model for the San Francisco Bay region (SFBR) created by the Working Group on California Earthquake Probabilities. The loss calculations are made using HAZUS-MH, a publicly available computer program developed by the Federal Emergency Management Agency (FEMA) for calculating future losses from earthquakes, floods and hurricanes within the United States. The database built into HAZUS-MH includes a detailed building inventory, population data, data on transportation corridors, bridges, utility lifelines, etc. Earthquake hazard in the loss calculations is based upon expected (median value) ground motion maps called ShakeMaps calculated for the scenario earthquake sources defined in WGCEP. The study considers the effect of relaxing certain assumptions in the WG02 model, and explores the effect of hypothetical reductions in epistemic uncertainty in parts of the model. For example, it addresses questions such as what would happen to the calculated loss distribution if the uncertainty in slip rate in the WG02 model were reduced (say, by obtaining additional geologic data)? What would happen if the geometry or amount of aseismic slip (creep) on the region's faults were better known? And what would be the effect on the calculated loss distribution if the time-dependent earthquake probability were better constrained, either by eliminating certain probability models or by better constraining the inherent randomness in earthquake recurrence? The study does not consider the effect of reducing uncertainty in the hazard introduced through models of attenuation and local site characteristics, although these may have a comparable or greater effect than does source-related uncertainty. Nor does it consider sources of uncertainty in the building inventory, building fragility curves, and other assumptions
Importance of tree basic density in biomass estimation and associated uncertainties
DEFF Research Database (Denmark)
Njana, Marco Andrew; Meilby, Henrik; Eid, Tron
2016-01-01
Key message Aboveground and belowground tree basic densities varied between and within the three mangrove species. If appropriately determined and applied, basic density may be useful in estimation of tree biomass. Predictive accuracy of the common (i.e. multi-species) models including aboveground...... of sustainable forest management, conservation and enhancement of carbon stocks (REDD+) initiatives offer an opportunity for sustainable management of forests including mangroves. In carbon accounting for REDD+, it is required that carbon estimates prepared for monitoring reporting and verification schemes...... and examine uncertainties in estimation of tree biomass using indirect methods. Methods This study focused on three dominant mangrove species (Avicennia marina (Forssk.) Vierh, Sonneratia alba J. Smith and Rhizophora mucronata Lam.) in Tanzania. A total of 120 trees were destructively sampled for aboveground...
Estimation of uncertainty in tracer gas measurement of air change rates.
Iizuka, Atsushi; Okuizumi, Yumiko; Yanagisawa, Yukio
2010-12-01
Simple and economical measurement of air change rates can be achieved with a passive-type tracer gas doser and sampler. However, this is made more complex by the fact many buildings are not a single fully mixed zone. This means many measurements are required to obtain information on ventilation conditions. In this study, we evaluated the uncertainty of tracer gas measurement of air change rate in n completely mixed zones. A single measurement with one tracer gas could be used to simply estimate the air change rate when n = 2. Accurate air change rates could not be obtained for n ≥ 2 due to a lack of information. However, the proposed method can be used to estimate an air change rate with an accuracy of air change rate can be avoided. The proposed estimation method will be useful in practical ventilation measurements.
PockDrug: A Model for Predicting Pocket Druggability That Overcomes Pocket Estimation Uncertainties.
Borrel, Alexandre; Regad, Leslie; Xhaard, Henri; Petitjean, Michel; Camproux, Anne-Claude
2015-04-27
Predicting protein druggability is a key interest in the target identification phase of drug discovery. Here, we assess the pocket estimation methods' influence on druggability predictions by comparing statistical models constructed from pockets estimated using different pocket estimation methods: a proximity of either 4 or 5.5 Å to a cocrystallized ligand or DoGSite and fpocket estimation methods. We developed PockDrug, a robust pocket druggability model that copes with uncertainties in pocket boundaries. It is based on a linear discriminant analysis from a pool of 52 descriptors combined with a selection of the most stable and efficient models using different pocket estimation methods. PockDrug retains the best combinations of three pocket properties which impact druggability: geometry, hydrophobicity, and aromaticity. It results in an average accuracy of 87.9% ± 4.7% using a test set and exhibits higher accuracy (∼5-10%) than previous studies that used an identical apo set. In conclusion, this study confirms the influence of pocket estimation on pocket druggability prediction and proposes PockDrug as a new model that overcomes pocket estimation variability.
Audit of the global carbon budget: estimate errors and their impact on uptake uncertainty
Ballantyne, A. P.; Andres, R.; Houghton, R.; Stocker, B. D.; Wanninkhof, R.; Anderegg, W.; Cooper, L. A.; DeGrandpre, M.; Tans, P. P.; Miller, J. B.; Alden, C.; White, J. W. C.
2015-04-01
Over the last 5 decades monitoring systems have been developed to detect changes in the accumulation of carbon (C) in the atmosphere and ocean; however, our ability to detect changes in the behavior of the global C cycle is still hindered by measurement and estimate errors. Here we present a rigorous and flexible framework for assessing the temporal and spatial components of estimate errors and their impact on uncertainty in net C uptake by the biosphere. We present a novel approach for incorporating temporally correlated random error into the error structure of emission estimates. Based on this approach, we conclude that the 2σ uncertainties of the atmospheric growth rate have decreased from 1.2 Pg C yr-1 in the 1960s to 0.3 Pg C yr-1 in the 2000s due to an expansion of the atmospheric observation network. The 2σ uncertainties in fossil fuel emissions have increased from 0.3 Pg C yr-1 in the 1960s to almost 1.0 Pg C yr-1 during the 2000s due to differences in national reporting errors and differences in energy inventories. Lastly, while land use emissions have remained fairly constant, their errors still remain high and thus their global C uptake uncertainty is not trivial. Currently, the absolute errors in fossil fuel emissions rival the total emissions from land use, highlighting the extent to which fossil fuels dominate the global C budget. Because errors in the atmospheric growth rate have decreased faster than errors in total emissions have increased, a ~20% reduction in the overall uncertainty of net C global uptake has occurred. Given all the major sources of error in the global C budget that we could identify, we are 93% confident that terrestrial C uptake has increased and 97% confident that ocean C uptake has increased over the last 5 decades. Thus, it is clear that arguably one of the most vital ecosystem services currently provided by the biosphere is the continued removal of approximately half of atmospheric CO2 emissions from the atmosphere
Uncertainties in neural network model based on carbon dioxide concentration for occupancy estimation
Energy Technology Data Exchange (ETDEWEB)
Alam, Azimil Gani; Rahman, Haolia; Kim, Jung-Kyung; Han, Hwataik [Kookmin University, Seoul (Korea, Republic of)
2017-05-15
Demand control ventilation is employed to save energy by adjusting airflow rate according to the ventilation load of a building. This paper investigates a method for occupancy estimation by using a dynamic neural network model based on carbon dioxide concentration in an occupied zone. The method can be applied to most commercial and residential buildings where human effluents to be ventilated. An indoor simulation program CONTAMW is used to generate indoor CO{sub 2} data corresponding to various occupancy schedules and airflow patterns to train neural network models. Coefficients of variation are obtained depending on the complexities of the physical parameters as well as the system parameters of neural networks, such as the numbers of hidden neurons and tapped delay lines. We intend to identify the uncertainties caused by the model parameters themselves, by excluding uncertainties in input data inherent in measurement. Our results show estimation accuracy is highly influenced by the frequency of occupancy variation but not significantly influenced by fluctuation in the airflow rate. Furthermore, we discuss the applicability and validity of the present method based on passive environmental conditions for estimating occupancy in a room from the viewpoint of demand control ventilation applications.
Linear estimation of coherent structures in wall-bounded turbulence at Re τ = 2000
Oehler, S.; Garcia–Gutiérrez, A.; Illingworth, S.
2018-04-01
The estimation problem for a fully-developed turbulent channel flow at Re τ = 2000 is considered. Specifically, a Kalman filter is designed using a Navier–Stokes-based linear model. The estimator uses time-resolved velocity measurements at a single wall-normal location (provided by DNS) to estimate the time-resolved velocity field at other wall-normal locations. The estimator is able to reproduce the largest scales with reasonable accuracy for a range of wavenumber pairs, measurement locations and estimation locations. Importantly, the linear model is also able to predict with reasonable accuracy the performance that will be achieved by the estimator when applied to the DNS. A more practical estimation scheme using the shear stress at the wall as measurement is also considered. The estimator is still able to estimate the largest scales with reasonable accuracy, although the estimator’s performance is reduced.
Yang, Ming; Zhu, X. Ronald; Park, Peter C.; Titt, Uwe; Mohan, Radhe; Virshup, Gary; Clayton, James E.; Dong, Lei
2012-07-01
The purpose of this study was to analyze factors affecting proton stopping-power-ratio (SPR) estimations and range uncertainties in proton therapy planning using the standard stoichiometric calibration. The SPR uncertainties were grouped into five categories according to their origins and then estimated based on previously published reports or measurements. For the first time, the impact of tissue composition variations on SPR estimation was assessed and the uncertainty estimates of each category were determined for low-density (lung), soft, and high-density (bone) tissues. A composite, 95th percentile water-equivalent-thickness uncertainty was calculated from multiple beam directions in 15 patients with various types of cancer undergoing proton therapy. The SPR uncertainties (1σ) were quite different (ranging from 1.6% to 5.0%) in different tissue groups, although the final combined uncertainty (95th percentile) for different treatment sites was fairly consistent at 3.0-3.4%, primarily because soft tissue is the dominant tissue type in the human body. The dominant contributing factor for uncertainties in soft tissues was the degeneracy of Hounsfield numbers in the presence of tissue composition variations. To reduce the overall uncertainties in SPR estimation, the use of dual-energy computed tomography is suggested. The values recommended in this study based on typical treatment sites and a small group of patients roughly agree with the commonly referenced value (3.5%) used for margin design. By using tissue-specific range uncertainties, one could estimate the beam-specific range margin by accounting for different types and amounts of tissues along a beam, which may allow for customization of range uncertainty for each beam direction.
Ndambiri, H.; Brouwer, R.; Mungatana, E.
2016-01-01
The effect of preference uncertainty on estimated willingness to pay (WTP) is examined using identical payment cards and alternative uncertainty elicitation procedures in three split samples, focusing on air quality improvement in Nairobi. The effect of the stochastic payment card (SPC) and
Lash, Timothy L
2007-11-26
The associations of pesticide exposure with disease outcomes are estimated without the benefit of a randomized design. For this reason and others, these studies are susceptible to systematic errors. I analyzed studies of the associations between alachlor and glyphosate exposure and cancer incidence, both derived from the Agricultural Health Study cohort, to quantify the bias and uncertainty potentially attributable to systematic error. For each study, I identified the prominent result and important sources of systematic error that might affect it. I assigned probability distributions to the bias parameters that allow quantification of the bias, drew a value at random from each assigned distribution, and calculated the estimate of effect adjusted for the biases. By repeating the draw and adjustment process over multiple iterations, I generated a frequency distribution of adjusted results, from which I obtained a point estimate and simulation interval. These methods were applied without access to the primary record-level dataset. The conventional estimates of effect associating alachlor and glyphosate exposure with cancer incidence were likely biased away from the null and understated the uncertainty by quantifying only random error. For example, the conventional p-value for a test of trend in the alachlor study equaled 0.02, whereas fewer than 20% of the bias analysis iterations yielded a p-value of 0.02 or lower. Similarly, the conventional fully-adjusted result associating glyphosate exposure with multiple myleoma equaled 2.6 with 95% confidence interval of 0.7 to 9.4. The frequency distribution generated by the bias analysis yielded a median hazard ratio equal to 1.5 with 95% simulation interval of 0.4 to 8.9, which was 66% wider than the conventional interval. Bias analysis provides a more complete picture of true uncertainty than conventional frequentist statistical analysis accompanied by a qualitative description of study limitations. The latter approach is
Directory of Open Access Journals (Sweden)
Lash Timothy L
2007-11-01
Full Text Available Abstract Background The associations of pesticide exposure with disease outcomes are estimated without the benefit of a randomized design. For this reason and others, these studies are susceptible to systematic errors. I analyzed studies of the associations between alachlor and glyphosate exposure and cancer incidence, both derived from the Agricultural Health Study cohort, to quantify the bias and uncertainty potentially attributable to systematic error. Methods For each study, I identified the prominent result and important sources of systematic error that might affect it. I assigned probability distributions to the bias parameters that allow quantification of the bias, drew a value at random from each assigned distribution, and calculated the estimate of effect adjusted for the biases. By repeating the draw and adjustment process over multiple iterations, I generated a frequency distribution of adjusted results, from which I obtained a point estimate and simulation interval. These methods were applied without access to the primary record-level dataset. Results The conventional estimates of effect associating alachlor and glyphosate exposure with cancer incidence were likely biased away from the null and understated the uncertainty by quantifying only random error. For example, the conventional p-value for a test of trend in the alachlor study equaled 0.02, whereas fewer than 20% of the bias analysis iterations yielded a p-value of 0.02 or lower. Similarly, the conventional fully-adjusted result associating glyphosate exposure with multiple myleoma equaled 2.6 with 95% confidence interval of 0.7 to 9.4. The frequency distribution generated by the bias analysis yielded a median hazard ratio equal to 1.5 with 95% simulation interval of 0.4 to 8.9, which was 66% wider than the conventional interval. Conclusion Bias analysis provides a more complete picture of true uncertainty than conventional frequentist statistical analysis accompanied by a
International Nuclear Information System (INIS)
Heo, Jaeseok; Kim, Kyung Doo
2015-01-01
Highlights: • We developed an interface between an engineering simulation code and statistical analysis software. • Multiple packages of the sensitivity analysis, uncertainty quantification, and parameter estimation algorithms are implemented in the framework. • Parallel computing algorithms are also implemented in the framework to solve multiple computational problems simultaneously. - Abstract: This paper introduces a statistical data analysis toolkit, PAPIRUS, designed to perform the model calibration, uncertainty propagation, Chi-square linearity test, and sensitivity analysis for both linear and nonlinear problems. The PAPIRUS was developed by implementing multiple packages of methodologies, and building an interface between an engineering simulation code and the statistical analysis algorithms. A parallel computing framework is implemented in the PAPIRUS with multiple computing resources and proper communications between the server and the clients of each processor. It was shown that even though a large amount of data is considered for the engineering calculation, the distributions of the model parameters and the calculation results can be quantified accurately with significant reductions in computational effort. A general description about the PAPIRUS with a graphical user interface is presented in Section 2. Sections 2.1–2.5 present the methodologies of data assimilation, uncertainty propagation, Chi-square linearity test, and sensitivity analysis implemented in the toolkit with some results obtained by each module of the software. Parallel computing algorithms adopted in the framework to solve multiple computational problems simultaneously are also summarized in the paper
International Nuclear Information System (INIS)
Heo, Jaeseok; Kim, Kyung Doo
2015-01-01
Statistical approaches to uncertainty quantification and sensitivity analysis are very important in estimating the safety margins for an engineering design application. This paper presents a system analysis and optimization toolkit developed by Korea Atomic Energy Research Institute (KAERI), which includes multiple packages of the sensitivity analysis and uncertainty quantification algorithms. In order to reduce the computing demand, multiple compute resources including multiprocessor computers and a network of workstations are simultaneously used. A Graphical User Interface (GUI) was also developed within the parallel computing framework for users to readily employ the toolkit for an engineering design and optimization problem. The goal of this work is to develop a GUI framework for engineering design and scientific analysis problems by implementing multiple packages of system analysis methods in the parallel computing toolkit. This was done by building an interface between an engineering simulation code and the system analysis software packages. The methods and strategies in the framework were designed to exploit parallel computing resources such as those found in a desktop multiprocessor workstation or a network of workstations. Available approaches in the framework include statistical and mathematical algorithms for use in science and engineering design problems. Currently the toolkit has 6 modules of the system analysis methodologies: deterministic and probabilistic approaches of data assimilation, uncertainty propagation, Chi-square linearity test, sensitivity analysis, and FFTBM
Energy Technology Data Exchange (ETDEWEB)
Heo, Jaeseok; Kim, Kyung Doo [KAERI, Daejeon (Korea, Republic of)
2015-05-15
Statistical approaches to uncertainty quantification and sensitivity analysis are very important in estimating the safety margins for an engineering design application. This paper presents a system analysis and optimization toolkit developed by Korea Atomic Energy Research Institute (KAERI), which includes multiple packages of the sensitivity analysis and uncertainty quantification algorithms. In order to reduce the computing demand, multiple compute resources including multiprocessor computers and a network of workstations are simultaneously used. A Graphical User Interface (GUI) was also developed within the parallel computing framework for users to readily employ the toolkit for an engineering design and optimization problem. The goal of this work is to develop a GUI framework for engineering design and scientific analysis problems by implementing multiple packages of system analysis methods in the parallel computing toolkit. This was done by building an interface between an engineering simulation code and the system analysis software packages. The methods and strategies in the framework were designed to exploit parallel computing resources such as those found in a desktop multiprocessor workstation or a network of workstations. Available approaches in the framework include statistical and mathematical algorithms for use in science and engineering design problems. Currently the toolkit has 6 modules of the system analysis methodologies: deterministic and probabilistic approaches of data assimilation, uncertainty propagation, Chi-square linearity test, sensitivity analysis, and FFTBM.
Nonlinear parameter estimation in inviscid compressible flows in presence of uncertainties
International Nuclear Information System (INIS)
Jemcov, A.; Mathur, S.
2004-01-01
The focus of this paper is on the formulation and solution of inverse problems of parameter estimation using algorithmic differentiation. The inverse problem formulated here seeks to determine the input parameters that minimize a least squares functional with respect to certain target data. The formulation allows for uncertainty in the target data by considering the least squares functional in a stochastic basis described by the covariance of the target data. Furthermore, to allow for robust design, the formulation also accounts for uncertainties in the input parameters. This is achieved using the method of propagation of uncertainties using the directional derivatives of the output parameters with respect to unknown parameters. The required derivatives are calculated simultaneously with the solution using generic programming exploiting the template and operator overloading features of the C++ language. The methodology described here is general and applicable to any numerical solution procedure for any set of governing equations but for the purpose of this paper we consider a finite volume solution of the compressible Euler equations. In particular, we illustrate the method for the case of supersonic flow in a duct with a wedge. The parameter to be determined is the inlet Mach number and the target data is the axial component of velocity at the exit of the duct. (author)
Uncertainty estimation with bias-correction for flow series based on rating curve
Shao, Quanxi; Lerat, Julien; Podger, Geoff; Dutta, Dushmanta
2014-03-01
Streamflow discharge constitutes one of the fundamental data required to perform water balance studies and develop hydrological models. A rating curve, designed based on a series of concurrent stage and discharge measurements at a gauging location, provides a way to generate complete discharge time series with a reasonable quality if sufficient measurement points are available. However, the associated uncertainty is frequently not available even though it has a significant impact on hydrological modelling. In this paper, we identify the discrepancy of the hydrographers' rating curves used to derive the historical discharge data series and proposed a modification by bias correction which is also in the form of power function as the traditional rating curve. In order to obtain the uncertainty estimation, we propose a further both-side Box-Cox transformation to stabilize the regression residuals as close to the normal distribution as possible, so that a proper uncertainty can be attached for the whole discharge series in the ensemble generation. We demonstrate the proposed method by applying it to the gauging stations in the Flinders and Gilbert rivers in north-west Queensland, Australia.
Energy Technology Data Exchange (ETDEWEB)
Heo, Jaeseok, E-mail: jheo@kaeri.re.kr; Kim, Kyung Doo, E-mail: kdkim@kaeri.re.kr
2015-10-15
Highlights: • We developed an interface between an engineering simulation code and statistical analysis software. • Multiple packages of the sensitivity analysis, uncertainty quantification, and parameter estimation algorithms are implemented in the framework. • Parallel computing algorithms are also implemented in the framework to solve multiple computational problems simultaneously. - Abstract: This paper introduces a statistical data analysis toolkit, PAPIRUS, designed to perform the model calibration, uncertainty propagation, Chi-square linearity test, and sensitivity analysis for both linear and nonlinear problems. The PAPIRUS was developed by implementing multiple packages of methodologies, and building an interface between an engineering simulation code and the statistical analysis algorithms. A parallel computing framework is implemented in the PAPIRUS with multiple computing resources and proper communications between the server and the clients of each processor. It was shown that even though a large amount of data is considered for the engineering calculation, the distributions of the model parameters and the calculation results can be quantified accurately with significant reductions in computational effort. A general description about the PAPIRUS with a graphical user interface is presented in Section 2. Sections 2.1–2.5 present the methodologies of data assimilation, uncertainty propagation, Chi-square linearity test, and sensitivity analysis implemented in the toolkit with some results obtained by each module of the software. Parallel computing algorithms adopted in the framework to solve multiple computational problems simultaneously are also summarized in the paper.
Wang, Lei; Xiong, Chuang; Wang, Xiaojun; Li, Yunlong; Xu, Menghui
2018-04-01
Considering that multi-source uncertainties from inherent nature as well as the external environment are unavoidable and severely affect the controller performance, the dynamic safety assessment with high confidence is of great significance for scientists and engineers. In view of this, the uncertainty quantification analysis and time-variant reliability estimation corresponding to the closed-loop control problems are conducted in this study under a mixture of random, interval, and convex uncertainties. By combining the state-space transformation and the natural set expansion, the boundary laws of controlled response histories are first confirmed with specific implementation of random items. For nonlinear cases, the collocation set methodology and fourth Rounge-Kutta algorithm are introduced as well. Enlightened by the first-passage model in random process theory as well as by the static probabilistic reliability ideas, a new definition of the hybrid time-variant reliability measurement is provided for the vibration control systems and the related solution details are further expounded. Two engineering examples are eventually presented to demonstrate the validity and applicability of the methodology developed.
A Bootstrap Approach to Computing Uncertainty in Inferred Oil and Gas Reserve Estimates
International Nuclear Information System (INIS)
Attanasi, Emil D.; Coburn, Timothy C.
2004-01-01
This study develops confidence intervals for estimates of inferred oil and gas reserves based on bootstrap procedures. Inferred reserves are expected additions to proved reserves in previously discovered conventional oil and gas fields. Estimates of inferred reserves accounted for 65% of the total oil and 34% of the total gas assessed in the U.S. Geological Survey's 1995 National Assessment of oil and gas in US onshore and State offshore areas. When the same computational methods used in the 1995 Assessment are applied to more recent data, the 80-year (from 1997 through 2076) inferred reserve estimates for pre-1997 discoveries located in the lower 48 onshore and state offshore areas amounted to a total of 39.7 billion barrels of oil (BBO) and 293 trillion cubic feet (TCF) of gas. The 90% confidence interval about the oil estimate derived from the bootstrap approach is 22.4 BBO to 69.5 BBO. The comparable 90% confidence interval for the inferred gas reserve estimate is 217 TCF to 413 TCF. The 90% confidence interval describes the uncertainty that should be attached to the estimates. It also provides a basis for developing scenarios to explore the implications for energy policy analysis
Cost Implications of Uncertainty in CO{sub 2} Storage Resource Estimates: A Review
Energy Technology Data Exchange (ETDEWEB)
Anderson, Steven T., E-mail: sanderson@usgs.gov [National Center, U.S. Geological Survey (United States)
2017-04-15
Carbon capture from stationary sources and geologic storage of carbon dioxide (CO{sub 2}) is an important option to include in strategies to mitigate greenhouse gas emissions. However, the potential costs of commercial-scale CO{sub 2} storage are not well constrained, stemming from the inherent uncertainty in storage resource estimates coupled with a lack of detailed estimates of the infrastructure needed to access those resources. Storage resource estimates are highly dependent on storage efficiency values or storage coefficients, which are calculated based on ranges of uncertain geological and physical reservoir parameters. If dynamic factors (such as variability in storage efficiencies, pressure interference, and acceptable injection rates over time), reservoir pressure limitations, boundaries on migration of CO{sub 2}, consideration of closed or semi-closed saline reservoir systems, and other possible constraints on the technically accessible CO{sub 2} storage resource (TASR) are accounted for, it is likely that only a fraction of the TASR could be available without incurring significant additional costs. Although storage resource estimates typically assume that any issues with pressure buildup due to CO{sub 2} injection will be mitigated by reservoir pressure management, estimates of the costs of CO{sub 2} storage generally do not include the costs of active pressure management. Production of saline waters (brines) could be essential to increasing the dynamic storage capacity of most reservoirs, but including the costs of this critical method of reservoir pressure management could increase current estimates of the costs of CO{sub 2} storage by two times, or more. Even without considering the implications for reservoir pressure management, geologic uncertainty can significantly impact CO{sub 2} storage capacities and costs, and contribute to uncertainty in carbon capture and storage (CCS) systems. Given the current state of available information and the
Supporting qualified database for V and V and uncertainty evaluation of best-estimate system codes
International Nuclear Information System (INIS)
Petruzzi, A.; D'Auria, F.
2014-01-01
Uncertainty evaluation constitutes a key feature of BEPU (Best Estimate Plus Uncertainty) process. The uncertainty can be the result of a Monte Carlo type analysis involving input uncertainty parameters or the outcome of a process involving the use of experimental data and connected code calculations. Those uncertainty methods are discussed in several papers and guidelines (IAEA-SRS- 52, OECD/NEA BEMUSE reports). The present paper aims at discussing the role and the depth of the analysis required for merging from one side suitable experimental data and on the other side qualified code calculation results. This aspect is mostly connected with the second approach for uncertainty mentioned above, but it can be used also in the framework of the first approach. Namely, the paper discusses the features and structure of the database that includes the following kinds of documents: 1. The' RDS-facility' (Reference Data Set for the selected facility): this includes the description of the facility, the geometrical characterization of any component of the facility, the instrumentations, the data acquisition system, the evaluation of pressure losses, the physical properties of the material and the characterization of pumps, valves and heat losses; 2. The 'RDS-test' (Reference Data Set for the selected test of the facility): this includes the description of the main phenomena investigated during the test, the configuration of the facility for the selected test (possible new evaluation of pressure and heat losses if needed) and the specific boundary and initial conditions; 3. The 'QP' (Qualification Report) of the code calculation results: this includes the description of the nodalization developed following a set of homogeneous techniques, the achievement of the steady state conditions and the qualitative and quantitative analysis of the transient with the characterization of the Relevant Thermal-Hydraulics Aspects (RTA); 4. The EH (Engineering
Estimation of plant sampling uncertainty: an example based on chemical analysis of moss samples.
Dołęgowska, Sabina
2016-11-01
In order to estimate the level of uncertainty arising from sampling, 54 samples (primary and duplicate) of the moss species Pleurozium schreberi (Brid.) Mitt. were collected within three forested areas (Wierna Rzeka, Piaski, Posłowice Range) in the Holy Cross Mountains (south-central Poland). During the fieldwork, each primary sample composed of 8 to 10 increments (subsamples) was taken over an area of 10 m 2 whereas duplicate samples were collected in the same way at a distance of 1-2 m. Subsequently, all samples were triple rinsed with deionized water, dried, milled, and digested (8 mL HNO 3 (1:1) + 1 mL 30 % H 2 O 2 ) in a closed microwave system Multiwave 3000. The prepared solutions were analyzed twice for Cu, Fe, Mn, and Zn using FAAS and GFAAS techniques. All datasets were checked for normality and for normally distributed elements (Cu from Piaski, Zn from Posłowice, Fe, Zn from Wierna Rzeka). The sampling uncertainty was computed with (i) classical ANOVA, (ii) classical RANOVA, (iii) modified RANOVA, and (iv) range statistics. For the remaining elements, the sampling uncertainty was calculated with traditional and/or modified RANOVA (if the amount of outliers did not exceed 10 %) or classical ANOVA after Box-Cox transformation (if the amount of outliers exceeded 10 %). The highest concentrations of all elements were found in moss samples from Piaski, whereas the sampling uncertainty calculated with different statistical methods ranged from 4.1 to 22 %.
Ershadi, Ali; McCabe, Matthew; Evans, Jason P.; Mariethoz, Gregoire; Kavetski, Dmitri
2013-01-01
The influence of uncertainty in land surface temperature, air temperature, and wind speed on the estimation of sensible heat flux is analyzed using a Bayesian inference technique applied to the Surface Energy Balance System (SEBS) model
Model uncertainty estimation and risk assessment is essential to environmental management and informed decision making on pollution mitigation strategies. In this study, we apply a probabilistic methodology, which combines Bayesian Monte Carlo simulation and Maximum Likelihood e...
International Nuclear Information System (INIS)
Santana, L V; Sarkis, J E S; Ulrich, J C; Hortellani, M A
2015-01-01
We provide an uncertainty estimates for homogeneity and stability studies of reference material used in proficiency test for determination of total mercury in fish fresh muscle tissue. Stability was estimated by linear regression and homogeneity by ANOVA. The results indicate that the reference material is both homogeneous and chemically stable over the short term. Total mercury concentration of the muscle tissue, with expanded uncertainty, was 0.294 ± 0.089 μg g −1
International Nuclear Information System (INIS)
Bae, In Ho; Naa, Man Gyun; Lee, Yoon Joon; Park, Goon Cherl
2009-01-01
The monitoring of detailed 3-dimensional (3D) reactor core power distribution is a prerequisite in the operation of nuclear power reactors to ensure that various safety limits imposed on the LPD and DNBR, are not violated during nuclear power reactor operation. The LPD and DNBR should be calculated in order to perform the two major functions of the core protection calculator system (CPCS) and the core operation limit supervisory system (COLSS). The LPD at the hottest part of a hot fuel rod, which is related to the power peaking factor (PPF, F q ), is more important than the LPD at any other position in a reactor core. The LPD needs to be estimated accurately to prevent nuclear fuel rods from melting. In this study, support vector regression (SVR) and uncertainty analysis have been applied to estimation of reactor core power peaking factor
The influence of climate change on flood risks in France - first estimates and uncertainty analysis
Dumas, P.; Hallegatte, S.; Quintana-Seguì, P.; Martin, E.
2013-03-01
This paper proposes a methodology to project the possible evolution of river flood damages due to climate change, and applies it to mainland France. Its main contributions are (i) to demonstrate a methodology to investigate the full causal chain from global climate change to local economic flood losses; (ii) to show that future flood losses may change in a very significant manner over France; (iii) to show that a very large uncertainty arises from the climate downscaling technique, since two techniques with comparable skills at reproducing reference river flows give very different estimates of future flows, and thus of future local losses. The main conclusion is thus that estimating future flood losses is still out of reach, especially at local scale, but that future national-scale losses may change significantly over this century, requiring policy changes in terms of risk management and land-use planning.
Directory of Open Access Journals (Sweden)
Beatriz Galindo-Prieto
2018-02-01
Full Text Available Independent component analysis combined with various strategies for cross-validation, uncertainty estimates by jack-knifing and critical Hotelling’s T2 limits estimation, proposed in this paper, is used for classification purposes in hyperspectral images. To the best of our knowledge, the combined approach of methods used in this paper has not been previously applied to hyperspectral imaging analysis for interpretation and classification in the literature. The data analysis performed here aims to distinguish between four different types of plastics, some of them containing brominated flame retardants, from their near infrared hyperspectral images. The results showed that the method approach used here can be successfully used for unsupervised classification. A comparison of validation approaches, especially leave-one-out cross-validation and regions of interest scheme validation is also evaluated.
Density meter algorithm and system for estimating sampling/mixing uncertainty
International Nuclear Information System (INIS)
Shine, E.P.
1986-01-01
The Laboratories Department at the Savannah River Plant (SRP) has installed a six-place density meter with an automatic sampling device. This paper describes the statistical software developed to analyze the density of uranyl nitrate solutions using this automated system. The purpose of this software is twofold: to estimate the sampling/mixing and measurement uncertainties in the process and to provide a measurement control program for the density meter. Non-uniformities in density are analyzed both analytically and graphically. The mean density and its limit of error are estimated. Quality control standards are analyzed concurrently with process samples and used to control the density meter measurement error. The analyses are corrected for concentration due to evaporation of samples waiting to be analyzed. The results of this program have been successful in identifying sampling/mixing problems and controlling the quality of analyses
Estimation of Uncertainty in Tracer Gas Measurement of Air Change Rates
Directory of Open Access Journals (Sweden)
Atsushi Iizuka
2010-12-01
Full Text Available Simple and economical measurement of air change rates can be achieved with a passive-type tracer gas doser and sampler. However, this is made more complex by the fact many buildings are not a single fully mixed zone. This means many measurements are required to obtain information on ventilation conditions. In this study, we evaluated the uncertainty of tracer gas measurement of air change rate in n completely mixed zones. A single measurement with one tracer gas could be used to simply estimate the air change rate when n = 2. Accurate air change rates could not be obtained for n ≥ 2 due to a lack of information. However, the proposed method can be used to estimate an air change rate with an accuracy of
Density meter algorithm and system for estimating sampling/mixing uncertainty
International Nuclear Information System (INIS)
Shine, E.P.
1986-01-01
The Laboratories Department at the Savannah River Plant (SRP) has installed a six-place density meter with an automatic sampling device. This paper describes the statisical software developed to analyze the density of uranyl nitrate solutions using this automated system. The purpose of this software is twofold: to estimate the sampling/mixing and measurement uncertainties in the process and to provide a measurement control program for the density meter. Non-uniformities in density are analyzed both analytically and graphically. The mean density and its limit of error are estimated. Quality control standards are analyzed concurrently with process samples and used to control the density meter measurement error. The analyses are corrected for concentration due to evaporation of samples waiting to be analyzed. The results of this program have been successful in identifying sampling/mixing problems and controlling the quality of analyses
Som, Nicholas A.; Goodman, Damon H.; Perry, Russell W.; Hardy, Thomas B.
2016-01-01
Previous methods for constructing univariate habitat suitability criteria (HSC) curves have ranged from professional judgement to kernel-smoothed density functions or combinations thereof. We present a new method of generating HSC curves that applies probability density functions as the mathematical representation of the curves. Compared with previous approaches, benefits of our method include (1) estimation of probability density function parameters directly from raw data, (2) quantitative methods for selecting among several candidate probability density functions, and (3) concise methods for expressing estimation uncertainty in the HSC curves. We demonstrate our method with a thorough example using data collected on the depth of water used by juvenile Chinook salmon (Oncorhynchus tschawytscha) in the Klamath River of northern California and southern Oregon. All R code needed to implement our example is provided in the appendix. Published 2015. This article is a U.S. Government work and is in the public domain in the USA.
A NEW METHOD FOR PREDICTING SURVIVAL AND ESTIMATING UNCERTAINTY IN TRAUMA PATIENTS
Directory of Open Access Journals (Sweden)
V. G. Schetinin
2017-01-01
Full Text Available The Trauma and Injury Severity Score (TRISS is the current “gold” standard of screening patient’s condition for purposes of predicting survival probability. More than 40 years of TRISS practice revealed a number of problems, particularly, 1 unexplained fluctuation of predicted values caused by aggregation of screening tests, and 2 low accuracy of uncertainty intervals estimations. We developed a new method made it available for practitioners as a web calculator to reduce negative effect of factors given above. The method involves Bayesian methodology of statistical inference which, being computationally expensive, in theory provides most accurate predictions. We implemented and tested this approach on a data set including 571,148 patients registered in the US National Trauma Data Bank (NTDB with 1–20 injuries. These patients were distributed over the following categories: (1 174,647 with 1 injury, (2 381,137 with 2–10 injuries, and (3 15,364 with 11–20 injuries. Survival rates in each category were 0.977, 0.953, and 0.831, respectively. The proposed method has improved prediction accuracy by 0.04%, 0.36%, and 3.64% (p-value <0.05 in the categories 1, 2, and 3, respectively. Hosmer-Lemeshow statistics showed a significant improvement of the new model calibration. The uncertainty 2σ intervals were reduced from 0.628 to 0.569 for patients of the second category and from 1.227 to 0.930 for patients of the third category, both with p-value <0.005. The new method shows the statistically significant improvement (p-value <0.05 in accuracy of predicting survival and estimating the uncertainty intervals. The largest improvement has been achieved for patients with 11–20 injuries. The method is available for practitioners as a web calculator http://www.traumacalc.org.
Verkade, J. S.; Brown, J. D.; Davids, F.; Reggiani, P.; Weerts, A. H.
2017-12-01
Two statistical post-processing approaches for estimation of predictive hydrological uncertainty are compared: (i) 'dressing' of a deterministic forecast by adding a single, combined estimate of both hydrological and meteorological uncertainty and (ii) 'dressing' of an ensemble streamflow forecast by adding an estimate of hydrological uncertainty to each individual streamflow ensemble member. Both approaches aim to produce an estimate of the 'total uncertainty' that captures both the meteorological and hydrological uncertainties. They differ in the degree to which they make use of statistical post-processing techniques. In the 'lumped' approach, both sources of uncertainty are lumped by post-processing deterministic forecasts using their verifying observations. In the 'source-specific' approach, the meteorological uncertainties are estimated by an ensemble of weather forecasts. These ensemble members are routed through a hydrological model and a realization of the probability distribution of hydrological uncertainties (only) is then added to each ensemble member to arrive at an estimate of the total uncertainty. The techniques are applied to one location in the Meuse basin and three locations in the Rhine basin. Resulting forecasts are assessed for their reliability and sharpness, as well as compared in terms of multiple verification scores including the relative mean error, Brier Skill Score, Mean Continuous Ranked Probability Skill Score, Relative Operating Characteristic Score and Relative Economic Value. The dressed deterministic forecasts are generally more reliable than the dressed ensemble forecasts, but the latter are sharper. On balance, however, they show similar quality across a range of verification metrics, with the dressed ensembles coming out slightly better. Some additional analyses are suggested. Notably, these include statistical post-processing of the meteorological forecasts in order to increase their reliability, thus increasing the reliability
Proof of concept and dose estimation with binary responses under model uncertainty.
Klingenberg, B
2009-01-30
This article suggests a unified framework for testing Proof of Concept (PoC) and estimating a target dose for the benefit of a more comprehensive, robust and powerful analysis in phase II or similar clinical trials. From a pre-specified set of candidate models, we choose the ones that best describe the observed dose-response. To decide which models, if any, significantly pick up a dose effect, we construct the permutation distribution of the minimum P-value over the candidate set. This allows us to find critical values and multiplicity adjusted P-values that control the familywise error rate of declaring any spurious effect in the candidate set as significant. Model averaging is then used to estimate a target dose. Popular single or multiple contrast tests for PoC, such as the Cochran-Armitage, Dunnett or Williams tests, are only optimal for specific dose-response shapes and do not provide target dose estimates with confidence limits. A thorough evaluation and comparison of our approach to these tests reveal that its power is as good or better in detecting a dose-response under various shapes with many more additional benefits: It incorporates model uncertainty in PoC decisions and target dose estimation, yields confidence intervals for target dose estimates and extends to more complicated data structures. We illustrate our method with the analysis of a Phase II clinical trial. Copyright (c) 2008 John Wiley & Sons, Ltd.
Duchêne, Sebastian; Lanfear, Robert
2015-09-01
Ancestral state reconstruction (ASR) is a popular method for exploring the evolutionary history of traits that leave little or no trace in the fossil record. For example, it has been used to test hypotheses about the number of evolutionary origins of key life-history traits such as oviparity, or key morphological structures such as wings. Many studies that use ASR have suggested that the number of evolutionary origins of such traits is higher than was previously thought. The scope of such inferences is increasing rapidly, facilitated by the construction of very large phylogenies and life-history databases. In this paper, we use simulations to show that the number of evolutionary origins of a trait tends to be overestimated when the phylogeny is not perfect. In some cases, the estimated number of transitions can be several fold higher than the true value. Furthermore, we show that the bias is not always corrected by standard approaches to account for phylogenetic uncertainty, such as repeating the analysis on a large collection of possible trees. These findings have important implications for studies that seek to estimate the number of origins of a trait, particularly those that use large phylogenies that are associated with considerable uncertainty. We discuss the implications of this bias, and methods to ameliorate it. © 2015 Wiley Periodicals, Inc.
Directory of Open Access Journals (Sweden)
Gunter eSpöck
2015-05-01
Full Text Available Recently, Spock and Pilz [38], demonstratedthat the spatial sampling design problem forthe Bayesian linear kriging predictor can betransformed to an equivalent experimentaldesign problem for a linear regression modelwith stochastic regression coefficients anduncorrelated errors. The stochastic regressioncoefficients derive from the polar spectralapproximation of the residual process. Thus,standard optimal convex experimental designtheory can be used to calculate optimal spatialsampling designs. The design functionals ̈considered in Spock and Pilz [38] did nottake into account the fact that kriging isactually a plug-in predictor which uses theestimated covariance function. The resultingoptimal designs were close to space-fillingconfigurations, because the design criteriondid not consider the uncertainty of thecovariance function.In this paper we also assume that thecovariance function is estimated, e.g., byrestricted maximum likelihood (REML. Wethen develop a design criterion that fully takesaccount of the covariance uncertainty. Theresulting designs are less regular and space-filling compared to those ignoring covarianceuncertainty. The new designs, however, alsorequire some closely spaced samples in orderto improve the estimate of the covariancefunction. We also relax the assumption ofGaussian observations and assume that thedata is transformed to Gaussianity by meansof the Box-Cox transformation. The resultingprediction method is known as trans-Gaussiankriging. We apply the Smith and Zhu [37]approach to this kriging method and show thatresulting optimal designs also depend on theavailable data. We illustrate our results witha data set of monthly rainfall measurementsfrom Upper Austria.
Energy Technology Data Exchange (ETDEWEB)
Secchi, Piercesare [MOX, Department of Mathematics, Polytechnic of Milan (Italy); Zio, Enrico [Department of Energy, Polytechnic of Milan, Via Ponzio 34/3, 20133 Milano (Italy)], E-mail: enrico.zio@polimi.it; Di Maio, Francesco [Department of Energy, Polytechnic of Milan, Via Ponzio 34/3, 20133 Milano (Italy)
2008-12-15
For licensing purposes, safety cases of Nuclear Power Plants (NPPs) must be presented at the Regulatory Authority with the necessary confidence on the models used to describe the plant safety behavior. In principle, this requires the repetition of a large number of model runs to account for the uncertainties inherent in the model description of the true plant behavior. The present paper propounds the use of bootstrapped Artificial Neural Networks (ANNs) for performing the numerous model output calculations needed for estimating safety margins with appropriate confidence intervals. Account is given both to the uncertainties inherent in the plant model and to those introduced by the ANN regression models used for performing the repeated safety parameter evaluations. The proposed framework of analysis is first illustrated with reference to a simple analytical model and then to the estimation of the safety margin on the maximum fuel cladding temperature reached during a complete group distribution header blockage scenario in a RBMK-1500 nuclear reactor. The results are compared with those obtained by a traditional parametric approach.
Quantifying Surface Energy Flux Estimation Uncertainty Using Land Surface Temperature Observations
French, A. N.; Hunsaker, D.; Thorp, K.; Bronson, K. F.
2015-12-01
Remote sensing with thermal infrared is widely recognized as good way to estimate surface heat fluxes, map crop water use, and detect water-stressed vegetation. When combined with net radiation and soil heat flux data, observations of sensible heat fluxes derived from surface temperatures (LST) are indicative of instantaneous evapotranspiration (ET). There are, however, substantial reasons LST data may not provide the best way to estimate of ET. For example, it is well known that observations and models of LST, air temperature, or estimates of transport resistances may be so inaccurate that physically based model nevertheless yield non-meaningful results. Furthermore, using visible and near infrared remote sensing observations collected at the same time as LST often yield physically plausible results because they are constrained by less dynamic surface conditions such as green fractional cover. Although sensitivity studies exist that help identify likely sources of error and uncertainty, ET studies typically do not provide a way to assess the relative importance of modeling ET with and without LST inputs. To better quantify model benefits and degradations due to LST observational inaccuracies, a Bayesian uncertainty study was undertaken using data collected in remote sensing experiments at Maricopa, Arizona. Visible, near infrared and thermal infrared data were obtained from an airborne platform. The prior probability distribution of ET estimates were modeled using fractional cover, local weather data and a Penman-Monteith mode, while the likelihood of LST data was modeled from a two-source energy balance model. Thus the posterior probabilities of ET represented the value added by using LST data. Results from an ET study over cotton grown in 2014 and 2015 showed significantly reduced ET confidence intervals when LST data were incorporated.
International Nuclear Information System (INIS)
Rechard, R.P.; Tierney, M.S.; Sanchez, L.C.; Martell, M.-A.
1996-05-01
This report presents one of 2 approaches (bounding calculations) which were used in a 1994 study to examine the possibility of a criticality in a repository. Bounding probabilities, although rough, point to the difficulty of creating conditions under which a critical mass could be assembled (container corrosion, separation of neutron absorbers from fissile material, collapse or precipitation of fissile material) and how significant the geochemical and hydrologic phenomena are. The study could not conceive of a mechanism consistent with conditions under which an atomic explosion could occur. Should a criticality occur in or near a container in the future, boundary consequence calculations showed that fissions from one critical event ( 20 fissions, if similar to aqueous and metal accidents and experiments) are quite small compared to the amount of fissions represented by the spent fuel itself. If it is assumed that the containers necessary to hold the highly enriched spent fuel went critical once per day for 1 million years, creating an energy release of about 10 20 fissions, the number of fissions equals about 10 28 , which corresponds to only 1% of the fission inventory in a repository containing 70,000 metric tons of heavy metal, the expected size for the proposed repository at Yucca Mountain, Nevada
Energy Technology Data Exchange (ETDEWEB)
Rechard, R.P.; Tierney, M.S.; Sanchez, L.C.; Martell, M.-A.
1996-05-01
This report presents one of 2 approaches (bounding calculations) which were used in a 1994 study to examine the possibility of a criticality in a repository. Bounding probabilities, although rough, point to the difficulty of creating conditions under which a critical mass could be assembled (container corrosion, separation of neutron absorbers from fissile material, collapse or precipitation of fissile material) and how significant the geochemical and hydrologic phenomena are. The study could not conceive of a mechanism consistent with conditions under which an atomic explosion could occur. Should a criticality occur in or near a container in the future, boundary consequence calculations showed that fissions from one critical event (<10{sup 20} fissions, if similar to aqueous and metal accidents and experiments) are quite small compared to the amount of fissions represented by the spent fuel itself. If it is assumed that the containers necessary to hold the highly enriched spent fuel went critical once per day for 1 million years, creating an energy release of about 10{sup 20} fissions, the number of fissions equals about 10{sup 28}, which corresponds to only 1% of the fission inventory in a repository containing 70,000 metric tons of heavy metal, the expected size for the proposed repository at Yucca Mountain, Nevada.
On the predictivity of pore-scale simulations: estimating uncertainties with multilevel Monte Carlo
Icardi, Matteo
2016-02-08
A fast method with tunable accuracy is proposed to estimate errors and uncertainties in pore-scale and Digital Rock Physics (DRP) problems. The overall predictivity of these studies can be, in fact, hindered by many factors including sample heterogeneity, computational and imaging limitations, model inadequacy and not perfectly known physical parameters. The typical objective of pore-scale studies is the estimation of macroscopic effective parameters such as permeability, effective diffusivity and hydrodynamic dispersion. However, these are often non-deterministic quantities (i.e., results obtained for specific pore-scale sample and setup are not totally reproducible by another “equivalent” sample and setup). The stochastic nature can arise due to the multi-scale heterogeneity, the computational and experimental limitations in considering large samples, and the complexity of the physical models. These approximations, in fact, introduce an error that, being dependent on a large number of complex factors, can be modeled as random. We propose a general simulation tool, based on multilevel Monte Carlo, that can reduce drastically the computational cost needed for computing accurate statistics of effective parameters and other quantities of interest, under any of these random errors. This is, to our knowledge, the first attempt to include Uncertainty Quantification (UQ) in pore-scale physics and simulation. The method can also provide estimates of the discretization error and it is tested on three-dimensional transport problems in heterogeneous materials, where the sampling procedure is done by generation algorithms able to reproduce realistic consolidated and unconsolidated random sphere and ellipsoid packings and arrangements. A totally automatic workflow is developed in an open-source code [2015. https://bitbucket.org/micardi/porescalemc.], that include rigid body physics and random packing algorithms, unstructured mesh discretization, finite volume solvers
Alvizuri, Celso R.
We present a catalog of full seismic moment tensors for 63 events from Uturuncu volcano in Bolivia. The events were recorded during 2011-2012 in the PLUTONS seismic array of 24 broadband stations. Most events had magnitudes between 0.5 and 2.0 and did not generate discernible surface waves; the largest event was Mw 2.8. For each event we computed the misfit between observed and synthetic waveforms, and we used first-motion polarity measurements to reduce the number of possible solutions. Each moment tensor solution was obtained using a grid search over the six-dimensional space of moment tensors. For each event we show the misfit function in eigenvalue space, represented by a lune. We identify three subsets of the catalog: (1) 6 isotropic events, (2) 5 tensional crack events, and (3) a swarm of 14 events southeast of the volcanic center that appear to be double couples. The occurrence of positively isotropic events is consistent with other published results from volcanic and geothermal regions. Several of these previous results, as well as our results, cannot be interpreted within the context of either an oblique opening crack or a crack-plus-double-couple model. Proper characterization of uncertainties for full moment tensors is critical for distinguishing among physical models of source processes. A seismic moment tensor is a 3x3 symmetric matrix that provides a compact representation of a seismic source. We develop an algorithm to estimate moment tensors and their uncertainties from observed seismic data. For a given event, the algorithm performs a grid search over the six-dimensional space of moment tensors by generating synthetic waveforms for each moment tensor and then evaluating a misfit function between the observed and synthetic waveforms. 'The' moment tensor M0 for the event is then the moment tensor with minimum misfit. To describe the uncertainty associated with M0, we first convert the misfit function to a probability function. The uncertainty, or
International Nuclear Information System (INIS)
Unal, C.; Williams, B.; Hemez, F.; Atamturktur, S.H.; McClure, P.
2011-01-01
Research highlights: → The best estimate plus uncertainty methodology (BEPU) is one option in the licensing of nuclear reactors. → The challenges for extending the BEPU method for fuel qualification for an advanced reactor fuel are primarily driven by schedule, the need for data, and the sufficiency of the data. → In this paper we develop an extended BEPU methodology that can potentially be used to address these new challenges in the design and licensing of advanced nuclear reactors. → The main components of the proposed methodology are verification, validation, calibration, and uncertainty quantification. → The methodology includes a formalism to quantify an adequate level of validation (predictive maturity) with respect to existing data, so that required new testing can be minimized, saving cost by demonstrating that further testing will not enhance the quality of the predictive tools. - Abstract: Many evolving nuclear energy technologies use advanced predictive multiscale, multiphysics modeling and simulation (M and S) capabilities to reduce the cost and schedule of design and licensing. Historically, the role of experiments has been as a primary tool for the design and understanding of nuclear system behavior, while M and S played the subordinate role of supporting experiments. In the new era of multiscale, multiphysics computational-based technology development, this role has been reversed. The experiments will still be needed, but they will be performed at different scales to calibrate and validate the models leading to predictive simulations for design and licensing. Minimizing the required number of validation experiments produces cost and time savings. The use of multiscale, multiphysics models introduces challenges in validating these predictive tools - traditional methodologies will have to be modified to address these challenges. This paper gives the basic aspects of a methodology that can potentially be used to address these new challenges in
Directory of Open Access Journals (Sweden)
Razi Ahmed
2013-06-01
Full Text Available Estimates of above ground biomass density in forests are crucial for refining global climate models and understanding climate change. Although data from field studies can be aggregated to estimate carbon stocks on global scales, the sparsity of such field data, temporal heterogeneity and methodological variations introduce large errors. Remote sensing measurements from spaceborne sensors are a realistic alternative for global carbon accounting; however, the uncertainty of such measurements is not well known and remains an active area of research. This article describes an effort to collect field data at the Harvard and Howland Forest sites, set in the temperate forests of the Northeastern United States in an attempt to establish ground truth forest biomass for calibration of remote sensing measurements. We present an assessment of the quality of ground truth biomass estimates derived from three different sets of diameter-based allometric equations over the Harvard and Howland Forests to establish the contribution of errors in ground truth data to the error in biomass estimates from remote sensing measurements.
Predictive Uncertainty Estimation in Water Demand Forecasting Using the Model Conditional Processor
Directory of Open Access Journals (Sweden)
Amos O. Anele
2018-04-01
Full Text Available In a previous paper, a number of potential models for short-term water demand (STWD prediction have been analysed to find the ones with the best fit. The results obtained in Anele et al. (2017 showed that hybrid models may be considered as the accurate and appropriate forecasting models for STWD prediction. However, such best single valued forecast does not guarantee reliable and robust decisions, which can be properly obtained via model uncertainty processors (MUPs. MUPs provide an estimate of the full predictive densities and not only the single valued expected prediction. Amongst other MUPs, the purpose of this paper is to use the multi-variate version of the model conditional processor (MCP, proposed by Todini (2008, to demonstrate how the estimation of the predictive probability conditional to a number of relatively good predictive models may improve our knowledge, thus reducing the predictive uncertainty (PU when forecasting into the unknown future. Through the MCP approach, the probability distribution of the future water demand can be assessed depending on the forecast provided by one or more deterministic forecasting models. Based on an average weekly data of 168 h, the probability density of the future demand is built conditional on three models’ predictions, namely the autoregressive-moving average (ARMA, feed-forward back propagation neural network (FFBP-NN and hybrid model (i.e., combined forecast from ARMA and FFBP-NN. The results obtained show that MCP may be effectively used for real-time STWD prediction since it brings out the PU connected to its forecast, and such information could help water utilities estimate the risk connected to a decision.
DEFF Research Database (Denmark)
Baadsgaard, Mikkel; Nielsen, Jan Nygaard; Madsen, Henrik
2000-01-01
An econometric analysis of continuous-timemodels of the term structure of interest rates is presented. A panel of coupon bond prices with different maturities is used to estimate the embedded parameters of a continuous-discrete state space model of unobserved state variables: the spot interest rate...... noise term should account for model errors. A nonlinear filtering method is used to compute estimates of the state variables, and the model parameters are estimated by a quasimaximum likelihood method provided that some assumptions are imposed on the model residuals. Both Monte Carlo simulation results...
Magnusson, Bertil; Ossowicki, Haakan; Rienitz, Olaf; Theodorsson, Elvar
2012-05-01
Healthcare laboratories are increasingly joining into larger laboratory organizations encompassing several physical laboratories. This caters for important new opportunities for re-defining the concept of a 'laboratory' to encompass all laboratories and measurement methods measuring the same measurand for a population of patients. In order to make measurement results, comparable bias should be minimized or eliminated and measurement uncertainty properly evaluated for all methods used for a particular patient population. The measurement as well as diagnostic uncertainty can be evaluated from internal and external quality control results using GUM principles. In this paper the uncertainty evaluations are described in detail using only two main components, within-laboratory reproducibility and uncertainty of the bias component according to a Nordtest guideline. The evaluation is exemplified for the determination of creatinine in serum for a conglomerate of laboratories both expressed in absolute units (μmol/L) and relative (%). An expanded measurement uncertainty of 12 μmol/L associated with concentrations of creatinine below 120 μmol/L and of 10% associated with concentrations above 120 μmol/L was estimated. The diagnostic uncertainty encompasses both measurement uncertainty and biological variation, and can be estimated for a single value and for a difference. This diagnostic uncertainty for the difference for two samples from the same patient was determined to be 14 μmol/L associated with concentrations of creatinine below 100 μmol/L and 14 % associated with concentrations above 100 μmol/L.
International Nuclear Information System (INIS)
Unal, Cetin; Williams, Brian; McClure, Patrick; Nelson, Ralph A.
2010-01-01
Many evolving nuclear energy programs plan to use advanced predictive multi-scale multi-physics simulation and modeling capabilities to reduce cost and time from design through licensing. Historically, the role of experiments was primary tool for design and understanding of nuclear system behavior while modeling and simulation played the subordinate role of supporting experiments. In the new era of multi-scale multi-physics computational based technology development, the experiments will still be needed but they will be performed at different scales to calibrate and validate models leading predictive simulations. Cost saving goals of programs will require us to minimize the required number of validation experiments. Utilization of more multi-scale multi-physics models introduces complexities in the validation of predictive tools. Traditional methodologies will have to be modified to address these arising issues. This paper lays out the basic aspects of a methodology that can be potentially used to address these new challenges in design and licensing of evolving nuclear technology programs. The main components of the proposed methodology are verification, validation, calibration, and uncertainty quantification. An enhanced calibration concept is introduced and is accomplished through data assimilation. The goal is to enable best-estimate prediction of system behaviors in both normal and safety related environments. To achieve this goal requires the additional steps of estimating the domain of validation and quantification of uncertainties that allow for extension of results to areas of the validation domain that are not directly tested with experiments, which might include extension of the modeling and simulation (M and S) capabilities for application to full-scale systems. The new methodology suggests a formalism to quantify an adequate level of validation (predictive maturity) with respect to required selective data so that required testing can be minimized for
Energy Technology Data Exchange (ETDEWEB)
Unal, Cetin [Los Alamos National Laboratory; Williams, Brian [Los Alamos National Laboratory; Mc Clure, Patrick [Los Alamos National Laboratory; Nelson, Ralph A [IDAHO NATIONAL LAB
2010-01-01
Many evolving nuclear energy programs plan to use advanced predictive multi-scale multi-physics simulation and modeling capabilities to reduce cost and time from design through licensing. Historically, the role of experiments was primary tool for design and understanding of nuclear system behavior while modeling and simulation played the subordinate role of supporting experiments. In the new era of multi-scale multi-physics computational based technology development, the experiments will still be needed but they will be performed at different scales to calibrate and validate models leading predictive simulations. Cost saving goals of programs will require us to minimize the required number of validation experiments. Utilization of more multi-scale multi-physics models introduces complexities in the validation of predictive tools. Traditional methodologies will have to be modified to address these arising issues. This paper lays out the basic aspects of a methodology that can be potentially used to address these new challenges in design and licensing of evolving nuclear technology programs. The main components of the proposed methodology are verification, validation, calibration, and uncertainty quantification. An enhanced calibration concept is introduced and is accomplished through data assimilation. The goal is to enable best-estimate prediction of system behaviors in both normal and safety related environments. To achieve this goal requires the additional steps of estimating the domain of validation and quantification of uncertainties that allow for extension of results to areas of the validation domain that are not directly tested with experiments, which might include extension of the modeling and simulation (M&S) capabilities for application to full-scale systems. The new methodology suggests a formalism to quantify an adequate level of validation (predictive maturity) with respect to required selective data so that required testing can be minimized for cost
Directory of Open Access Journals (Sweden)
M. P. Mittermaier
2008-05-01
Full Text Available A simple measure of the uncertainty associated with using radar-derived rainfall estimates as "truth" has been introduced to the Numerical Weather Prediction (NWP verification process to assess the effect on forecast skill and errors. Deterministic precipitation forecasts from the mesoscale version of the UK Met Office Unified Model for a two-day high-impact event and for a month were verified at the daily and six-hourly time scale using a spatially-based intensity-scale method and various traditional skill scores such as the Equitable Threat Score (ETS and log-odds ratio. Radar-rainfall accumulations from the UK Nimrod radar-composite were used.
The results show that the inclusion of uncertainty has some effect, shifting the forecast errors and skill. The study also allowed for the comparison of results from the intensity-scale method and traditional skill scores. It showed that the two methods complement each other, one detailing the scale and rainfall accumulation thresholds where the errors occur, the other showing how skillful the forecast is. It was also found that for the six-hourly forecasts the error distributions remain similar with forecast lead time but skill decreases. This highlights the difference between forecast error and forecast skill, and that they are not necessarily the same.
Sun, Ruochen; Yuan, Huiling; Liu, Xiaoli
2017-11-01
The heteroscedasticity treatment in residual error models directly impacts the model calibration and prediction uncertainty estimation. This study compares three methods to deal with the heteroscedasticity, including the explicit linear modeling (LM) method and nonlinear modeling (NL) method using hyperbolic tangent function, as well as the implicit Box-Cox transformation (BC). Then a combined approach (CA) combining the advantages of both LM and BC methods has been proposed. In conjunction with the first order autoregressive model and the skew exponential power (SEP) distribution, four residual error models are generated, namely LM-SEP, NL-SEP, BC-SEP and CA-SEP, and their corresponding likelihood functions are applied to the Variable Infiltration Capacity (VIC) hydrologic model over the Huaihe River basin, China. Results show that the LM-SEP yields the poorest streamflow predictions with the widest uncertainty band and unrealistic negative flows. The NL and BC methods can better deal with the heteroscedasticity and hence their corresponding predictive performances are improved, yet the negative flows cannot be avoided. The CA-SEP produces the most accurate predictions with the highest reliability and effectively avoids the negative flows, because the CA approach is capable of addressing the complicated heteroscedasticity over the study basin.
Freni, G; La Loggia, G; Notaro, V
2010-01-01
Due to the increased occurrence of flooding events in urban areas, many procedures for flood damage quantification have been defined in recent decades. The lack of large databases in most cases is overcome by combining the output of urban drainage models and damage curves linking flooding to expected damage. The application of advanced hydraulic models as diagnostic, design and decision-making support tools has become a standard practice in hydraulic research and application. Flooding damage functions are usually evaluated by a priori estimation of potential damage (based on the value of exposed goods) or by interpolating real damage data (recorded during historical flooding events). Hydraulic models have undergone continuous advancements, pushed forward by increasing computer capacity. The details of the flooding propagation process on the surface and the details of the interconnections between underground and surface drainage systems have been studied extensively in recent years, resulting in progressively more reliable models. The same level of was advancement has not been reached with regard to damage curves, for which improvements are highly connected to data availability; this remains the main bottleneck in the expected flooding damage estimation. Such functions are usually affected by significant uncertainty intrinsically related to the collected data and to the simplified structure of the adopted functional relationships. The present paper aimed to evaluate this uncertainty by comparing the intrinsic uncertainty connected to the construction of the damage-depth function to the hydraulic model uncertainty. In this way, the paper sought to evaluate the role of hydraulic model detail level in the wider context of flood damage estimation. This paper demonstrated that the use of detailed hydraulic models might not be justified because of the higher computational cost and the significant uncertainty in damage estimation curves. This uncertainty occurs mainly
Vera-Sánchez, Juan Antonio; Ruiz-Morales, Carmen; González-López, Antonio
2018-03-01
To provide a multi-stage model to calculate uncertainty in radiochromic film dosimetry with Monte-Carlo techniques. This new approach is applied to single-channel and multichannel algorithms. Two lots of Gafchromic EBT3 are exposed in two different Varian linacs. They are read with an EPSON V800 flatbed scanner. The Monte-Carlo techniques in uncertainty analysis provide a numerical representation of the probability density functions of the output magnitudes. From this numerical representation, traditional parameters of uncertainty analysis as the standard deviations and bias are calculated. Moreover, these numerical representations are used to investigate the shape of the probability density functions of the output magnitudes. Also, another calibration film is read in four EPSON scanners (two V800 and two 10000XL) and the uncertainty analysis is carried out with the four images. The dose estimates of single-channel and multichannel algorithms show a Gaussian behavior and low bias. The multichannel algorithms lead to less uncertainty in the final dose estimates when the EPSON V800 is employed as reading device. In the case of the EPSON 10000XL, the single-channel algorithms provide less uncertainty in the dose estimates for doses higher than four Gy. A multi-stage model has been presented. With the aid of this model and the use of the Monte-Carlo techniques, the uncertainty of dose estimates for single-channel and multichannel algorithms are estimated. The application of the model together with Monte-Carlo techniques leads to a complete characterization of the uncertainties in radiochromic film dosimetry. Copyright © 2018 Associazione Italiana di Fisica Medica. Published by Elsevier Ltd. All rights reserved.
Study of the uncertainty in estimation of the exposure of non-human biota to ionising radiation.
Avila, R; Beresford, N A; Agüero, A; Broed, R; Brown, J; Iospje, M; Robles, B; Suañez, A
2004-12-01
Uncertainty in estimations of the exposure of non-human biota to ionising radiation may arise from a number of sources including values of the model parameters, empirical data, measurement errors and biases in the sampling. The significance of the overall uncertainty of an exposure assessment will depend on how the estimated dose compares with reference doses used for risk characterisation. In this paper, we present the results of a study of the uncertainty in estimation of the exposure of non-human biota using some of the models and parameters recommended in the FASSET methodology. The study was carried out for semi-natural terrestrial, agricultural and marine ecosystems, and for four radionuclides (137Cs, 239Pu, 129I and 237Np). The parameters of the radionuclide transfer models showed the highest sensitivity and contributed the most to the uncertainty in the predictions of doses to biota. The most important ones were related to the bioavailability and mobility of radionuclides in the environment, for example soil-to-plant transfer factors, the bioaccumulation factors for marine biota and the gut uptake fraction for terrestrial mammals. In contrast, the dose conversion coefficients showed low sensitivity and contributed little to the overall uncertainty. Radiobiological effectiveness contributed to the overall uncertainty of the dose estimations for alpha emitters although to a lesser degree than a number of transfer model parameters.
Use and application of 'best estimate plus uncertainty' methods. A regulatory view
International Nuclear Information System (INIS)
Mendizabal, Rafael; Pelayo, Fernando
2013-01-01
Regulatory environment is characterized by its prevention to change. Any move has to be solidly founded. Application in licensing of Best Estimate Plus Uncertainty (BEPU) methods is not an exception. Typically a fully deterministic approach as described in IAEA SSG-2 is used for design bases accident analyses in current Nuclear Power Plants. In recent years the use of BEPU methodologies is gaining favor in the nuclear technology community as a way to optimize design and operation while preserving full compliance with applicable regulation. This paper has its focus on the regulatory relevance of the use of BEPU in licensing practices. A regulatory analysis describing the rationale of the evolution from classic deterministic methods to BEPU as well as a selected set of topics around the implications of the use of BEPU methods is shown. To finalize some conclusions and thoughts of possible further developments of these methods are drawn. (authors)
Summary of uncertainty estimation results for Hanford tank chemical and radionuclide inventories
International Nuclear Information System (INIS)
Ferryman, T.A.; Amidan, B.G.; Chen, G.
1998-09-01
The exact physical and chemical nature of 55 million gallons of radioactive waste held in 177 underground waste tanks at the Hanford Site is not known in sufficient detail to support safety, retrieval, and immobilization missions. The Hanford Engineering Analysis Best-Basis team has made point estimates of the inventories in each tank. The purpose of this study is to estimate probability distributions for each of the analytes and tanks for which the Hanford Best-Basis team has made point estimates. Uncertainty intervals can then be calculated for the Best-Basis inventories and should facilitate the cleanup missions. The methodology used to generate the results published in the Tank Characterization Database (TCD) and summarized in this paper is based on scientific principles, sound technical knowledge of the realities associated with the Hanford waste tanks, the chemical analysis of actual samples from the tanks, the Hanford Best-Basic research, and historical data records. The methodology builds on research conducted by Pacific Northwest National Laboratory (PNNL) over the last few years. Appendix A of this report summarizes the results of the study. The full set of results (in percentiles, 1--99) is available through the TCD, (http://twins.pnl.gov:8001)
Summary of uncertainty estimation results for Hanford tank chemical and radionuclide inventories
Energy Technology Data Exchange (ETDEWEB)
Ferryman, T.A.; Amidan, B.G.; Chen, G. [and others
1998-09-01
The exact physical and chemical nature of 55 million gallons of radioactive waste held in 177 underground waste tanks at the Hanford Site is not known in sufficient detail to support safety, retrieval, and immobilization missions. The Hanford Engineering Analysis Best-Basis team has made point estimates of the inventories in each tank. The purpose of this study is to estimate probability distributions for each of the analytes and tanks for which the Hanford Best-Basis team has made point estimates. Uncertainty intervals can then be calculated for the Best-Basis inventories and should facilitate the cleanup missions. The methodology used to generate the results published in the Tank Characterization Database (TCD) and summarized in this paper is based on scientific principles, sound technical knowledge of the realities associated with the Hanford waste tanks, the chemical analysis of actual samples from the tanks, the Hanford Best-Basic research, and historical data records. The methodology builds on research conducted by Pacific Northwest National Laboratory (PNNL) over the last few years. Appendix A of this report summarizes the results of the study. The full set of results (in percentiles, 1--99) is available through the TCD, (http://twins.pnl.gov:8001).
Directory of Open Access Journals (Sweden)
Igor Stubelj
2014-03-01
Full Text Available The paper deals with the estimation of weighted average cost of capital (WACC for regulated industries in developing financial markets from the perspective of the current financial-economic crisis. In current financial market situation some evident changes have occurred: risk-free rates in solid and developed financial markets (e. g. USA, Germany have fallen, but due to increased market volatility, the risk premiums have increased. The latter is especially evident in transition economies where the amplitude of market volatility is extremely high. In such circumstances, there is a question of how to calculate WACC properly. WACC is an important measure in financial management decisions and in our case, business regulation. We argue in the paper that the most accurate method for calculating WACC is the estimation of the long-term WACC, which takes into consideration a long-term stable yield of capital and not the current market conditions. Following this, we propose some solutions that could be used for calculating WACC for regulated industries on the developing financial markets in times of market uncertainty. As an example, we present an estimation of the capital cost for a selected Slovenian company, which operates in the regulated industry of electric distribution.
Estimation of gross land-use change and its uncertainty using a Bayesian data assimilation approach
Levy, Peter; van Oijen, Marcel; Buys, Gwen; Tomlinson, Sam
2018-03-01
We present a method for estimating land-use change using a Bayesian data assimilation approach. The approach provides a general framework for combining multiple disparate data sources with a simple model. This allows us to constrain estimates of gross land-use change with reliable national-scale census data, whilst retaining the detailed information available from several other sources. Eight different data sources, with three different data structures, were combined in our posterior estimate of land use and land-use change, and other data sources could easily be added in future. The tendency for observations to underestimate gross land-use change is accounted for by allowing for a skewed distribution in the likelihood function. The data structure produced has high temporal and spatial resolution, and is appropriate for dynamic process-based modelling. Uncertainty is propagated appropriately into the output, so we have a full posterior distribution of output and parameters. The data are available in the widely used netCDF file format from http://eidc.ceh.ac.uk/.
Gehrmann, R. A. S.; Schwalenberg, K.; Hölz, S.; Zander, T.; Dettmer, J.; Bialas, J.
2016-12-01
In 2014 an interdisciplinary survey was conducted as part of the German SUGAR project in the Western Black Sea targeting gas hydrate occurrences in the Danube Delta. Marine controlled source electromagnetic (CSEM) data were acquired with an inline seafloor-towed array (BGR), and a two-polarization horizontal ocean-bottom source and receiver configuration (GEOMAR). The CSEM data are co-located with high-resolution 2-D and 3-D seismic reflection data (GEOMAR). We present results from 2-D regularized inversion (MARE2DEM by Kerry Key), which provides a smooth model of the electrical resistivity distribution beneath the source and multiple receivers. The 2-D approach includes seafloor topography and structural constraints from seismic data. We estimate uncertainties from the regularized inversion and compare them to 1-D Bayesian inversion results. The probabilistic inversion for a layered subsurface treats the parameter values and the number of layers as unknown by applying reversible-jump Markov-chain Monte Carlo sampling. A non-diagonal data covariance matrix obtained from residual error analysis accounts for correlated errors. The resulting resistivity models show generally high resistivity values between 3 and 10 Ωm on average which can be partly attributed to depleted pore water salinities due to sea-level low stands in the past, and locally up to 30 Ωm which is likely caused by gas hydrates. At the base of the gas hydrate stability zone resistivities rise up to more than 100 Ωm which could be due to gas hydrate as well as a layer of free gas underneath. However, the deeper parts also show the largest model parameter uncertainties. Archie's Law is used to derive estimates of the gas hydrate saturation, which vary between 30 and 80% within the anomalous layers considering salinity and porosity profiles from a distant DSDP bore hole.
Huang, Yibin; Zhan, Hongbin; Knappett, Peter S. K.
2018-04-01
Past studies modeling stream-aquifer interaction commonly account for vertical anisotropy in hydraulic conductivity, but rarely address horizontal anisotropy, which may exist in certain sedimentary environments. If present, horizontal anisotropy will greatly impact stream depletion and the amount of recharge a pumped aquifer captures from the river. This scenario requires a different and somewhat more sophisticated mathematical approach to model and interpret pumping test results than previous models used to describe captured recharge from rivers. In this study, a new mathematical model is developed to describe the spatiotemporal distribution of drawdown from stream-bank pumping with a well screened across a horizontally anisotropic, confined aquifer, laterally bounded by a river. This new model is used to estimate four aquifer parameters including the magnitude and directions of major and minor principal transmissivities and storativity based on the observed drawdown-time curves within a minimum of three non-collinear observation wells. In order to approve the efficacy of the new model, a MATLAB script file is programmed to conduct a four-parameter inversion to estimate the four parameters of concern. By comparing the results of analytical and numerical inversions, the accuracy of estimated results from both inversions is acceptable, but the MATLAB program sometimes becomes problematic because of the difficulty of separating the local minima from the global minima. It appears that the new analytical model of this study is applicable and robust in estimating parameter values for a horizontally anisotropic aquifer laterally bounded by a stream. Besides that, the new model calculates stream depletion rate as a function of stream-bank pumping. Unique to horizontally anisotropic and homogeneous aquifers, the stream depletion rate at any given pumping rate depends closely on the horizontal anisotropy ratio and the direction of the principle transmissivities relative to
Assouline, Dan; Mohajeri, Nahid; Scartezzini, Jean-Louis
2017-04-01
Solar energy is clean, widely available, and arguably the most promising renewable energy resource. Taking full advantage of solar power, however, requires a deep understanding of its patterns and dependencies in space and time. The recent advances in Machine Learning brought powerful algorithms to estimate the spatio-temporal variations of solar irradiance (the power per unit area received from the Sun, W/m2), using local weather and terrain information. Such algorithms include Deep Learning (e.g. Artificial Neural Networks), or kernel methods (e.g. Support Vector Machines). However, most of these methods have some disadvantages, as they: (i) are complex to tune, (ii) are mainly used as a black box and offering no interpretation on the variables contributions, (iii) often do not provide uncertainty predictions (Assouline et al., 2016). To provide a reasonable solar mapping with good accuracy, these gaps would ideally need to be filled. We present here simple steps using one ensemble learning algorithm namely, Random Forests (Breiman, 2001) to (i) estimate monthly solar potential with good accuracy, (ii) provide information on the contribution of each feature in the estimation, and (iii) offer prediction intervals for each point estimate. We have selected Switzerland as an example. Using a Digital Elevation Model (DEM) along with monthly solar irradiance time series and weather data, we build monthly solar maps for Global Horizontal Irradiance (GHI), Diffuse Horizontal Irradiance (GHI), and Extraterrestrial Irradiance (EI). The weather data include monthly values for temperature, precipitation, sunshine duration, and cloud cover. In order to explain the impact of each feature on the solar irradiance of each point estimate, we extend the contribution method (Kuz'min et al., 2011) to a regression setting. Contribution maps for all features can then be computed for each solar map. This provides precious information on the spatial variation of the features impact all
Directory of Open Access Journals (Sweden)
Gurkan eSin
2015-02-01
Full Text Available Capital investment, next to the product demand, sales and production costs, is one of the key metrics commonly used for project evaluation and feasibility assessment. Estimating the investment costs of a new product/process alternative during early stage design is a challenging task. This is especially important in biorefinery research, where available information and experiences with new technologies is limited. A systematic methodology for uncertainty analysis of cost data is proposed that employs (a Bootstrapping as a regression method when cost data is available and (b the Monte Carlo technique as an error propagation method based on expert input when cost data is not available. Four well-known models for early stage cost estimation are reviewed an analyzed using the methodology. The significance of uncertainties of cost data for early stage process design is highlighted using the synthesis and design of a biorefinery as a case study. The impact of uncertainties in cost estimation on the identification of optimal processing paths is found to be profound. To tackle this challenge, a comprehensive techno-economic risk analysis framework is presented to enable robust decision making under uncertainties. One of the results using an order-of-magnitude estimate shows that the production of diethyl ether and 1,3-butadiene are the most promising with economic risks of 0.24 MM$/a and 4.6 MM$/a due to uncertainties in cost estimations, respectively.
Ciurean, R. L.; Glade, T.
2012-04-01
Decision under uncertainty is a constant of everyday life and an important component of risk management and governance. Recently, experts have emphasized the importance of quantifying uncertainty in all phases of landslide risk analysis. Due to its multi-dimensional and dynamic nature, (physical) vulnerability is inherently complex and the "degree of loss" estimates imprecise and to some extent even subjective. Uncertainty analysis introduces quantitative modeling approaches that allow for a more explicitly objective output, improving the risk management process as well as enhancing communication between various stakeholders for better risk governance. This study presents a review of concepts for uncertainty analysis in vulnerability of elements at risk to landslides. Different semi-quantitative and quantitative methods are compared based on their feasibility in real-world situations, hazard dependency, process stage in vulnerability assessment (i.e. input data, model, output), and applicability within an integrated landslide hazard and risk framework. The resulted observations will help to identify current gaps and future needs in vulnerability assessment, including estimation of uncertainty propagation, transferability of the methods, development of visualization tools, but also address basic questions like what is uncertainty and how uncertainty can be quantified or treated in a reliable and reproducible way.
International Nuclear Information System (INIS)
Tanaka, Yohei; Momma, Akihiko; Kato, Ken; Negishi, Akira; Takano, Kiyonami; Nozaki, Ken; Kato, Tohru
2009-01-01
Uncertainty of electrical efficiency measurement was investigated for a 10 kW-class SOFC system using town gas. Uncertainty of heating value measured by the gas chromatography method on a mole base was estimated as ±0.12% at 95% level of confidence. Micro-gas chromatography with/without CH 4 quantification may be able to reduce uncertainty of measurement. Calibration and uncertainty estimation methods are proposed for flow-rate measurement of town gas with thermal mass-flow meters or controllers. By adequate calibrations for flowmeters, flow rate of town gas or natural gas at 35 standard litters per minute can be measured within relative uncertainty ±1.0% at 95 % level of confidence. Uncertainty of power measurement can be as low as ±0.14% when a precise wattmeter is used and calibrated properly. It is clarified that electrical efficiency for non-pressurized 10 kW-class SOFC systems can be measured within ±1.0% relative uncertainty at 95% level of confidence with the developed techniques when the SOFC systems are operated relatively stably
Directory of Open Access Journals (Sweden)
George Maldonado
2009-09-01
Full Text Available Abstract: In a follow-up study of mortality among North American synthetic rubber industry workers, cumulative exposure to 1,3-butadiene was positively associated with leukemia. Problems with historical exposure estimation, however, may have distorted the association. To evaluate the impact of potential inaccuracies in exposure estimation, we conducted uncertainty analyses of the relation between cumulative exposure to butadiene and leukemia. We created the 1,000 sets of butadiene estimates using job-exposure matrices consisting of exposure values that corresponded to randomly selected percentiles of the approximate probability distribution of plant-, work area/job group-, and year specific butadiene ppm. We then analyzed the relation between cumulative exposure to butadiene and leukemia for each of the 1,000 sets of butadiene estimates. In the uncertainty analysis, the point estimate of the RR for the first non zero exposure category (>0–<37.5 ppm-years was most likely to be about 1.5. The rate ratio for the second exposure category (37.5–<184.7 ppm-years was most likely to range from 1.5 to 1.8. The RR for category 3 of exposure (184.7–<425.0 ppm-years was most likely between 2.1 and 3.0. The RR for the highest exposure category (425.0+ ppm-years was likely to be between 2.9 and 3.7. This range off RR point estimates can best be interpreted as a probability distribution that describes our uncertainty in RR point estimates due to uncertainty in exposure estimation. After considering the complete probability distributions of butadiene exposure estimates, the exposure-response association of butadiene and leukemia was maintained. This exercise was a unique example of how uncertainty analyses can be used to investigate and support an observed measure of effect when occupational exposure estimates are employed in the absence of direct exposure measurements.
Czech Academy of Sciences Publication Activity Database
Feireisl, Eduard; Hošek, Radim; Maltese, D.; Novotný, A.
2017-01-01
Roč. 33, č. 4 (2017), s. 1208-1223 ISSN 0749-159X EU Projects: European Commission(XE) 320078 - MATH EF Institutional support: RVO:67985840 Keywords : convergence * error estimates * mixed numerical method * Navier–Stokes system Subject RIV: BA - General Math ematics OBOR OECD: Pure math ematics Impact factor: 1.079, year: 2016 http://onlinelibrary.wiley.com/doi/10.1002/num.22140/abstract
Simon, Aaron B; Dubowitz, David J; Blockley, Nicholas P; Buxton, Richard B
2016-04-01
Calibrated blood oxygenation level dependent (BOLD) imaging is a multimodal functional MRI technique designed to estimate changes in cerebral oxygen metabolism from measured changes in cerebral blood flow and the BOLD signal. This technique addresses fundamental ambiguities associated with quantitative BOLD signal analysis; however, its dependence on biophysical modeling creates uncertainty in the resulting oxygen metabolism estimates. In this work, we developed a Bayesian approach to estimating the oxygen metabolism response to a neural stimulus and used it to examine the uncertainty that arises in calibrated BOLD estimation due to the presence of unmeasured model parameters. We applied our approach to estimate the CMRO2 response to a visual task using the traditional hypercapnia calibration experiment as well as to estimate the metabolic response to both a visual task and hypercapnia using the measurement of baseline apparent R2' as a calibration technique. Further, in order to examine the effects of cerebral spinal fluid (CSF) signal contamination on the measurement of apparent R2', we examined the effects of measuring this parameter with and without CSF-nulling. We found that the two calibration techniques provided consistent estimates of the metabolic response on average, with a median R2'-based estimate of the metabolic response to CO2 of 1.4%, and R2'- and hypercapnia-calibrated estimates of the visual response of 27% and 24%, respectively. However, these estimates were sensitive to different sources of estimation uncertainty. The R2'-calibrated estimate was highly sensitive to CSF contamination and to uncertainty in unmeasured model parameters describing flow-volume coupling, capillary bed characteristics, and the iso-susceptibility saturation of blood. The hypercapnia-calibrated estimate was relatively insensitive to these parameters but highly sensitive to the assumed metabolic response to CO2. Copyright © 2016 Elsevier Inc. All rights reserved.
Simon, Aaron B.; Dubowitz, David J.; Blockley, Nicholas P.; Buxton, Richard B.
2016-01-01
Calibrated blood oxygenation level dependent (BOLD) imaging is a multimodal functional MRI technique designed to estimate changes in cerebral oxygen metabolism from measured changes in cerebral blood flow and the BOLD signal. This technique addresses fundamental ambiguities associated with quantitative BOLD signal analysis; however, its dependence on biophysical modeling creates uncertainty in the resulting oxygen metabolism estimates. In this work, we developed a Bayesian approach to estimating the oxygen metabolism response to a neural stimulus and used it to examine the uncertainty that arises in calibrated BOLD estimation due to the presence of unmeasured model parameters. We applied our approach to estimate the CMRO2 response to a visual task using the traditional hypercapnia calibration experiment as well as to estimate the metabolic response to both a visual task and hypercapnia using the measurement of baseline apparent R2′ as a calibration technique. Further, in order to examine the effects of cerebral spinal fluid (CSF) signal contamination on the measurement of apparent R2′, we examined the effects of measuring this parameter with and without CSF-nulling. We found that the two calibration techniques provided consistent estimates of the metabolic response on average, with a median R2′-based estimate of the metabolic response to CO2 of 1.4%, and R2′- and hypercapnia-calibrated estimates of the visual response of 27% and 24%, respectively. However, these estimates were sensitive to different sources of estimation uncertainty. The R2′-calibrated estimate was highly sensitive to CSF contamination and to uncertainty in unmeasured model parameters describing flow-volume coupling, capillary bed characteristics, and the iso-susceptibility saturation of blood. The hypercapnia-calibrated estimate was relatively insensitive to these parameters but highly sensitive to the assumed metabolic response to CO2. PMID:26790354
International Nuclear Information System (INIS)
Orellana Salas, A.; Melgar Perez, J.; Arrocha Acevedo, J. F.
2013-01-01
The determination of the activity to prescribe the hyperthyroid patients presented difficult consideration uncertainties. The uncertainties associated with the experimental design can exceed 20%, so it should be valued to customize activity therapy of 1 31 I. (Author)
McGuire, Jeffrey J.; Kaneko, Yoshihiro
2018-06-01
The key kinematic earthquake source parameters: rupture velocity, duration and area, shed light on earthquake dynamics, provide direct constraints on stress-drop, and have implications for seismic hazard. However, for moderate and small earthquakes, these parameters are usually poorly constrained due to limitations of the standard analysis methods. Numerical experiments by Kaneko and Shearer [2014,2015] demonstrated that standard spectral fitting techniques can lead to roughly 1 order of magnitude variation in stress-drop estimates that do not reflect the actual rupture properties even for simple crack models. We utilize these models to explore an alternative approach where we estimate the rupture area directly. For the suite of models, the area averaged static stress drop is nearly constant for models with the same underlying friction law, yet corner frequency based stress-drop estimates vary by a factor of 5-10 even for noise free data. Alternatively, we simulated inversions for the rupture area as parameterized by the second moments of the slip distribution. A natural estimate for the rupture area derived from the second moments is A=πLcWc, where Lc and Wc are the characteristic rupture length and width. This definition yields estimates of stress drop that vary by only 10% between the models but are slightly larger than the true area-averaged values. We simulate inversions for the second moments for the various models and find that the area can be estimated well when there are at least 15 available measurements of apparent duration at a variety of take-off angles. The improvement compared to azimuthally-averaged corner-frequency based approaches results from the second moments accounting for directivity and removing the assumption of a circular rupture area, both of which bias the standard approach. We also develop a new method that determines the minimum and maximum values of rupture area that are consistent with a particular dataset at the 95% confidence
Uncertainty estimates of a GRACE inversion modelling technique over Greenland using a simulation
Bonin, Jennifer; Chambers, Don
2013-07-01
The low spatial resolution of GRACE causes leakage, where signals in one location spread out into nearby regions. Because of this leakage, using simple techniques such as basin averages may result in an incorrect estimate of the true mass change in a region. A fairly simple least squares inversion technique can be used to more specifically localize mass changes into a pre-determined set of basins of uniform internal mass distribution. However, the accuracy of these higher resolution basin mass amplitudes has not been determined, nor is it known how the distribution of the chosen basins affects the results. We use a simple `truth' model over Greenland as an example case, to estimate the uncertainties of this inversion method and expose those design parameters which may result in an incorrect high-resolution mass distribution. We determine that an appropriate level of smoothing (300-400 km) and process noise (0.30 cm2 of water) gets the best results. The trends of the Greenland internal basins and Iceland can be reasonably estimated with this method, with average systematic errors of 3.5 cm yr-1 per basin. The largest mass losses found from GRACE RL04 occur in the coastal northwest (-19.9 and -33.0 cm yr-1) and southeast (-24.2 and -27.9 cm yr-1), with small mass gains (+1.4 to +7.7 cm yr-1) found across the northern interior. Acceleration of mass change is measurable at the 95 per cent confidence level in four northwestern basins, but not elsewhere in Greenland. Due to an insufficiently detailed distribution of basins across internal Canada, the trend estimates of Baffin and Ellesmere Islands are expected to be incorrect due to systematic errors caused by the inversion technique.
Uncertainty in CH4 and N2O emission estimates from a managed fen meadow using EC measurements
International Nuclear Information System (INIS)
Kroon, P.S.; Hensen, A.; Van 't Veen, W.H.; Vermeulen, A.T.; Jonker, H.
2009-02-01
The overall uncertainty in annual flux estimates derived from chamber measurements may be as high as 50% due to the temporal and spatial variability in the fluxes. As even a large number of chamber plots still cover typically less than 1% of the total field area, the field-scale integrated emission necessarily remains a matter of speculation. High frequency micrometeorological methods are a good option for obtaining integrated estimates on a hectare scale with a continuous coverage in time. Instrumentation is now becoming available that meets the requirements for CH4 and N2O eddy covariance (EC) measurements. A system consisting of a quantum cascade laser (QCL) spectrometer and a sonic anemometer has recently been proven to be suitable for performing EC measurements. This study analyses the EC flux measurements of CH4 and N2O and its corrections, like calibration, Webb-correction, and corrections for high and low frequency losses, and assesses the magnitude of the uncertainties associated with the precision of the measurement instruments, measurement set-up and the methodology. The uncertainty of one single EC flux measurement, a daily, monthly and 3-monthly average EC flux is estimated. In addition, the cumulative emission of C-CH4 and N-N2O and their uncertainties are determined over several fertilizing events at a dairy farm site in the Netherlands. These fertilizing events are selected from the continuously EC flux measurements from August 2006 to September 2008. The EC flux uncertainties are compared by the overall uncertainty in annual flux estimates derived from chamber measurements. It will be shown that EC flux measurements can decrease the overall uncertainty in annual flux estimates
Uncertainty in CH4 and N2O emission estimates from a managed fen meadow using EC measurements
Energy Technology Data Exchange (ETDEWEB)
Kroon, P.S.; Hensen, A.; Van ' t Veen, W.H.; Vermeulen, A.T. [ECN Biomass, Coal and Environment, Petten (Netherlands); Jonker, H. [Delft University of Technology, Delft (Netherlands)
2009-02-15
The overall uncertainty in annual flux estimates derived from chamber measurements may be as high as 50% due to the temporal and spatial variability in the fluxes. As even a large number of chamber plots still cover typically less than 1% of the total field area, the field-scale integrated emission necessarily remains a matter of speculation. High frequency micrometeorological methods are a good option for obtaining integrated estimates on a hectare scale with a continuous coverage in time. Instrumentation is now becoming available that meets the requirements for CH4 and N2O eddy covariance (EC) measurements. A system consisting of a quantum cascade laser (QCL) spectrometer and a sonic anemometer has recently been proven to be suitable for performing EC measurements. This study analyses the EC flux measurements of CH4 and N2O and its corrections, like calibration, Webb-correction, and corrections for high and low frequency losses, and assesses the magnitude of the uncertainties associated with the precision of the measurement instruments, measurement set-up and the methodology. The uncertainty of one single EC flux measurement, a daily, monthly and 3-monthly average EC flux is estimated. In addition, the cumulative emission of C-CH4 and N-N2O and their uncertainties are determined over several fertilizing events at a dairy farm site in the Netherlands. These fertilizing events are selected from the continuously EC flux measurements from August 2006 to September 2008. The EC flux uncertainties are compared by the overall uncertainty in annual flux estimates derived from chamber measurements. It will be shown that EC flux measurements can decrease the overall uncertainty in annual flux estimates.
Energy Technology Data Exchange (ETDEWEB)
Cologne, John B [Department of Statistics, Radiation Effects Research Foundation, 5-2 Hijiyama Park, Minami-ku, Hiroshima 732-0815 (Japan); Pawel, David J [Office of Radiation and Indoor Air, US Environmental Protection Agency, 1200 Pennsylvania Ave NW, Washington DC 20460 (United States); Sharp, Gerald B [Department of Epidemiology, Radiation Effects Research Foundation, 5-2 Hijiyama Park, Minami-ku, Hiroshima 732-0815 (Japan); Fujiwara, Saeko [Department of Clinical Studies, Radiation Effects Research Foundation, 5-2 Hijiyama Park, Minami-ku, Hiroshima 732-0815 (Japan)
2004-06-01
Exposure to other risk factors is an important consideration in assessing the role played by radiation in producing disease. A cross-sectional study of atomic-bomb survivors suggested an interaction between whole-body radiation exposure and chronic hepatitis-C viral (HCV) infection in the etiology of chronic liver disease (chronic hepatitis and cirrhosis), but did not allow determination of the joint-effect mechanism. Different estimates of probability of causation (POC) conditional on HCV status resulted from additive and multiplicative models. We therefore estimated the risk for radiation conditional on HCV status using a more general, mixture model that does not require choosing between additivity or multiplicativity, or deciding whether there is interaction, in the face of the large uncertainty. The results support the conclusion that POC increases with radiation dose in persons without HCV infection, but are inconclusive regarding individuals with HCV infection, the lower confidence bound on estimated POC for radiation with HCV infection being zero over the entire dose range. Although the mixture model may not reflect the true joint-effect mechanism, it avoids restrictive model assumptions that cannot be validated using the available data yet have a profound influence on estimated POC. These considerations apply more generally, given that the additive and multiplicative models are often used in POC related work. We therefore consider that an empirical approach may be preferable to assuming a specific mechanistic model for estimating POC in epidemiological studies where the joint-effect mechanism is in doubt.
International Nuclear Information System (INIS)
Cologne, John B; Pawel, David J; Sharp, Gerald B; Fujiwara, Saeko
2004-01-01
Exposure to other risk factors is an important consideration in assessing the role played by radiation in producing disease. A cross-sectional study of atomic-bomb survivors suggested an interaction between whole-body radiation exposure and chronic hepatitis-C viral (HCV) infection in the etiology of chronic liver disease (chronic hepatitis and cirrhosis), but did not allow determination of the joint-effect mechanism. Different estimates of probability of causation (POC) conditional on HCV status resulted from additive and multiplicative models. We therefore estimated the risk for radiation conditional on HCV status using a more general, mixture model that does not require choosing between additivity or multiplicativity, or deciding whether there is interaction, in the face of the large uncertainty. The results support the conclusion that POC increases with radiation dose in persons without HCV infection, but are inconclusive regarding individuals with HCV infection, the lower confidence bound on estimated POC for radiation with HCV infection being zero over the entire dose range. Although the mixture model may not reflect the true joint-effect mechanism, it avoids restrictive model assumptions that cannot be validated using the available data yet have a profound influence on estimated POC. These considerations apply more generally, given that the additive and multiplicative models are often used in POC related work. We therefore consider that an empirical approach may be preferable to assuming a specific mechanistic model for estimating POC in epidemiological studies where the joint-effect mechanism is in doubt
The first Australian gravimetric quasigeoid model with location-specific uncertainty estimates
Featherstone, W. E.; McCubbine, J. C.; Brown, N. J.; Claessens, S. J.; Filmer, M. S.; Kirby, J. F.
2018-02-01
We describe the computation of the first Australian quasigeoid model to include error estimates as a function of location that have been propagated from uncertainties in the EGM2008 global model, land and altimeter-derived gravity anomalies and terrain corrections. The model has been extended to include Australia's offshore territories and maritime boundaries using newer datasets comprising an additional {˜ }280,000 land gravity observations, a newer altimeter-derived marine gravity anomaly grid, and terrain corrections at 1^' ' }× 1^' ' } resolution. The error propagation uses a remove-restore approach, where the EGM2008 quasigeoid and gravity anomaly error grids are augmented by errors propagated through a modified Stokes integral from the errors in the altimeter gravity anomalies, land gravity observations and terrain corrections. The gravimetric quasigeoid errors (one sigma) are 50-60 mm across most of the Australian landmass, increasing to {˜ }100 mm in regions of steep horizontal gravity gradients or the mountains, and are commensurate with external estimates.
OECD/CSNI Workshop on Best Estimate Methods and Uncertainty Evaluations - Workshop Proceedings
International Nuclear Information System (INIS)
2013-01-01
Best-Estimate Methods plus Uncertainty Evaluation are gaining increased interest in the licensing process. On the other hand, lessons learnt from the BEMUSE (NEA/CSNI/R(2011)3) and SM2A (NEA/CSNI/R(2011)3) benchmarks, progress of UAM benchmark, and answers to the WGAMA questionnaire on the Use of Best-Estimate Methodologies show that improvements of the present methods are necessary and new applications appear. The objective of this workshop is to provide a forum for a wide range of experts to exchange information in the area of best estimate analysis and uncertainty evaluation methods and address issues drawn-up from BEMUSE, UAM and SM2A activities. Both, improvement of existing methods and recent new developments are included. As a result of the workshop development, a set of recommendations, including lines for future activities were proposed. The organisation of the Workshop was divided into three parts: Opening session including key notes from OECD and IAEA representatives, Technical sessions, and a Wrap-up session. All sessions included a debate with participation from the audience constituted by 71 attendees. The workshop consisted of four technical sessions: a) Development achievements of BEPU methods and State of the Art: The objective of this session was to present the different approaches to deal with Best Estimate codes and uncertainties evaluations. A total of six papers were presented. One initial paper summarized the existing methods; the following open papers were focused on specific methods stressing their bases, peculiarities and advantages. As a result of the session a picture of the current State of the Art was obtained. b) International comparative activities: This session reviewed the set of international activities around the subject of BEPU methods benchmarking and development. From each of the activities a description of the objectives, development, main results, conclusions and recommendations (in case it is finalized) was presented. This
Kawabata, E.; Main, I. G.; Naylor, M.; Chandler, R. E.
2016-12-01
In moderate to low seismicity areas such as the UK, earthquakes represent a small but not negligible risk to sensitive structures such as nuclear power plants. As a part of the safety case in the planning and regulation of such structures, seismic activity must first be monitored and quantified to form a catalogue of past events. In a low or moderate seismicity zone, most of our knowledge of the most significant events comes from macroseismic intensity measures from the pre-instrumental period (before 1900). These historical records must then be combined and calibrated with modern analogue and digitally-recorded instrumental data on a common source magnitude scale, the most useful of which is the moment magnitude. The result is a unified catalogue that can be used for probabilistic seismic hazard analysis. An isoseismal map involves a set of contours that enclose the areas at which the event was felt at particular intensity values or higher, called felt areas. It has been common practice to draw these contours by hand with varying degrees of subjectivity. Here, we demonstrate a Bayesian method for constructing such maps objectively from macroseismic intensity measures and their observed locations. It involves using mathematical expressions to represent concentric ellipses and estimating their optimal parameters and uncertainties in a Bayesian framework. Inferred fault orientations in the UK are predominantly vertical, so the elliptical assumption is reasonable at least to first order or as a null hypothesis. Relevant physical constraints are used as priors where available. The resulting posterior distributions are used to calculate felt area at a given intensity, as well as a probability density function for the inferred epicentre. We then describe another Bayesian approach for deriving moment magnitude from felt areas based on their relationship and known constraints such as the frequency-magnitude distribution. The use of Bayesian inference allows us to quantify
International Nuclear Information System (INIS)
Reutter, Bryan W.; Gullberg, Grant T.; Huesman, Ronald H.
2001-01-01
The estimation of time-activity curves and kinetic model parameters directly from projection data is potentially useful for clinical dynamic single photon emission computed tomography (SPECT) studies, particularly in those clinics that have only single-detector systems and thus are not able to perform rapid tomographic acquisitions. Because the radiopharmaceutical distribution changes while the SPECT gantry rotates, projections at different angles come from different tracer distributions. A dynamic image sequence reconstructed from the inconsistent projections acquired by a slowly rotating gantry can contain artifacts that lead to biases in kinetic parameters estimated from time-activity curves generated by overlaying regions of interest on the images. If cone beam collimators are used and the focal point of the collimators always remains in a particular transaxial plane, additional artifacts can arise in other planes reconstructed using insufficient projection samples [1]. If the projection samples truncate the patient's body, this can result in additional image artifacts. To overcome these sources of bias in conventional image based dynamic data analysis, we and others have been investigating the estimation of time-activity curves and kinetic model parameters directly from dynamic SPECT projection data by modeling the spatial and temporal distribution of the radiopharmaceutical throughout the projected field of view [2-8]. In our previous work we developed a computationally efficient method for fully four-dimensional (4-D) direct estimation of spatiotemporal distributions from dynamic SPECT projection data [5], which extended Formiconi's least squares algorithm for reconstructing temporally static distributions [9]. In addition, we studied the biases that result from modeling various orders temporal continuity and using various time samplings [5]. the present work, we address computational issues associated with evaluating the statistical uncertainty of
Odman, M. T.; Hu, Y.; Russell, A. G.
2016-12-01
Prescribed burning is practiced throughout the US, and most widely in the Southeast, for the purpose of maintaining and improving the ecosystem, and reducing the wildfire risk. However, prescribed burn emissions contribute significantly to the of trace gas and particulate matter loads in the atmosphere. In places where air quality is already stressed by other anthropogenic emissions, prescribed burns can lead to major health and environmental problems. Air quality modeling efforts are under way to assess the impacts of prescribed burn emissions. Operational forecasts of the impacts are also emerging for use in dynamic management of air quality as well as the burns. Unfortunately, large uncertainties exist in the process of estimating prescribed burn emissions and these uncertainties limit the accuracy of the burn impact predictions. Prescribed burn emissions are estimated by using either ground-based information or satellite observations. When there is sufficient local information about the burn area, the types of fuels, their consumption amounts, and the progression of the fire, ground-based estimates are more accurate. In the absence of such information satellites remain as the only reliable source for emission estimation. To determine the level of uncertainty in prescribed burn emissions, we compared estimates derived from a burn permit database and other ground-based information to the estimates by the Biomass Burning Emissions Product derived from a constellation of NOAA and NASA satellites. Using these emissions estimates we conducted simulations with the Community Multiscale Air Quality (CMAQ) model and predicted trace gas and particulate matter concentrations throughout the Southeast for two consecutive burn seasons (2015 and 2016). In this presentation, we will compare model predicted concentrations to measurements at monitoring stations and evaluate if the differences are commensurate with our emission uncertainty estimates. We will also investigate if
The Application Programming Interface (API) for Uncertainty Analysis, Sensitivity Analysis, andParameter Estimation (UA/SA/PE API) (also known as Calibration, Optimization and Sensitivity and Uncertainty (CUSO)) was developed in a joint effort between several members of both ...
Funamizu, Akihiro; Ito, Makoto; Doya, Kenji; Kanzaki, Ryohei; Takahashi, Hirokazu
2012-04-01
The estimation of reward outcomes for action candidates is essential for decision making. In this study, we examined whether and how the uncertainty in reward outcome estimation affects the action choice and learning rate. We designed a choice task in which rats selected either the left-poking or right-poking hole and received a reward of a food pellet stochastically. The reward probabilities of the left and right holes were chosen from six settings (high, 100% vs. 66%; mid, 66% vs. 33%; low, 33% vs. 0% for the left vs. right holes, and the opposites) in every 20-549 trials. We used Bayesian Q-learning models to estimate the time course of the probability distribution of action values and tested if they better explain the behaviors of rats than standard Q-learning models that estimate only the mean of action values. Model comparison by cross-validation revealed that a Bayesian Q-learning model with an asymmetric update for reward and non-reward outcomes fit the choice time course of the rats best. In the action-choice equation of the Bayesian Q-learning model, the estimated coefficient for the variance of action value was positive, meaning that rats were uncertainty seeking. Further analysis of the Bayesian Q-learning model suggested that the uncertainty facilitated the effective learning rate. These results suggest that the rats consider uncertainty in action-value estimation and that they have an uncertainty-seeking action policy and uncertainty-dependent modulation of the effective learning rate. © 2012 The Authors. European Journal of Neuroscience © 2012 Federation of European Neuroscience Societies and Blackwell Publishing Ltd.
Schiavazzi, Daniele E.; Baretta, Alessia; Pennati, Giancarlo; Hsia, Tain-Yen; Marsden, Alison L.
2017-01-01
Summary Computational models of cardiovascular physiology can inform clinical decision-making, providing a physically consistent framework to assess vascular pressures and flow distributions, and aiding in treatment planning. In particular, lumped parameter network (LPN) models that make an analogy to electrical circuits offer a fast and surprisingly realistic method to reproduce the circulatory physiology. The complexity of LPN models can vary significantly to account, for example, for cardiac and valve function, respiration, autoregulation, and time-dependent hemodynamics. More complex models provide insight into detailed physiological mechanisms, but their utility is maximized if one can quickly identify patient specific parameters. The clinical utility of LPN models with many parameters will be greatly enhanced by automated parameter identification, particularly if parameter tuning can match non-invasively obtained clinical data. We present a framework for automated tuning of 0D lumped model parameters to match clinical data. We demonstrate the utility of this framework through application to single ventricle pediatric patients with Norwood physiology. Through a combination of local identifiability, Bayesian estimation and maximum a posteriori simplex optimization, we show the ability to automatically determine physiologically consistent point estimates of the parameters and to quantify uncertainty induced by errors and assumptions in the collected clinical data. We show that multi-level estimation, that is, updating the parameter prior information through sub-model analysis, can lead to a significant reduction in the parameter marginal posterior variance. We first consider virtual patient conditions, with clinical targets generated through model solutions, and second application to a cohort of four single-ventricle patients with Norwood physiology. PMID:27155892
Wang, Wei; Wu, Jian-Guo; Han, Xing-Guo
2012-01-01
Based on the measurements in the enclosure and uncontrolled grazing plots in the typical steppe of Xilinguole, Inner Mongolia, this paper studied the soil carbon storage and carbon sequestration in the grasslands dominated by Leymus chinensis, Stipa grandis, and Stipa krylovii, respectively, and estimated the regional scale soil carbon sequestration potential in the heavily degraded grassland after restoration. At local scale, the annual soil carbon sequestration in the three grasslands all decreased with increasing year of enclosure. The soil organic carbon storage was significantly higher in the grasslands dominated by L. chinensis and Stipa grandis than in that dominated by Stipa krylovii, but the latter had much higher soil carbon sequestration potential, because of the greater loss of soil organic carbon during the degradation process due to overgrazing. At regional scale, the soil carbon sequestration potential at the depth of 0-20 cm varied from -0.03 x 10(4) to 3.71 x 10(4) kg C x a(-1), and the total carbon sequestration potential was 12.1 x 10(8) kg C x a(-1). Uncertainty analysis indicated that soil gravel content had less effect on the estimated carbon sequestration potential, but the estimation errors resulted from the spatial interpolation of climate data could be about +/- 4.7 x 10(9) kg C x a(-1). In the future, if the growth season precipitation in this region had an average variation of -3.2 mm x (10 a)(-1), the soil carbon sequestration potential would be de- creased by 1.07 x 10(8) kg C x (10 a)(-1).
Directory of Open Access Journals (Sweden)
Simon Willcock
Full Text Available Monitoring landscape carbon storage is critical for supporting and validating climate change mitigation policies. These may be aimed at reducing deforestation and degradation, or increasing terrestrial carbon storage at local, regional and global levels. However, due to data-deficiencies, default global carbon storage values for given land cover types such as 'lowland tropical forest' are often used, termed 'Tier 1 type' analyses by the Intergovernmental Panel on Climate Change (IPCC. Such estimates may be erroneous when used at regional scales. Furthermore uncertainty assessments are rarely provided leading to estimates of land cover change carbon fluxes of unknown precision which may undermine efforts to properly evaluate land cover policies aimed at altering land cover dynamics. Here, we present a repeatable method to estimate carbon storage values and associated 95% confidence intervals (CI for all five IPCC carbon pools (aboveground live carbon, litter, coarse woody debris, belowground live carbon and soil carbon for data-deficient regions, using a combination of existing inventory data and systematic literature searches, weighted to ensure the final values are regionally specific. The method meets the IPCC 'Tier 2' reporting standard. We use this method to estimate carbon storage over an area of33.9 million hectares of eastern Tanzania, reporting values for 30 land cover types. We estimate that this area stored 6.33 (5.92-6.74 Pg C in the year 2000. Carbon storage estimates for the same study area extracted from five published Africa-wide or global studies show a mean carbon storage value of ∼50% of that reported using our regional values, with four of the five studies reporting lower carbon storage values. This suggests that carbon storage may have been underestimated for this region of Africa. Our study demonstrates the importance of obtaining regionally appropriate carbon storage estimates, and shows how such values can be produced
International Nuclear Information System (INIS)
Yao, Cong; Wada, Takashige; Shimizu, Akinobu; Kobatake, Hidefumi; Nawano, Shigeru
2006-01-01
We propose an approach for the simultaneous bounding box estimation of multiple organs in volumetric CT images. Local eigen-organ spaces are constructed for different types of training organs, and a global eigen-space, which describes the spatial relationships between the organs, is also constructed. Each volume of interest in the abdominal CT image is projected into the local eigen-organ spaces, and several candidate locations are determined. The final selection of the organ locations is made by projecting the set of candidate locations into the global eigen-space. A probabilistic atlas of organs is used to eliminate locations with low probability and to guide the selection of candidate locations. Evaluation by the leave-one-out method using 10 volumetric abdominal CT images showed that the proposed method provided an average accuracy of 80.38% for 11 different organ types. (author)
Estimating bounds on the macroeconomic effects of the Clean Energy Future policy scenarios
Energy Technology Data Exchange (ETDEWEB)
Sanstad, A. H.; DeCanio, S. J.; Boyd, G. A.
2000-04-04
The Clean Energy Future (CEF) is a partial equilibrium study in that it focuses specifically on markets for energy services. It is also important, however, to consider potential effects of the CEF policies on overall economic performance. The purpose of this paper is: (1) to provide a framework for interpreting the macroeconomic (or second-order) effects that might occur under the types of scenarios analyzed in the CEF, and (2) to obtain a range of estimates of these effects associated with the Moderate and Advanced scenarios as described in the CEF study. In this paper the authors consider results from both types of model in the context of the CEF study. The primary framework and calculations focus on the second meaning given above of the term macroeconomic and the associated CGE models, because these are appropriate for analysis on the time scales of the CEF, through 2010 or 2020. Because the Keynesian-style macroeconomic models are designed and suited for short-term forecasting, they also discuss the application of one such model to the analysis of the shorter-horizon effects of certain policies to reduce carbon emissions.
Frequency Diverse Array Radar Cramér-Rao Lower Bounds for Estimating Direction, Range, and Velocity
Directory of Open Access Journals (Sweden)
Yongbing Wang
2014-01-01
Full Text Available Different from phased-array radar, frequency diverse array (FDA radar offers range-dependent beampattern and thus provides new application potentials. But there is a fundamental question: what estimation performance can achieve for an FDA radar? In this paper, we derive FDA radar Cramér-Rao lower bounds (CRLBs for estimating direction, range (time delay, and velocity (Doppler shift. Two different data models including pre- and postmatched filtering are investigated separately. As the FDA radar has range-angle coupling, we use a simple transmit subaperturing strategy which divides the whole array into two subarrays, each uses a distinct frequency increment. Assuming temporally white Gaussian noise and linear frequency modulated transmit signal, extensive simulation examples are performed. When compared to conventional phased-array radar, FDA can yield better CRLBs for estimating the direction, range, and velocity. Moreover, the impacts of the element number and frequency increment are also analyzed. Simulation results show that the CRLBs decrease with the increase of the elements number and frequency increment.
International Nuclear Information System (INIS)
Mallet, Vivien
2005-01-01
The thesis deals with the evaluation of a chemistry-transport model, not primarily with classical comparisons to observations, but through the estimation of its a priori uncertainties due to input data, model formulation and numerical approximations. These three uncertainty sources are studied respectively on the basis of Monte Carlos simulations, multi-models simulations and numerical schemes inter-comparisons. A high uncertainty is found, in output ozone concentrations. In order to overtake the limitations due to the uncertainty, a solution is ensemble forecast. Through combinations of several models (up to forty-eight models) on the basis of past observations, the forecast can be significantly improved. The achievement of this work has also led to develop the innovative modelling-system Polyphemus. (author) [fr
Souverijns, Niels; Gossart, Alexandra; Lhermitte, Stef; Gorodetskaya, Irina; Kneifel, Stefan; Maahn, Maximilian; Bliven, Francis; van Lipzig, Nicole
2017-04-01
The Antarctic Ice Sheet (AIS) is the largest ice body on earth, having a volume equivalent to 58.3 m global mean sea level rise. Precipitation is the dominant source term in the surface mass balance of the AIS. However, this quantity is not well constrained in both models and observations. Direct observations over the AIS are also not coherent, as they are sparse in space and time and acquisition techniques differ. As a result, precipitation observations stay mostly limited to continent-wide averages based on satellite radar observations. Snowfall rate (SR) at high temporal resolution can be derived from the ground-based radar effective reflectivity factor (Z) using information about snow particle size and shape. Here we present reflectivity snowfall rate relations (Z = aSRb) for the East Antarctic escarpment region using the measurements at the Princess Elisabeth (PE) station and an overview of their uncertainties. A novel technique is developed by combining an optical disdrometer (NASA's Precipitation Imaging Package; PIP) and a vertically pointing 24 GHz FMCW micro rain radar (Metek's MRR) in order to reduce the uncertainty in SR estimates. PIP is used to obtain information about snow particle characteristics and to get an estimate of Z, SR and the Z-SR relation. For PE, located 173 km inland, the relation equals Z = 18SR1.1. The prefactor (a) of the relation is sensitive to the median diameter of the particles. Larger particles, found closer to the coast, lead to an increase of the value of the prefactor. More inland locations, where smaller snow particles are found, obtain lower values for the prefactor. The exponent of the Z-SR relation (b) is insensitive to the median diameter of the snow particles. This dependence of the prefactor of the Z-SR relation to the particle size needs to be taken into account when converting radar reflectivities to snowfall rates over Antarctica. The uncertainty on the Z-SR relations is quantified using a bootstrapping approach
International Nuclear Information System (INIS)
Sunardi; Samin Prihatin
2010-01-01
Validation and uncertainty estimation of Fast Neutron Activation Analysis (FNAA) method for Cu, Fe, Al, Si elements in sediment samples has been conduced. The aim of the research is to confirm whether FNAA method is still matches to ISO/lEC 17025-2005 standard. The research covered the verification, performance, validation of FNM and uncertainty estimation. Standard of SRM 8704 and sediments were weighted for certain weight and irradiated with 14 MeV fast neutron and then counted using gamma spectrometry. The result of validation method for Cu, Fe, Al, Si element showed that the accuracy were in the range of 95.89-98.68 %, while the precision were in the range 1.13-2.29 %. The result of uncertainty estimation for Cu, Fe, Al, and Si were 2.67, 1.46, 1.71 and 1.20 % respectively. From this data, it can be concluded that the FNM method is still reliable and valid for element contents analysis in samples, because the accuracy is up to 95 % and the precision is under 5 %, while the uncertainty are relatively small and suitable for the range 95 % level of confidence where the uncertainty maximum is 5 %. (author)
Statistical characterization of roughness uncertainty and impact on wind resource estimation
DEFF Research Database (Denmark)
Kelly, Mark C.; Ejsing Jørgensen, Hans
2017-01-01
In this work we relate uncertainty in background roughness length (z0) to uncertainty in wind speeds, where the latter are predicted at a wind farm location based on wind statistics observed at a different site. Sensitivity of predicted winds to roughness is derived analytically for the industry...... between mean wind speed and AEP. Following our developments, we provide guidance on approximate roughness uncertainty magnitudes to be expected in industry practice, and we also find that sites with larger background roughness incur relatively larger uncertainties.......-standard European Wind Atlas method, which is based on the geostrophic drag law. We statistically consider roughness and its corresponding uncertainty, in terms of both z0 derived from measured wind speeds as well as that chosen in practice by wind engineers. We show the combined effect of roughness uncertainty...
Best Estimate plus Uncertainty (BEPU) Analyses in the IAEA Safety Standards
International Nuclear Information System (INIS)
Dusic, Milorad; )
2013-01-01
The Safety Standards Series establishes an essential basis for safety and represents the broadest international consensus. Safety Standards Series publications are categorized into: Safety Fundamental (Present the overall objectives, concepts and principles of protection and safety, they are the policy documents of the safety standards), Safety Requirements (Establish requirements that must be met to ensure the protection and safety of people and the environment, both now and in the future), and Safety Guides (Provide guidance, in the form of more detailed actions, conditions or procedures that can be used to comply with the Requirements). The incorporation of more detailed requirements, in accordance with national practice, may still be necessary. There should be only one set of international safety standards. Each safety standard will be reviewed by the relevant committee or by the commission every five years. Best Estimate plus Uncertainty (BEPU) Analyses are approached in the following IAEA Safety Standards: - Safety Requirements SSR 2/1 - Safety of NPPs, Design (Revision of NS-R-1); - General Safety Requirement GSR Part 4: Safety Assessment for Facilities and Activities; - Safety Guide SSG-2 Deterministic Safety Analysis for Nuclear Power Plants. NUSSC suggested that new safety guides should be accompanied by documents like TECDOCs or Safety Reports describing in detail their recommendations where appropriate. Special review is currently underway to identify needs for revision in the light of the Fukushima accident. Revision will concern, first, the Safety Requirements, and then, the Selected Safety Guides
International Nuclear Information System (INIS)
Luxat, J.C.; Huget, R.G.
2001-01-01
Development of a methodology to perform best estimate and uncertainty nuclear safety analysis has been underway at Ontario Power Generation for the past two and one half years. A key driver for the methodology development, and one of the major challenges faced, is the need to re-establish demonstrated safety margins that have progressively been undermined through excessive and compounding conservatism in deterministic analyses. The major focus of the prototyping applications was to quantify the safety margins that exist at the probable range of high power operating conditions, rather than the highly improbable operating states associated with Limit of the Envelope (LOE) assumptions. In LOE, all parameters of significance to the consequences of a postulated accident are assumed to simultaneously deviate to their limiting values. Another equally important objective of the prototyping was to demonstrate the feasibility of conducting safety analysis as an incremental analysis activity, as opposed to a major re-analysis activity. The prototype analysis solely employed prior analyses of Bruce B large break LOCA events - no new computer simulations were undertaken. This is a significant and novel feature of the prototyping work. This methodology framework has been applied to a postulated large break LOCA in a Bruce generating unit on a prototype basis. This paper presents results of the application. (author)
Adaptive multiscale MCMC algorithm for uncertainty quantification in seismic parameter estimation
Tan, Xiaosi
2014-08-05
Formulating an inverse problem in a Bayesian framework has several major advantages (Sen and Stoffa, 1996). It allows finding multiple solutions subject to flexible a priori information and performing uncertainty quantification in the inverse problem. In this paper, we consider Bayesian inversion for the parameter estimation in seismic wave propagation. The Bayes\\' theorem allows writing the posterior distribution via the likelihood function and the prior distribution where the latter represents our prior knowledge about physical properties. One of the popular algorithms for sampling this posterior distribution is Markov chain Monte Carlo (MCMC), which involves making proposals and calculating their acceptance probabilities. However, for large-scale problems, MCMC is prohibitevely expensive as it requires many forward runs. In this paper, we propose a multilevel MCMC algorithm that employs multilevel forward simulations. Multilevel forward simulations are derived using Generalized Multiscale Finite Element Methods that we have proposed earlier (Efendiev et al., 2013a; Chung et al., 2013). Our overall Bayesian inversion approach provides a substantial speed-up both in the process of the sampling via preconditioning using approximate posteriors and the computation of the forward problems for different proposals by using the adaptive nature of multiscale methods. These aspects of the method are discussed n the paper. This paper is motivated by earlier work of M. Sen and his collaborators (Hong and Sen, 2007; Hong, 2008) who proposed the development of efficient MCMC techniques for seismic applications. In the paper, we present some preliminary numerical results.
Estimating Uncertainty of Point-Cloud Based Single-Tree Segmentation with Ensemble Based Filtering
Directory of Open Access Journals (Sweden)
Matthew Parkan
2018-02-01
Full Text Available Individual tree crown segmentation from Airborne Laser Scanning data is a nodal problem in forest remote sensing. Focusing on single layered spruce and fir dominated coniferous forests, this article addresses the problem of directly estimating 3D segment shape uncertainty (i.e., without field/reference surveys, using a probabilistic approach. First, a coarse segmentation (marker controlled watershed is applied. Then, the 3D alpha hull and several descriptors are computed for each segment. Based on these descriptors, the alpha hulls are grouped to form ensembles (i.e., groups of similar tree shapes. By examining how frequently regions of a shape occur within an ensemble, it is possible to assign a shape probability to each point within a segment. The shape probability can subsequently be thresholded to obtain improved (filtered tree segments. Results indicate this approach can be used to produce segmentation reliability maps. A comparison to manually segmented tree crowns also indicates that the approach is able to produce more reliable tree shapes than the initial (unfiltered segmentation.
Uncertainties and Systematic Effects on the estimate of stellar masses in high z galaxies
Salimbeni, S.; Fontana, A.; Giallongo, E.; Grazian, A.; Menci, N.; Pentericci, L.; Santini, P.
2009-05-01
We discuss the uncertainties and the systematic effects that exist in the estimates of the stellar masses of high redshift galaxies, using broad band photometry, and how they affect the deduced galaxy stellar mass function. We use at this purpose the latest version of the GOODS-MUSIC catalog. In particular, we discuss the impact of different synthetic models, of the assumed initial mass function and of the selection band. Using Chariot & Bruzual 2007 and Maraston 2005 models we find masses lower than those obtained from Bruzual & Chariot 2003 models. In addition, we find a slight trend as a function of the mass itself comparing these two mass determinations with that from Bruzual & Chariot 2003 models. As consequence, the derived galaxy stellar mass functions show diverse shapes, and their slope depends on the assumed models. Despite these differences, the overall results and scenario is observed in all these cases. The masses obtained with the assumption of the Chabrier initial mass function are in average 0.24 dex lower than those from the Salpeter assumption, at all redshifts, causing a shift of galaxy stellar mass function of the same amount. Finally, using a 4.5 μm-selected sample instead of a Ks-selected one, we add a new population of highly absorbed, dusty galaxies at z~=2-3 of relatively low masses, yielding stronger constraints on the slope of the galaxy stellar mass function at lower masses.
Directory of Open Access Journals (Sweden)
J. Florian Wellmann
2013-04-01
Full Text Available The quantification and analysis of uncertainties is important in all cases where maps and models of uncertain properties are the basis for further decisions. Once these uncertainties are identified, the logical next step is to determine how they can be reduced. Information theory provides a framework for the analysis of spatial uncertainties when different subregions are considered as random variables. In the work presented here, joint entropy, conditional entropy, and mutual information are applied for a detailed analysis of spatial uncertainty correlations. The aim is to determine (i which areas in a spatial analysis share information, and (ii where, and by how much, additional information would reduce uncertainties. As an illustration, a typical geological example is evaluated: the case of a subsurface layer with uncertain depth, shape and thickness. Mutual information and multivariate conditional entropies are determined based on multiple simulated model realisations. Even for this simple case, the measures not only provide a clear picture of uncertainties and their correlations but also give detailed insights into the potential reduction of uncertainties at each position, given additional information at a different location. The methods are directly applicable to other types of spatial uncertainty evaluations, especially where multiple realisations of a model simulation are analysed. In summary, the application of information theoretic measures opens up the path to a better understanding of spatial uncertainties, and their relationship to information and prior knowledge, for cases where uncertain property distributions are spatially analysed and visualised in maps and models.
International Nuclear Information System (INIS)
Hong, Kee Jeung; Kim, Jee Sang
2009-01-01
As concrete ages, the surrounding environment is expected to have growing influences on the concrete. As all the impacts of the environment cannot be considered in the strength-estimating model of a nondestructive concrete test, the increase in concrete age leads to growing uncertainty in the strength-estimating model. Therefore, the variation of the model error increases. It is necessary to include those impacts in the probability model of concrete strength attained from the nondestructive tests so as to build a more accurate reliability model for structural performance evaluation. This paper reviews and categorizes the existing strength-estimating statistical models of nondestructive concrete test, and suggests a new form of the strength-estimating statistical models to properly reflect the model uncertainty due to aging of the concrete. This new form of the statistical models will lay foundation for more accurate structural performance evaluation.
Krishnan Kutty, S.; Sekhar, M.; Ruiz, L.; Tomer, S. K.; Bandyopadhyay, S.; Buis, S.; Guerif, M.; Gascuel-odoux, C.
2012-12-01
Groundwater recharge in a semi-arid region is generally low, but could exhibit high spatial variability depending on the soil type and plant cover. The potential recharge (the drainage flux just beneath the root zone) is found to be sensitive to water holding capacity and rooting depth (Rushton, 2003). Simple water balance approaches for recharge estimation often fail to consider the effect of plant cover, growth phases and rooting depth. Hence a crop model based approach might be better suited to assess sensitivity of recharge for various crop-soil combinations in agricultural catchments. Martinez et al. (2009) using a root zone modelling approach to estimate groundwater recharge stressed that future studies should focus on quantifying the uncertainty in recharge estimates due to uncertainty in soil water parameters such as soil layers, field capacity, rooting depth etc. Uncertainty in the parameters may arise due to the uncertainties in retrieved variables (surface soil moisture and leaf area index) from satellite. Hence a good estimate of parameters as well as their uncertainty is essential for a reliable estimate of the potential recharge. In this study we focus on assessing the sensitivity of crop and soil types on the potential recharge by using a generic crop model STICS. The effect of uncertainty in the soil parameters on the estimates of recharge and its uncertainty is investigated. The multi-layer soil water parameters and their uncertainty is estimated by inversion of STICS model using the GLUE approach. Surface soil moisture and LAI either retrieved from microwave remote sensing data or measured in field plots (Sreelash et al., 2012) were found to provide good estimates of the soil water properties and therefore both these data sets were used in this study to estimate the parameters and the potential recharge for a combination of soil-crop systems. These investigations were made in two field experimental catchments. The first one is in the tropical semi
International Nuclear Information System (INIS)
Cheali, Peam; Gernaey, Krist V.; Sin, Gürkan
2015-01-01
Capital investment, next to the product demand, sales, and production costs, is one of the key metrics commonly used for project evaluation and feasibility assessment. Estimating the investment costs of a new product/process alternative during early-stage design is a challenging task, which is especially relevant in biorefinery research where information about new technologies and experience with new technologies is limited. A systematic methodology for uncertainty analysis of cost data is proposed that employs: (a) bootstrapping as a regression method when cost data are available; and, (b) the Monte Carlo technique as an error propagation method based on expert input when cost data are not available. Four well-known models for early-stage cost estimation are reviewed and analyzed using the methodology. The significance of uncertainties of cost data for early-stage process design is highlighted using the synthesis and design of a biorefinery as a case study. The impact of uncertainties in cost estimation on the identification of optimal processing paths is indeed found to be profound. To tackle this challenge, a comprehensive techno-economic risk analysis framework is presented to enable robust decision-making under uncertainties. One of the results using order-of-magnitude estimates shows that the production of diethyl ether and 1,3-butadiene are the most promising with the lowest economic risks (among the alternatives considered) of 0.24 MM$/a and 4.6 MM$/a, respectively.
David M. Bell; Eric J. Ward; A. Christopher Oishi; Ram Oren; Paul G. Flikkema; James S. Clark; David Whitehead
2015-01-01
Uncertainties in ecophysiological responses to environment, such as the impact of atmospheric and soil moisture conditions on plant water regulation, limit our ability to estimate key inputs for ecosystem models. Advanced statistical frameworks provide coherent methodologies for relating observed data, such as stem sap flux density, to unobserved processes, such as...
Energy Technology Data Exchange (ETDEWEB)
Cheali, Peam; Gernaey, Krist V.; Sin, Gürkan, E-mail: gsi@kt.dtu.dk [Department of Chemical and Biochemical Engineering, Technical University of Denmark, Lyngby (Denmark)
2015-02-06
Capital investment, next to the product demand, sales, and production costs, is one of the key metrics commonly used for project evaluation and feasibility assessment. Estimating the investment costs of a new product/process alternative during early-stage design is a challenging task, which is especially relevant in biorefinery research where information about new technologies and experience with new technologies is limited. A systematic methodology for uncertainty analysis of cost data is proposed that employs: (a) bootstrapping as a regression method when cost data are available; and, (b) the Monte Carlo technique as an error propagation method based on expert input when cost data are not available. Four well-known models for early-stage cost estimation are reviewed and analyzed using the methodology. The significance of uncertainties of cost data for early-stage process design is highlighted using the synthesis and design of a biorefinery as a case study. The impact of uncertainties in cost estimation on the identification of optimal processing paths is indeed found to be profound. To tackle this challenge, a comprehensive techno-economic risk analysis framework is presented to enable robust decision-making under uncertainties. One of the results using order-of-magnitude estimates shows that the production of diethyl ether and 1,3-butadiene are the most promising with the lowest economic risks (among the alternatives considered) of 0.24 MM$/a and 4.6 MM$/a, respectively.
Energy Technology Data Exchange (ETDEWEB)
Nakagawa, Tsuneo; Shibata, Keiichi [Japan Atomic Energy Research Inst., Tokai, Ibaraki (Japan). Tokai Research Establishment
1997-05-01
Uncertainties have been estimated for the resonance parameters of {sup 56}Fe, {sup 239}Pu, {sup 240}Pu and {sup 238}U contained in JENDL-3.2. Errors of the parameters were determined from the measurements which the evaluation was based on. The estimated errors have been compiled in the MF32 of the ENDF format. The numerical results are given in tables. (author)
Directory of Open Access Journals (Sweden)
Enrico Zio
2008-01-01
Full Text Available In the present work, the uncertainties affecting the safety margins estimated from thermal-hydraulic code calculations are captured quantitatively by resorting to the order statistics and the bootstrap technique. The proposed framework of analysis is applied to the estimation of the safety margin, with its confidence interval, of the maximum fuel cladding temperature reached during a complete group distribution blockage scenario in a RBMK-1500 nuclear reactor.
Directory of Open Access Journals (Sweden)
Ephraim S. Gower
2014-12-01
Full Text Available In this study, a generic analysis of sensor impulse response effects on linearly filtered channel noise is presented to illustrate the resulting variations to the Cramèr–Rao lower bounds (CRLBs of signal parameter estimators in signal processing and communication applications. The authors start by deriving the density function of a filtered signal, which is shown to be a mixture density, and hence the exact expressions for the mean and variance. Simulation results are used to confirm the derivations, which are then used to investigate the effects of impulse response length and variance, as well as channel noise length and variance effects on the resulting CRLBs. Results indicate that for non-zero-mean channel noise and impulse responses, the resulting mean of filtered noise can be relatively large causing adverse deviations to parameter estimations. The filtered noise variance is shown to be proportional to the impulse response energy, where for long duration of signal capture the CRLB is significantly increased.
Parameter uncertainty analysis for the annual phosphorus loss estimator (APLE) model
Technical abstract: Models are often used to predict phosphorus (P) loss from agricultural fields. While it is commonly recognized that model predictions are inherently uncertain, few studies have addressed prediction uncertainties using P loss models. In this study, we conduct an uncertainty analys...
Estimating the magnitude of prediction uncertainties for field-scale P loss models
Models are often used to predict phosphorus (P) loss from agricultural fields. While it is commonly recognized that model predictions are inherently uncertain, few studies have addressed prediction uncertainties using P loss models. In this study, an uncertainty analysis for the Annual P Loss Estima...
DEFF Research Database (Denmark)
Troldborg, Mads; Nowak, W.; Binning, Philip John
2010-01-01
with an uncertain geostatistical model and iii) measurement uncertainty. The method is tested on a TCE contaminated site for which four different conceptual models were set up. The mass discharge and the associated uncertainty are hereby determined. It is discussed which of the conceptual models is most likely...
Implementation of unscented transform to estimate the uncertainty of a liquid flow standard system
Energy Technology Data Exchange (ETDEWEB)
Chun, Sejong; Choi, Hae-Man; Yoon, Byung-Ro; Kang, Woong [Korea Research Institute of Standards and Science, Daejeon (Korea, Republic of)
2017-03-15
First-order partial derivatives of a mathematical model are an essential part of evaluating the measurement uncertainty of a liquid flow standard system according to the Guide to the expression of uncertainty in measurement (GUM). Although the GUM provides a straightforward method to evaluate the measurement uncertainty of volume flow rate, the first-order partial derivatives can be complicated. The mathematical model of volume flow rate in a liquid flow standard system has a cross-correlation between liquid density and buoyancy correction factor. This cross-correlation can make derivation of the first-order partial derivatives difficult. Monte Carlo simulation can be used as an alternative method to circumvent the difficulty in partial derivation. However, the Monte Carlo simulation requires large computational resources for a correct simulation because it considers the completeness issue whether an ideal or a real operator conducts an experiment to evaluate the measurement uncertainty. Thus, the Monte Carlo simulation needs a large number of samples to ensure that the uncertainty evaluation is as close to the GUM as possible. Unscented transform can alleviate this problem because unscented transform can be regarded as a Monte Carlo simulation with an infinite number of samples. This idea means that unscented transform considers the uncertainty evaluation with respect to the ideal operator. Thus, unscented transform can evaluate the measurement uncertainty the same as the uncertainty that the GUM provides.
Pulles, M.P.J.; Kok, H.; Quass, U.
2006-01-01
This study uses an improved emission inventory model to assess the uncertainties in emissions of dioxins and furans associated with both knowledge on the exact technologies and processes used, and with the uncertainties of both activity data and emission factors. The annual total emissions for the
Estimating the uncertainty in thermochemical calculations for oxygen-hydrogen combustors
Sims, Joseph David
The thermochemistry program CEA2 was combined with the statistical thermodynamics program PAC99 in a Monte Carlo simulation to determine the uncertainty in several CEA2 output variables due to uncertainty in thermodynamic reference values for the reactant and combustion species. In all, six typical performance parameters were examined, along with the required intermediate calculations (five gas properties and eight stoichiometric coefficients), for three hydrogen-oxygen combustors: a main combustor, an oxidizer preburner and a fuel preburner. The three combustors were analyzed in two different modes: design mode, where, for the first time, the uncertainty in thermodynamic reference values---taken from the literature---was considered (inputs to CEA2 were specified and so had no uncertainty); and data reduction mode, where inputs to CEA2 did have uncertainty. The inputs to CEA2 were contrived experimental measurements that were intended to represent the typical combustor testing facility. In design mode, uncertainties in the performance parameters were on the order of 0.1% for the main combustor, on the order of 0.05% for the oxidizer preburner and on the order of 0.01% for the fuel preburner. Thermodynamic reference values for H2O were the dominant sources of uncertainty, as was the assigned enthalpy for liquid oxygen. In data reduction mode, uncertainties in performance parameters increased significantly as a result of the uncertainties in experimental measurements compared to uncertainties in thermodynamic reference values. Main combustor and fuel preburner theoretical performance values had uncertainties of about 0.5%, while the oxidizer preburner had nearly 2%. Associated experimentally-determined performance values for all three combustors were 3% to 4%. The dominant sources of uncertainty in this mode were the propellant flowrates. These results only apply to hydrogen-oxygen combustors and should not be generalized to every propellant combination. Species for
Kenneth E. Skog; Kim Pingoud; James E. Smith
2004-01-01
A method is suggested for estimating additions to carbon stored in harvested wood products (HWP) and for evaluating uncertainty. The method uses data on HWP production and trade from several decades and tracks annual additions to pools of HWP in use, removals from use, additions to solid waste disposal sites (SWDS), and decay from SWDS. The method is consistent with...
International Nuclear Information System (INIS)
Hüser, Dorothee; Thomsen-Schmidt, Peter; Hüser, Jonathan; Rief, Sebastian; Seewig, Jörg
2016-01-01
Roughness parameters that characterize contacting surfaces with regard to friction and wear are commonly stated without uncertainties, or with an uncertainty only taking into account a very limited amount of aspects such as repeatability of reproducibility (homogeneity) of the specimen. This makes it difficult to discriminate between different values of single roughness parameters. Therefore uncertainty assessment methods are required that take all relevant aspects into account. In the literature this is rarely performed and examples specific for parameters used in friction and wear are not yet given. We propose a procedure to derive the uncertainty from a single profile employing a statistical method that is based on the statistical moments of the amplitude distribution and the autocorrelation length of the profile. To show the possibilities and the limitations of this method we compare the uncertainty derived from a single profile with that derived from a high statistics experiment. (paper)
Verhulst, Kristal R.; Karion, Anna; Kim, Jooil; Salameh, Peter K.; Keeling, Ralph F.; Newman, Sally; Miller, John; Sloop, Christopher; Pongetti, Thomas; Rao, Preeti; Wong, Clare; Hopkins, Francesca M.; Yadav, Vineet; Weiss, Ray F.; Duren, Riley M.; Miller, Charles E.
2017-07-01
We report continuous surface observations of carbon dioxide (CO2) and methane (CH4) from the Los Angeles (LA) Megacity Carbon Project during 2015. We devised a calibration strategy, methods for selection of background air masses, calculation of urban enhancements, and a detailed algorithm for estimating uncertainties in urban-scale CO2 and CH4 measurements. These methods are essential for understanding carbon fluxes from the LA megacity and other complex urban environments globally. We estimate background mole fractions entering LA using observations from four extra-urban sites including two marine sites located south of LA in La Jolla (LJO) and offshore on San Clemente Island (SCI), one continental site located in Victorville (VIC), in the high desert northeast of LA, and one continental/mid-troposphere site located on Mount Wilson (MWO) in the San Gabriel Mountains. We find that a local marine background can be established to within ˜ 1 ppm CO2 and ˜ 10 ppb CH4 using these local measurement sites. Overall, atmospheric carbon dioxide and methane levels are highly variable across Los Angeles. Urban and suburban sites show moderate to large CO2 and CH4 enhancements relative to a marine background estimate. The USC (University of Southern California) site near downtown LA exhibits median hourly enhancements of ˜ 20 ppm CO2 and ˜ 150 ppb CH4 during 2015 as well as ˜ 15 ppm CO2 and ˜ 80 ppb CH4 during mid-afternoon hours (12:00-16:00 LT, local time), which is the typical period of focus for flux inversions. The estimated measurement uncertainty is typically better than 0.1 ppm CO2 and 1 ppb CH4 based on the repeated standard gas measurements from the LA sites during the last 2 years, similar to Andrews et al. (2014). The largest component of the measurement uncertainty is due to the single-point calibration method; however, the uncertainty in the background mole fraction is much larger than the measurement uncertainty. The background uncertainty for the marine
International Nuclear Information System (INIS)
Ohnishi, S.; Thornton, B.; Kamada, S.; Hirao, Y.; Ura, T.; Odano, N.
2016-01-01
Factors to convert the count rate of a NaI(Tl) scintillation detector to the concentration of radioactive cesium in marine sediments are estimated for a towed gamma-ray detector system. The response of the detector against a unit concentration of radioactive cesium is calculated by Monte Carlo radiation transport simulation considering the vertical profile of radioactive material measured in core samples. The conversion factors are acquired by integrating the contribution of each layer and are normalized by the concentration in the surface sediment layer. At the same time, the uncertainty of the conversion factors are formulated and estimated. The combined standard uncertainty of the radioactive cesium concentration by the towed gamma-ray detector is around 25 percent. The values of uncertainty, often referred to as relative root mean squat errors in other works, between sediment core sampling measurements and towed detector measurements were 16 percent in the investigation made near the Abukuma River mouth and 5.2 percent in Sendai Bay, respectively. Most of the uncertainty is due to interpolation of the conversion factors between core samples and uncertainty of the detector's burial depth. The results of the towed measurements agree well with laboratory analysed sediment samples. Also, the concentrations of radioactive cesium at the intersection of each survey line are consistent. The consistency with sampling results and between different lines' transects demonstrate the availability and reproducibility of towed gamma-ray detector system.
AUTHOR|(INSPIRE)INSPIRE-00534683; The ATLAS collaboration
2016-01-01
The jet energy scale (JES) uncertainty is estimated using different methods at different pT ranges. In situ techniques exploiting the pT balance between a jet and a reference object (e.g. Z or gamma) are used at lower pT, but at very high pT (> 2.5 TeV) there is not enough statistics for in-situ techniques. The JES uncertainty at high-pT is important in several searches for new phenomena, e.g. the dijet resonance and angular searches. In the highest pT range, the JES uncertainty is estimated using the calorimeter response to single hadrons. In this method, jets are treated as a superposition of energy depositions of single particles. An uncertainty is applied to each energy depositions belonging to the particles within the jet, and propagated to the final jet energy scale. This poster presents the JES uncertainty found with this method at sqrt(s) = 8 TeV and its developments.
Energy Technology Data Exchange (ETDEWEB)
Ohnishi, S., E-mail: ohnishi@nmri.go.jp [National Maritime Research Institute, 6-38-1, Shinkawa, Mitaka, Tokyo 181-0004 (Japan); Thornton, B. [Institute of Industrial Science, The University of Tokyo, 4-6-1, Komaba, Meguro-ku, Tokyo 153-8505 (Japan); Kamada, S.; Hirao, Y.; Ura, T.; Odano, N. [National Maritime Research Institute, 6-38-1, Shinkawa, Mitaka, Tokyo 181-0004 (Japan)
2016-05-21
Factors to convert the count rate of a NaI(Tl) scintillation detector to the concentration of radioactive cesium in marine sediments are estimated for a towed gamma-ray detector system. The response of the detector against a unit concentration of radioactive cesium is calculated by Monte Carlo radiation transport simulation considering the vertical profile of radioactive material measured in core samples. The conversion factors are acquired by integrating the contribution of each layer and are normalized by the concentration in the surface sediment layer. At the same time, the uncertainty of the conversion factors are formulated and estimated. The combined standard uncertainty of the radioactive cesium concentration by the towed gamma-ray detector is around 25 percent. The values of uncertainty, often referred to as relative root mean squat errors in other works, between sediment core sampling measurements and towed detector measurements were 16 percent in the investigation made near the Abukuma River mouth and 5.2 percent in Sendai Bay, respectively. Most of the uncertainty is due to interpolation of the conversion factors between core samples and uncertainty of the detector's burial depth. The results of the towed measurements agree well with laboratory analysed sediment samples. Also, the concentrations of radioactive cesium at the intersection of each survey line are consistent. The consistency with sampling results and between different lines' transects demonstrate the availability and reproducibility of towed gamma-ray detector system.
Barazzetti Barbieri, Cristina; de Souza Sarkis, Jorge Eduardo
2018-07-01
The forensic interpretation of environmental analytical data is usually challenging due to the high geospatial variability of these data. The measurements' uncertainty includes contributions from the sampling and from the sample handling and preparation processes. These contributions are often disregarded in analytical techniques results' quality assurance. A pollution crime investigation case was used to carry out a methodology able to address these uncertainties in two different environmental compartments, freshwater sediments and landfill leachate. The methodology used to estimate the uncertainty was the duplicate method (that replicates predefined steps of the measurement procedure in order to assess its precision) and the parameters used to investigate the pollution were metals (Cr, Cu, Ni, and Zn) in the leachate, the suspect source, and in the sediment, the possible sink. The metal analysis results were compared to statutory limits and it was demonstrated that Cr and Ni concentrations in sediment samples exceeded the threshold levels at all sites downstream the pollution sources, considering the expanded uncertainty U of the measurements and a probability of contamination >0.975, at most sites. Cu and Zn concentrations were above the statutory limits at two sites, but the classification was inconclusive considering the uncertainties of the measurements. Metal analyses in leachate revealed that Cr concentrations were above the statutory limits with a probability of contamination >0.975 in all leachate ponds while the Cu, Ni and Zn probability of contamination was below 0.025. The results demonstrated that the estimation of the sampling uncertainty, which was the dominant component of the combined uncertainty, is required for a comprehensive interpretation of the environmental analyses results, particularly in forensic cases. Copyright © 2018 Elsevier B.V. All rights reserved.
Lahiri, B. B.; Ranoo, Surojit; Philip, John
2017-11-01
Magnetic fluid hyperthermia (MFH) is becoming a viable cancer treatment methodology where the alternating magnetic field induced heating of magnetic fluid is utilized for ablating the cancerous cells or making them more susceptible to the conventional treatments. The heating efficiency in MFH is quantified in terms of specific absorption rate (SAR), which is defined as the heating power generated per unit mass. In majority of the experimental studies, SAR is evaluated from the temperature rise curves, obtained under non-adiabatic experimental conditions, which is prone to various thermodynamic uncertainties. A proper understanding of the experimental uncertainties and its remedies is a prerequisite for obtaining accurate and reproducible SAR. Here, we study the thermodynamic uncertainties associated with peripheral heating, delayed heating, heat loss from the sample and spatial variation in the temperature profile within the sample. Using first order approximations, an adiabatic reconstruction protocol for the measured temperature rise curves is developed for SAR estimation, which is found to be in good agreement with those obtained from the computationally intense slope corrected method. Our experimental findings clearly show that the peripheral and delayed heating are due to radiation heat transfer from the heating coils and slower response time of the sensor, respectively. Our results suggest that the peripheral heating is linearly proportional to the sample area to volume ratio and coil temperature. It is also observed that peripheral heating decreases in presence of a non-magnetic insulating shielding. The delayed heating is found to contribute up to ~25% uncertainties in SAR values. As the SAR values are very sensitive to the initial slope determination method, explicit mention of the range of linear regression analysis is appropriate to reproduce the results. The effect of sample volume to area ratio on linear heat loss rate is systematically studied and the
International Nuclear Information System (INIS)
Lahiri, B B; Ranoo, Surojit; Philip, John
2017-01-01
Magnetic fluid hyperthermia (MFH) is becoming a viable cancer treatment methodology where the alternating magnetic field induced heating of magnetic fluid is utilized for ablating the cancerous cells or making them more susceptible to the conventional treatments. The heating efficiency in MFH is quantified in terms of specific absorption rate (SAR), which is defined as the heating power generated per unit mass. In majority of the experimental studies, SAR is evaluated from the temperature rise curves, obtained under non-adiabatic experimental conditions, which is prone to various thermodynamic uncertainties. A proper understanding of the experimental uncertainties and its remedies is a prerequisite for obtaining accurate and reproducible SAR. Here, we study the thermodynamic uncertainties associated with peripheral heating, delayed heating, heat loss from the sample and spatial variation in the temperature profile within the sample. Using first order approximations, an adiabatic reconstruction protocol for the measured temperature rise curves is developed for SAR estimation, which is found to be in good agreement with those obtained from the computationally intense slope corrected method. Our experimental findings clearly show that the peripheral and delayed heating are due to radiation heat transfer from the heating coils and slower response time of the sensor, respectively. Our results suggest that the peripheral heating is linearly proportional to the sample area to volume ratio and coil temperature. It is also observed that peripheral heating decreases in presence of a non-magnetic insulating shielding. The delayed heating is found to contribute up to ∼25% uncertainties in SAR values. As the SAR values are very sensitive to the initial slope determination method, explicit mention of the range of linear regression analysis is appropriate to reproduce the results. The effect of sample volume to area ratio on linear heat loss rate is systematically studied and
Estimation of Setup Uncertainty Using Planar and MVCT Imaging for Gynecologic Malignancies
International Nuclear Information System (INIS)
Santanam, Lakshmi; Esthappan, Jacqueline; Mutic, Sasa; Klein, Eric E.; Goddu, S. Murty; Chaudhari, Summer; Wahab, Sasha; El Naqa, Issam M.; Low, Daniel A.; Grigsby, Perry W.
2008-01-01
Purpose: This prospective study investigates gynecologic malignancy online treatment setup error corrections using planar kilovoltage/megavoltage (KV/MV) imaging and helical MV computed tomography (MVCT) imaging. Methods and Materials: Twenty patients were divided into two groups. The first group (10 patients) was imaged and treated using a conventional linear accelerator (LINAC) with image-guidance capabilities, whereas the second group (10 patients) was treated using tomotherapy with MVCT capabilities. Patients treated on the LINAC underwent planar KV and portal MV imaging and a two-dimensional image registration algorithm was used to match these images to digitally reconstructed radiographs (DRRs). Patients that were treated using tomotherapy underwent MVCT imaging, and a three-dimensional image registration algorithm was used to match planning CT to MVCT images. Subsequent repositioning shifts were applied before each treatment and recorded for further analysis. To assess intrafraction motion, 5 of the 10 patients treated on the LINAC underwent posttreatment planar imaging and DRR matching. Based on these data, patient position uncertainties along with estimated margins based on well-known recipes were determined. Results: The errors associated with patient positioning ranged from 0.13 cm to 0.38 cm, for patients imaged on LINAC and 0.13 cm to 0.48 cm for patients imaged on tomotherapy. Our institutional clinical target volume-PTV margin value of 0.7 cm lies inside the confidence interval of the margins established using both planar and MVCT imaging. Conclusion: Use of high-quality daily planar imaging, volumetric MVCT imaging, and setup corrections yields excellent setup accuracy and can help reduce margins for the external beam treatment of gynecologic malignancies
Energy Technology Data Exchange (ETDEWEB)
Habte, Aron M [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Sengupta, Manajit [National Renewable Energy Laboratory (NREL), Golden, CO (United States)
2017-12-19
It is essential to apply a traceable and standard approach to determine the uncertainty of solar resource data. Solar resource data are used for all phases of solar energy conversion projects, from the conceptual phase to routine solar power plant operation, and to determine performance guarantees of solar energy conversion systems. These guarantees are based on the available solar resource derived from a measurement station or modeled data set such as the National Solar Radiation Database (NSRDB). Therefore, quantifying the uncertainty of these data sets provides confidence to financiers, developers, and site operators of solar energy conversion systems and ultimately reduces deployment costs. In this study, we implemented the Guide to the Expression of Uncertainty in Measurement (GUM) 1 to quantify the overall uncertainty of the NSRDB data. First, we start with quantifying measurement uncertainty, then we determine each uncertainty statistic of the NSRDB data, and we combine them using the root-sum-of-the-squares method. The statistics were derived by comparing the NSRDB data to the seven measurement stations from the National Oceanic and Atmospheric Administration's Surface Radiation Budget Network, National Renewable Energy Laboratory's Solar Radiation Research Laboratory, and the Atmospheric Radiation Measurement program's Southern Great Plains Central Facility, in Billings, Oklahoma. The evaluation was conducted for hourly values, daily totals, monthly mean daily totals, and annual mean monthly mean daily totals. Varying time averages assist to capture the temporal uncertainty of the specific modeled solar resource data required for each phase of a solar energy project; some phases require higher temporal resolution than others. Overall, by including the uncertainty of measurements of solar radiation made at ground stations, bias, and root mean square error, the NSRDB data demonstrated expanded uncertainty of 17 percent - 29 percent on hourly
Shen, Mingxi; Chen, Jie; Zhuan, Meijia; Chen, Hua; Xu, Chong-Yu; Xiong, Lihua
2018-01-01
Uncertainty estimation of climate change impacts on hydrology has received much attention in the research community. The choice of a global climate model (GCM) is usually considered as the largest contributor to the uncertainty of climate change impacts. The temporal variation of GCM uncertainty needs to be investigated for making long-term decisions to deal with climate change. Accordingly, this study investigated the temporal variation (mainly long-term) of uncertainty related to the choice of a GCM in predicting climate change impacts on hydrology by using multi-GCMs over multiple continuous future periods. Specifically, twenty CMIP5 GCMs under RCP4.5 and RCP8.5 emission scenarios were adapted to adequately represent this uncertainty envelope, fifty-one 30-year future periods moving from 2021 to 2100 with 1-year interval were produced to express the temporal variation. Future climatic and hydrological regimes over all future periods were compared to those in the reference period (1971-2000) using a set of metrics, including mean and extremes. The periodicity of climatic and hydrological changes and their uncertainty were analyzed using wavelet analysis, while the trend was analyzed using Mann-Kendall trend test and regression analysis. The results showed that both future climate change (precipitation and temperature) and hydrological response predicted by the twenty GCMs were highly uncertain, and the uncertainty increased significantly over time. For example, the change of mean annual precipitation increased from 1.4% in 2021-2050 to 6.5% in 2071-2100 for RCP4.5 in terms of the median value of multi-models, but the projected uncertainty reached 21.7% in 2021-2050 and 25.1% in 2071-2100 for RCP4.5. The uncertainty under a high emission scenario (RCP8.5) was much larger than that under a relatively low emission scenario (RCP4.5). Almost all climatic and hydrological regimes and their uncertainty did not show significant periodicity at the P = .05 significance
Li, Aihua; Dhakal, Shital; Glenn, Nancy F.; Spaete, Luke P.; Shinneman, Douglas; Pilliod, David S.; Arkle, Robert; McIlroy, Susan
2017-01-01
Our study objectives were to model the aboveground biomass in a xeric shrub-steppe landscape with airborne light detection and ranging (Lidar) and explore the uncertainty associated with the models we created. We incorporated vegetation vertical structure information obtained from Lidar with ground-measured biomass data, allowing us to scale shrub biomass from small field sites (1 m subplots and 1 ha plots) to a larger landscape. A series of airborne Lidar-derived vegetation metrics were trained and linked with the field-measured biomass in Random Forests (RF) regression models. A Stepwise Multiple Regression (SMR) model was also explored as a comparison. Our results demonstrated that the important predictors from Lidar-derived metrics had a strong correlation with field-measured biomass in the RF regression models with a pseudo R2 of 0.76 and RMSE of 125 g/m2 for shrub biomass and a pseudo R2 of 0.74 and RMSE of 141 g/m2 for total biomass, and a weak correlation with field-measured herbaceous biomass. The SMR results were similar but slightly better than RF, explaining 77–79% of the variance, with RMSE ranging from 120 to 129 g/m2 for shrub and total biomass, respectively. We further explored the computational efficiency and relative accuracies of using point cloud and raster Lidar metrics at different resolutions (1 m to 1 ha). Metrics derived from the Lidar point cloud processing led to improved biomass estimates at nearly all resolutions in comparison to raster-derived Lidar metrics. Only at 1 m were the results from the point cloud and raster products nearly equivalent. The best Lidar prediction models of biomass at the plot-level (1 ha) were achieved when Lidar metrics were derived from an average of fine resolution (1 m) metrics to minimize boundary effects and to smooth variability. Overall, both RF and SMR methods explained more than 74% of the variance in biomass, with the most important Lidar variables being associated with vegetation structure
Ershadi, Ali
2013-05-01
The influence of uncertainty in land surface temperature, air temperature, and wind speed on the estimation of sensible heat flux is analyzed using a Bayesian inference technique applied to the Surface Energy Balance System (SEBS) model. The Bayesian approach allows for an explicit quantification of the uncertainties in input variables: a source of error generally ignored in surface heat flux estimation. An application using field measurements from the Soil Moisture Experiment 2002 is presented. The spatial variability of selected input meteorological variables in a multitower site is used to formulate the prior estimates for the sampling uncertainties, and the likelihood function is formulated assuming Gaussian errors in the SEBS model. Land surface temperature, air temperature, and wind speed were estimated by sampling their posterior distribution using a Markov chain Monte Carlo algorithm. Results verify that Bayesian-inferred air temperature and wind speed were generally consistent with those observed at the towers, suggesting that local observations of these variables were spatially representative. Uncertainties in the land surface temperature appear to have the strongest effect on the estimated sensible heat flux, with Bayesian-inferred values differing by up to ±5°C from the observed data. These differences suggest that the footprint of the in situ measured land surface temperature is not representative of the larger-scale variability. As such, these measurements should be used with caution in the calculation of surface heat fluxes and highlight the importance of capturing the spatial variability in the land surface temperature: particularly, for remote sensing retrieval algorithms that use this variable for flux estimation.
Prudencio, Ernesto E.; Schulz, Karl W.
2012-01-01
QUESO is a collection of statistical algorithms and programming constructs supporting research into the uncertainty quantification (UQ) of models and their predictions. It has been designed with three objectives: it should (a) be sufficiently
Ali, E. S. M.; Spencer, B.; McEwen, M. R.; Rogers, D. W. O.
2015-02-01
In this study, a quantitative estimate is derived for the uncertainty in the XCOM photon mass attenuation coefficients in the energy range of interest to external beam radiation therapy—i.e. 100 keV (orthovoltage) to 25 MeV—using direct comparisons of experimental data against Monte Carlo models and theoretical XCOM data. Two independent datasets are used. The first dataset is from our recent transmission measurements and the corresponding EGSnrc calculations (Ali et al 2012 Med. Phys. 39 5990-6003) for 10-30 MV photon beams from the research linac at the National Research Council Canada. The attenuators are graphite and lead, with a total of 140 data points and an experimental uncertainty of ˜0.5% (k = 1). An optimum energy-independent cross section scaling factor that minimizes the discrepancies between measurements and calculations is used to deduce cross section uncertainty. The second dataset is from the aggregate of cross section measurements in the literature for graphite and lead (49 experiments, 288 data points). The dataset is compared to the sum of the XCOM data plus the IAEA photonuclear data. Again, an optimum energy-independent cross section scaling factor is used to deduce the cross section uncertainty. Using the average result from the two datasets, the energy-independent cross section uncertainty estimate is 0.5% (68% confidence) and 0.7% (95% confidence). The potential for energy-dependent errors is discussed. Photon cross section uncertainty is shown to be smaller than the current qualitative ‘envelope of uncertainty’ of the order of 1-2%, as given by Hubbell (1999 Phys. Med. Biol 44 R1-22).
Xu, Zhuocan; Mace, Jay; Avalone, Linnea; Wang, Zhien
2015-04-01
The extreme variability of ice particle habits in precipitating clouds affects our understanding of these cloud systems in every aspect (i.e. radiation transfer, dynamics, precipitation rate, etc) and largely contributes to the uncertainties in the model representation of related processes. Ice particle mass-dimensional power law relationships, M=a*(D ^ b), are commonly assumed in models and retrieval algorithms, while very little knowledge exists regarding the uncertainties of these M-D parameters in real-world situations. In this study, we apply Optimal Estimation (OE) methodology to infer ice particle mass-dimensional relationship from ice particle size distributions and bulk water contents independently measured on board the University of Wyoming King Air during the Colorado Airborne Multi-Phase Cloud Study (CAMPS). We also utilize W-band radar reflectivity obtained on the same platform (King Air) offering a further constraint to this ill-posed problem (Heymsfield et al. 2010). In addition to the values of retrieved M-D parameters, the associated uncertainties are conveniently acquired in the OE framework, within the limitations of assumed Gaussian statistics. We find, given the constraints provided by the bulk water measurement and in situ radar reflectivity, that the relative uncertainty of mass-dimensional power law prefactor (a) is approximately 80% and the relative uncertainty of exponent (b) is 10-15%. With this level of uncertainty, the forward model uncertainty in radar reflectivity would be on the order of 4 dB or a factor of approximately 2.5 in ice water content. The implications of this finding are that inferences of bulk water from either remote or in situ measurements of particle spectra cannot be more certain than this when the mass-dimensional relationships are not known a priori which is almost never the case.
Hazenberg, P.; Torfs, P. J. J. F.; Leijnse, H.; Delrieu, G.; Uijlenhoet, R.
2013-09-01
This paper presents a novel approach to estimate the vertical profile of reflectivity (VPR) from volumetric weather radar data using both a traditional Eulerian as well as a newly proposed Lagrangian implementation. For this latter implementation, the recently developed Rotational Carpenter Square Cluster Algorithm (RoCaSCA) is