mBEEF: An accurate semi-local Bayesian error estimation density functional
NASA Astrophysics Data System (ADS)
Wellendorff, Jess; Lundgaard, Keld T.; Jacobsen, Karsten W.; Bligaard, Thomas
2014-04-01
We present a general-purpose meta-generalized gradient approximation (MGGA) exchange-correlation functional generated within the Bayesian error estimation functional framework [J. Wellendorff, K. T. Lundgaard, A. Møgelhøj, V. Petzold, D. D. Landis, J. K. Nørskov, T. Bligaard, and K. W. Jacobsen, Phys. Rev. B 85, 235149 (2012)]. The functional is designed to give reasonably accurate density functional theory (DFT) predictions of a broad range of properties in materials physics and chemistry, while exhibiting a high degree of transferability. Particularly, it improves upon solid cohesive energies and lattice constants over the BEEF-vdW functional without compromising high performance on adsorption and reaction energies. We thus expect it to be particularly well-suited for studies in surface science and catalysis. An ensemble of functionals for error estimation in DFT is an intrinsic feature of exchange-correlation models designed this way, and we show how the Bayesian ensemble may provide a systematic analysis of the reliability of DFT based simulations.
Estimating Bias Error Distributions
NASA Technical Reports Server (NTRS)
Liu, Tian-Shu; Finley, Tom D.
2001-01-01
This paper formulates the general methodology for estimating the bias error distribution of a device in a measuring domain from less accurate measurements when a minimal number of standard values (typically two values) are available. A new perspective is that the bias error distribution can be found as a solution of an intrinsic functional equation in a domain. Based on this theory, the scaling- and translation-based methods for determining the bias error distribution arc developed. These methods are virtually applicable to any device as long as the bias error distribution of the device can be sufficiently described by a power series (a polynomial) or a Fourier series in a domain. These methods have been validated through computational simulations and laboratory calibration experiments for a number of different devices.
Micromagnetometer calibration for accurate orientation estimation.
Zhang, Zhi-Qiang; Yang, Guang-Zhong
2015-02-01
Micromagnetometers, together with inertial sensors, are widely used for attitude estimation for a wide variety of applications. However, appropriate sensor calibration, which is essential to the accuracy of attitude reconstruction, must be performed in advance. Thus far, many different magnetometer calibration methods have been proposed to compensate for errors such as scale, offset, and nonorthogonality. They have also been used for obviate magnetic errors due to soft and hard iron. However, in order to combine the magnetometer with inertial sensor for attitude reconstruction, alignment difference between the magnetometer and the axes of the inertial sensor must be determined as well. This paper proposes a practical means of sensor error correction by simultaneous consideration of sensor errors, magnetic errors, and alignment difference. We take the summation of the offset and hard iron error as the combined bias and then amalgamate the alignment difference and all the other errors as a transformation matrix. A two-step approach is presented to determine the combined bias and transformation matrix separately. In the first step, the combined bias is determined by finding an optimal ellipsoid that can best fit the sensor readings. In the second step, the intrinsic relationships of the raw sensor readings are explored to estimate the transformation matrix as a homogeneous linear least-squares problem. Singular value decomposition is then applied to estimate both the transformation matrix and magnetic vector. The proposed method is then applied to calibrate our sensor node. Although there is no ground truth for the combined bias and transformation matrix for our node, the consistency of calibration results among different trials and less than 3(°) root mean square error for orientation estimation have been achieved, which illustrates the effectiveness of the proposed sensor calibration method for practical applications. PMID:25265625
Control by model error estimation
NASA Technical Reports Server (NTRS)
Likins, P. W.; Skelton, R. E.
1976-01-01
Modern control theory relies upon the fidelity of the mathematical model of the system. Truncated modes, external disturbances, and parameter errors in linear system models are corrected by augmenting to the original system of equations an 'error system' which is designed to approximate the effects of such model errors. A Chebyshev error system is developed for application to the Large Space Telescope (LST).
NASA Astrophysics Data System (ADS)
Lasemi, Ali; Xue, Deyi; Gu, Peihua
2016-05-01
Five-axis CNC machine tools are widely used in manufacturing of parts with free-form surfaces. Geometric errors of machine tools have significant effects on the quality of manufactured parts. This research focuses on development of a new method to accurately identify geometric errors of 5-axis CNC machines, especially the errors due to rotary axes, using the magnetic double ball bar. A theoretical model for identification of geometric errors is provided. In this model, both position-independent errors and position-dependent errors are considered as the error sources. This model is simplified by identification and removal of the correlated and insignificant error sources of the machine. Insignificant error sources are identified using the sensitivity analysis technique. Simulation results reveal that the simplified error identification model can result in more accurate estimations of the error parameters. Experiments on a 5-axis CNC machine tool also demonstrate significant reduction in the volumetric error after error compensation.
Error estimation and structural shape optimization
NASA Astrophysics Data System (ADS)
Song, Xiaoguang
This work is concerned with three topics: error estimation, data smoothing process and the structural shape optimization design and analysis. In particular, the superconvergent stress recovery technique, the dual kriging B-spline curve and surface fittings, the development and the implementation of a novel node-based numerical shape optimization package are addressed. Concept and new technique of accurate stress recovery are developed and applied in finding the lateral buckling parameters of plate structures. Some useful conclusions are made for the finite element Reissner-Mindlin plate solutions. The powerful dual kriging B-spline fitting technique is reviewed and a set of new compact formulations are developed. This data smoothing method is then applied in accurately recovering curves and surfaces. The new node-based shape optimization method is based on the consideration that the critical stress and displacement constraints are generally located along or near the structural boundary. The method puts the maximum weights on the selected boundary nodes, referred to as the design points, so that the time-consuming sensitivity analysis is related to the perturbation of only these nodes. The method also allows large shape changes to achieve the optimal shape. The design variables are specified as the moving magnitudes for the prescribed design points that are always located at the structural boundary. Theories, implementations and applications are presented for various modules by which the package is constructed. Especially, techniques involving finite element error estimation, adaptive mesh generation, design sensitivity analysis, and data smoothing are emphasized.
Error estimation for ORION baseline vector determination
NASA Technical Reports Server (NTRS)
Wu, S. C.
1980-01-01
Effects of error sources on Operational Radio Interferometry Observing Network (ORION) baseline vector determination are studied. Partial derivatives of delay observations with respect to each error source are formulated. Covariance analysis is performed to estimate the contribution of each error source to baseline vector error. System design parameters such as antenna sizes, system temperatures and provision for dual frequency operation are discussed.
Systematic Error Estimation for Chemical Reaction Energies.
Simm, Gregor N; Reiher, Markus
2016-06-14
For a theoretical understanding of the reactivity of complex chemical systems, accurate relative energies between intermediates and transition states are required. Despite its popularity, density functional theory (DFT) often fails to provide sufficiently accurate data, especially for molecules containing transition metals. Due to the huge number of intermediates that need to be studied for all but the simplest chemical processes, DFT is, to date, the only method that is computationally feasible. Here, we present a Bayesian framework for DFT that allows for error estimation of calculated properties. Since the optimal choice of parameters in present-day density functionals is strongly system dependent, we advocate for a system-focused reparameterization. While, at first sight, this approach conflicts with the first-principles character of DFT that should make it, in principle, system independent, we deliberately introduce system dependence to be able to assign a stochastically meaningful error to the system-dependent parametrization, which makes it nonarbitrary. By reparameterizing a functional that was derived on a sound physical basis to a chemical system of interest, we obtain a functional that yields reliable confidence intervals for reaction energies. We demonstrate our approach on the example of catalytic nitrogen fixation.
Accurate Parameter Estimation for Unbalanced Three-Phase System
Chen, Yuan
2014-01-01
Smart grid is an intelligent power generation and control console in modern electricity networks, where the unbalanced three-phase power system is the commonly used model. Here, parameter estimation for this system is addressed. After converting the three-phase waveforms into a pair of orthogonal signals via the α β-transformation, the nonlinear least squares (NLS) estimator is developed for accurately finding the frequency, phase, and voltage parameters. The estimator is realized by the Newton-Raphson scheme, whose global convergence is studied in this paper. Computer simulations show that the mean square error performance of NLS method can attain the Cramér-Rao lower bound. Moreover, our proposal provides more accurate frequency estimation when compared with the complex least mean square (CLMS) and augmented CLMS. PMID:25162056
Accurate parameter estimation for unbalanced three-phase system.
Chen, Yuan; So, Hing Cheung
2014-01-01
Smart grid is an intelligent power generation and control console in modern electricity networks, where the unbalanced three-phase power system is the commonly used model. Here, parameter estimation for this system is addressed. After converting the three-phase waveforms into a pair of orthogonal signals via the α β-transformation, the nonlinear least squares (NLS) estimator is developed for accurately finding the frequency, phase, and voltage parameters. The estimator is realized by the Newton-Raphson scheme, whose global convergence is studied in this paper. Computer simulations show that the mean square error performance of NLS method can attain the Cramér-Rao lower bound. Moreover, our proposal provides more accurate frequency estimation when compared with the complex least mean square (CLMS) and augmented CLMS.
Adjoint Error Estimation for Linear Advection
Connors, J M; Banks, J W; Hittinger, J A; Woodward, C S
2011-03-30
An a posteriori error formula is described when a statistical measurement of the solution to a hyperbolic conservation law in 1D is estimated by finite volume approximations. This is accomplished using adjoint error estimation. In contrast to previously studied methods, the adjoint problem is divorced from the finite volume method used to approximate the forward solution variables. An exact error formula and computable error estimate are derived based on an abstractly defined approximation of the adjoint solution. This framework allows the error to be computed to an arbitrary accuracy given a sufficiently well resolved approximation of the adjoint solution. The accuracy of the computable error estimate provably satisfies an a priori error bound for sufficiently smooth solutions of the forward and adjoint problems. The theory does not currently account for discontinuities. Computational examples are provided that show support of the theory for smooth solutions. The application to problems with discontinuities is also investigated computationally.
Estimates of Random Error in Satellite Rainfall Averages
NASA Technical Reports Server (NTRS)
Bell, Thomas L.; Kundu, Prasun K.
2003-01-01
Satellite rain estimates are most accurate when obtained with microwave instruments on low earth-orbiting satellites. Estimation of daily or monthly total areal rainfall, typically of interest to hydrologists and climate researchers, is made difficult, however, by the relatively poor coverage generally available from such satellites. Intermittent coverage by the satellites leads to random "sampling error" in the satellite products. The inexact information about hydrometeors inferred from microwave data also leads to random "retrieval errors" in the rain estimates. In this talk we will review approaches to quantitative estimation of the sampling error in area/time averages of satellite rain retrievals using ground-based observations, and methods of estimating rms random error, both sampling and retrieval, in averages using satellite measurements themselves.
Accurate estimation of sigma(exp 0) using AIRSAR data
NASA Technical Reports Server (NTRS)
Holecz, Francesco; Rignot, Eric
1995-01-01
During recent years signature analysis, classification, and modeling of Synthetic Aperture Radar (SAR) data as well as estimation of geophysical parameters from SAR data have received a great deal of interest. An important requirement for the quantitative use of SAR data is the accurate estimation of the backscattering coefficient sigma(exp 0). In terrain with relief variations radar signals are distorted due to the projection of the scene topography into the slant range-Doppler plane. The effect of these variations is to change the physical size of the scattering area, leading to errors in the radar backscatter values and incidence angle. For this reason the local incidence angle, derived from sensor position and Digital Elevation Model (DEM) data must always be considered. Especially in the airborne case, the antenna gain pattern can be an additional source of radiometric error, because the radar look angle is not known precisely as a result of the the aircraft motions and the local surface topography. Consequently, radiometric distortions due to the antenna gain pattern must also be corrected for each resolution cell, by taking into account aircraft displacements (position and attitude) and position of the backscatter element, defined by the DEM data. In this paper, a method to derive an accurate estimation of the backscattering coefficient using NASA/JPL AIRSAR data is presented. The results are evaluated in terms of geometric accuracy, radiometric variations of sigma(exp 0), and precision of the estimated forest biomass.
Stress Recovery and Error Estimation for 3-D Shell Structures
NASA Technical Reports Server (NTRS)
Riggs, H. R.
2000-01-01
The C1-continuous stress fields obtained from finite element analyses are in general lower- order accurate than are the corresponding displacement fields. Much effort has focussed on increasing their accuracy and/or their continuity, both for improved stress prediction and especially error estimation. A previous project developed a penalized, discrete least squares variational procedure that increases the accuracy and continuity of the stress field. The variational problem is solved by a post-processing, 'finite-element-type' analysis to recover a smooth, more accurate, C1-continuous stress field given the 'raw' finite element stresses. This analysis has been named the SEA/PDLS. The recovered stress field can be used in a posteriori error estimators, such as the Zienkiewicz-Zhu error estimator or equilibrium error estimators. The procedure was well-developed for the two-dimensional (plane) case involving low-order finite elements. It has been demonstrated that, if optimal finite element stresses are used for the post-processing, the recovered stress field is globally superconvergent. Extension of this work to three dimensional solids is straightforward. Attachment: Stress recovery and error estimation for shell structure (abstract only). A 4-node, shear-deformable flat shell element developed via explicit Kirchhoff constraints (abstract only). A novel four-node quadrilateral smoothing element for stress enhancement and error estimation (abstract only).
Wind Power Error Estimation in Resource Assessments
Rodríguez, Osvaldo; del Río, Jesús A.; Jaramillo, Oscar A.; Martínez, Manuel
2015-01-01
Estimating the power output is one of the elements that determine the techno-economic feasibility of a renewable project. At present, there is a need to develop reliable methods that achieve this goal, thereby contributing to wind power penetration. In this study, we propose a method for wind power error estimation based on the wind speed measurement error, probability density function, and wind turbine power curves. This method uses the actual wind speed data without prior statistical treatment based on 28 wind turbine power curves, which were fitted by Lagrange's method, to calculate the estimate wind power output and the corresponding error propagation. We found that wind speed percentage errors of 10% were propagated into the power output estimates, thereby yielding an error of 5%. The proposed error propagation complements the traditional power resource assessments. The wind power estimation error also allows us to estimate intervals for the power production leveled cost or the investment time return. The implementation of this method increases the reliability of techno-economic resource assessment studies. PMID:26000444
Wind power error estimation in resource assessments.
Rodríguez, Osvaldo; Del Río, Jesús A; Jaramillo, Oscar A; Martínez, Manuel
2015-01-01
Estimating the power output is one of the elements that determine the techno-economic feasibility of a renewable project. At present, there is a need to develop reliable methods that achieve this goal, thereby contributing to wind power penetration. In this study, we propose a method for wind power error estimation based on the wind speed measurement error, probability density function, and wind turbine power curves. This method uses the actual wind speed data without prior statistical treatment based on 28 wind turbine power curves, which were fitted by Lagrange's method, to calculate the estimate wind power output and the corresponding error propagation. We found that wind speed percentage errors of 10% were propagated into the power output estimates, thereby yielding an error of 5%. The proposed error propagation complements the traditional power resource assessments. The wind power estimation error also allows us to estimate intervals for the power production leveled cost or the investment time return. The implementation of this method increases the reliability of techno-economic resource assessment studies.
Wind power error estimation in resource assessments.
Rodríguez, Osvaldo; Del Río, Jesús A; Jaramillo, Oscar A; Martínez, Manuel
2015-01-01
Estimating the power output is one of the elements that determine the techno-economic feasibility of a renewable project. At present, there is a need to develop reliable methods that achieve this goal, thereby contributing to wind power penetration. In this study, we propose a method for wind power error estimation based on the wind speed measurement error, probability density function, and wind turbine power curves. This method uses the actual wind speed data without prior statistical treatment based on 28 wind turbine power curves, which were fitted by Lagrange's method, to calculate the estimate wind power output and the corresponding error propagation. We found that wind speed percentage errors of 10% were propagated into the power output estimates, thereby yielding an error of 5%. The proposed error propagation complements the traditional power resource assessments. The wind power estimation error also allows us to estimate intervals for the power production leveled cost or the investment time return. The implementation of this method increases the reliability of techno-economic resource assessment studies. PMID:26000444
Approaches to relativistic positioning around Earth and error estimations
NASA Astrophysics Data System (ADS)
Puchades, Neus; Sáez, Diego
2016-01-01
In the context of relativistic positioning, the coordinates of a given user may be calculated by using suitable information broadcast by a 4-tuple of satellites. Our 4-tuples belong to the Galileo constellation. Recently, we estimated the positioning errors due to uncertainties in the satellite world lines (U-errors). A distribution of U-errors was obtained, at various times, in a set of points covering a large region surrounding Earth. Here, the positioning errors associated to the simplifying assumption that photons move in Minkowski space-time (S-errors) are estimated and compared with the U-errors. Both errors have been calculated for the same points and times to make comparisons possible. For a certain realistic modeling of the world line uncertainties, the estimated S-errors have proved to be smaller than the U-errors, which shows that the approach based on the assumption that the Earth's gravitational field produces negligible effects on photons may be used in a large region surrounding Earth. The applicability of this approach - which simplifies numerical calculations - to positioning problems, and the usefulness of our S-error maps, are pointed out. A better approach, based on the assumption that photons move in the Schwarzschild space-time governed by an idealized Earth, is also analyzed. More accurate descriptions of photon propagation involving non symmetric space-time structures are not necessary for ordinary positioning and spacecraft navigation around Earth.
Estimating Filtering Errors Using the Peano Kernel Theorem
Jerome Blair
2008-03-01
The Peano Kernel Theorem is introduced and a frequency domain derivation is given. It is demonstrated that the application of this theorem yields simple and accurate formulas for estimating the error introduced into a signal by filtering it to reduce noise.
Estimating Filtering Errors Using the Peano Kernel Theorem
Jerome Blair
2009-02-20
The Peano Kernel Theorem is introduced and a frequency domain derivation is given. It is demonstrated that the application of this theorem yields simple and accurate formulas for estimating the error introduced into a signal by filtering it to reduce noise.
NASA Astrophysics Data System (ADS)
tang, ling; tian, yudong; lin, xin
2014-05-01
Precipitation retrievals from space-borne Passive Microwave (PMW) radiometers are the major source in modern satellite-based global rainfall dataset. The error characteristics in these individual retrievals directly affect the merged end products and applications, but have not been systematically studied. In this paper, we undertake a critical investigation of the seasonal and sensor type skill and errors of both in PMW radiometers over the continental United States (CONUS). A high-resolution ground radar-based datasets - NOAA's National Severe Storms Laboratory (NSSL) Q2 radar derived precipitation estimates are used as the ground reference. The high spatial and temporal resolution of the reference data allows near-instantaneous collocation (within 5 minutes) and relatively more precise comparison with the satellite overpasses. We compare precipitation retrievals from twelve satellites, including six imagers (one TMI, AMSR-E, SSM/I and three SSMIS) and six sounders (three AMSU-B and three MHS) against the Q2 radar precipitation. Results show that precipitation retrievals from PMW radiometers exhibit fairly systematic biases depending on season and precipitation intensity, with overestimates in summer at moderate to high precipitation rates and underestimates in winter at low and moderate precipitation rates. This result is also showing in satellite-based multi-sensor precipitation products, indicating the transferring of uncertainties from single sensor input to multi-sensor precipitation estimates. Meanwhile, retrievals from the microwave imagers have notably better performance than those from the microwave sounders. The sounders have higher biases, about two times at small rain rates and two-three times at the moderate to high end rain rates, compared to the imagers. The sounders also have a narrower dynamic range, and higher random errors, which are also detailed in the paper.
Conditional Density Estimation in Measurement Error Problems.
Wang, Xiao-Feng; Ye, Deping
2015-01-01
This paper is motivated by a wide range of background correction problems in gene array data analysis, where the raw gene expression intensities are measured with error. Estimating a conditional density function from the contaminated expression data is a key aspect of statistical inference and visualization in these studies. We propose re-weighted deconvolution kernel methods to estimate the conditional density function in an additive error model, when the error distribution is known as well as when it is unknown. Theoretical properties of the proposed estimators are investigated with respect to the mean absolute error from a "double asymptotic" view. Practical rules are developed for the selection of smoothing-parameters. Simulated examples and an application to an Illumina bead microarray study are presented to illustrate the viability of the methods. PMID:25284902
Optimal input design for aircraft instrumentation systematic error estimation
NASA Technical Reports Server (NTRS)
Morelli, Eugene A.
1991-01-01
A new technique for designing optimal flight test inputs for accurate estimation of instrumentation systematic errors was developed and demonstrated. A simulation model of the F-18 High Angle of Attack Research Vehicle (HARV) aircraft was used to evaluate the effectiveness of the optimal input compared to input recorded during flight test. Instrumentation systematic error parameter estimates and their standard errors were compared. It was found that the optimal input design improved error parameter estimates and their accuracies for a fixed time input design. Pilot acceptability of the optimal input design was demonstrated using a six degree-of-freedom fixed base piloted simulation of the F-18 HARV. The technique described in this work provides a practical, optimal procedure for designing inputs for data compatibility experiments.
Zollanvari, Amin; Dougherty, Edward R
2014-06-01
The most important aspect of any classifier is its error rate, because this quantifies its predictive capacity. Thus, the accuracy of error estimation is critical. Error estimation is problematic in small-sample classifier design because the error must be estimated using the same data from which the classifier has been designed. Use of prior knowledge, in the form of a prior distribution on an uncertainty class of feature-label distributions to which the true, but unknown, feature-distribution belongs, can facilitate accurate error estimation (in the mean-square sense) in circumstances where accurate completely model-free error estimation is impossible. This paper provides analytic asymptotically exact finite-sample approximations for various performance metrics of the resulting Bayesian Minimum Mean-Square-Error (MMSE) error estimator in the case of linear discriminant analysis (LDA) in the multivariate Gaussian model. These performance metrics include the first, second, and cross moments of the Bayesian MMSE error estimator with the true error of LDA, and therefore, the Root-Mean-Square (RMS) error of the estimator. We lay down the theoretical groundwork for Kolmogorov double-asymptotics in a Bayesian setting, which enables us to derive asymptotic expressions of the desired performance metrics. From these we produce analytic finite-sample approximations and demonstrate their accuracy via numerical examples. Various examples illustrate the behavior of these approximations and their use in determining the necessary sample size to achieve a desired RMS. The Supplementary Material contains derivations for some equations and added figures.
Practical Aspects of the Equation-Error Method for Aircraft Parameter Estimation
NASA Technical Reports Server (NTRS)
Morelli, Eugene a.
2006-01-01
Various practical aspects of the equation-error approach to aircraft parameter estimation were examined. The analysis was based on simulated flight data from an F-16 nonlinear simulation, with realistic noise sequences added to the computed aircraft responses. This approach exposes issues related to the parameter estimation techniques and results, because the true parameter values are known for simulation data. The issues studied include differentiating noisy time series, maximum likelihood parameter estimation, biases in equation-error parameter estimates, accurate computation of estimated parameter error bounds, comparisons of equation-error parameter estimates with output-error parameter estimates, analyzing data from multiple maneuvers, data collinearity, and frequency-domain methods.
First- and second-order error estimates in Monte Carlo integration
NASA Astrophysics Data System (ADS)
Bakx, R.; Kleiss, R. H. P.; Versteegen, F.
2016-11-01
In Monte Carlo integration an accurate and reliable determination of the numerical integration error is essential. We point out the need for an independent estimate of the error on this error, for which we present an unbiased estimator. In contrast to the usual (first-order) error estimator, this second-order estimator can be shown to be not necessarily positive in an actual Monte Carlo computation. We propose an alternative and indicate how this can be computed in linear time without risk of large rounding errors. In addition, we comment on the relatively very slow convergence of the second-order error estimate.
Reducing Measurement Error in Student Achievement Estimation
ERIC Educational Resources Information Center
Battauz, Michela; Bellio, Ruggero; Gori, Enrico
2008-01-01
The achievement level is a variable measured with error, that can be estimated by means of the Rasch model. Teacher grades also measure the achievement level but they are expressed on a different scale. This paper proposes a method for combining these two scores to obtain a synthetic measure of the achievement level based on the theory developed…
Accurate Biomass Estimation via Bayesian Adaptive Sampling
NASA Technical Reports Server (NTRS)
Wheeler, Kevin R.; Knuth, Kevin H.; Castle, Joseph P.; Lvov, Nikolay
2005-01-01
The following concepts were introduced: a) Bayesian adaptive sampling for solving biomass estimation; b) Characterization of MISR Rahman model parameters conditioned upon MODIS landcover. c) Rigorous non-parametric Bayesian approach to analytic mixture model determination. d) Unique U.S. asset for science product validation and verification.
31 CFR 205.24 - How are accurate estimates maintained?
Code of Federal Regulations, 2010 CFR
2010-07-01
... 31 Money and Finance: Treasury 2 2010-07-01 2010-07-01 false How are accurate estimates maintained... Treasury-State Agreement § 205.24 How are accurate estimates maintained? (a) If a State has knowledge that an estimate does not reasonably correspond to the State's cash needs for a Federal assistance...
Cao, Youfang; Terebus, Anna; Liang, Jie
2016-01-01
The discrete chemical master equation (dCME) provides a general framework for studying stochasticity in mesoscopic reaction networks. Since its direct solution rapidly becomes intractable due to the increasing size of the state space, truncation of the state space is necessary for solving most dCMEs. It is therefore important to assess the consequences of state space truncations so errors can be quantified and minimized. Here we describe a novel method for state space truncation. By partitioning a reaction network into multiple molecular equivalence groups (MEG), we truncate the state space by limiting the total molecular copy numbers in each MEG. We further describe a theoretical framework for analysis of the truncation error in the steady state probability landscape using reflecting boundaries. By aggregating the state space based on the usage of a MEG and constructing an aggregated Markov process, we show that the truncation error of a MEG can be asymptotically bounded by the probability of states on the reflecting boundary of the MEG. Furthermore, truncating states of an arbitrary MEG will not undermine the estimated error of truncating any other MEGs. We then provide an overall error estimate for networks with multiple MEGs. To rapidly determine the appropriate size of an arbitrary MEG, we also introduce an a priori method to estimate the upper bound of its truncation error. This a priori estimate can be rapidly computed from reaction rates of the network, without the need of costly trial solutions of the dCME. As examples, we show results of applying our methods to the four stochastic networks of 1) the birth and death model, 2) the single gene expression model, 3) the genetic toggle switch model, and 4) the phage lambda bistable epigenetic switch model. We demonstrate how truncation errors and steady state probability landscapes can be computed using different sizes of the MEG(s) and how the results validate out theories. Overall, the novel state space
Cao, Youfang; Terebus, Anna; Liang, Jie
2016-04-01
The discrete chemical master equation (dCME) provides a general framework for studying stochasticity in mesoscopic reaction networks. Since its direct solution rapidly becomes intractable due to the increasing size of the state space, truncation of the state space is necessary for solving most dCMEs. It is therefore important to assess the consequences of state space truncations so errors can be quantified and minimized. Here we describe a novel method for state space truncation. By partitioning a reaction network into multiple molecular equivalence groups (MEGs), we truncate the state space by limiting the total molecular copy numbers in each MEG. We further describe a theoretical framework for analysis of the truncation error in the steady-state probability landscape using reflecting boundaries. By aggregating the state space based on the usage of a MEG and constructing an aggregated Markov process, we show that the truncation error of a MEG can be asymptotically bounded by the probability of states on the reflecting boundary of the MEG. Furthermore, truncating states of an arbitrary MEG will not undermine the estimated error of truncating any other MEGs. We then provide an overall error estimate for networks with multiple MEGs. To rapidly determine the appropriate size of an arbitrary MEG, we also introduce an a priori method to estimate the upper bound of its truncation error. This a priori estimate can be rapidly computed from reaction rates of the network, without the need of costly trial solutions of the dCME. As examples, we show results of applying our methods to the four stochastic networks of (1) the birth and death model, (2) the single gene expression model, (3) the genetic toggle switch model, and (4) the phage lambda bistable epigenetic switch model. We demonstrate how truncation errors and steady-state probability landscapes can be computed using different sizes of the MEG(s) and how the results validate our theories. Overall, the novel state space
Estimating errors in least-squares fitting
NASA Technical Reports Server (NTRS)
Richter, P. H.
1995-01-01
While least-squares fitting procedures are commonly used in data analysis and are extensively discussed in the literature devoted to this subject, the proper assessment of errors resulting from such fits has received relatively little attention. The present work considers statistical errors in the fitted parameters, as well as in the values of the fitted function itself, resulting from random errors in the data. Expressions are derived for the standard error of the fit, as a function of the independent variable, for the general nonlinear and linear fitting problems. Additionally, closed-form expressions are derived for some examples commonly encountered in the scientific and engineering fields, namely ordinary polynomial and Gaussian fitting functions. These results have direct application to the assessment of the antenna gain and system temperature characteristics, in addition to a broad range of problems in data analysis. The effects of the nature of the data and the choice of fitting function on the ability to accurately model the system under study are discussed, and some general rules are deduced to assist workers intent on maximizing the amount of information obtained form a given set of measurements.
Accurate Orientation Estimation Using AHRS under Conditions of Magnetic Distortion
Yadav, Nagesh; Bleakley, Chris
2014-01-01
Low cost, compact attitude heading reference systems (AHRS) are now being used to track human body movements in indoor environments by estimation of the 3D orientation of body segments. In many of these systems, heading estimation is achieved by monitoring the strength of the Earth's magnetic field. However, the Earth's magnetic field can be locally distorted due to the proximity of ferrous and/or magnetic objects. Herein, we propose a novel method for accurate 3D orientation estimation using an AHRS, comprised of an accelerometer, gyroscope and magnetometer, under conditions of magnetic field distortion. The system performs online detection and compensation for magnetic disturbances, due to, for example, the presence of ferrous objects. The magnetic distortions are detected by exploiting variations in magnetic dip angle, relative to the gravity vector, and in magnetic strength. We investigate and show the advantages of using both magnetic strength and magnetic dip angle for detecting the presence of magnetic distortions. The correction method is based on a particle filter, which performs the correction using an adaptive cost function and by adapting the variance during particle resampling, so as to place more emphasis on the results of dead reckoning of the gyroscope measurements and less on the magnetometer readings. The proposed method was tested in an indoor environment in the presence of various magnetic distortions and under various accelerations (up to 3 g). In the experiments, the proposed algorithm achieves <2° static peak-to-peak error and <5° dynamic peak-to-peak error, significantly outperforming previous methods. PMID:25347584
Large manual pointing errors, but accurate verbal reports, for indications of target azimuth
Philbeck, John; Sargent, Jesse; Arthur, Joeanna; Dopkins, Steve
2008-01-01
Many tasks have been used to probe human directional knowledge, but relatively little is known about the comparative merits of different means of indicating target azimuth. Few studies have compared action-based versus non-action-based judgments for targets encircling the observer. This comparison promises to illuminate not only the perception of azimuths in the front and rear hemispaces, but also the frames of reference underlying various azimuth judgments, and ultimately their neural underpinnings. We compared a response in which participants aimed a pointer at a nearby target, with verbal azimuth estimates. Target locations were distributed between 20 and 340 deg. Non-visual pointing responses exhibited large constant errors (up to −32 deg) that tended to increase with target eccentricity. Pointing with eyes open also showed large errors (up to −21 deg). In striking contrast, verbal reports were highly accurate, with constant errors rarely exceeding +/− 5 deg. Under our testing conditions, these results are not likely to stem from differences in perception-based vs. action-based responses, but instead reflect the frames of reference underlying the pointing and verbal responses. When participants used the pointer to match the egocentric target azimuth rather than the exocentric target azimuth relative to the pointer, errors were reduced. PMID:18546661
Ultraspectral Sounding Retrieval Error Budget and Estimation
NASA Technical Reports Server (NTRS)
Zhou, Daniel K.; Larar, Allen M.; Liu, Xu; Smith, William L.; Strow, L. Larrabee; Yang, Ping
2011-01-01
The ultraspectral infrared radiances obtained from satellite observations provide atmospheric, surface, and/or cloud information. The intent of the measurement of the thermodynamic state is the initialization of weather and climate models. Great effort has been given to retrieving and validating these atmospheric, surface, and/or cloud properties. Error Consistency Analysis Scheme (ECAS), through fast radiative transfer model (RTM) forward and inverse calculations, has been developed to estimate the error budget in terms of absolute and standard deviation of differences in both spectral radiance and retrieved geophysical parameter domains. The retrieval error is assessed through ECAS without assistance of other independent measurements such as radiosonde data. ECAS re-evaluates instrument random noise, and establishes the link between radiometric accuracy and retrieved geophysical parameter accuracy. ECAS can be applied to measurements of any ultraspectral instrument and any retrieval scheme with associated RTM. In this paper, ECAS is described and demonstration is made with the measurements of the METOP-A satellite Infrared Atmospheric Sounding Interferometer (IASI)..
Factoring Algebraic Error for Relative Pose Estimation
Lindstrom, P; Duchaineau, M
2009-03-09
We address the problem of estimating the relative pose, i.e. translation and rotation, of two calibrated cameras from image point correspondences. Our approach is to factor the nonlinear algebraic pose error functional into translational and rotational components, and to optimize translation and rotation independently. This factorization admits subproblems that can be solved using direct methods with practical guarantees on global optimality. That is, for a given translation, the corresponding optimal rotation can directly be determined, and vice versa. We show that these subproblems are equivalent to computing the least eigenvector of second- and fourth-order symmetric tensors. When neither translation or rotation is known, alternating translation and rotation optimization leads to a simple, efficient, and robust algorithm for pose estimation that improves on the well-known 5- and 8-point methods.
Target parameter and error estimation using magnetometry
NASA Astrophysics Data System (ADS)
Norton, S. J.; Witten, A. J.; Won, I. J.; Taylor, D.
The problem of locating and identifying buried unexploded ordnance from magnetometry measurements is addressed within the context of maximum likelihood estimation. In this approach, the magnetostatic theory is used to develop data templates, which represent the modeled magnetic response of a buried ferrous object of arbitrary location, iron content, size, shape, and orientation. It is assumed that these objects are characterized both by a magnetic susceptibility representing their passive response to the earth's magnetic field and by a three-dimensional magnetization vector representing a permanent dipole magnetization. Analytical models were derived for four types of targets: spheres, spherical shells, ellipsoids, and ellipsoidal shells. The models can be used to quantify the Cramer-Rao (error) bounds on the parameter estimates. These bounds give the minimum variance in the estimated parameters as a function of measurement signal-to-noise ratio, spatial sampling, and target characteristics. For cases where analytic expressions for the Cramer-Rao bounds can be derived, these expressions prove quite useful in establishing optimal sampling strategies. Analytic expressions for various Cramer-Rao bounds have been developed for spherical- and spherical shell-type objects. An maximum likelihood estimation algorithm has been developed and tested on data acquired at the Magnetic Test Range at the Naval Explosive Ordnance Disposal Tech Center in Indian Head, Maryland. This algorithm estimates seven target parameters. These parameters are the three Cartesian coordinates (x, y, z) identifying the buried ordnance's location, the three Cartesian components of the permanent dipole magnetization vector, and the equivalent radius of the ordnance assuming it is a passive solid iron sphere.
Rigorous Error Estimates for Reynolds' Lubrication Approximation
NASA Astrophysics Data System (ADS)
Wilkening, Jon
2006-11-01
Reynolds' lubrication equation is used extensively in engineering calculations to study flows between moving machine parts, e.g. in journal bearings or computer disk drives. It is also used extensively in micro- and bio-fluid mechanics to model creeping flows through narrow channels and in thin films. To date, the only rigorous justification of this equation (due to Bayada and Chambat in 1986 and to Nazarov in 1987) states that the solution of the Navier-Stokes equations converges to the solution of Reynolds' equation in the limit as the aspect ratio ɛ approaches zero. In this talk, I will show how the constants in these error bounds depend on the geometry. More specifically, I will show how to compute expansion solutions of the Stokes equations in a 2-d periodic geometry to arbitrary order and exhibit error estimates with constants which are either (1) given in the problem statement or easily computable from h(x), or (2) difficult to compute but universal (independent of h(x)). Studying the constants in the latter category, we find that the effective radius of convergence actually increases through 10th order, but then begins to decrease as the inverse of the order, indicating that the expansion solution is probably an asymptotic series rather than a convergent series.
NASA Technical Reports Server (NTRS)
Lang, Christapher G.; Bey, Kim S. (Technical Monitor)
2002-01-01
This research investigates residual-based a posteriori error estimates for finite element approximations of heat conduction in single-layer and multi-layered materials. The finite element approximation, based upon hierarchical modelling combined with p-version finite elements, is described with specific application to a two-dimensional, steady state, heat-conduction problem. Element error indicators are determined by solving an element equation for the error with the element residual as a source, and a global error estimate in the energy norm is computed by collecting the element contributions. Numerical results of the performance of the error estimate are presented by comparisons to the actual error. Two methods are discussed and compared for approximating the element boundary flux. The equilibrated flux method provides more accurate results for estimating the error than the average flux method. The error estimation is applied to multi-layered materials with a modification to the equilibrated flux method to approximate the discontinuous flux along a boundary at the material interfaces. A directional error indicator is developed which distinguishes between the hierarchical modeling error and the finite element error. Numerical results are presented for single-layered materials which show that the directional indicators accurately determine which contribution to the total error dominates.
Quantifying Accurate Calorie Estimation Using the "Think Aloud" Method
ERIC Educational Resources Information Center
Holmstrup, Michael E.; Stearns-Bruening, Kay; Rozelle, Jeffrey
2013-01-01
Objective: Clients often have limited time in a nutrition education setting. An improved understanding of the strategies used to accurately estimate calories may help to identify areas of focused instruction to improve nutrition knowledge. Methods: A "Think Aloud" exercise was recorded during the estimation of calories in a standard dinner meal…
Does more accurate exposure prediction necessarily improve health effect estimates?
Szpiro, Adam A; Paciorek, Christopher J; Sheppard, Lianne
2011-09-01
A unique challenge in air pollution cohort studies and similar applications in environmental epidemiology is that exposure is not measured directly at subjects' locations. Instead, pollution data from monitoring stations at some distance from the study subjects are used to predict exposures, and these predicted exposures are used to estimate the health effect parameter of interest. It is usually assumed that minimizing the error in predicting the true exposure will improve health effect estimation. We show in a simulation study that this is not always the case. We interpret our results in light of recently developed statistical theory for measurement error, and we discuss implications for the design and analysis of epidemiologic research.
Analysis of Measurement Error and Estimator Shape in Three-Point Hydraulic Gradient Estimators
NASA Astrophysics Data System (ADS)
McKenna, S. A.; Wahi, A. K.
2003-12-01
Three spatially separated measurements of head provide a means of estimating the magnitude and orientation of the hydraulic gradient. Previous work with three-point estimators has focused on the effect of the size (area) of the three-point estimator and measurement error on the final estimates of the gradient magnitude and orientation in laboratory and field studies (Mizell, 1980; Silliman and Frost, 1995; Silliman and Mantz, 2000; Ruskauff and Rumbaugh, 1996). However, a systematic analysis of the combined effects of measurement error, estimator shape and estimator orientation relative to the gradient orientation has not previously been conducted. Monte Carlo simulation with an underlying assumption of a homogeneous transmissivity field is used to examine the effects of uncorrelated measurement error on a series of eleven different three-point estimators having the same size but different shapes as a function of the orientation of the true gradient. Results show that the variance in the estimate of both the magnitude and the orientation increase linearly with the increase in measurement error in agreement with the results of stochastic theory for estimators that are small relative to the correlation length of transmissivity (Mizell, 1980). Three-point estimator shapes with base to height ratios between 0.5 and 5.0 provide accurate estimates of magnitude and orientation across all orientations of the true gradient. As an example, these results are applied to data collected from a monitoring network of 25 wells at the WIPP site during two different time periods. The simulation results are used to reduce the set of all possible combinations of three wells to those combinations with acceptable measurement errors relative to the amount of head drop across the estimator and base to height ratios between 0.5 and 5.0. These limitations reduce the set of all possible well combinations by 98 percent and show that size alone as defined by triangle area is not a valid
Kassabian, Nazelie; Lo Presti, Letizia; Rispoli, Francesco
2014-06-11
Railway signaling is a safety system that has evolved over the last couple of centuries towards autonomous functionality. Recently, great effort is being devoted in this field, towards the use and exploitation of Global Navigation Satellite System (GNSS) signals and GNSS augmentation systems in view of lower railway track equipments and maintenance costs, that is a priority to sustain the investments for modernizing the local and regional lines most of which lack automatic train protection systems and are still manually operated. The objective of this paper is to assess the sensitivity of the Linear Minimum Mean Square Error (LMMSE) algorithm to modeling errors in the spatial correlation function that characterizes true pseudorange Differential Corrections (DCs). This study is inspired by the railway application; however, it applies to all transportation systems, including the road sector, that need to be complemented by an augmentation system in order to deliver accurate and reliable positioning with integrity specifications. A vector of noisy pseudorange DC measurements are simulated, assuming a Gauss-Markov model with a decay rate parameter inversely proportional to the correlation distance that exists between two points of a certain environment. The LMMSE algorithm is applied on this vector to estimate the true DC, and the estimation error is compared to the noise added during simulation. The results show that for large enough correlation distance to Reference Stations (RSs) distance separation ratio values, the LMMSE brings considerable advantage in terms of estimation error accuracy and precision. Conversely, the LMMSE algorithm may deteriorate the quality of the DC measurements whenever the ratio falls below a certain threshold.
Kassabian, Nazelie; Presti, Letizia Lo; Rispoli, Francesco
2014-01-01
Railway signaling is a safety system that has evolved over the last couple of centuries towards autonomous functionality. Recently, great effort is being devoted in this field, towards the use and exploitation of Global Navigation Satellite System (GNSS) signals and GNSS augmentation systems in view of lower railway track equipments and maintenance costs, that is a priority to sustain the investments for modernizing the local and regional lines most of which lack automatic train protection systems and are still manually operated. The objective of this paper is to assess the sensitivity of the Linear Minimum Mean Square Error (LMMSE) algorithm to modeling errors in the spatial correlation function that characterizes true pseudorange Differential Corrections (DCs). This study is inspired by the railway application; however, it applies to all transportation systems, including the road sector, that need to be complemented by an augmentation system in order to deliver accurate and reliable positioning with integrity specifications. A vector of noisy pseudorange DC measurements are simulated, assuming a Gauss-Markov model with a decay rate parameter inversely proportional to the correlation distance that exists between two points of a certain environment. The LMMSE algorithm is applied on this vector to estimate the true DC, and the estimation error is compared to the noise added during simulation. The results show that for large enough correlation distance to Reference Stations (RSs) distance separation ratio values, the LMMSE brings considerable advantage in terms of estimation error accuracy and precision. Conversely, the LMMSE algorithm may deteriorate the quality of the DC measurements whenever the ratio falls below a certain threshold. PMID:24922454
A posteriori pointwise error estimates for the boundary element method
Paulino, G.H.; Gray, L.J.; Zarikian, V.
1995-01-01
This report presents a new approach for a posteriori pointwise error estimation in the boundary element method. The estimator relies upon the evaluation of hypersingular integral equations, and is therefore intrinsic to the boundary integral equation approach. This property allows some theoretical justification by mathematically correlating the exact and estimated errors. A methodology is developed for approximating the error on the boundary as well as in the interior of the domain. In the interior, error estimates for both the function and its derivatives (e.g. potential and interior gradients for potential problems, displacements and stresses for elasticity problems) are presented. Extensive computational experiments have been performed for the two dimensional Laplace equation on interior domains, employing Dirichlet and mixed boundary conditions. The results indicate that the error estimates successfully track the form of the exact error curve. Moreover, a reasonable estimate of the magnitude of the actual error is also obtained.
Real-Time Parameter Estimation Using Output Error
NASA Technical Reports Server (NTRS)
Grauer, Jared A.
2014-01-01
Output-error parameter estimation, normally a post- ight batch technique, was applied to real-time dynamic modeling problems. Variations on the traditional algorithm were investigated with the goal of making the method suitable for operation in real time. Im- plementation recommendations are given that are dependent on the modeling problem of interest. Application to ight test data showed that accurate parameter estimates and un- certainties for the short-period dynamics model were available every 2 s using time domain data, or every 3 s using frequency domain data. The data compatibility problem was also solved in real time, providing corrected sensor measurements every 4 s. If uncertainty corrections for colored residuals are omitted, this rate can be increased to every 0.5 s.
Estimating IMU heading error from SAR images.
Doerry, Armin Walter
2009-03-01
Angular orientation errors of the real antenna for Synthetic Aperture Radar (SAR) will manifest as undesired illumination gradients in SAR images. These gradients can be measured, and the pointing error can be calculated. This can be done for single images, but done more robustly using multi-image methods. Several methods are provided in this report. The pointing error can then be fed back to the navigation Kalman filter to correct for problematic heading (yaw) error drift. This can mitigate the need for uncomfortable and undesired IMU alignment maneuvers such as S-turns.
Inertial and Magnetic Sensor Data Compression Considering the Estimation Error
Suh, Young Soo
2009-01-01
This paper presents a compression method for inertial and magnetic sensor data, where the compressed data are used to estimate some states. When sensor data are bounded, the proposed compression method guarantees that the compression error is smaller than a prescribed bound. The manner in which this error bound affects the bit rate and the estimation error is investigated. Through the simulation, it is shown that the estimation error is improved by 18.81% over a test set of 12 cases compared with a filter that does not use the compression error bound. PMID:22454564
A Note on Confidence Interval Estimation and Margin of Error
ERIC Educational Resources Information Center
Gilliland, Dennis; Melfi, Vince
2010-01-01
Confidence interval estimation is a fundamental technique in statistical inference. Margin of error is used to delimit the error in estimation. Dispelling misinterpretations that teachers and students give to these terms is important. In this note, we give examples of the confusion that can arise in regard to confidence interval estimation and…
Improved Soundings and Error Estimates using AIRS/AMSU Data
NASA Technical Reports Server (NTRS)
Susskind, Joel
2006-01-01
AIRS was launched on EOS Aqua on May 4, 2002, together with AMSU A and HSB, to form a next generation polar orbiting infrared and microwave atmospheric sounding system. The primary products of AIRS/AMSU are twice daily global fields of atmospheric temperature-humidity profiles, ozone profiles, sea/land surface skin temperature, and cloud related parameters including OLR. The sounding goals of AIRS are to produce 1 km tropospheric layer mean temperatures with an rms error of 1 K, and layer precipitable water with an rms error of 20 percent, in cases with up to 80 percent effective cloud cover. The basic theory used to analyze AIRS/AMSU/HSB data in the presence of clouds, called the at-launch algorithm, and a post-launch algorithm which differed only in the minor details from the at-launch algorithm, have been described previously. The post-launch algorithm, referred to as AIRS Version 4.0, has been used by the Goddard DAAC to analyze and distribute AIRS retrieval products. In this paper we show progress made toward the AIRS Version 5.0 algorithm which will be used by the Goddard DAAC starting late in 2006. A new methodology has been developed to provide accurate case by case error estimates for retrieved geophysical parameters and for the channel by channel cloud cleared radiances used to derive the geophysical parameters from the AIRS/AMSU observations. These error estimates are in turn used for quality control of the derived geophysical parameters and clear column radiances. Improvements made to the retrieval algorithm since Version 4.0 are described as well as results comparing Version 5.0 retrieval accuracy and spatial coverage with those obtained using Version 4.0.
Field evaluation of distance-estimation error during wetland-dependent bird surveys
Nadeau, Christopher P.; Conway, Courtney J.
2012-01-01
Context: The most common methods to estimate detection probability during avian point-count surveys involve recording a distance between the survey point and individual birds detected during the survey period. Accurately measuring or estimating distance is an important assumption of these methods; however, this assumption is rarely tested in the context of aural avian point-count surveys. Aims: We expand on recent bird-simulation studies to document the error associated with estimating distance to calling birds in a wetland ecosystem. Methods: We used two approaches to estimate the error associated with five surveyor's distance estimates between the survey point and calling birds, and to determine the factors that affect a surveyor's ability to estimate distance. Key results: We observed biased and imprecise distance estimates when estimating distance to simulated birds in a point-count scenario (x̄error = -9 m, s.d.error = 47 m) and when estimating distances to real birds during field trials (x̄error = 39 m, s.d.error = 79 m). The amount of bias and precision in distance estimates differed among surveyors; surveyors with more training and experience were less biased and more precise when estimating distance to both real and simulated birds. Three environmental factors were important in explaining the error associated with distance estimates, including the measured distance from the bird to the surveyor, the volume of the call and the species of bird. Surveyors tended to make large overestimations to birds close to the survey point, which is an especially serious error in distance sampling. Conclusions: Our results suggest that distance-estimation error is prevalent, but surveyor training may be the easiest way to reduce distance-estimation error. Implications: The present study has demonstrated how relatively simple field trials can be used to estimate the error associated with distance estimates used to estimate detection probability during avian point
Eldred, Michael Scott; Subia, Samuel Ramirez; Neckels, David; Hopkins, Matthew Morgan; Notz, Patrick K.; Adams, Brian M.; Carnes, Brian; Wittwer, Jonathan W.; Bichon, Barron J.; Copps, Kevin D.
2006-10-01
This report documents the results for an FY06 ASC Algorithms Level 2 milestone combining error estimation and adaptivity, uncertainty quantification, and probabilistic design capabilities applied to the analysis and design of bistable MEMS. Through the use of error estimation and adaptive mesh refinement, solution verification can be performed in an automated and parameter-adaptive manner. The resulting uncertainty analysis and probabilistic design studies are shown to be more accurate, efficient, reliable, and convenient.
Semiclassical Dynamicswith Exponentially Small Error Estimates
NASA Astrophysics Data System (ADS)
Hagedorn, George A.; Joye, Alain
We construct approximate solutions to the time-dependent Schrödingerequation
Adaptive error covariances estimation methods for ensemble Kalman filters
Zhen, Yicun; Harlim, John
2015-08-01
This paper presents a computationally fast algorithm for estimating, both, the system and observation noise covariances of nonlinear dynamics, that can be used in an ensemble Kalman filtering framework. The new method is a modification of Belanger's recursive method, to avoid an expensive computational cost in inverting error covariance matrices of product of innovation processes of different lags when the number of observations becomes large. When we use only product of innovation processes up to one-lag, the computational cost is indeed comparable to a recently proposed method by Berry–Sauer's. However, our method is more flexible since it allows for using information from product of innovation processes of more than one-lag. Extensive numerical comparisons between the proposed method and both the original Belanger's and Berry–Sauer's schemes are shown in various examples, ranging from low-dimensional linear and nonlinear systems of SDEs and 40-dimensional stochastically forced Lorenz-96 model. Our numerical results suggest that the proposed scheme is as accurate as the original Belanger's scheme on low-dimensional problems and has a wider range of more accurate estimates compared to Berry–Sauer's method on L-96 example.
An Accurate Link Correlation Estimator for Improving Wireless Protocol Performance
Zhao, Zhiwei; Xu, Xianghua; Dong, Wei; Bu, Jiajun
2015-01-01
Wireless link correlation has shown significant impact on the performance of various sensor network protocols. Many works have been devoted to exploiting link correlation for protocol improvements. However, the effectiveness of these designs heavily relies on the accuracy of link correlation measurement. In this paper, we investigate state-of-the-art link correlation measurement and analyze the limitations of existing works. We then propose a novel lightweight and accurate link correlation estimation (LACE) approach based on the reasoning of link correlation formation. LACE combines both long-term and short-term link behaviors for link correlation estimation. We implement LACE as a stand-alone interface in TinyOS and incorporate it into both routing and flooding protocols. Simulation and testbed results show that LACE: (1) achieves more accurate and lightweight link correlation measurements than the state-of-the-art work; and (2) greatly improves the performance of protocols exploiting link correlation. PMID:25686314
An accurate link correlation estimator for improving wireless protocol performance.
Zhao, Zhiwei; Xu, Xianghua; Dong, Wei; Bu, Jiajun
2015-02-12
Wireless link correlation has shown significant impact on the performance of various sensor network protocols. Many works have been devoted to exploiting link correlation for protocol improvements. However, the effectiveness of these designs heavily relies on the accuracy of link correlation measurement. In this paper, we investigate state-of-the-art link correlation measurement and analyze the limitations of existing works. We then propose a novel lightweight and accurate link correlation estimation (LACE) approach based on the reasoning of link correlation formation. LACE combines both long-term and short-term link behaviors for link correlation estimation. We implement LACE as a stand-alone interface in TinyOS and incorporate it into both routing and flooding protocols. Simulation and testbed results show that LACE: (1) achieves more accurate and lightweight link correlation measurements than the state-of-the-art work; and (2) greatly improves the performance of protocols exploiting link correlation.
Improved Margin of Error Estimates for Proportions in Business: An Educational Example
ERIC Educational Resources Information Center
Arzumanyan, George; Halcoussis, Dennis; Phillips, G. Michael
2015-01-01
This paper presents the Agresti & Coull "Adjusted Wald" method for computing confidence intervals and margins of error for common proportion estimates. The presented method is easily implementable by business students and practitioners and provides more accurate estimates of proportions particularly in extreme samples and small…
Fisher classifier and its probability of error estimation
NASA Technical Reports Server (NTRS)
Chittineni, C. B.
1979-01-01
Computationally efficient expressions are derived for estimating the probability of error using the leave-one-out method. The optimal threshold for the classification of patterns projected onto Fisher's direction is derived. A simple generalization of the Fisher classifier to multiple classes is presented. Computational expressions are developed for estimating the probability of error of the multiclass Fisher classifier.
A fast and accurate frequency estimation algorithm for sinusoidal signal with harmonic components
NASA Astrophysics Data System (ADS)
Hu, Jinghua; Pan, Mengchun; Zeng, Zhidun; Hu, Jiafei; Chen, Dixiang; Tian, Wugang; Zhao, Jianqiang; Du, Qingfa
2016-10-01
Frequency estimation is a fundamental problem in many applications, such as traditional vibration measurement, power system supervision, and microelectromechanical system sensors control. In this paper, a fast and accurate frequency estimation algorithm is proposed to deal with low efficiency problem in traditional methods. The proposed algorithm consists of coarse and fine frequency estimation steps, and we demonstrate that it is more efficient than conventional searching methods to achieve coarse frequency estimation (location peak of FFT amplitude) by applying modified zero-crossing technique. Thus, the proposed estimation algorithm requires less hardware and software sources and can achieve even higher efficiency when the experimental data increase. Experimental results with modulated magnetic signal show that the root mean square error of frequency estimation is below 0.032 Hz with the proposed algorithm, which has lower computational complexity and better global performance than conventional frequency estimation methods.
NASA Technical Reports Server (NTRS)
Dee, D. P.; Cohn, S. E.; Ghil, M.
1985-01-01
A two-part algorithm is presented for reliably computing weather forecast model and observational error covariances during data assimilation. Data errors arise from instrumental inaccuracies and sub-grid scale variability, whereas forecast errors occur because of modeling errors and the propagation of previous analysis errors. A Kalman filter is defined as the primary algorithm for estimating the forecast and analysis error convariance matrices. A second algorithm is described for quantifying the noise covariance matrices of any degree to obtain accurate values for the observational error covariances. Numerical results are provided from a linearized one-dimensional shallow-water model. The results cover observational noise covariances, initial instrumental errors and erroneous model values.
An Ensemble-type Approach to Numerical Error Estimation
NASA Astrophysics Data System (ADS)
Ackmann, J.; Marotzke, J.; Korn, P.
2015-12-01
The estimation of the numerical error in a specific physical quantity of interest (goal) is of key importance in geophysical modelling. Towards this aim, we have formulated an algorithm that combines elements of the classical dual-weighted error estimation with stochastic methods. Our algorithm is based on the Dual-weighted Residual method in which the residual of the model solution is weighed by the adjoint solution, i.e. by the sensitivities of the goal towards the residual. We extend this method by modelling the residual as a stochastic process. Parameterizing the residual by a stochastic process was motivated by the Mori-Zwanzig formalism from statistical mechanics.Here, we apply our approach to two-dimensional shallow-water flows with lateral boundaries and an eddy viscosity parameterization. We employ different parameters of the stochastic process for different dynamical regimes in different regions. We find that for each region the temporal fluctuations of local truncation errors (discrete residuals) can be interpreted stochastically by a Laplace-distributed random variable. Assuming that these random variables are fully correlated in time leads to a stochastic process that parameterizes a problem-dependent temporal evolution of local truncation errors. The parameters of this stochastic process are estimated from short, near-initial, high-resolution simulations. Under the assumption that the estimated parameters can be extrapolated to the full time window of the error estimation, the estimated stochastic process is proven to be a valid surrogate for the local truncation errors.Replacing the local truncation errors by a stochastic process puts our method within the class of ensemble methods and makes the resulting error estimator a random variable. The result of our error estimator is thus a confidence interval on the error in the respective goal. We will show error estimates for two 2D ocean-type experiments and provide an outlook for the 3D case.
Accurate photometric redshift probability density estimation - method comparison and application
NASA Astrophysics Data System (ADS)
Rau, Markus Michael; Seitz, Stella; Brimioulle, Fabrice; Frank, Eibe; Friedrich, Oliver; Gruen, Daniel; Hoyle, Ben
2015-10-01
We introduce an ordinal classification algorithm for photometric redshift estimation, which significantly improves the reconstruction of photometric redshift probability density functions (PDFs) for individual galaxies and galaxy samples. As a use case we apply our method to CFHTLS galaxies. The ordinal classification algorithm treats distinct redshift bins as ordered values, which improves the quality of photometric redshift PDFs, compared with non-ordinal classification architectures. We also propose a new single value point estimate of the galaxy redshift, which can be used to estimate the full redshift PDF of a galaxy sample. This method is competitive in terms of accuracy with contemporary algorithms, which stack the full redshift PDFs of all galaxies in the sample, but requires orders of magnitude less storage space. The methods described in this paper greatly improve the log-likelihood of individual object redshift PDFs, when compared with a popular neural network code (ANNZ). In our use case, this improvement reaches 50 per cent for high-redshift objects (z ≥ 0.75). We show that using these more accurate photometric redshift PDFs will lead to a reduction in the systematic biases by up to a factor of 4, when compared with less accurate PDFs obtained from commonly used methods. The cosmological analyses we examine and find improvement upon are the following: gravitational lensing cluster mass estimates, modelling of angular correlation functions and modelling of cosmic shear correlation functions.
Empirical State Error Covariance Matrix for Batch Estimation
NASA Technical Reports Server (NTRS)
Frisbee, Joe
2015-01-01
State estimation techniques effectively provide mean state estimates. However, the theoretical state error covariance matrices provided as part of these techniques often suffer from a lack of confidence in their ability to describe the uncertainty in the estimated states. By a reinterpretation of the equations involved in the weighted batch least squares algorithm, it is possible to directly arrive at an empirical state error covariance matrix. The proposed empirical state error covariance matrix will contain the effect of all error sources, known or not. This empirical error covariance matrix may be calculated as a side computation for each unique batch solution. Results based on the proposed technique will be presented for a simple, two observer and measurement error only problem.
Methods for accurate estimation of net discharge in a tidal channel
Simpson, M.R.; Bland, R.
2000-01-01
Accurate estimates of net residual discharge in tidally affected rivers and estuaries are possible because of recently developed ultrasonic discharge measurement techniques. Previous discharge estimates using conventional mechanical current meters and methods based on stage/discharge relations or water slope measurements often yielded errors that were as great as or greater than the computed residual discharge. Ultrasonic measurement methods consist of: 1) the use of ultrasonic instruments for the measurement of a representative 'index' velocity used for in situ estimation of mean water velocity and 2) the use of the acoustic Doppler current discharge measurement system to calibrate the index velocity measurement data. Methods used to calibrate (rate) the index velocity to the channel velocity measured using the Acoustic Doppler Current Profiler are the most critical factors affecting the accuracy of net discharge estimation. The index velocity first must be related to mean channel velocity and then used to calculate instantaneous channel discharge. Finally, discharge is low-pass filtered to remove the effects of the tides. An ultrasonic velocity meter discharge-measurement site in a tidally affected region of the Sacramento-San Joaquin Rivers was used to study the accuracy of the index velocity calibration procedure. Calibration data consisting of ultrasonic velocity meter index velocity and concurrent acoustic Doppler discharge measurement data were collected during three time periods. Two sets of data were collected during a spring tide (monthly maximum tidal current) and one of data collected during a neap tide (monthly minimum tidal current). The relative magnitude of instrumental errors, acoustic Doppler discharge measurement errors, and calibration errors were evaluated. Calibration error was found to be the most significant source of error in estimating net discharge. Using a comprehensive calibration method, net discharge estimates developed from the three
Accurate Satellite-Derived Estimates of Tropospheric Ozone Radiative Forcing
NASA Technical Reports Server (NTRS)
Joiner, Joanna; Schoeberl, Mark R.; Vasilkov, Alexander P.; Oreopoulos, Lazaros; Platnick, Steven; Livesey, Nathaniel J.; Levelt, Pieternel F.
2008-01-01
Estimates of the radiative forcing due to anthropogenically-produced tropospheric O3 are derived primarily from models. Here, we use tropospheric ozone and cloud data from several instruments in the A-train constellation of satellites as well as information from the GEOS-5 Data Assimilation System to accurately estimate the instantaneous radiative forcing from tropospheric O3 for January and July 2005. We improve upon previous estimates of tropospheric ozone mixing ratios from a residual approach using the NASA Earth Observing System (EOS) Aura Ozone Monitoring Instrument (OMI) and Microwave Limb Sounder (MLS) by incorporating cloud pressure information from OMI. Since we cannot distinguish between natural and anthropogenic sources with the satellite data, our estimates reflect the total forcing due to tropospheric O3. We focus specifically on the magnitude and spatial structure of the cloud effect on both the shortand long-wave radiative forcing. The estimates presented here can be used to validate present day O3 radiative forcing produced by models.
Bootstrap Estimates of Standard Errors in Generalizability Theory
ERIC Educational Resources Information Center
Tong, Ye; Brennan, Robert L.
2007-01-01
Estimating standard errors of estimated variance components has long been a challenging task in generalizability theory. Researchers have speculated about the potential applicability of the bootstrap for obtaining such estimates, but they have identified problems (especially bias) in using the bootstrap. Using Brennan's bias-correcting procedures…
Nonparametric Item Response Curve Estimation with Correction for Measurement Error
ERIC Educational Resources Information Center
Guo, Hongwen; Sinharay, Sandip
2011-01-01
Nonparametric or kernel regression estimation of item response curves (IRCs) is often used in item analysis in testing programs. These estimates are biased when the observed scores are used as the regressor because the observed scores are contaminated by measurement error. Accuracy of this estimation is a concern theoretically and operationally.…
Accurate estimators of correlation functions in Fourier space
NASA Astrophysics Data System (ADS)
Sefusatti, E.; Crocce, M.; Scoccimarro, R.; Couchman, H. M. P.
2016-08-01
Efficient estimators of Fourier-space statistics for large number of objects rely on fast Fourier transforms (FFTs), which are affected by aliasing from unresolved small-scale modes due to the finite FFT grid. Aliasing takes the form of a sum over images, each of them corresponding to the Fourier content displaced by increasing multiples of the sampling frequency of the grid. These spurious contributions limit the accuracy in the estimation of Fourier-space statistics, and are typically ameliorated by simultaneously increasing grid size and discarding high-frequency modes. This results in inefficient estimates for e.g. the power spectrum when desired systematic biases are well under per cent level. We show that using interlaced grids removes odd images, which include the dominant contribution to aliasing. In addition, we discuss the choice of interpolation kernel used to define density perturbations on the FFT grid and demonstrate that using higher order interpolation kernels than the standard Cloud-In-Cell algorithm results in significant reduction of the remaining images. We show that combining fourth-order interpolation with interlacing gives very accurate Fourier amplitudes and phases of density perturbations. This results in power spectrum and bispectrum estimates that have systematic biases below 0.01 per cent all the way to the Nyquist frequency of the grid, thus maximizing the use of unbiased Fourier coefficients for a given grid size and greatly reducing systematics for applications to large cosmological data sets.
Using doppler radar images to estimate aircraft navigational heading error
Doerry, Armin W.; Jordan, Jay D.; Kim, Theodore J.
2012-07-03
A yaw angle error of a motion measurement system carried on an aircraft for navigation is estimated from Doppler radar images captured using the aircraft. At least two radar pulses aimed at respectively different physical locations in a targeted area are transmitted from a radar antenna carried on the aircraft. At least two Doppler radar images that respectively correspond to the at least two transmitted radar pulses are produced. These images are used to produce an estimate of the yaw angle error.
Stability and error estimation for Component Adaptive Grid methods
NASA Technical Reports Server (NTRS)
Oliger, Joseph; Zhu, Xiaolei
1994-01-01
Component adaptive grid (CAG) methods for solving hyperbolic partial differential equations (PDE's) are discussed in this paper. Applying recent stability results for a class of numerical methods on uniform grids. The convergence of these methods for linear problems on component adaptive grids is established here. Furthermore, the computational error can be estimated on CAG's using the stability results. Using these estimates, the error can be controlled on CAG's. Thus, the solution can be computed efficiently on CAG's within a given error tolerance. Computational results for time dependent linear problems in one and two space dimensions are presented.
PERIOD ERROR ESTIMATION FOR THE KEPLER ECLIPSING BINARY CATALOG
Mighell, Kenneth J.; Plavchan, Peter
2013-06-15
The Kepler Eclipsing Binary Catalog (KEBC) describes 2165 eclipsing binaries identified in the 115 deg{sup 2} Kepler Field based on observations from Kepler quarters Q0, Q1, and Q2. The periods in the KEBC are given in units of days out to six decimal places but no period errors are provided. We present the PEC (Period Error Calculator) algorithm, which can be used to estimate the period errors of strictly periodic variables observed by the Kepler Mission. The PEC algorithm is based on propagation of error theory and assumes that observation of every light curve peak/minimum in a long time-series observation can be unambiguously identified. The PEC algorithm can be efficiently programmed using just a few lines of C computer language code. The PEC algorithm was used to develop a simple model that provides period error estimates for eclipsing binaries in the KEBC with periods less than 62.5 days: log {sigma}{sub P} Almost-Equal-To - 5.8908 + 1.4425(1 + log P), where P is the period of an eclipsing binary in the KEBC in units of days. KEBC systems with periods {>=}62.5 days have KEBC period errors of {approx}0.0144 days. Periods and period errors of seven eclipsing binary systems in the KEBC were measured using the NASA Exoplanet Archive Periodogram Service and compared to period errors estimated using the PEC algorithm.
An Empirical State Error Covariance Matrix for Batch State Estimation
NASA Technical Reports Server (NTRS)
Frisbee, Joseph H., Jr.
2011-01-01
State estimation techniques serve effectively to provide mean state estimates. However, the state error covariance matrices provided as part of these techniques suffer from some degree of lack of confidence in their ability to adequately describe the uncertainty in the estimated states. A specific problem with the traditional form of state error covariance matrices is that they represent only a mapping of the assumed observation error characteristics into the state space. Any errors that arise from other sources (environment modeling, precision, etc.) are not directly represented in a traditional, theoretical state error covariance matrix. Consider that an actual observation contains only measurement error and that an estimated observation contains all other errors, known and unknown. It then follows that a measurement residual (the difference between expected and observed measurements) contains all errors for that measurement. Therefore, a direct and appropriate inclusion of the actual measurement residuals in the state error covariance matrix will result in an empirical state error covariance matrix. This empirical state error covariance matrix will fully account for the error in the state estimate. By way of a literal reinterpretation of the equations involved in the weighted least squares estimation algorithm, it is possible to arrive at an appropriate, and formally correct, empirical state error covariance matrix. The first specific step of the method is to use the average form of the weighted measurement residual variance performance index rather than its usual total weighted residual form. Next it is helpful to interpret the solution to the normal equations as the average of a collection of sample vectors drawn from a hypothetical parent population. From here, using a standard statistical analysis approach, it directly follows as to how to determine the standard empirical state error covariance matrix. This matrix will contain the total uncertainty in the
Functional error estimators for the adaptive discretization of inverse problems
NASA Astrophysics Data System (ADS)
Clason, Christian; Kaltenbacher, Barbara; Wachsmuth, Daniel
2016-10-01
So-called functional error estimators provide a valuable tool for reliably estimating the discretization error for a sum of two convex functions. We apply this concept to Tikhonov regularization for the solution of inverse problems for partial differential equations, not only for quadratic Hilbert space regularization terms but also for nonsmooth Banach space penalties. Examples include the measure-space norm (i.e., sparsity regularization) or the indicator function of an {L}∞ ball (i.e., Ivanov regularization). The error estimators can be written in terms of residuals in the optimality system that can then be estimated by conventional techniques, thus leading to explicit estimators. This is illustrated by means of an elliptic inverse source problem with the above-mentioned penalties, and numerical results are provided for the case of sparsity regularization.
Accurate heart rate estimation from camera recording via MUSIC algorithm.
Fouladi, Seyyed Hamed; Balasingham, Ilangko; Ramstad, Tor Audun; Kansanen, Kimmo
2015-01-01
In this paper, we propose an algorithm to extract heart rate frequency from video camera using the Multiple SIgnal Classification (MUSIC) algorithm. This leads to improved accuracy of the estimated heart rate frequency in cases the performance is limited by the number of samples and frame rate. Monitoring vital signs remotely can be exploited for both non-contact physiological and psychological diagnosis. The color variation recorded by ordinary cameras is used for heart rate monitoring. The orthogonality between signal space and noise space is used to find more accurate heart rate frequency in comparison with traditional methods. It is shown via experimental results that the limitation of previous methods can be overcome by using subspace methods. PMID:26738015
Research on Parameter Estimation Methods for Alpha Stable Noise in a Laser Gyroscope's Random Error.
Wang, Xueyun; Li, Kui; Gao, Pengyu; Meng, Suxia
2015-01-01
Alpha stable noise, determined by four parameters, has been found in the random error of a laser gyroscope. Accurate estimation of the four parameters is the key process for analyzing the properties of alpha stable noise. Three widely used estimation methods-quantile, empirical characteristic function (ECF) and logarithmic moment method-are analyzed in contrast with Monte Carlo simulation in this paper. The estimation accuracy and the application conditions of all methods, as well as the causes of poor estimation accuracy, are illustrated. Finally, the highest precision method, ECF, is applied to 27 groups of experimental data to estimate the parameters of alpha stable noise in a laser gyroscope's random error. The cumulative probability density curve of the experimental data fitted by an alpha stable distribution is better than that by a Gaussian distribution, which verifies the existence of alpha stable noise in a laser gyroscope's random error.
NASA Astrophysics Data System (ADS)
Száz, Dénes; Farkas, Alexandra; Barta, András; Kretzer, Balázs; Egri, Ádám; Horváth, Gábor
2016-07-01
The theory of sky-polarimetric Viking navigation has been widely accepted for decades without any information about the accuracy of this method. Previously, we have measured the accuracy of the first and second steps of this navigation method in psychophysical laboratory and planetarium experiments. Now, we have tested the accuracy of the third step in a planetarium experiment, assuming that the first and second steps are errorless. Using the fists of their outstretched arms, 10 test persons had to estimate the elevation angles (measured in numbers of fists and fingers) of black dots (representing the position of the occluded Sun) projected onto the planetarium dome. The test persons performed 2400 elevation estimations, 48% of which were more accurate than ±1°. We selected three test persons with the (i) largest and (ii) smallest elevation errors and (iii) highest standard deviation of the elevation error. From the errors of these three persons, we calculated their error function, from which the North errors (the angles with which they deviated from the geographical North) were determined for summer solstice and spring equinox, two specific dates of the Viking sailing period. The range of possible North errors ΔωN was the lowest and highest at low and high solar elevations, respectively. At high elevations, the maximal ΔωN was 35.6° and 73.7° at summer solstice and 23.8° and 43.9° at spring equinox for the best and worst test person (navigator), respectively. Thus, the best navigator was twice as good as the worst one. At solstice and equinox, high elevations occur the most frequently during the day, thus high North errors could occur more frequently than expected before. According to our findings, the ideal periods for sky-polarimetric Viking navigation are immediately after sunrise and before sunset, because the North errors are the lowest at low solar elevations.
Factor Loading Estimation Error and Stability Using Exploratory Factor Analysis
ERIC Educational Resources Information Center
Sass, Daniel A.
2010-01-01
Exploratory factor analysis (EFA) is commonly employed to evaluate the factor structure of measures with dichotomously scored items. Generally, only the estimated factor loadings are provided with no reference to significance tests, confidence intervals, and/or estimated factor loading standard errors. This simulation study assessed factor loading…
Adaptive Error Estimation in Linearized Ocean General Circulation Models
NASA Technical Reports Server (NTRS)
Chechelnitsky, Michael Y.
1999-01-01
Data assimilation methods are routinely used in oceanography. The statistics of the model and measurement errors need to be specified a priori. This study addresses the problem of estimating model and measurement error statistics from observations. We start by testing innovation based methods of adaptive error estimation with low-dimensional models in the North Pacific (5-60 deg N, 132-252 deg E) to TOPEX/POSEIDON (TIP) sea level anomaly data, acoustic tomography data from the ATOC project, and the MIT General Circulation Model (GCM). A reduced state linear model that describes large scale internal (baroclinic) error dynamics is used. The methods are shown to be sensitive to the initial guess for the error statistics and the type of observations. A new off-line approach is developed, the covariance matching approach (CMA), where covariance matrices of model-data residuals are "matched" to their theoretical expectations using familiar least squares methods. This method uses observations directly instead of the innovations sequence and is shown to be related to the MT method and the method of Fu et al. (1993). Twin experiments using the same linearized MIT GCM suggest that altimetric data are ill-suited to the estimation of internal GCM errors, but that such estimates can in theory be obtained using acoustic data. The CMA is then applied to T/P sea level anomaly data and a linearization of a global GFDL GCM which uses two vertical modes. We show that the CMA method can be used with a global model and a global data set, and that the estimates of the error statistics are robust. We show that the fraction of the GCM-T/P residual variance explained by the model error is larger than that derived in Fukumori et al.(1999) with the method of Fu et al.(1993). Most of the model error is explained by the barotropic mode. However, we find that impact of the change in the error statistics on the data assimilation estimates is very small. This is explained by the large
Error decomposition and estimation of inherent optical properties.
Salama, Mhd Suhyb; Stein, Alfred
2009-09-10
We describe a methodology to quantify and separate the errors of inherent optical properties (IOPs) derived from ocean-color model inversion. Their total error is decomposed into three different sources, namely, model approximations and inversion, sensor noise, and atmospheric correction. Prior information on plausible ranges of observation, sensor noise, and inversion goodness-of-fit are employed to derive the posterior probability distribution of the IOPs. The relative contribution of each error component to the total error budget of the IOPs, all being of stochastic nature, is then quantified. The method is validated with the International Ocean Colour Coordinating Group (IOCCG) data set and the NASA bio-Optical Marine Algorithm Data set (NOMAD). The derived errors are close to the known values with correlation coefficients of 60-90% and 67-90% for IOCCG and NOMAD data sets, respectively. Model-induced errors inherent to the derived IOPs are between 10% and 57% of the total error, whereas atmospheric-induced errors are in general above 43% and up to 90% for both data sets. The proposed method is applied to synthesized and in situ measured populations of IOPs. The mean relative errors of the derived values are between 2% and 20%. A specific error table to the Medium Resolution Imaging Spectrometer (MERIS) sensor is constructed. It serves as a benchmark to evaluate the performance of the atmospheric correction method and to compute atmospheric-induced errors. Our method has a better performance and is more appropriate to estimate actual errors of ocean-color derived products than the previously suggested methods. Moreover, it is generic and can be applied to quantify the error of any derived biogeophysical parameter regardless of the used derivation. PMID:19745859
Error Estimation for Reduced Order Models of Dynamical systems
Homescu, C; Petzold, L R; Serban, R
2003-12-16
The use of reduced order models to describe a dynamical system is pervasive in science and engineering. Often these models are used without an estimate of their error or range of validity. In this paper we consider dynamical systems and reduced models built using proper orthogonal decomposition. We show how to compute estimates and bounds for these errors, by a combination of the small sample statistical condition estimation method and of error estimation using the adjoint method. More importantly, the proposed approach allows the assessment of so-called regions of validity for reduced models, i.e., ranges of perturbations in the original system over which the reduced model is still appropriate. This question is particularly important for applications in which reduced models are used not just to approximate the solution to the system that provided the data used in constructing the reduced model, but rather to approximate the solution of systems perturbed from the original one. Numerical examples validate our approach: the error norm estimates approximate well the forward error while the derived bounds are within an order of magnitude.
Sampling errors in satellite estimates of tropical rain
NASA Technical Reports Server (NTRS)
Mcconnell, Alan; North, Gerald R.
1987-01-01
The GATE rainfall data set is used in a statistical study to estimate the sampling errors that might be expected for the type of snapshot sampling that a low earth-orbiting satellite makes. For averages over the entire 400-km square and for the duration of several weeks, strong evidence is found that sampling errors less than 10 percent can be expected in contributions from each of four rain rate categories which individually account for about one quarter of the total rain.
Estimation of rod scale errors in geodetic leveling
Craymer, Michael R.; Vaníček, Petr; Castle, Robert O.
1995-01-01
Comparisons among repeated geodetic levelings have often been used for detecting and estimating residual rod scale errors in leveled heights. Individual rod-pair scale errors are estimated by a two-step procedure using a model based on either differences in heights, differences in section height differences, or differences in section tilts. It is shown that the estimated rod-pair scale errors derived from each model are identical only when the data are correctly weighted, and the mathematical correlations are accounted for in the model based on heights. Analyses based on simple regressions of changes in height versus height can easily lead to incorrect conclusions. We also show that the statistically estimated scale errors are not a simple function of height, height difference, or tilt. The models are valid only when terrain slope is constant over adjacent pairs of setups (i.e., smoothly varying terrain). In order to discriminate between rod scale errors and vertical displacements due to crustal motion, the individual rod-pairs should be used in more than one leveling, preferably in areas of contrasting tectonic activity. From an analysis of 37 separately calibrated rod-pairs used in 55 levelings in southern California, we found eight statistically significant coefficients that could be reasonably attributed to rod scale errors, only one of which was larger than the expected random error in the applied calibration-based scale correction. However, significant differences with other independent checks indicate that caution should be exercised before accepting these results as evidence of scale error. Further refinements of the technique are clearly needed if the results are to be routinely applied in practice.
NASA Astrophysics Data System (ADS)
Jones, Reese E.; Mandadapu, Kranthi K.
2012-04-01
We present a rigorous Green-Kubo methodology for calculating transport coefficients based on on-the-fly estimates of: (a) statistical stationarity of the relevant process, and (b) error in the resulting coefficient. The methodology uses time samples efficiently across an ensemble of parallel replicas to yield accurate estimates, which is particularly useful for estimating the thermal conductivity of semi-conductors near their Debye temperatures where the characteristic decay times of the heat flux correlation functions are large. Employing and extending the error analysis of Zwanzig and Ailawadi [Phys. Rev. 182, 280 (1969)], 10.1103/PhysRev.182.280 and Frenkel [in Proceedings of the International School of Physics "Enrico Fermi", Course LXXV (North-Holland Publishing Company, Amsterdam, 1980)] to the integral of correlation, we are able to provide tight theoretical bounds for the error in the estimate of the transport coefficient. To demonstrate the performance of the method, four test cases of increasing computational cost and complexity are presented: the viscosity of Ar and water, and the thermal conductivity of Si and GaN. In addition to producing accurate estimates of the transport coefficients for these materials, this work demonstrates precise agreement of the computed variances in the estimates of the correlation and the transport coefficient with the extended theory based on the assumption that fluctuations follow a Gaussian process. The proposed algorithm in conjunction with the extended theory enables the calculation of transport coefficients with the Green-Kubo method accurately and efficiently.
Jones, Reese E; Mandadapu, Kranthi K
2012-04-21
We present a rigorous Green-Kubo methodology for calculating transport coefficients based on on-the-fly estimates of: (a) statistical stationarity of the relevant process, and (b) error in the resulting coefficient. The methodology uses time samples efficiently across an ensemble of parallel replicas to yield accurate estimates, which is particularly useful for estimating the thermal conductivity of semi-conductors near their Debye temperatures where the characteristic decay times of the heat flux correlation functions are large. Employing and extending the error analysis of Zwanzig and Ailawadi [Phys. Rev. 182, 280 (1969)] and Frenkel [in Proceedings of the International School of Physics "Enrico Fermi", Course LXXV (North-Holland Publishing Company, Amsterdam, 1980)] to the integral of correlation, we are able to provide tight theoretical bounds for the error in the estimate of the transport coefficient. To demonstrate the performance of the method, four test cases of increasing computational cost and complexity are presented: the viscosity of Ar and water, and the thermal conductivity of Si and GaN. In addition to producing accurate estimates of the transport coefficients for these materials, this work demonstrates precise agreement of the computed variances in the estimates of the correlation and the transport coefficient with the extended theory based on the assumption that fluctuations follow a Gaussian process. The proposed algorithm in conjunction with the extended theory enables the calculation of transport coefficients with the Green-Kubo method accurately and efficiently.
Verification of unfold error estimates in the unfold operator code
Fehl, D.L.; Biggs, F.
1997-01-01
Spectral unfolding is an inverse mathematical operation that attempts to obtain spectral source information from a set of response functions and data measurements. Several unfold algorithms have appeared over the past 30 years; among them is the unfold operator (UFO) code written at Sandia National Laboratories. In addition to an unfolded spectrum, the UFO code also estimates the unfold uncertainty (error) induced by estimated random uncertainties in the data. In UFO the unfold uncertainty is obtained from the error matrix. This built-in estimate has now been compared to error estimates obtained by running the code in a Monte Carlo fashion with prescribed data distributions (Gaussian deviates). In the test problem studied, data were simulated from an arbitrarily chosen blackbody spectrum (10 keV) and a set of overlapping response functions. The data were assumed to have an imprecision of 5{percent} (standard deviation). One hundred random data sets were generated. The built-in estimate of unfold uncertainty agreed with the Monte Carlo estimate to within the statistical resolution of this relatively small sample size (95{percent} confidence level). A possible 10{percent} bias between the two methods was unresolved. The Monte Carlo technique is also useful in underdetermined problems, for which the error matrix method does not apply. UFO has been applied to the diagnosis of low energy x rays emitted by Z-pinch and ion-beam driven hohlraums. {copyright} {ital 1997 American Institute of Physics.}
Verification of unfold error estimates in the unfold operator code
NASA Astrophysics Data System (ADS)
Fehl, D. L.; Biggs, F.
1997-01-01
Spectral unfolding is an inverse mathematical operation that attempts to obtain spectral source information from a set of response functions and data measurements. Several unfold algorithms have appeared over the past 30 years; among them is the unfold operator (UFO) code written at Sandia National Laboratories. In addition to an unfolded spectrum, the UFO code also estimates the unfold uncertainty (error) induced by estimated random uncertainties in the data. In UFO the unfold uncertainty is obtained from the error matrix. This built-in estimate has now been compared to error estimates obtained by running the code in a Monte Carlo fashion with prescribed data distributions (Gaussian deviates). In the test problem studied, data were simulated from an arbitrarily chosen blackbody spectrum (10 keV) and a set of overlapping response functions. The data were assumed to have an imprecision of 5% (standard deviation). One hundred random data sets were generated. The built-in estimate of unfold uncertainty agreed with the Monte Carlo estimate to within the statistical resolution of this relatively small sample size (95% confidence level). A possible 10% bias between the two methods was unresolved. The Monte Carlo technique is also useful in underdetermined problems, for which the error matrix method does not apply. UFO has been applied to the diagnosis of low energy x rays emitted by Z-pinch and ion-beam driven hohlraums.
Accurate estimation of forest carbon stocks by 3-D remote sensing of individual trees.
Omasa, Kenji; Qiu, Guo Yu; Watanuki, Kenichi; Yoshimi, Kenji; Akiyama, Yukihide
2003-03-15
Forests are one of the most important carbon sinks on Earth. However, owing to the complex structure, variable geography, and large area of forests, accurate estimation of forest carbon stocks is still a challenge for both site surveying and remote sensing. For these reasons, the Kyoto Protocol requires the establishment of methodologies for estimating the carbon stocks of forests (Kyoto Protocol, Article 5). A possible solution to this challenge is to remotely measure the carbon stocks of every tree in an entire forest. Here, we present a methodology for estimating carbon stocks of a Japanese cedar forest by using a high-resolution, helicopter-borne 3-dimensional (3-D) scanning lidar system that measures the 3-D canopy structure of every tree in a forest. Results show that a digital image (10-cm mesh) of woody canopy can be acquired. The treetop can be detected automatically with a reasonable accuracy. The absolute error ranges for tree height measurements are within 42 cm. Allometric relationships of height to carbon stocks then permit estimation of total carbon storage by measurement of carbon stocks of every tree. Thus, we suggest that our methodology can be used to accurately estimate the carbon stocks of Japanese cedar forests at a stand scale. Periodic measurements will reveal changes in forest carbon stocks.
Error propagation and scaling for tropical forest biomass estimates.
Chave, Jerome; Condit, Richard; Aguilar, Salomon; Hernandez, Andres; Lao, Suzanne; Perez, Rolando
2004-01-01
The above-ground biomass (AGB) of tropical forests is a crucial variable for ecologists, biogeochemists, foresters and policymakers. Tree inventories are an efficient way of assessing forest carbon stocks and emissions to the atmosphere during deforestation. To make correct inferences about long-term changes in biomass stocks, it is essential to know the uncertainty associated with AGB estimates, yet this uncertainty is rarely evaluated carefully. Here, we quantify four types of uncertainty that could lead to statistical error in AGB estimates: (i) error due to tree measurement; (ii) error due to the choice of an allometric model relating AGB to other tree dimensions; (iii) sampling uncertainty, related to the size of the study plot; (iv) representativeness of a network of small plots across a vast forest landscape. In previous studies, these sources of error were reported but rarely integrated into a consistent framework. We estimate all four terms in a 50 hectare (ha, where 1 ha = 10(4) m2) plot on Barro Colorado Island, Panama, and in a network of 1 ha plots scattered across central Panama. We find that the most important source of error is currently related to the choice of the allometric model. More work should be devoted to improving the predictive power of allometric models for biomass. PMID:15212093
Error estimation for the linearized auto-localization algorithm.
Guevara, Jorge; Jiménez, Antonio R; Prieto, Jose Carlos; Seco, Fernando
2012-01-01
The Linearized Auto-Localization (LAL) algorithm estimates the position of beacon nodes in Local Positioning Systems (LPSs), using only the distance measurements to a mobile node whose position is also unknown. The LAL algorithm calculates the inter-beacon distances, used for the estimation of the beacons' positions, from the linearized trilateration equations. In this paper we propose a method to estimate the propagation of the errors of the inter-beacon distances obtained with the LAL algorithm, based on a first order Taylor approximation of the equations. Since the method depends on such approximation, a confidence parameter τ is defined to measure the reliability of the estimated error. Field evaluations showed that by applying this information to an improved weighted-based auto-localization algorithm (WLAL), the standard deviation of the inter-beacon distances can be improved by more than 30% on average with respect to the original LAL method.
Error estimation for the linearized auto-localization algorithm.
Guevara, Jorge; Jiménez, Antonio R; Prieto, Jose Carlos; Seco, Fernando
2012-01-01
The Linearized Auto-Localization (LAL) algorithm estimates the position of beacon nodes in Local Positioning Systems (LPSs), using only the distance measurements to a mobile node whose position is also unknown. The LAL algorithm calculates the inter-beacon distances, used for the estimation of the beacons' positions, from the linearized trilateration equations. In this paper we propose a method to estimate the propagation of the errors of the inter-beacon distances obtained with the LAL algorithm, based on a first order Taylor approximation of the equations. Since the method depends on such approximation, a confidence parameter τ is defined to measure the reliability of the estimated error. Field evaluations showed that by applying this information to an improved weighted-based auto-localization algorithm (WLAL), the standard deviation of the inter-beacon distances can be improved by more than 30% on average with respect to the original LAL method. PMID:22736965
Error Estimation for the Linearized Auto-Localization Algorithm
Guevara, Jorge; Jiménez, Antonio R.; Prieto, Jose Carlos; Seco, Fernando
2012-01-01
The Linearized Auto-Localization (LAL) algorithm estimates the position of beacon nodes in Local Positioning Systems (LPSs), using only the distance measurements to a mobile node whose position is also unknown. The LAL algorithm calculates the inter-beacon distances, used for the estimation of the beacons’ positions, from the linearized trilateration equations. In this paper we propose a method to estimate the propagation of the errors of the inter-beacon distances obtained with the LAL algorithm, based on a first order Taylor approximation of the equations. Since the method depends on such approximation, a confidence parameter τ is defined to measure the reliability of the estimated error. Field evaluations showed that by applying this information to an improved weighted-based auto-localization algorithm (WLAL), the standard deviation of the inter-beacon distances can be improved by more than 30% on average with respect to the original LAL method. PMID:22736965
Crop area estimation based on remotely-sensed data with an accurate but costly subsample
NASA Technical Reports Server (NTRS)
Gunst, R. F.
1983-01-01
Alternatives to sampling-theory stratified and regression estimators of crop production and timber biomass were examined. An alternative estimator which is viewed as especially promising is the errors-in-variable regression estimator. Investigations established the need for caution with this estimator when the ratio of two error variances is not precisely known.
ORAN- ORBITAL AND GEODETIC PARAMETER ESTIMATION ERROR ANALYSIS
NASA Technical Reports Server (NTRS)
Putney, B.
1994-01-01
The Orbital and Geodetic Parameter Estimation Error Analysis program, ORAN, was developed as a Bayesian least squares simulation program for orbital trajectories. ORAN does not process data, but is intended to compute the accuracy of the results of a data reduction, if measurements of a given accuracy are available and are processed by a minimum variance data reduction program. Actual data may be used to provide the time when a given measurement was available and the estimated noise on that measurement. ORAN is designed to consider a data reduction process in which a number of satellite data periods are reduced simultaneously. If there is more than one satellite in a data period, satellite-to-satellite tracking may be analyzed. The least squares estimator in most orbital determination programs assumes that measurements can be modeled by a nonlinear regression equation containing a function of parameters to be estimated and parameters which are assumed to be constant. The partitioning of parameters into those to be estimated (adjusted) and those assumed to be known (unadjusted) is somewhat arbitrary. For any particular problem, the data will be insufficient to adjust all parameters subject to uncertainty, and some reasonable subset of these parameters is selected for estimation. The final errors in the adjusted parameters may be decomposed into a component due to measurement noise and a component due to errors in the assumed values of the unadjusted parameters. Error statistics associated with the first component are generally evaluated in an orbital determination program. ORAN is used to simulate the orbital determination processing and to compute error statistics associated with the second component. Satellite observations may be simulated with desired noise levels given in many forms including range and range rate, altimeter height, right ascension and declination, direction cosines, X and Y angles, azimuth and elevation, and satellite-to-satellite range and
Condition and Error Estimates in Numerical Matrix Computations
Konstantinov, M. M.; Petkov, P. H.
2008-10-30
This tutorial paper deals with sensitivity and error estimates in matrix computational processes. The main factors determining the accuracy of the result computed in floating--point machine arithmetics are considered. Special attention is paid to the perturbation analysis of matrix algebraic equations and unitary matrix decompositions.
Error analysis for the Fourier domain offset estimation algorithm
NASA Astrophysics Data System (ADS)
Wei, Ling; He, Jieling; He, Yi; Yang, Jinsheng; Li, Xiqi; Shi, Guohua; Zhang, Yudong
2016-02-01
The offset estimation algorithm is crucial for the accuracy of the Shack-Hartmann wave-front sensor. Recently, the Fourier Domain Offset (FDO) algorithm has been proposed for offset estimation. Similar to other algorithms, the accuracy of FDO is affected by noise such as background noise, photon noise, and 'fake' spots. However, no adequate quantitative error analysis has been performed for FDO in previous studies, which is of great importance for practical applications of the FDO. In this study, we quantitatively analysed how the estimation error of FDO is affected by noise based on theoretical deduction, numerical simulation, and experiments. The results demonstrate that the standard deviation of the wobbling error is: (1) inversely proportional to the raw signal to noise ratio, and proportional to the square of the sub-aperture size in the presence of background noise; and (2) proportional to the square root of the intensity in the presence of photonic noise. Furthermore, the upper bound of the estimation error is proportional to the intensity of 'fake' spots and the sub-aperture size. The results of the simulation and experiments agreed with the theoretical analysis.
Concise Formulas for the Standard Errors of Component Loading Estimates.
ERIC Educational Resources Information Center
Ogasawara, Haruhiko
2002-01-01
Derived formulas for the asymptotic standard errors of component loading estimates to cover the cases of principal component analysis for unstandardized and standardized variables with orthogonal and oblique rotations. Used the formulas with a real correlation matrix of 355 subjects who took 12 psychological tests. (SLD)
Bootstrap Standard Error Estimates in Dynamic Factor Analysis
ERIC Educational Resources Information Center
Zhang, Guangjian; Browne, Michael W.
2010-01-01
Dynamic factor analysis summarizes changes in scores on a battery of manifest variables over repeated measurements in terms of a time series in a substantially smaller number of latent factors. Algebraic formulae for standard errors of parameter estimates are more difficult to obtain than in the usual intersubject factor analysis because of the…
Jakeman, J.D. Wildey, T.
2015-01-01
In this paper we present an algorithm for adaptive sparse grid approximations of quantities of interest computed from discretized partial differential equations. We use adjoint-based a posteriori error estimates of the physical discretization error and the interpolation error in the sparse grid to enhance the sparse grid approximation and to drive adaptivity of the sparse grid. Utilizing these error estimates provides significantly more accurate functional values for random samples of the sparse grid approximation. We also demonstrate that alternative refinement strategies based upon a posteriori error estimates can lead to further increases in accuracy in the approximation over traditional hierarchical surplus based strategies. Throughout this paper we also provide and test a framework for balancing the physical discretization error with the stochastic interpolation error of the enhanced sparse grid approximation.
Sensitivity of LIDAR Canopy Height Estimate to Geolocation Error
NASA Astrophysics Data System (ADS)
Tang, H.; Dubayah, R.
2010-12-01
Many factors affect the quality of canopy height structure data derived from space-based lidar such as DESDynI. Among these is geolocation accuracy. Inadequate geolocation information hinders subsequent analyses because a different portion of the canopy is observed relative to what is assumed. This is especially true in mountainous terrain where the effects of slope magnify geolocation errors. Mission engineering design must trade the expense of providing more accurate geolocation with the potential improvement in measurement accuracy. The objective of our work is to assess the effects of small errors in geolocation on subsequent retrievals of maximum canopy height for a varying set of canopy structures and terrains. Dense discrete lidar data from different forest sites (from La Selva Biological Station, Costa Rica, Sierra National Forest, California, and Hubbard Brook and Bartlett Experimental Forests in New Hampshire) are used to simulate DESDynI height retrievals using various geolocation accuracies. Results show that canopy height measurement errors generally increase as the geolocation error increases. Interestingly, most of the height errors are caused by variation of canopy height rather than topography (slope and aspect).
Error estimates and specification parameters for functional renormalization
Schnoerr, David; Boettcher, Igor; Pawlowski, Jan M.; Wetterich, Christof
2013-07-15
We present a strategy for estimating the error of truncated functional flow equations. While the basic functional renormalization group equation is exact, approximated solutions by means of truncations do not only depend on the choice of the retained information, but also on the precise definition of the truncation. Therefore, results depend on specification parameters that can be used to quantify the error of a given truncation. We demonstrate this for the BCS–BEC crossover in ultracold atoms. Within a simple truncation the precise definition of the frequency dependence of the truncated propagator affects the results, indicating a shortcoming of the choice of a frequency independent cutoff function.
Accurate estimation of motion blur parameters in noisy remote sensing image
NASA Astrophysics Data System (ADS)
Shi, Xueyan; Wang, Lin; Shao, Xiaopeng; Wang, Huilin; Tao, Zhong
2015-05-01
The relative motion between remote sensing satellite sensor and objects is one of the most common reasons for remote sensing image degradation. It seriously weakens image data interpretation and information extraction. In practice, point spread function (PSF) should be estimated firstly for image restoration. Identifying motion blur direction and length accurately is very crucial for PSF and restoring image with precision. In general, the regular light-and-dark stripes in the spectrum can be employed to obtain the parameters by using Radon transform. However, serious noise existing in actual remote sensing images often causes the stripes unobvious. The parameters would be difficult to calculate and the error of the result relatively big. In this paper, an improved motion blur parameter identification method to noisy remote sensing image is proposed to solve this problem. The spectrum characteristic of noisy remote sensing image is analyzed firstly. An interactive image segmentation method based on graph theory called GrabCut is adopted to effectively extract the edge of the light center in the spectrum. Motion blur direction is estimated by applying Radon transform on the segmentation result. In order to reduce random error, a method based on whole column statistics is used during calculating blur length. Finally, Lucy-Richardson algorithm is applied to restore the remote sensing images of the moon after estimating blur parameters. The experimental results verify the effectiveness and robustness of our algorithm.
Fast and Accurate Learning When Making Discrete Numerical Estimates.
Sanborn, Adam N; Beierholm, Ulrik R
2016-04-01
Many everyday estimation tasks have an inherently discrete nature, whether the task is counting objects (e.g., a number of paint buckets) or estimating discretized continuous variables (e.g., the number of paint buckets needed to paint a room). While Bayesian inference is often used for modeling estimates made along continuous scales, discrete numerical estimates have not received as much attention, despite their common everyday occurrence. Using two tasks, a numerosity task and an area estimation task, we invoke Bayesian decision theory to characterize how people learn discrete numerical distributions and make numerical estimates. Across three experiments with novel stimulus distributions we found that participants fell between two common decision functions for converting their uncertain representation into a response: drawing a sample from their posterior distribution and taking the maximum of their posterior distribution. While this was consistent with the decision function found in previous work using continuous estimation tasks, surprisingly the prior distributions learned by participants in our experiments were much more adaptive: When making continuous estimates, participants have required thousands of trials to learn bimodal priors, but in our tasks participants learned discrete bimodal and even discrete quadrimodal priors within a few hundred trials. This makes discrete numerical estimation tasks good testbeds for investigating how people learn and make estimates. PMID:27070155
Fast and Accurate Learning When Making Discrete Numerical Estimates.
Sanborn, Adam N; Beierholm, Ulrik R
2016-04-01
Many everyday estimation tasks have an inherently discrete nature, whether the task is counting objects (e.g., a number of paint buckets) or estimating discretized continuous variables (e.g., the number of paint buckets needed to paint a room). While Bayesian inference is often used for modeling estimates made along continuous scales, discrete numerical estimates have not received as much attention, despite their common everyday occurrence. Using two tasks, a numerosity task and an area estimation task, we invoke Bayesian decision theory to characterize how people learn discrete numerical distributions and make numerical estimates. Across three experiments with novel stimulus distributions we found that participants fell between two common decision functions for converting their uncertain representation into a response: drawing a sample from their posterior distribution and taking the maximum of their posterior distribution. While this was consistent with the decision function found in previous work using continuous estimation tasks, surprisingly the prior distributions learned by participants in our experiments were much more adaptive: When making continuous estimates, participants have required thousands of trials to learn bimodal priors, but in our tasks participants learned discrete bimodal and even discrete quadrimodal priors within a few hundred trials. This makes discrete numerical estimation tasks good testbeds for investigating how people learn and make estimates.
Fast and Accurate Learning When Making Discrete Numerical Estimates
Sanborn, Adam N.; Beierholm, Ulrik R.
2016-01-01
Many everyday estimation tasks have an inherently discrete nature, whether the task is counting objects (e.g., a number of paint buckets) or estimating discretized continuous variables (e.g., the number of paint buckets needed to paint a room). While Bayesian inference is often used for modeling estimates made along continuous scales, discrete numerical estimates have not received as much attention, despite their common everyday occurrence. Using two tasks, a numerosity task and an area estimation task, we invoke Bayesian decision theory to characterize how people learn discrete numerical distributions and make numerical estimates. Across three experiments with novel stimulus distributions we found that participants fell between two common decision functions for converting their uncertain representation into a response: drawing a sample from their posterior distribution and taking the maximum of their posterior distribution. While this was consistent with the decision function found in previous work using continuous estimation tasks, surprisingly the prior distributions learned by participants in our experiments were much more adaptive: When making continuous estimates, participants have required thousands of trials to learn bimodal priors, but in our tasks participants learned discrete bimodal and even discrete quadrimodal priors within a few hundred trials. This makes discrete numerical estimation tasks good testbeds for investigating how people learn and make estimates. PMID:27070155
An Anisotropic A posteriori Error Estimator for CFD
NASA Astrophysics Data System (ADS)
Feijóo, Raúl A.; Padra, Claudio; Quintana, Fernando
In this article, a robust anisotropic adaptive algorithm is presented, to solve compressible-flow equations using a stabilized CFD solver and automatic mesh generators. The association includes a mesh generator, a flow solver, and an a posteriori error-estimator code. The estimator was selected among several choices available (Almeida et al. (2000). Comput. Methods Appl. Mech. Engng, 182, 379-400; Borges et al. (1998). "Computational mechanics: new trends and applications". Proceedings of the 4th World Congress on Computational Mechanics, Bs.As., Argentina) giving a powerful computational tool. The main aim is to capture solution discontinuities, in this case, shocks, using the least amount of computational resources, i.e. elements, compatible with a solution of good quality. This leads to high aspect-ratio elements (stretching). To achieve this, a directional error estimator was specifically selected. The numerical results show good behavior of the error estimator, resulting in strongly-adapted meshes in few steps, typically three or four iterations, enough to capture shocks using a moderate and well-distributed amount of elements.
NASA Astrophysics Data System (ADS)
Yang, Que; Wang, Shanshan; Wang, Kai; Zhang, Chunyu; Zhang, Lu; Meng, Qingyu; Zhu, Qiudong
2015-08-01
For normal eyes without history of any ocular surgery, traditional equations for calculating intraocular lens (IOL) power, such as SRK-T, Holladay, Higis, SRK-II, et al., all were relativley accurate. However, for eyes underwent refractive surgeries, such as LASIK, or eyes diagnosed as keratoconus, these equations may cause significant postoperative refractive error, which may cause poor satisfaction after cataract surgery. Although some methods have been carried out to solve this problem, such as Hagis-L equation[1], or using preoperative data (data before LASIK) to estimate K value[2], no precise equations were available for these eyes. Here, we introduced a novel intraocular lens power estimation method by accurate ray tracing with optical design software ZEMAX. Instead of using traditional regression formula, we adopted the exact measured corneal elevation distribution, central corneal thickness, anterior chamber depth, axial length, and estimated effective lens plane as the input parameters. The calculation of intraocular lens power for a patient with keratoconus and another LASIK postoperative patient met very well with their visual capacity after cataract surgery.
Error Estimation and Uncertainty Propagation in Computational Fluid Mechanics
NASA Technical Reports Server (NTRS)
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.
Burnecki, Krzysztof; Kepten, Eldad; Garini, Yuval; Sikora, Grzegorz; Weron, Aleksander
2015-01-01
Accurately characterizing the anomalous diffusion of a tracer particle has become a central issue in biophysics. However, measurement errors raise difficulty in the characterization of single trajectories, which is usually performed through the time-averaged mean square displacement (TAMSD). In this paper, we study a fractionally integrated moving average (FIMA) process as an appropriate model for anomalous diffusion data with measurement errors. We compare FIMA and traditional TAMSD estimators for the anomalous diffusion exponent. The ability of the FIMA framework to characterize dynamics in a wide range of anomalous exponents and noise levels through the simulation of a toy model (fractional Brownian motion disturbed by Gaussian white noise) is discussed. Comparison to the TAMSD technique, shows that FIMA estimation is superior in many scenarios. This is expected to enable new measurement regimes for single particle tracking (SPT) experiments even in the presence of high measurement errors. PMID:26065707
NASA Astrophysics Data System (ADS)
Burnecki, Krzysztof; Kepten, Eldad; Garini, Yuval; Sikora, Grzegorz; Weron, Aleksander
2015-06-01
Accurately characterizing the anomalous diffusion of a tracer particle has become a central issue in biophysics. However, measurement errors raise difficulty in the characterization of single trajectories, which is usually performed through the time-averaged mean square displacement (TAMSD). In this paper, we study a fractionally integrated moving average (FIMA) process as an appropriate model for anomalous diffusion data with measurement errors. We compare FIMA and traditional TAMSD estimators for the anomalous diffusion exponent. The ability of the FIMA framework to characterize dynamics in a wide range of anomalous exponents and noise levels through the simulation of a toy model (fractional Brownian motion disturbed by Gaussian white noise) is discussed. Comparison to the TAMSD technique, shows that FIMA estimation is superior in many scenarios. This is expected to enable new measurement regimes for single particle tracking (SPT) experiments even in the presence of high measurement errors.
Motion estimation performance models with application to hardware error tolerance
NASA Astrophysics Data System (ADS)
Cheong, Hye-Yeon; Ortega, Antonio
2007-01-01
The progress of VLSI technology towards deep sub-micron feature sizes, e.g., sub-100 nanometer technology, has created a growing impact of hardware defects and fabrication process variability, which lead to reductions in yield rate. To address these problems, a new approach, system-level error tolerance (ET), has been recently introduced. Considering that a significant percentage of the entire chip production is discarded due to minor imperfections, this approach is based on accepting imperfect chips that introduce imperceptible/acceptable system-level degradation; this leads to increases in overall effective yield. In this paper, we investigate the impact of hardware faults on the video compression performance, with a focus on the motion estimation (ME) process. More specifically, we provide an analytical formulation of the impact of single and multiple stuck-at-faults within ME computation. We further present a model for estimating the system-level performance degradation due to such faults, which can be used for the error tolerance based decision strategy of accepting a given faulty chip. We also show how different faults and ME search algorithms compare in terms of error tolerance and define the characteristics of search algorithm that lead to increased error tolerance. Finally, we show that different hardware architectures performing the same metric computation have different error tolerance characteristics and we present the optimal ME hardware architecture in terms of error tolerance. While we focus on ME hardware, our work could also applied to systems (e.g., classifiers, matching pursuits, vector quantization) where a selection is made among several alternatives (e.g., class label, basis function, quantization codeword) based on which choice minimizes an additive metric of interest.
Modeling the Error of Estimated River Discharge in the Eurasian Pan-Arctic
NASA Astrophysics Data System (ADS)
Shiklomanov, A.; Lammers, R.; Yakovleva, T.; Vorosmarty, C.
2004-05-01
Recent work by Peterson et al. (2002) has shown increases in the river discharge to the Arctic Ocean of the six largest Eurasian Rivers to be 7% (2.0 +/- 0.7 km3/year) from 1936 to 1999. As with most measures of the natural environment this increase represents the trend of a highly variable time series containing annual, seasonal, and daily cycles. Accurate estimates of the error associated with these time and space aggregated annual series have not been well characterized. We seek here to define the expected error surrounding the time series of annual river discharge in the pan-Arctic region. Using standard hydrometric data along with information on the i) frequency and precision of measurements, ii) characteristics of river channel capacity, and iii) method of discharge computation, we develop a model of error for river discharge. We focus on the random error in the daily river discharge of the major down-stream gauges of the pan-Arctic drainage system. A simplified method to define possible errors in the average discharge data has been developed to estimate the reliability of monthly and yearly data over the long-term period of observation (up to 64 years). Results to date have shown that the accuracy of daily discharge estimates for the large Eurasian rivers is highly variable throughout any given year. Maximum errors, exceeding 40% for some rivers take place during ice and backwater conditions when the stage-discharge rating curves cannot be applied. The annual discharge over the long-term is more accurate with 3-10% error. This error range for annual data holds even for more recent periods, such as for the last 15 years, when the actual number of discharge measurements declined significantly. With this error model we have found the estimated error for river discharge in the six largest Eurasian watersheds can be reduced by up to 50% to 2.0 +/- 0.4 km3/year over the previous estimates
Bioaccessibility tests accurately estimate bioavailability of lead to quail
Technology Transfer Automated Retrieval System (TEKTRAN)
Hazards of soil-borne Pb to wild birds may be more accurately quantified if the bioavailability of that Pb is known. To better understand the bioavailability of Pb, we incorporated Pb-contaminated soils or Pb acetate into diets for Japanese quail (Coturnix japonica), fed the quail for 15 days, and ...
BIOACCESSIBILITY TESTS ACCURATELY ESTIMATE BIOAVAILABILITY OF LEAD TO QUAIL
Hazards of soil-borne Pb to wild birds may be more accurately quantified if the bioavailability of that Pb is known. To better understand the bioavailability of Pb to birds, we measured blood Pb concentrations in Japanese quail (Coturnix japonica) fed diets containing Pb-contami...
NASA Technical Reports Server (NTRS)
Zhou, Daniel K.; Larar, Allen M.; Liu, Xu; Smith, William L.; Strow, Larry, L.
2013-01-01
Great effort has been devoted towards validating geophysical parameters retrieved from ultraspectral infrared radiances obtained from satellite remote sensors. An error consistency analysis scheme (ECAS), utilizing fast radiative transfer model (RTM) forward and inverse calculations, has been developed to estimate the error budget in terms of mean difference and standard deviation of error in both spectral radiance and retrieval domains. The retrieval error is assessed through ECAS without relying on other independent measurements such as radiosonde data. ECAS establishes a link between the accuracies of radiances and retrieved geophysical parameters. ECAS can be applied to measurements from any ultraspectral instrument and any retrieval scheme with its associated RTM. In this manuscript, ECAS is described and demonstrated with measurements from the MetOp-A satellite Infrared Atmospheric Sounding Interferometer (IASI). This scheme can be used together with other validation methodologies to give a more definitive characterization of the error and/or uncertainty of geophysical parameters retrieved from ultraspectral radiances observed from current and future satellite remote sensors such as IASI, the Atmospheric Infrared Sounder (AIRS), and the Cross-track Infrared Sounder (CrIS).
GPS/DR Error Estimation for Autonomous Vehicle Localization.
Lee, Byung-Hyun; Song, Jong-Hwa; Im, Jun-Hyuck; Im, Sung-Hyuck; Heo, Moon-Beom; Jee, Gyu-In
2015-08-21
Autonomous vehicles require highly reliable navigation capabilities. For example, a lane-following method cannot be applied in an intersection without lanes, and since typical lane detection is performed using a straight-line model, errors can occur when the lateral distance is estimated in curved sections due to a model mismatch. Therefore, this paper proposes a localization method that uses GPS/DR error estimation based on a lane detection method with curved lane models, stop line detection, and curve matching in order to improve the performance during waypoint following procedures. The advantage of using the proposed method is that position information can be provided for autonomous driving through intersections, in sections with sharp curves, and in curved sections following a straight section. The proposed method was applied in autonomous vehicles at an experimental site to evaluate its performance, and the results indicate that the positioning achieved accuracy at the sub-meter level.
GPS/DR Error Estimation for Autonomous Vehicle Localization
Lee, Byung-Hyun; Song, Jong-Hwa; Im, Jun-Hyuck; Im, Sung-Hyuck; Heo, Moon-Beom; Jee, Gyu-In
2015-01-01
Autonomous vehicles require highly reliable navigation capabilities. For example, a lane-following method cannot be applied in an intersection without lanes, and since typical lane detection is performed using a straight-line model, errors can occur when the lateral distance is estimated in curved sections due to a model mismatch. Therefore, this paper proposes a localization method that uses GPS/DR error estimation based on a lane detection method with curved lane models, stop line detection, and curve matching in order to improve the performance during waypoint following procedures. The advantage of using the proposed method is that position information can be provided for autonomous driving through intersections, in sections with sharp curves, and in curved sections following a straight section. The proposed method was applied in autonomous vehicles at an experimental site to evaluate its performance, and the results indicate that the positioning achieved accuracy at the sub-meter level. PMID:26307997
GPS/DR Error Estimation for Autonomous Vehicle Localization.
Lee, Byung-Hyun; Song, Jong-Hwa; Im, Jun-Hyuck; Im, Sung-Hyuck; Heo, Moon-Beom; Jee, Gyu-In
2015-01-01
Autonomous vehicles require highly reliable navigation capabilities. For example, a lane-following method cannot be applied in an intersection without lanes, and since typical lane detection is performed using a straight-line model, errors can occur when the lateral distance is estimated in curved sections due to a model mismatch. Therefore, this paper proposes a localization method that uses GPS/DR error estimation based on a lane detection method with curved lane models, stop line detection, and curve matching in order to improve the performance during waypoint following procedures. The advantage of using the proposed method is that position information can be provided for autonomous driving through intersections, in sections with sharp curves, and in curved sections following a straight section. The proposed method was applied in autonomous vehicles at an experimental site to evaluate its performance, and the results indicate that the positioning achieved accuracy at the sub-meter level. PMID:26307997
Divergent estimation error in portfolio optimization and in linear regression
NASA Astrophysics Data System (ADS)
Kondor, I.; Varga-Haszonits, I.
2008-08-01
The problem of estimation error in portfolio optimization is discussed, in the limit where the portfolio size N and the sample size T go to infinity such that their ratio is fixed. The estimation error strongly depends on the ratio N/T and diverges for a critical value of this parameter. This divergence is the manifestation of an algorithmic phase transition, it is accompanied by a number of critical phenomena, and displays universality. As the structure of a large number of multidimensional regression and modelling problems is very similar to portfolio optimization, the scope of the above observations extends far beyond finance, and covers a large number of problems in operations research, machine learning, bioinformatics, medical science, economics, and technology.
Jakeman, J. D.; Wildey, T.
2015-01-01
In this paper we present an algorithm for adaptive sparse grid approximations of quantities of interest computed from discretized partial differential equations. We use adjoint-based a posteriori error estimates of the interpolation error in the sparse grid to enhance the sparse grid approximation and to drive adaptivity. We show that utilizing these error estimates provides significantly more accurate functional values for random samples of the sparse grid approximation. We also demonstrate that alternative refinement strategies based upon a posteriori error estimates can lead to further increases in accuracy in the approximation over traditional hierarchical surplus based strategies. Throughout this papermore » we also provide and test a framework for balancing the physical discretization error with the stochastic interpolation error of the enhanced sparse grid approximation.« less
Jakeman, J. D.; Wildey, T.
2015-01-01
In this paper we present an algorithm for adaptive sparse grid approximations of quantities of interest computed from discretized partial differential equations. We use adjoint-based a posteriori error estimates of the interpolation error in the sparse grid to enhance the sparse grid approximation and to drive adaptivity. We show that utilizing these error estimates provides significantly more accurate functional values for random samples of the sparse grid approximation. We also demonstrate that alternative refinement strategies based upon a posteriori error estimates can lead to further increases in accuracy in the approximation over traditional hierarchical surplus based strategies. Throughout this paper we also provide and test a framework for balancing the physical discretization error with the stochastic interpolation error of the enhanced sparse grid approximation.
Real-Time Baseline Error Estimation and Correction for GNSS/Strong Motion Seismometer Integration
NASA Astrophysics Data System (ADS)
Li, C. Y. N.; Groves, P. D.; Ziebart, M. K.
2014-12-01
Accurate and rapid estimation of permanent surface displacement is required immediately after a slip event for earthquake monitoring or tsunami early warning. It is difficult to achieve the necessary accuracy and precision at high- and low-frequencies using GNSS or seismometry alone. GNSS and seismic sensors can be integrated to overcome the limitations of each. Kalman filter algorithms with displacement and velocity states have been developed to combine GNSS and accelerometer observations to obtain the optimal displacement solutions. However, the sawtooth-like phenomena caused by the bias or tilting of the sensor decrease the accuracy of the displacement estimates. A three-dimensional Kalman filter algorithm with an additional baseline error state has been developed. An experiment with both a GNSS receiver and a strong motion seismometer mounted on a movable platform and subjected to known displacements was carried out. The results clearly show that the additional baseline error state enables the Kalman filter to estimate the instrument's sensor bias and tilt effects and correct the state estimates in real time. Furthermore, the proposed Kalman filter algorithm has been validated with data sets from the 2010 Mw 7.2 El Mayor-Cucapah Earthquake. The results indicate that the additional baseline error state can not only eliminate the linear and quadratic drifts but also reduce the sawtooth-like effects from the displacement solutions. The conventional zero-mean baseline-corrected results cannot show the permanent displacements after an earthquake; the two-state Kalman filter can only provide stable and optimal solutions if the strong motion seismometer had not been moved or tilted by the earthquake. Yet the proposed Kalman filter can achieve the precise and accurate displacements by estimating and correcting for the baseline error at each epoch. The integration filters out noise-like distortions and thus improves the real-time detection and measurement capability
Interpolation Error Estimates for Mean Value Coordinates over Convex Polygons.
Rand, Alexander; Gillette, Andrew; Bajaj, Chandrajit
2013-08-01
In a similar fashion to estimates shown for Harmonic, Wachspress, and Sibson coordinates in [Gillette et al., AiCM, to appear], we prove interpolation error estimates for the mean value coordinates on convex polygons suitable for standard finite element analysis. Our analysis is based on providing a uniform bound on the gradient of the mean value functions for all convex polygons of diameter one satisfying certain simple geometric restrictions. This work makes rigorous an observed practical advantage of the mean value coordinates: unlike Wachspress coordinates, the gradient of the mean value coordinates does not become large as interior angles of the polygon approach π.
Model error estimation and correction by solving a inverse problem
NASA Astrophysics Data System (ADS)
Xue, Haile
2016-04-01
Nowadays, the weather forecasts and climate predictions are increasingly relied on numerical models. Yet, errors inevitably exist in model due to the imperfect numeric and parameterizations. From the practical point of view, model correction is an efficient strategy. Despite of the different complexity of forecast error correction algorithms, the general idea is to estimate the forecast errors by considering the NWP as a direct problem. Chou (1974) suggested an alternative view by considering the NWP as an inverse problem. The model error tendency term (ME) due to the model deficiency is assumed as an unknown term in NWP model, which can be discretized into short intervals (for example 6 hour) and considered as a constant or linear form in each interval. Given the past re-analyses and NWP model, the discretized MEs in the past intervals can be solved iteratively as a constant or linear-increased tendency term in each interval. These MEs can be further used as the online corrections. In this study, an iterative method for obtaining the MEs in past intervals was presented, and its convergence had been confirmed with sets of experiments in the global forecast system of the Global and Regional Assimilation and Prediction System (GRAPES-GFS) for July-August (JA) 2009 and January-February (JF) 2010. Then these MEs were used to get online model corretions based of systematic errors of GRAPES-GFS for July 2009 and January 2010. The data sets associated with initial condition and sea surface temperature (SST) used in this study are both based on NCEP final (FNL) data. According to the iterative numerical experiments, the following key conclusions can be drawn:(1) Batches of iteration test results indicated that the hour 6 forecast errors were reduced to 10% of their original value after 20 steps of iteration.(2) By offlinely comparing the error corrections estimated by MEs to the mean forecast errors, the patterns of estimated errors were considered to agree well with those
Quantifying Error in the CMORPH Satellite Precipitation Estimates
NASA Astrophysics Data System (ADS)
Xu, B.; Yoo, S.; Xie, P.
2010-12-01
As part of the collaboration between China Meteorological Administration (CMA) National Meteorological Information Centre (NMIC) and NOAA Climate Prediction Center (CPC), a new system is being developed to construct hourly precipitation analysis on a 0.25olat/lon grid over China by merging information derived from gauge observations and CMORPH satellite precipitation estimates. Foundation to the development of the gauge-satellite merging algorithm is the definition of the systematic and random error inherent in the CMORPH satellite precipitation estimates. In this study, we quantify the CMORPH error structures through comparisons against a gauge-based analysis of hourly precipitation derived from station reports from a dense network over China. First, systematic error (bias) of the CMORPH satellite estimates are examined with co-located hourly gauge precipitation analysis over 0.25olat/lon grid boxes with at least one reporting station. The CMORPH exhibits biases of regional variations showing over-estimates over eastern China, and seasonal changes with over-/under-estimates during warm/cold seasons. The CMORPH bias presents range-dependency. In general, the CMORPH tends to over-/under-estimate weak / strong rainfall. The bias, when expressed in the form of ratio between the gauge observations and the CMORPH satellite estimates, increases with the rainfall intensity but tends to saturate at a certain level for high rainfall. Based on the above results, a prototype algorithm is developed to remove the CMORPH bias through matching the PDF of original CMORPH estimates against that of the gauge analysis using data pairs co-located over grid boxes with at least one reporting gauge over a 30-day period ending at the target date. The spatial domain for collecting the co-located data pairs is expanded so that at least 5000 pairs of data are available to ensure statistical availability. The bias-corrected CMORPH is then compared against the gauge data to quantify the
Rigorous covariance propagation of geoid errors to geodetic MDT estimates
NASA Astrophysics Data System (ADS)
Pail, R.; Albertella, A.; Fecher, T.; Savcenko, R.
2012-04-01
The mean dynamic topography (MDT) is defined as the difference between the mean sea surface (MSS) derived from satellite altimetry, averaged over several years, and the static geoid. Assuming geostrophic conditions, from the MDT the ocean surface velocities as important component of global ocean circulation can be derived from it. Due to the availability of GOCE gravity field models, for the very first time MDT can now be derived solely from satellite observations (altimetry and gravity) down to spatial length-scales of 100 km and even below. Global gravity field models, parameterized in terms of spherical harmonic coefficients, are complemented by the full variance-covariance matrix (VCM). Therefore, for the geoid component a realistic statistical error estimate is available, while the error description of the altimetric component is still an open issue and is, if at all, attacked empirically. In this study we make the attempt to perform, based on the full gravity VCM, rigorous error propagation to derived geostrophic surface velocities, thus also considering all correlations. For the definition of the static geoid we use the third release of the time-wise GOCE model, as well as the satellite-only combination model GOCO03S. In detail, we will investigate the velocity errors resulting from the geoid component in dependence of the harmonic degree, and the impact of using/no using covariances on the MDT errors and its correlations. When deriving an MDT, it is spectrally filtered to a certain maximum degree, which is usually driven by the signal content of the geoid model, by applying isotropic or non-isotropic filters. Since this filtering is acting also on the geoid component, the consistent integration of this filter process into the covariance propagation shall be performed, and its impact shall be quantified. The study will be performed for MDT estimates in specific test areas of particular oceanographic interest.
A Foundation for the Accurate Prediction of the Soft Error Vulnerability of Scientific Applications
Bronevetsky, G; de Supinski, B; Schulz, M
2009-02-13
Understanding the soft error vulnerability of supercomputer applications is critical as these systems are using ever larger numbers of devices that have decreasing feature sizes and, thus, increasing frequency of soft errors. As many large scale parallel scientific applications use BLAS and LAPACK linear algebra routines, the soft error vulnerability of these methods constitutes a large fraction of the applications overall vulnerability. This paper analyzes the vulnerability of these routines to soft errors by characterizing how their outputs are affected by injected errors and by evaluating several techniques for predicting how errors propagate from the input to the output of each routine. The resulting error profiles can be used to understand the fault vulnerability of full applications that use these routines.
NASA Technical Reports Server (NTRS)
Consiglio, Maria C.; Hoadley, Sherwood T.; Allen, B. Danette
2009-01-01
Wind prediction errors are known to affect the performance of automated air traffic management tools that rely on aircraft trajectory predictions. In particular, automated separation assurance tools, planned as part of the NextGen concept of operations, must be designed to account and compensate for the impact of wind prediction errors and other system uncertainties. In this paper we describe a high fidelity batch simulation study designed to estimate the separation distance required to compensate for the effects of wind-prediction errors throughout increasing traffic density on an airborne separation assistance system. These experimental runs are part of the Safety Performance of Airborne Separation experiment suite that examines the safety implications of prediction errors and system uncertainties on airborne separation assurance systems. In this experiment, wind-prediction errors were varied between zero and forty knots while traffic density was increased several times current traffic levels. In order to accurately measure the full unmitigated impact of wind-prediction errors, no uncertainty buffers were added to the separation minima. The goal of the study was to measure the impact of wind-prediction errors in order to estimate the additional separation buffers necessary to preserve separation and to provide a baseline for future analyses. Buffer estimations from this study will be used and verified in upcoming safety evaluation experiments under similar simulation conditions. Results suggest that the strategic airborne separation functions exercised in this experiment can sustain wind prediction errors up to 40kts at current day air traffic density with no additional separation distance buffer and at eight times the current day with no more than a 60% increase in separation distance buffer.
Impacts of Characteristics of Errors in Radar Rainfall Estimates for Rainfall-Runoff Simulation
NASA Astrophysics Data System (ADS)
KO, D.; PARK, T.; Lee, T. S.; Shin, J. Y.; Lee, D.
2015-12-01
For flood prediction, weather radar has been commonly employed to measure the amount of precipitation and its spatial distribution. However, estimated rainfall from the radar contains uncertainty caused by its errors such as beam blockage and ground clutter. Even though, previous studies have been focused on removing error of radar data, it is crucial to evaluate runoff volumes which are influenced primarily by the radar errors. Furthermore, resolution of rainfall modeled by previous studies for rainfall uncertainty analysis or distributed hydrological simulation are quite coarse to apply to real application. Therefore, in the current study, we tested the effects of radar rainfall errors on rainfall runoff with a high resolution approach, called spatial error model (SEM). In the current study, the synthetic generation of random and cross-correlated radar errors were employed as SEM. A number of events for the Nam River dam region were tested to investigate the peak discharge from a basin according to error variance. The results indicate that the dependent error brings much higher variations in peak discharge than the independent random error. To further investigate the effect of the magnitude of cross-correlation between radar errors, the different magnitudes of spatial cross-correlations were employed for the rainfall-runoff simulation. The results demonstrate that the stronger correlation leads to higher variation of peak discharge and vice versa. We conclude that the error structure in radar rainfall estimates significantly affects on predicting the runoff peak. Therefore, the efforts must take into consideration on not only removing radar rainfall error itself but also weakening the cross-correlation structure of radar errors in order to forecast flood events more accurately. Acknowledgements This research was supported by a grant from a Strategic Research Project (Development of Flood Warning and Snowfall Estimation Platform Using Hydrological Radars), which
Removing the thermal component from heart rate provides an accurate VO2 estimation in forest work.
Dubé, Philippe-Antoine; Imbeau, Daniel; Dubeau, Denise; Lebel, Luc; Kolus, Ahmet
2016-05-01
Heart rate (HR) was monitored continuously in 41 forest workers performing brushcutting or tree planting work. 10-min seated rest periods were imposed during the workday to estimate the HR thermal component (ΔHRT) per Vogt et al. (1970, 1973). VO2 was measured using a portable gas analyzer during a morning submaximal step-test conducted at the work site, during a work bout over the course of the day (range: 9-74 min), and during an ensuing 10-min rest pause taken at the worksite. The VO2 estimated, from measured HR and from corrected HR (thermal component removed), were compared to VO2 measured during work and rest. Varied levels of HR thermal component (ΔHRTavg range: 0-38 bpm) originating from a wide range of ambient thermal conditions, thermal clothing insulation worn, and physical load exerted during work were observed. Using raw HR significantly overestimated measured work VO2 by 30% on average (range: 1%-64%). 74% of VO2 prediction error variance was explained by the HR thermal component. VO2 estimated from corrected HR, was not statistically different from measured VO2. Work VO2 can be estimated accurately in the presence of thermal stress using Vogt et al.'s method, which can be implemented easily by the practitioner with inexpensive instruments.
Accurate biopsy-needle depth estimation in limited-angle tomography using multi-view geometry
NASA Astrophysics Data System (ADS)
van der Sommen, Fons; Zinger, Sveta; de With, Peter H. N.
2016-03-01
Recently, compressed-sensing based algorithms have enabled volume reconstruction from projection images acquired over a relatively small angle (θ < 20°). These methods enable accurate depth estimation of surgical tools with respect to anatomical structures. However, they are computationally expensive and time consuming, rendering them unattractive for image-guided interventions. We propose an alternative approach for depth estimation of biopsy needles during image-guided interventions, in which we split the problem into two parts and solve them independently: needle-depth estimation and volume reconstruction. The complete proposed system consists of the previous two steps, preceded by needle extraction. First, we detect the biopsy needle in the projection images and remove it by interpolation. Next, we exploit epipolar geometry to find point-to-point correspondences in the projection images to triangulate the 3D position of the needle in the volume. Finally, we use the interpolated projection images to reconstruct the local anatomical structures and indicate the position of the needle within this volume. For validation of the algorithm, we have recorded a full CT scan of a phantom with an inserted biopsy needle. The performance of our approach ranges from a median error of 2.94 mm for an distributed viewing angle of 1° down to an error of 0.30 mm for an angle larger than 10°. Based on the results of this initial phantom study, we conclude that multi-view geometry offers an attractive alternative to time-consuming iterative methods for the depth estimation of surgical tools during C-arm-based image-guided interventions.
Local error estimates for discontinuous solutions of nonlinear hyperbolic equations
NASA Technical Reports Server (NTRS)
Tadmor, Eitan
1989-01-01
Let u(x,t) be the possibly discontinuous entropy solution of a nonlinear scalar conservation law with smooth initial data. Suppose u sub epsilon(x,t) is the solution of an approximate viscosity regularization, where epsilon greater than 0 is the small viscosity amplitude. It is shown that by post-processing the small viscosity approximation u sub epsilon, pointwise values of u and its derivatives can be recovered with an error as close to epsilon as desired. The analysis relies on the adjoint problem of the forward error equation, which in this case amounts to a backward linear transport with discontinuous coefficients. The novelty of this approach is to use a (generalized) E-condition of the forward problem in order to deduce a W(exp 1,infinity) energy estimate for the discontinuous backward transport equation; this, in turn, leads one to an epsilon-uniform estimate on moments of the error u(sub epsilon) - u. This approach does not follow the characteristics and, therefore, applies mutatis mutandis to other approximate solutions such as E-difference schemes.
Close-range radar rainfall estimation and error analysis
NASA Astrophysics Data System (ADS)
van de Beek, C. Z.; Leijnse, H.; Hazenberg, P.; Uijlenhoet, R.
2016-08-01
Quantitative precipitation estimation (QPE) using ground-based weather radar is affected by many sources of error. The most important of these are (1) radar calibration, (2) ground clutter, (3) wet-radome attenuation, (4) rain-induced attenuation, (5) vertical variability in rain drop size distribution (DSD), (6) non-uniform beam filling and (7) variations in DSD. This study presents an attempt to separate and quantify these sources of error in flat terrain very close to the radar (1-2 km), where (4), (5) and (6) only play a minor role. Other important errors exist, like beam blockage, WLAN interferences and hail contamination and are briefly mentioned, but not considered in the analysis. A 3-day rainfall event (25-27 August 2010) that produced more than 50 mm of precipitation in De Bilt, the Netherlands, is analyzed using radar, rain gauge and disdrometer data. Without any correction, it is found that the radar severely underestimates the total rain amount (by more than 50 %). The calibration of the radar receiver is operationally monitored by analyzing the received power from the sun. This turns out to cause a 1 dB underestimation. The operational clutter filter applied by KNMI is found to incorrectly identify precipitation as clutter, especially at near-zero Doppler velocities. An alternative simple clutter removal scheme using a clear sky clutter map improves the rainfall estimation slightly. To investigate the effect of wet-radome attenuation, stable returns from buildings close to the radar are analyzed. It is shown that this may have caused an underestimation of up to 4 dB. Finally, a disdrometer is used to derive event and intra-event specific Z-R relations due to variations in the observed DSDs. Such variations may result in errors when applying the operational Marshall-Palmer Z-R relation. Correcting for all of these effects has a large positive impact on the radar-derived precipitation estimates and yields a good match between radar QPE and gauge
NASA Technical Reports Server (NTRS)
Lu, Hui-Ling; Cheng, Victor H. L.; Leitner, Jesse A.; Carpenter, Kenneth G.
2004-01-01
Long-baseline space interferometers involving formation flying of multiple spacecraft hold great promise as future space missions for high-resolution imagery. The major challenge of obtaining high-quality interferometric synthesized images from long-baseline space interferometers is to control these spacecraft and their optics payloads in the specified configuration accurately. In this paper, we describe our effort toward fine control of long-baseline space interferometers without resorting to additional sensing equipment. We present an estimation procedure that effectively extracts relative x/y translational exit pupil aperture deviations from the raw interferometric image with small estimation errors.
Accurate feature detection and estimation using nonlinear and multiresolution analysis
NASA Astrophysics Data System (ADS)
Rudin, Leonid; Osher, Stanley
1994-11-01
A program for feature detection and estimation using nonlinear and multiscale analysis was completed. The state-of-the-art edge detection was combined with multiscale restoration (as suggested by the first author) and robust results in the presence of noise were obtained. Successful applications to numerous images of interest to DOD were made. Also, a new market in the criminal justice field was developed, based in part, on this work.
Simulation model accurately estimates total dietary iodine intake.
Verkaik-Kloosterman, Janneke; van 't Veer, Pieter; Ocké, Marga C
2009-07-01
One problem with estimating iodine intake is the lack of detailed data about the discretionary use of iodized kitchen salt and iodization of industrially processed foods. To be able to take into account these uncertainties in estimating iodine intake, a simulation model combining deterministic and probabilistic techniques was developed. Data from the Dutch National Food Consumption Survey (1997-1998) and an update of the Food Composition database were used to simulate 3 different scenarios: Dutch iodine legislation until July 2008, Dutch iodine legislation after July 2008, and a potential future situation. Results from studies measuring iodine excretion during the former legislation are comparable with the iodine intakes estimated with our model. For both former and current legislation, iodine intake was adequate for a large part of the Dutch population, but some young children (<5%) were at risk of intakes that were too low. In the scenario of a potential future situation using lower salt iodine levels, the percentage of the Dutch population with intakes that were too low increased (almost 10% of young children). To keep iodine intakes adequate, salt iodine levels should not be decreased, unless many more foods will contain iodized salt. Our model should be useful in predicting the effects of food reformulation or fortification on habitual nutrient intakes.
Error Estimation of An Ensemble Statistical Seasonal Precipitation Prediction Model
NASA Technical Reports Server (NTRS)
Shen, Samuel S. P.; Lau, William K. M.; Kim, Kyu-Myong; Li, Gui-Long
2001-01-01
This NASA Technical Memorandum describes an optimal ensemble canonical correlation forecasting model for seasonal precipitation. Each individual forecast is based on the canonical correlation analysis (CCA) in the spectral spaces whose bases are empirical orthogonal functions (EOF). The optimal weights in the ensemble forecasting crucially depend on the mean square error of each individual forecast. An estimate of the mean square error of a CCA prediction is made also using the spectral method. The error is decomposed onto EOFs of the predictand and decreases linearly according to the correlation between the predictor and predictand. Since new CCA scheme is derived for continuous fields of predictor and predictand, an area-factor is automatically included. Thus our model is an improvement of the spectral CCA scheme of Barnett and Preisendorfer. The improvements include (1) the use of area-factor, (2) the estimation of prediction error, and (3) the optimal ensemble of multiple forecasts. The new CCA model is applied to the seasonal forecasting of the United States (US) precipitation field. The predictor is the sea surface temperature (SST). The US Climate Prediction Center's reconstructed SST is used as the predictor's historical data. The US National Center for Environmental Prediction's optimally interpolated precipitation (1951-2000) is used as the predictand's historical data. Our forecast experiments show that the new ensemble canonical correlation scheme renders a reasonable forecasting skill. For example, when using September-October-November SST to predict the next season December-January-February precipitation, the spatial pattern correlation between the observed and predicted are positive in 46 years among the 50 years of experiments. The positive correlations are close to or greater than 0.4 in 29 years, which indicates excellent performance of the forecasting model. The forecasting skill can be further enhanced when several predictors are used.
Effects of measurement error on estimating biological half-life
Caudill, S.P.; Pirkle, J.L.; Michalek, J.E. )
1992-10-01
Direct computation of the observed biological half-life of a toxic compound in a person can lead to an undefined estimate when subsequent concentration measurements are greater than or equal to previous measurements. The likelihood of such an occurrence depends upon the length of time between measurements and the variance (intra-subject biological and inter-sample analytical) associated with the measurements. If the compound is lipophilic the subject's percentage of body fat at the times of measurement can also affect this likelihood. We present formulas for computing a model-predicted half-life estimate and its variance; and we derive expressions for the effect of sample size, measurement error, time between measurements, and any relevant covariates on the variability in model-predicted half-life estimates. We also use statistical modeling to estimate the probability of obtaining an undefined half-life estimate and to compute the expected number of undefined half-life estimates for a sample from a study population. Finally, we illustrate our methods using data from a study of 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) exposure among 36 members of Operation Ranch Hand, the Air Force unit responsible for the aerial spraying of Agent Orange in Vietnam.
Standard Errors of Estimated Latent Variable Scores with Estimated Structural Parameters
ERIC Educational Resources Information Center
Hoshino, Takahiro; Shigemasu, Kazuo
2008-01-01
The authors propose a concise formula to evaluate the standard error of the estimated latent variable score when the true values of the structural parameters are not known and must be estimated. The formula can be applied to factor scores in factor analysis or ability parameters in item response theory, without bootstrap or Markov chain Monte…
Verification of unfold error estimates in the UFO code
Fehl, D.L.; Biggs, F.
1996-07-01
Spectral unfolding is an inverse mathematical operation which attempts to obtain spectral source information from a set of tabulated response functions and data measurements. Several unfold algorithms have appeared over the past 30 years; among them is the UFO (UnFold Operator) code. In addition to an unfolded spectrum, UFO also estimates the unfold uncertainty (error) induced by running the code in a Monte Carlo fashion with prescribed data distributions (Gaussian deviates). In the problem studied, data were simulated from an arbitrarily chosen blackbody spectrum (10 keV) and a set of overlapping response functions. The data were assumed to have an imprecision of 5% (standard deviation). 100 random data sets were generated. The built-in estimate of unfold uncertainty agreed with the Monte Carlo estimate to within the statistical resolution of this relatively small sample size (95% confidence level). A possible 10% bias between the two methods was unresolved. The Monte Carlo technique is also useful in underdetemined problems, for which the error matrix method does not apply. UFO has been applied to the diagnosis of low energy x rays emitted by Z-Pinch and ion-beam driven hohlraums.
Bootstrap Standard Error Estimates in Dynamic Factor Analysis.
Zhang, Guangjian; Browne, Michael W
2010-05-28
Dynamic factor analysis summarizes changes in scores on a battery of manifest variables over repeated measurements in terms of a time series in a substantially smaller number of latent factors. Algebraic formulae for standard errors of parameter estimates are more difficult to obtain than in the usual intersubject factor analysis because of the interdependence of successive observations. Bootstrap methods can fill this need, however. The standard bootstrap of individual timepoints is not appropriate because it destroys their order in time and consequently gives incorrect standard error estimates. Two bootstrap procedures that are appropriate for dynamic factor analysis are described. The moving block bootstrap breaks down the original time series into blocks and draws samples of blocks instead of individual timepoints. A parametric bootstrap is essentially a Monte Carlo study in which the population parameters are taken to be estimates obtained from the available sample. These bootstrap procedures are demonstrated using 103 days of affective mood self-ratings from a pregnant woman, 90 days of personality self-ratings from a psychology freshman, and a simulation study.
Error estimates for the Skyrme-Hartree-Fock model
NASA Astrophysics Data System (ADS)
Erler, J.; Reinhard, P.-G.
2015-03-01
There are many complementary strategies to estimate the extrapolation errors of a model calibrated in least-squares fits. We consider the Skyrme-Hartree-Fock model for nuclear structure and dynamics and exemplify the following five strategies: uncertainties from statistical analysis, covariances between observables, trends of residuals, variation of fit data, and dedicated variation of model parameters. This gives useful insight into the impact of the key fit data as they consist of binding energies, charge rms radii, and charge formfactor. Amongst others, we check in particular the predictive value for observables in the stable nucleus 208Pb, the super-heavy element 266Hs, r-process nuclei, and neutron stars.
Estimation of flood warning runoff thresholds in ungauged basins with asymmetric error functions
NASA Astrophysics Data System (ADS)
Toth, Elena
2016-06-01
In many real-world flood forecasting systems, the runoff thresholds for activating warnings or mitigation measures correspond to the flow peaks with a given return period (often 2 years, which may be associated with the bankfull discharge). At locations where the historical streamflow records are absent or very limited, the threshold can be estimated with regionally derived empirical relationships between catchment descriptors and the desired flood quantile. Whatever the function form, such models are generally parameterised by minimising the mean square error, which assigns equal importance to overprediction or underprediction errors. Considering that the consequences of an overestimated warning threshold (leading to the risk of missing alarms) generally have a much lower level of acceptance than those of an underestimated threshold (leading to the issuance of false alarms), the present work proposes to parameterise the regression model through an asymmetric error function, which penalises the overpredictions more. The estimates by models (feedforward neural networks) with increasing degree of asymmetry are compared with those of a traditional, symmetrically trained network, in a rigorous cross-validation experiment referred to a database of catchments covering the country of Italy. The analysis shows that the use of the asymmetric error function can substantially reduce the number and extent of overestimation errors, if compared to the use of the traditional square errors. Of course such reduction is at the expense of increasing underestimation errors, but the overall accurateness is still acceptable and the results illustrate the potential value of choosing an asymmetric error function when the consequences of missed alarms are more severe than those of false alarms.
Estimation of flood warning runoff thresholds in ungauged basins with asymmetric error functions
NASA Astrophysics Data System (ADS)
Toth, E.
2015-06-01
In many real-world flood forecasting systems, the runoff thresholds for activating warnings or mitigation measures correspond to the flow peaks with a given return period (often the 2-year one, that may be associated with the bankfull discharge). At locations where the historical streamflow records are absent or very limited, the threshold can be estimated with regionally-derived empirical relationships between catchment descriptors and the desired flood quantile. Whatever is the function form, such models are generally parameterised by minimising the mean square error, that assigns equal importance to overprediction or underprediction errors. Considering that the consequences of an overestimated warning threshold (leading to the risk of missing alarms) generally have a much lower level of acceptance than those of an underestimated threshold (leading to the issuance of false alarms), the present work proposes to parameterise the regression model through an asymmetric error function, that penalises more the overpredictions. The estimates by models (feedforward neural networks) with increasing degree of asymmetry are compared with those of a traditional, symmetrically-trained network, in a rigorous cross-validation experiment referred to a database of catchments covering the Italian country. The analysis shows that the use of the asymmetric error function can substantially reduce the number and extent of overestimation errors, if compared to the use of the traditional square errors. Of course such reduction is at the expense of increasing underestimation errors, but the overall accurateness is still acceptable and the results illustrate the potential value of choosing an asymmetric error function when the consequences of missed alarms are more severe than those of false alarms.
Bioaccessibility tests accurately estimate bioavailability of lead to quail
Beyer, W. Nelson; Basta, Nicholas T; Chaney, Rufus L.; Henry, Paula F.; Mosby, David; Rattner, Barnett A.; Scheckel, Kirk G.; Sprague, Dan; Weber, John
2016-01-01
Hazards of soil-borne Pb to wild birds may be more accurately quantified if the bioavailability of that Pb is known. To better understand the bioavailability of Pb to birds, we measured blood Pb concentrations in Japanese quail (Coturnix japonica) fed diets containing Pb-contaminated soils. Relative bioavailabilities were expressed by comparison with blood Pb concentrations in quail fed a Pb acetate reference diet. Diets containing soil from five Pb-contaminated Superfund sites had relative bioavailabilities from 33%-63%, with a mean of about 50%. Treatment of two of the soils with phosphorus significantly reduced the bioavailability of Pb. Bioaccessibility of Pb in the test soils was then measured in six in vitro tests and regressed on bioavailability. They were: the “Relative Bioavailability Leaching Procedure” (RBALP) at pH 1.5, the same test conducted at pH 2.5, the “Ohio State University In vitro Gastrointestinal” method (OSU IVG), the “Urban Soil Bioaccessible Lead Test”, the modified “Physiologically Based Extraction Test” and the “Waterfowl Physiologically Based Extraction Test.” All regressions had positive slopes. Based on criteria of slope and coefficient of determination, the RBALP pH 2.5 and OSU IVG tests performed very well. Speciation by X-ray absorption spectroscopy demonstrated that, on average, most of the Pb in the sampled soils was sorbed to minerals (30%), bound to organic matter (24%), or present as Pb sulfate (18%). Additional Pb was associated with P (chloropyromorphite, hydroxypyromorphite and tertiary Pb phosphate), and with Pb carbonates, leadhillite (a lead sulfate carbonate hydroxide), and Pb sulfide. The formation of chloropyromorphite reduced the bioavailability of Pb and the amendment of Pb-contaminated soils with P may be a thermodynamically favored means to sequester Pb.
Meyvis, Tom; Ratner, Rebecca K; Levav, Jonathan
2010-11-01
Why do affective forecasting errors persist in the face of repeated disconfirming evidence? Five studies demonstrate that people misremember their forecasts as consistent with their experience and thus fail to perceive the extent of their forecasting error. As a result, people do not learn from past forecasting errors and fail to adjust subsequent forecasts. In the context of a Super Bowl loss (Study 1), a presidential election (Studies 2 and 3), an important purchase (Study 4), and the consumption of candies (Study 5), individuals mispredicted their affective reactions to these experiences and subsequently misremembered their predictions as more accurate than they actually had been. The findings indicate that this recall error results from people's tendency to anchor on their current affective state when trying to recall their affective forecasts. Further, those who showed larger recall errors were less likely to learn to adjust their subsequent forecasts and reminding people of their actual forecasts enhanced learning. These results suggest that a failure to accurately recall one's past predictions contributes to the perpetuation of forecasting errors.
Bioaccessibility tests accurately estimate bioavailability of lead to quail.
Beyer, W Nelson; Basta, Nicholas T; Chaney, Rufus L; Henry, Paula F P; Mosby, David E; Rattner, Barnett A; Scheckel, Kirk G; Sprague, Daniel T; Weber, John S
2016-09-01
Hazards of soil-borne lead (Pb) to wild birds may be more accurately quantified if the bioavailability of that Pb is known. To better understand the bioavailability of Pb to birds, the authors measured blood Pb concentrations in Japanese quail (Coturnix japonica) fed diets containing Pb-contaminated soils. Relative bioavailabilities were expressed by comparison with blood Pb concentrations in quail fed a Pb acetate reference diet. Diets containing soil from 5 Pb-contaminated Superfund sites had relative bioavailabilities from 33% to 63%, with a mean of approximately 50%. Treatment of 2 of the soils with phosphorus (P) significantly reduced the bioavailability of Pb. Bioaccessibility of Pb in the test soils was then measured in 6 in vitro tests and regressed on bioavailability: the relative bioavailability leaching procedure at pH 1.5, the same test conducted at pH 2.5, the Ohio State University in vitro gastrointestinal method, the urban soil bioaccessible lead test, the modified physiologically based extraction test, and the waterfowl physiologically based extraction test. All regressions had positive slopes. Based on criteria of slope and coefficient of determination, the relative bioavailability leaching procedure at pH 2.5 and Ohio State University in vitro gastrointestinal tests performed very well. Speciation by X-ray absorption spectroscopy demonstrated that, on average, most of the Pb in the sampled soils was sorbed to minerals (30%), bound to organic matter (24%), or present as Pb sulfate (18%). Additional Pb was associated with P (chloropyromorphite, hydroxypyromorphite, and tertiary Pb phosphate) and with Pb carbonates, leadhillite (a lead sulfate carbonate hydroxide), and Pb sulfide. The formation of chloropyromorphite reduced the bioavailability of Pb, and the amendment of Pb-contaminated soils with P may be a thermodynamically favored means to sequester Pb. Environ Toxicol Chem 2016;35:2311-2319. Published 2016 Wiley Periodicals Inc. on behalf of
Kretzschmar, A; Durand, E; Maisonnasse, A; Vallon, J; Le Conte, Y
2015-06-01
A new procedure of stratified sampling is proposed in order to establish an accurate estimation of Varroa destructor populations on sticky bottom boards of the hive. It is based on the spatial sampling theory that recommends using regular grid stratification in the case of spatially structured process. The distribution of varroa mites on sticky board being observed as spatially structured, we designed a sampling scheme based on a regular grid with circles centered on each grid element. This new procedure is then compared with a former method using partially random sampling. Relative error improvements are exposed on the basis of a large sample of simulated sticky boards (n=20,000) which provides a complete range of spatial structures, from a random structure to a highly frame driven structure. The improvement of varroa mite number estimation is then measured by the percentage of counts with an error greater than a given level.
Kretzschmar, A; Durand, E; Maisonnasse, A; Vallon, J; Le Conte, Y
2015-06-01
A new procedure of stratified sampling is proposed in order to establish an accurate estimation of Varroa destructor populations on sticky bottom boards of the hive. It is based on the spatial sampling theory that recommends using regular grid stratification in the case of spatially structured process. The distribution of varroa mites on sticky board being observed as spatially structured, we designed a sampling scheme based on a regular grid with circles centered on each grid element. This new procedure is then compared with a former method using partially random sampling. Relative error improvements are exposed on the basis of a large sample of simulated sticky boards (n=20,000) which provides a complete range of spatial structures, from a random structure to a highly frame driven structure. The improvement of varroa mite number estimation is then measured by the percentage of counts with an error greater than a given level. PMID:26470273
Hausmann, M; Brandenburger, U; Brauers, T; Dorn, H P
1999-01-20
Differential-optical-absorption spectroscopy (DOAS) permits the sensitive measurement of concentrations of trace gases in the atmosphere. DOAS is a technique of well-defined accuracy; however, the calculation of a statistically sound measurement precision is still an unsolved problem. Usually one evaluates DOAS spectra by performing least-squares fits of reference absorption spectra to the measured atmospheric absorption spectra. Inasmuch as the absorbance from atmospheric trace gases is usually very weak, with optical densities in the range from 10(-5) to 10(-3), interference caused by the occurrence of nonreproducible spectral artifacts often determines the detection limit and the measurement precision. These spectral artifacts bias the least-squares fitting result in two respects. First, spectral artifacts to some extent are falsely interpreted as real absorption, and second, spectral artifacts add nonstatistical noise to spectral residuals, which results in a significant misestimation of the least-squares fitting error. We introduce two new approaches to investigate the evaluation errors of DOAS spectra accurately. The first method, residual inspection by cyclic displacement, estimates the effect of false interpretation of the artifact structures. The second method applies a statistical bootstrap algorithm to estimate properly the error of fitting, even in cases when the condition of random and independent scatter of the residual signal is not fulfilled. Evaluation of simulated atmospheric measurement spectra shows that a combination of the results of both methods yields a good estimate of the spectra evaluation error to within an uncertainty of ~10%.
Kunin, Victor; Engelbrektson, Anna; Ochman, Howard; Hugenholtz, Philip
2009-08-01
Massively parallel pyrosequencing of the small subunit (16S) ribosomal RNA gene has revealed that the extent of rare microbial populations in several environments, the 'rare biosphere', is orders of magnitude higher than previously thought. One important caveat with this method is that sequencing error could artificially inflate diversity estimates. Although the per-base error of 16S rDNA amplicon pyrosequencing has been shown to be as good as or lower than Sanger sequencing, no direct assessments of pyrosequencing errors on diversity estimates have been reported. Using only Escherichia coli MG1655 as a reference template, we find that 16S rDNA diversity is grossly overestimated unless relatively stringent read quality filtering and low clustering thresholds are applied. In particular, the common practice of removing reads with unresolved bases and anomalous read lengths is insufficient to ensure accurate estimates of microbial diversity. Furthermore, common and reproducible homopolymer length errors can result in relatively abundant spurious phylotypes further confounding data interpretation. We suggest that stringent quality-based trimming of 16S pyrotags and clustering thresholds no greater than 97% identity should be used to avoid overestimates of the rare biosphere.
Xiao, Yongling; Abrahamowicz, Michal
2010-03-30
We propose two bootstrap-based methods to correct the standard errors (SEs) from Cox's model for within-cluster correlation of right-censored event times. The cluster-bootstrap method resamples, with replacement, only the clusters, whereas the two-step bootstrap method resamples (i) the clusters, and (ii) individuals within each selected cluster, with replacement. In simulations, we evaluate both methods and compare them with the existing robust variance estimator and the shared gamma frailty model, which are available in statistical software packages. We simulate clustered event time data, with latent cluster-level random effects, which are ignored in the conventional Cox's model. For cluster-level covariates, both proposed bootstrap methods yield accurate SEs, and type I error rates, and acceptable coverage rates, regardless of the true random effects distribution, and avoid serious variance under-estimation by conventional Cox-based standard errors. However, the two-step bootstrap method over-estimates the variance for individual-level covariates. We also apply the proposed bootstrap methods to obtain confidence bands around flexible estimates of time-dependent effects in a real-life analysis of cluster event times.
Can student health professionals accurately estimate alcohol content in commonly occurring drinks?
Sinclair, Julia; Searle, Emma
2016-01-01
Objectives: Correct identification of alcohol as a contributor to, or comorbidity of, many psychiatric diseases requires health professionals to be competent and confident to take an accurate alcohol history. Being able to estimate (or calculate) the alcohol content in commonly consumed drinks is a prerequisite for quantifying levels of alcohol consumption. The aim of this study was to assess this ability in medical and nursing students. Methods: A cross-sectional survey of 891 medical and nursing students across different years of training was conducted. Students were asked the alcohol content of 10 different alcoholic drinks by seeing a slide of the drink (with picture, volume and percentage of alcohol by volume) for 30 s. Results: Overall, the mean number of correctly estimated drinks (out of the 10 tested) was 2.4, increasing to just over 3 if a 10% margin of error was used. Wine and premium strength beers were underestimated by over 50% of students. Those who drank alcohol themselves, or who were further on in their clinical training, did better on the task, but overall the levels remained low. Conclusions: Knowledge of, or the ability to work out, the alcohol content of commonly consumed drinks is poor, and further research is needed to understand the reasons for this and the impact this may have on the likelihood to undertake screening or initiate treatment. PMID:27536344
Close-range radar rainfall estimation and error analysis
NASA Astrophysics Data System (ADS)
van de Beek, C. Z.; Leijnse, H.; Hazenberg, P.; Uijlenhoet, R.
2012-04-01
It is well-known that quantitative precipitation estimation (QPE) is affected by many sources of error. The most important of these are 1) radar calibration, 2) wet radome attenuation, 3) rain attenuation, 4) vertical profile of reflectivity, 5) variations in drop size distribution, and 6) sampling effects. The study presented here is an attempt to separate and quantify these sources of error. For this purpose, QPE is performed very close to the radar (~1-2 km) so that 3), 4), and 6) will only play a minor role. Error source 5) can be corrected for because of the availability of two disdrometers (instruments that measure the drop size distribution). A 3-day rainfall event (25-27 August 2010) that produced more than 50 mm in De Bilt, The Netherlands is analyzed. Radar, rain gauge, and disdrometer data from De Bilt are used for this. It is clear from the analyses that without any corrections, the radar severely underestimates the total rain amount (only 25 mm). To investigate the effect of wet radome attenuation, stable returns from buildings close to the radar are analyzed. It is shown that this may have caused an underestimation up to ~4 dB. The calibration of the radar is checked by looking at received power from the sun. This turns out to cause another 1 dB of underestimation. The effect of variability of drop size distributions is shown to cause further underestimation. Correcting for all of these effects yields a good match between radar QPE and gauge measurements.
Sampling errors in rainfall estimates by multiple satellites
NASA Technical Reports Server (NTRS)
North, Gerald R.; Shen, Samuel S. P.; Upson, Robert
1993-01-01
This paper examines the sampling characteristics of combining data collected by several low-orbiting satellites attempting to estimate the space-time average of rain rates. The several satellites can have different orbital and swath-width parameters. The satellite overpasses are allowed to make partial coverage snapshots of the grid box with each overpass. Such partial visits are considered in an approximate way, letting each intersection area fraction of the grid box by a particular satellite swath be a random variable with mean and variance parameters computed from exact orbit calculations. The derivation procedure is based upon the spectral minimum mean-square error formalism introduced by North and Nakamoto. By using a simple parametric form for the spacetime spectral density, simple formulas are derived for a large number of examples, including the combination of the Tropical Rainfall Measuring Mission with an operational sun-synchronous orbiter. The approximations and results are discussed and directions for future research are summarized.
NASA Astrophysics Data System (ADS)
Wellendorff, Jess; Lundgaard, Keld T.; Møgelhøj, Andreas; Petzold, Vivien; Landis, David D.; Nørskov, Jens K.; Bligaard, Thomas; Jacobsen, Karsten W.
2012-06-01
A methodology for semiempirical density functional optimization, using regularization and cross-validation methods from machine learning, is developed. We demonstrate that such methods enable well-behaved exchange-correlation approximations in very flexible model spaces, thus avoiding the overfitting found when standard least-squares methods are applied to high-order polynomial expansions. A general-purpose density functional for surface science and catalysis studies should accurately describe bond breaking and formation in chemistry, solid state physics, and surface chemistry, and should preferably also include van der Waals dispersion interactions. Such a functional necessarily compromises between describing fundamentally different types of interactions, making transferability of the density functional approximation a key issue. We investigate this trade-off between describing the energetics of intramolecular and intermolecular, bulk solid, and surface chemical bonding, and the developed optimization method explicitly handles making the compromise based on the directions in model space favored by different materials properties. The approach is applied to designing the Bayesian error estimation functional with van der Waals correlation (BEEF-vdW), a semilocal approximation with an additional nonlocal correlation term. Furthermore, an ensemble of functionals around BEEF-vdW comes out naturally, offering an estimate of the computational error. An extensive assessment on a range of data sets validates the applicability of BEEF-vdW to studies in chemistry and condensed matter physics. Applications of the approximation and its Bayesian ensemble error estimate to two intricate surface science problems support this.
Model Error Estimation for the CPTEC Eta Model
NASA Technical Reports Server (NTRS)
Tippett, Michael K.; daSilva, Arlindo
1999-01-01
Statistical data assimilation systems require the specification of forecast and observation error statistics. Forecast error is due to model imperfections and differences between the initial condition and the actual state of the atmosphere. Practical four-dimensional variational (4D-Var) methods try to fit the forecast state to the observations and assume that the model error is negligible. Here with a number of simplifying assumption, a framework is developed for isolating the model error given the forecast error at two lead-times. Two definitions are proposed for the Talagrand ratio tau, the fraction of the forecast error due to model error rather than initial condition error. Data from the CPTEC Eta Model running operationally over South America are used to calculate forecast error statistics and lower bounds for tau.
Error analysis of leaf area estimates made from allometric regression models
NASA Technical Reports Server (NTRS)
Feiveson, A. H.; Chhikara, R. S.
1986-01-01
Biological net productivity, measured in terms of the change in biomass with time, affects global productivity and the quality of life through biochemical and hydrological cycles and by its effect on the overall energy balance. Estimating leaf area for large ecosystems is one of the more important means of monitoring this productivity. For a particular forest plot, the leaf area is often estimated by a two-stage process. In the first stage, known as dimension analysis, a small number of trees are felled so that their areas can be measured as accurately as possible. These leaf areas are then related to non-destructive, easily-measured features such as bole diameter and tree height, by using a regression model. In the second stage, the non-destructive features are measured for all or for a sample of trees in the plots and then used as input into the regression model to estimate the total leaf area. Because both stages of the estimation process are subject to error, it is difficult to evaluate the accuracy of the final plot leaf area estimates. This paper illustrates how a complete error analysis can be made, using an example from a study made on aspen trees in northern Minnesota. The study was a joint effort by NASA and the University of California at Santa Barbara known as COVER (Characterization of Vegetation with Remote Sensing).
Techniques for accurate estimation of net discharge in a tidal channel
Simpson, Michael R.; Bland, Roger
1999-01-01
An ultrasonic velocity meter discharge-measurement site in a tidally affected region of the Sacramento-San Joaquin rivers was used to study the accuracy of the index velocity calibration procedure. Calibration data consisting of ultrasonic velocity meter index velocity and concurrent acoustic Doppler discharge measurement data were collected during three time periods. The relative magnitude of equipment errors, acoustic Doppler discharge measurement errors, and calibration errors were evaluated. Calibration error was the most significant source of error in estimating net discharge. Using a comprehensive calibration method, net discharge estimates developed from the three sets of calibration data differed by less than an average of 4 cubic meters per second. Typical maximum flow rates during the data-collection period averaged 750 cubic meters per second.
Detecting Positioning Errors and Estimating Correct Positions by Moving Window
Song, Ha Yoon; Lee, Jun Seok
2015-01-01
In recent times, improvements in smart mobile devices have led to new functionalities related to their embedded positioning abilities. Many related applications that use positioning data have been introduced and are widely being used. However, the positioning data acquired by such devices are prone to erroneous values caused by environmental factors. In this research, a detection algorithm is implemented to detect erroneous data over a continuous positioning data set with several options. Our algorithm is based on a moving window for speed values derived by consecutive positioning data. Both the moving average of the speed and standard deviation in a moving window compose a moving significant interval at a given time, which is utilized to detect erroneous positioning data along with other parameters by checking the newly obtained speed value. In order to fulfill the designated operation, we need to examine the physical parameters and also determine the parameters for the moving windows. Along with the detection of erroneous speed data, estimations of correct positioning are presented. The proposed algorithm first estimates the speed, and then the correct positions. In addition, it removes the effect of errors on the moving window statistics in order to maintain accuracy. Experimental verifications based on our algorithm are presented in various ways. We hope that our approach can help other researchers with regard to positioning applications and human mobility research. PMID:26624282
Detecting Positioning Errors and Estimating Correct Positions by Moving Window.
Song, Ha Yoon; Lee, Jun Seok
2015-01-01
In recent times, improvements in smart mobile devices have led to new functionalities related to their embedded positioning abilities. Many related applications that use positioning data have been introduced and are widely being used. However, the positioning data acquired by such devices are prone to erroneous values caused by environmental factors. In this research, a detection algorithm is implemented to detect erroneous data over a continuous positioning data set with several options. Our algorithm is based on a moving window for speed values derived by consecutive positioning data. Both the moving average of the speed and standard deviation in a moving window compose a moving significant interval at a given time, which is utilized to detect erroneous positioning data along with other parameters by checking the newly obtained speed value. In order to fulfill the designated operation, we need to examine the physical parameters and also determine the parameters for the moving windows. Along with the detection of erroneous speed data, estimations of correct positioning are presented. The proposed algorithm first estimates the speed, and then the correct positions. In addition, it removes the effect of errors on the moving window statistics in order to maintain accuracy. Experimental verifications based on our algorithm are presented in various ways. We hope that our approach can help other researchers with regard to positioning applications and human mobility research.
Detecting Positioning Errors and Estimating Correct Positions by Moving Window.
Song, Ha Yoon; Lee, Jun Seok
2015-01-01
In recent times, improvements in smart mobile devices have led to new functionalities related to their embedded positioning abilities. Many related applications that use positioning data have been introduced and are widely being used. However, the positioning data acquired by such devices are prone to erroneous values caused by environmental factors. In this research, a detection algorithm is implemented to detect erroneous data over a continuous positioning data set with several options. Our algorithm is based on a moving window for speed values derived by consecutive positioning data. Both the moving average of the speed and standard deviation in a moving window compose a moving significant interval at a given time, which is utilized to detect erroneous positioning data along with other parameters by checking the newly obtained speed value. In order to fulfill the designated operation, we need to examine the physical parameters and also determine the parameters for the moving windows. Along with the detection of erroneous speed data, estimations of correct positioning are presented. The proposed algorithm first estimates the speed, and then the correct positions. In addition, it removes the effect of errors on the moving window statistics in order to maintain accuracy. Experimental verifications based on our algorithm are presented in various ways. We hope that our approach can help other researchers with regard to positioning applications and human mobility research. PMID:26624282
Zhang, Zhijun; Ashraf, Muhammad; Sahn, David J.; Song, Xubo
2014-01-01
Purpose: Quantitative analysis of cardiac motion is important for evaluation of heart function. Three dimensional (3D) echocardiography is among the most frequently used imaging modalities for motion estimation because it is convenient, real-time, low-cost, and nonionizing. However, motion estimation from 3D echocardiographic sequences is still a challenging problem due to low image quality and image corruption by noise and artifacts. Methods: The authors have developed a temporally diffeomorphic motion estimation approach in which the velocity field instead of the displacement field was optimized. The optimal velocity field optimizes a novel similarity function, which we call the intensity consistency error, defined as multiple consecutive frames evolving to each time point. The optimization problem is solved by using the steepest descent method. Results: Experiments with simulated datasets, images of an ex vivo rabbit phantom, images of in vivo open-chest pig hearts, and healthy human images were used to validate the authors’ method. Simulated and real cardiac sequences tests showed that results in the authors’ method are more accurate than other competing temporal diffeomorphic methods. Tests with sonomicrometry showed that the tracked crystal positions have good agreement with ground truth and the authors’ method has higher accuracy than the temporal diffeomorphic free-form deformation (TDFFD) method. Validation with an open-access human cardiac dataset showed that the authors’ method has smaller feature tracking errors than both TDFFD and frame-to-frame methods. Conclusions: The authors proposed a diffeomorphic motion estimation method with temporal smoothness by constraining the velocity field to have maximum local intensity consistency within multiple consecutive frames. The estimated motion using the authors’ method has good temporal consistency and is more accurate than other temporally diffeomorphic motion estimation methods. PMID:24784402
Hess, Maureen A; Rhydderch, James G; LeClair, Larry L; Buckley, Raymond M; Kawase, Mitsuhiro; Hauser, Lorenz
2012-11-01
Genotyping errors are present in almost all genetic data and can affect biological conclusions of a study, particularly for studies based on individual identification and parentage. Many statistical approaches can incorporate genotyping errors, but usually need accurate estimates of error rates. Here, we used a new microsatellite data set developed for brown rockfish (Sebastes auriculatus) to estimate genotyping error using three approaches: (i) repeat genotyping 5% of samples, (ii) comparing unintentionally recaptured individuals and (iii) Mendelian inheritance error checking for known parent-offspring pairs. In each data set, we quantified genotyping error rate per allele due to allele drop-out and false alleles. Genotyping error rate per locus revealed an average overall genotyping error rate by direct count of 0.3%, 1.5% and 1.7% (0.002, 0.007 and 0.008 per allele error rate) from replicate genotypes, known parent-offspring pairs and unintentionally recaptured individuals, respectively. By direct-count error estimates, the recapture and known parent-offspring data sets revealed an error rate four times greater than estimated using repeat genotypes. There was no evidence of correlation between error rates and locus variability for all three data sets, and errors appeared to occur randomly over loci in the repeat genotypes, but not in recaptures and parent-offspring comparisons. Furthermore, there was no correlation in locus-specific error rates between any two of the three data sets. Our data suggest that repeat genotyping may underestimate true error rates and may not estimate locus-specific error rates accurately. We therefore suggest using methods for error estimation that correspond to the overall aim of the study (e.g. known parent-offspring comparisons in parentage studies).
Highnam, Gareth; Franck, Christopher; Martin, Andy; Stephens, Calvin; Puthige, Ashwin; Mittelman, David
2013-01-01
Repetitive sequences are biologically and clinically important because they can influence traits and disease, but repeats are challenging to analyse using short-read sequencing technology. We present a tool for genotyping microsatellite repeats called RepeatSeq, which uses Bayesian model selection guided by an empirically derived error model that incorporates sequence and read properties. Next, we apply RepeatSeq to high-coverage genomes from the 1000 Genomes Project to evaluate performance and accuracy. The software uses common formats, such as VCF, for compatibility with existing genome analysis pipelines. Source code and binaries are available at http://github.com/adaptivegenome/repeatseq. PMID:23090981
CO2 Flux Estimation Errors Associated with Moist Atmospheric Processes
NASA Technical Reports Server (NTRS)
Parazoo, N. C.; Denning, A. S.; Kawa, S. R.; Pawson, S.; Lokupitiya, R.
2012-01-01
Vertical transport by moist sub-grid scale processes such as deep convection is a well-known source of uncertainty in CO2 source/sink inversion. However, a dynamical link between vertical transport, satellite based retrievals of column mole fractions of CO2, and source/sink inversion has not yet been established. By using the same offline transport model with meteorological fields from slightly different data assimilation systems, we examine sensitivity of frontal CO2 transport and retrieved fluxes to different parameterizations of sub-grid vertical transport. We find that frontal transport feeds off background vertical CO2 gradients, which are modulated by sub-grid vertical transport. The implication for source/sink estimation is two-fold. First, CO2 variations contained in moist poleward moving air masses are systematically different from variations in dry equatorward moving air. Moist poleward transport is hidden from orbital sensors on satellites, causing a sampling bias, which leads directly to small but systematic flux retrieval errors in northern mid-latitudes. Second, differences in the representation of moist sub-grid vertical transport in GEOS-4 and GEOS-5 meteorological fields cause differences in vertical gradients of CO2, which leads to systematic differences in moist poleward and dry equatorward CO2 transport and therefore the fraction of CO2 variations hidden in moist air from satellites. As a result, sampling biases are amplified and regional scale flux errors enhanced, most notably in Europe (0.43+/-0.35 PgC /yr). These results, cast from the perspective of moist frontal transport processes, support previous arguments that the vertical gradient of CO2 is a major source of uncertainty in source/sink inversion.
Bootstrap Standard Errors for Maximum Likelihood Ability Estimates When Item Parameters Are Unknown
ERIC Educational Resources Information Center
Patton, Jeffrey M.; Cheng, Ying; Yuan, Ke-Hai; Diao, Qi
2014-01-01
When item parameter estimates are used to estimate the ability parameter in item response models, the standard error (SE) of the ability estimate must be corrected to reflect the error carried over from item calibration. For maximum likelihood (ML) ability estimates, a corrected asymptotic SE is available, but it requires a long test and the…
A machine learning approach to the accurate prediction of multi-leaf collimator positional errors
NASA Astrophysics Data System (ADS)
Carlson, Joel N. K.; Park, Jong Min; Park, So-Yeon; In Park, Jong; Choi, Yunseok; Ye, Sung-Joon
2016-03-01
Discrepancies between planned and delivered movements of multi-leaf collimators (MLCs) are an important source of errors in dose distributions during radiotherapy. In this work we used machine learning techniques to train models to predict these discrepancies, assessed the accuracy of the model predictions, and examined the impact these errors have on quality assurance (QA) procedures and dosimetry. Predictive leaf motion parameters for the models were calculated from the plan files, such as leaf position and velocity, whether the leaf was moving towards or away from the isocenter of the MLC, and many others. Differences in positions between synchronized DICOM-RT planning files and DynaLog files reported during QA delivery were used as a target response for training of the models. The final model is capable of predicting MLC positions during delivery to a high degree of accuracy. For moving MLC leaves, predicted positions were shown to be significantly closer to delivered positions than were planned positions. By incorporating predicted positions into dose calculations in the TPS, increases were shown in gamma passing rates against measured dose distributions recorded during QA delivery. For instance, head and neck plans with 1%/2 mm gamma criteria had an average increase in passing rate of 4.17% (SD = 1.54%). This indicates that the inclusion of predictions during dose calculation leads to a more realistic representation of plan delivery. To assess impact on the patient, dose volumetric histograms (DVH) using delivered positions were calculated for comparison with planned and predicted DVHs. In all cases, predicted dose volumetric parameters were in closer agreement to the delivered parameters than were the planned parameters, particularly for organs at risk on the periphery of the treatment area. By incorporating the predicted positions into the TPS, the treatment planner is given a more realistic view of the dose distribution as it will truly be
Types of Possible Survey Errors in Estimates Published in the Weekly Natural Gas Storage Report
2016-01-01
This document lists types of potential errors in EIA estimates published in the WNGSR. Survey errors are an unavoidable aspect of data collection. Error is inherent in all collected data, regardless of the source of the data and the care and competence of data collectors. The type and extent of error depends on the type and characteristics of the survey.
Estimating Root Mean Square Errors in Remotely Sensed Soil Moisture over Continental Scale Domains
NASA Technical Reports Server (NTRS)
Draper, Clara S.; Reichle, Rolf; de Jeu, Richard; Naeimi, Vahid; Parinussa, Robert; Wagner, Wolfgang
2013-01-01
Root Mean Square Errors (RMSE) in the soil moisture anomaly time series obtained from the Advanced Scatterometer (ASCAT) and the Advanced Microwave Scanning Radiometer (AMSR-E; using the Land Parameter Retrieval Model) are estimated over a continental scale domain centered on North America, using two methods: triple colocation (RMSETC ) and error propagation through the soil moisture retrieval models (RMSEEP ). In the absence of an established consensus for the climatology of soil moisture over large domains, presenting a RMSE in soil moisture units requires that it be specified relative to a selected reference data set. To avoid the complications that arise from the use of a reference, the RMSE is presented as a fraction of the time series standard deviation (fRMSE). For both sensors, the fRMSETC and fRMSEEP show similar spatial patterns of relatively highlow errors, and the mean fRMSE for each land cover class is consistent with expectations. Triple colocation is also shown to be surprisingly robust to representativity differences between the soil moisture data sets used, and it is believed to accurately estimate the fRMSE in the remotely sensed soil moisture anomaly time series. Comparing the ASCAT and AMSR-E fRMSETC shows that both data sets have very similar accuracy across a range of land cover classes, although the AMSR-E accuracy is more directly related to vegetation cover. In general, both data sets have good skill up to moderate vegetation conditions.
Complex phase error and motion estimation in synthetic aperture radar imaging
NASA Astrophysics Data System (ADS)
Soumekh, M.; Yang, H.
1991-06-01
Attention is given to a SAR wave equation-based system model that accurately represents the interaction of the impinging radar signal with the target to be imaged. The model is used to estimate the complex phase error across the synthesized aperture from the measured corrupted SAR data by combining the two wave equation models governing the collected SAR data at two temporal frequencies of the radar signal. The SAR system model shows that the motion of an object in a static scene results in coupled Doppler shifts in both the temporal frequency domain and the spatial frequency domain of the synthetic aperture. The velocity of the moving object is estimated through these two Doppler shifts. It is shown that once the dynamic target's velocity is known, its reconstruction can be formulated via a squint-mode SAR geometry with parameters that depend upon the dynamic target's velocity.
NASA Astrophysics Data System (ADS)
Li, Y.; Ryu, D.; Western, A. W.; Wang, Q.; Robertson, D.; Crow, W. T.
2013-12-01
significantly. The EnKS streamflow forecasts are more accurate and reliable than the EnKF for the synthetic scenario. They also alleviate instability in the EnKF due to overcorrection of current state variables. For the operational forecasting case, the forecasts benefit less from state updating and the difference between the EnKS and EnKF becomes less significant. This is because the uncertainty in the NWP rainfall forecasts becomes dominant with increasing lead time. Forecast discharge in 2010. Solid curves are observations and gray areas indicate 95% of probabilistic forecasts. (a) openloop ensemble spread based on the error parameters estimated by the MAP; (b) 60-h lead time forecasts based on the EnKS.
ERIC Educational Resources Information Center
Culpepper, Steven Andrew
2012-01-01
Measurement error significantly biases interaction effects and distorts researchers' inferences regarding interactive hypotheses. This article focuses on the single-indicator case and shows how to accurately estimate group slope differences by disattenuating interaction effects with errors-in-variables (EIV) regression. New analytic findings were…
Evaluating concentration estimation errors in ELISA microarray experiments
Daly, Don S.; White, Amanda M.; Varnum, Susan M.; Anderson, Kevin K.; Zangar, Richard C.
2005-01-26
Enzyme-linked immunosorbent assay (ELISA) is a standard immunoassay to predict a protein concentration in a sample. Deploying ELISA in a microarray format permits simultaneous prediction of the concentrations of numerous proteins in a small sample. These predictions, however, are uncertain due to processing error and biological variability. Evaluating prediction error is critical to interpreting biological significance and improving the ELISA microarray process. Evaluating prediction error must be automated to realize a reliable high-throughput ELISA microarray system. Methods: In this paper, we present a statistical method based on propagation of error to evaluate prediction errors in the ELISA microarray process. Although propagation of error is central to this method, it is effective only when comparable data are available. Therefore, we briefly discuss the roles of experimental design, data screening, normalization and statistical diagnostics when evaluating ELISA microarray prediction errors. We use an ELISA microarray investigation of breast cancer biomarkers to illustrate the evaluation of prediction errors. The illustration begins with a description of the design and resulting data, followed by a brief discussion of data screening and normalization. In our illustration, we fit a standard curve to the screened and normalized data, review the modeling diagnostics, and apply propagation of error.
Han, Mira V; Thomas, Gregg W C; Lugo-Martinez, Jose; Hahn, Matthew W
2013-08-01
Current sequencing methods produce large amounts of data, but genome assemblies constructed from these data are often fragmented and incomplete. Incomplete and error-filled assemblies result in many annotation errors, especially in the number of genes present in a genome. This means that methods attempting to estimate rates of gene duplication and loss often will be misled by such errors and that rates of gene family evolution will be consistently overestimated. Here, we present a method that takes these errors into account, allowing one to accurately infer rates of gene gain and loss among genomes even with low assembly and annotation quality. The method is implemented in the newest version of the software package CAFE, along with several other novel features. We demonstrate the accuracy of the method with extensive simulations and reanalyze several previously published data sets. Our results show that errors in genome annotation do lead to higher inferred rates of gene gain and loss but that CAFE 3 sufficiently accounts for these errors to provide accurate estimates of important evolutionary parameters.
Estimating Equating Error in Observed-Score Equating. Research Report.
ERIC Educational Resources Information Center
van der Linden, Wim J.
Traditionally, error in equating observed scores on two versions of a test is defined as the difference between the transformations that equate the quantiles of their distributions in the sample and in the population of examinees. This definition underlies, for example, the well-known approximation to the standard error of equating by Lord (1982).…
Improved Atmospheric Soundings and Error Estimates from Analysis of AIRS/AMSU Data
NASA Technical Reports Server (NTRS)
Susskind, Joel
2007-01-01
The AIRS Science Team Version 5.0 retrieval algorithm became operational at the Goddard DAAC in July 2007 generating near real-time products from analysis of AIRS/AMSU sounding data. This algorithm contains many significant theoretical advances over the AIRS Science Team Version 4.0 retrieval algorithm used previously. Three very significant developments of Version 5 are: 1) the development and implementation of an improved Radiative Transfer Algorithm (RTA) which allows for accurate treatment of non-Local Thermodynamic Equilibrium (non-LTE) effects on shortwave sounding channels; 2) the development of methodology to obtain very accurate case by case product error estimates which are in turn used for quality control; and 3) development of an accurate AIRS only cloud clearing and retrieval system. These theoretical improvements taken together enabled a new methodology to be developed which further improves soundings in partially cloudy conditions, without the need for microwave observations in the cloud clearing step as has been done previously. In this methodology, longwave C02 channel observations in the spectral region 700 cm-' to 750 cm-' are used exclusively for cloud clearing purposes, while shortwave C02 channels in the spectral region 2195 cm-' to 2395 cm-' are used for temperature sounding purposes. The new methodology for improved error estimates and their use in quality control is described briefly and results are shown indicative of their accuracy. Results are also shown of forecast impact experiments assimilating AIRS Version 5.0 retrieval products in the Goddard GEOS 5 Data Assimilation System using different quality control thresholds.
An Empirical State Error Covariance Matrix for the Weighted Least Squares Estimation Method
NASA Technical Reports Server (NTRS)
Frisbee, Joseph H., Jr.
2011-01-01
State estimation techniques effectively provide mean state estimates. However, the theoretical state error covariance matrices provided as part of these techniques often suffer from a lack of confidence in their ability to describe the un-certainty in the estimated states. By a reinterpretation of the equations involved in the weighted least squares algorithm, it is possible to directly arrive at an empirical state error covariance matrix. This proposed empirical state error covariance matrix will contain the effect of all error sources, known or not. Results based on the proposed technique will be presented for a simple, two observer, measurement error only problem.
Impact of the Born approximation on the estimation error in 2D inverse scattering
NASA Astrophysics Data System (ADS)
Diong, M. L.; Roueff, A.; Lasaygues, P.; Litman, A.
2016-06-01
The aim is to quantify the impact of the Born approximation on the estimation error for a simple inverse scattering problem, while taking into account the noise measurement features. The proposed method consists of comparing two estimation errors: the error obtained with the Born approximation and the error obtained without it. The first error is characterized by the mean and variance of the maximum likelihood estimator, which are straightforward to compute with the Born approximation because the corresponding estimator is linear. The second error is evaluated with the Cramer–Rao bound (CRB). The CRB is a lower bound on the variance of unbiased estimators and thus does not depend on the choice of the estimation method. Beyond the conclusions that will be established under the Born approximation, this study lays out a general methodology that can be generalized to any other approximation.
Butt, Nathalie; Slade, Eleanor; Thompson, Jill; Malhi, Yadvinder; Riutta, Terhi
2013-06-01
A typical way to quantify aboveground carbon in forests is to measure tree diameters and use species-specific allometric equations to estimate biomass and carbon stocks. Using "citizen scientists" to collect data that are usually time-consuming and labor-intensive can play a valuable role in ecological research. However, data validation, such as establishing the sampling error in volunteer measurements, is a crucial, but little studied, part of utilizing citizen science data. The aims of this study were to (1) evaluate the quality of tree diameter and height measurements carried out by volunteers compared to expert scientists and (2) estimate how sensitive carbon stock estimates are to these measurement sampling errors. Using all diameter data measured with a diameter tape, the volunteer mean sampling error (difference between repeated measurements of the same stem) was 9.9 mm, and the expert sampling error was 1.8 mm. Excluding those sampling errors > 1 cm, the mean sampling errors were 2.3 mm (volunteers) and 1.4 mm (experts) (this excluded 14% [volunteer] and 3% [expert] of the data). The sampling error in diameter measurements had a small effect on the biomass estimates of the plots: a volunteer (expert) diameter sampling error of 2.3 mm (1.4 mm) translated into 1.7% (0.9%) change in the biomass estimates calculated from species-specific allometric equations based upon diameter. Height sampling error had a dependent relationship with tree height. Including height measurements in biomass calculations compounded the sampling error markedly; the impact of volunteer sampling error on biomass estimates was +/- 15%, and the expert range was +/- 9%. Using dendrometer bands, used to measure growth rates, we calculated that the volunteer (vs. expert) sampling error was 0.6 mm (vs. 0.3 mm), which is equivalent to a difference in carbon storage of +/- 0.011 kg C/yr (vs. +/- 0.002 kg C/yr) per stem. Using a citizen science model for monitoring carbon stocks not only has
Butt, Nathalie; Slade, Eleanor; Thompson, Jill; Malhi, Yadvinder; Riutta, Terhi
2013-06-01
A typical way to quantify aboveground carbon in forests is to measure tree diameters and use species-specific allometric equations to estimate biomass and carbon stocks. Using "citizen scientists" to collect data that are usually time-consuming and labor-intensive can play a valuable role in ecological research. However, data validation, such as establishing the sampling error in volunteer measurements, is a crucial, but little studied, part of utilizing citizen science data. The aims of this study were to (1) evaluate the quality of tree diameter and height measurements carried out by volunteers compared to expert scientists and (2) estimate how sensitive carbon stock estimates are to these measurement sampling errors. Using all diameter data measured with a diameter tape, the volunteer mean sampling error (difference between repeated measurements of the same stem) was 9.9 mm, and the expert sampling error was 1.8 mm. Excluding those sampling errors > 1 cm, the mean sampling errors were 2.3 mm (volunteers) and 1.4 mm (experts) (this excluded 14% [volunteer] and 3% [expert] of the data). The sampling error in diameter measurements had a small effect on the biomass estimates of the plots: a volunteer (expert) diameter sampling error of 2.3 mm (1.4 mm) translated into 1.7% (0.9%) change in the biomass estimates calculated from species-specific allometric equations based upon diameter. Height sampling error had a dependent relationship with tree height. Including height measurements in biomass calculations compounded the sampling error markedly; the impact of volunteer sampling error on biomass estimates was +/- 15%, and the expert range was +/- 9%. Using dendrometer bands, used to measure growth rates, we calculated that the volunteer (vs. expert) sampling error was 0.6 mm (vs. 0.3 mm), which is equivalent to a difference in carbon storage of +/- 0.011 kg C/yr (vs. +/- 0.002 kg C/yr) per stem. Using a citizen science model for monitoring carbon stocks not only has
Bayesian parameter estimation of a k-ε model for accurate jet-in-crossflow simulations
Ray, Jaideep; Lefantzi, Sophia; Arunajatesan, Srinivasan; Dechant, Lawrence
2016-05-31
Reynolds-averaged Navier–Stokes models are not very accurate for high-Reynolds-number compressible jet-in-crossflow interactions. The inaccuracy arises from the use of inappropriate model parameters and model-form errors in the Reynolds-averaged Navier–Stokes model. In this study, the hypothesis is pursued that Reynolds-averaged Navier–Stokes predictions can be significantly improved by using parameters inferred from experimental measurements of a supersonic jet interacting with a transonic crossflow.
NASA Astrophysics Data System (ADS)
Tinkham, W. T.; Hoffman, C. M.; Falkowski, M. J.; Smith, A. M.; Link, T. E.; Marshall, H.
2011-12-01
Light Detection and Ranging (LiDAR) has become one of the most effective and reliable means of characterizing surface topography and vegetation structure. Most LiDAR-derived estimates such as vegetation height, snow depth, and floodplain boundaries rely on the accurate creation of digital terrain models (DTM). As a result of the importance of an accurate DTM in using LiDAR data to estimate snow depth, it is necessary to understand the variables that influence the DTM accuracy in order to assess snow depth error. A series of 4 x 4 m plots that were surveyed at 0.5 m spacing in a semi-arid catchment were used for training the Random Forests algorithm along with a series of 35 variables in order to spatially predict vertical error within a LiDAR derived DTM. The final model was utilized to predict the combined error resulting from snow volume and snow water equivalent estimates derived from a snow-free LiDAR DTM and a snow-on LiDAR acquisition of the same site. The methodology allows for a statistical quantification of the spatially-distributed error patterns that are incorporated into the estimation of snow volume and snow water equivalents from LiDAR.
Estimating Climatological Bias Errors for the Global Precipitation Climatology Project (GPCP)
NASA Technical Reports Server (NTRS)
Adler, Robert; Gu, Guojun; Huffman, George
2012-01-01
A procedure is described to estimate bias errors for mean precipitation by using multiple estimates from different algorithms, satellite sources, and merged products. The Global Precipitation Climatology Project (GPCP) monthly product is used as a base precipitation estimate, with other input products included when they are within +/- 50% of the GPCP estimates on a zonal-mean basis (ocean and land separately). The standard deviation s of the included products is then taken to be the estimated systematic, or bias, error. The results allow one to examine monthly climatologies and the annual climatology, producing maps of estimated bias errors, zonal-mean errors, and estimated errors over large areas such as ocean and land for both the tropics and the globe. For ocean areas, where there is the largest question as to absolute magnitude of precipitation, the analysis shows spatial variations in the estimated bias errors, indicating areas where one should have more or less confidence in the mean precipitation estimates. In the tropics, relative bias error estimates (s/m, where m is the mean precipitation) over the eastern Pacific Ocean are as large as 20%, as compared with 10%-15% in the western Pacific part of the ITCZ. An examination of latitudinal differences over ocean clearly shows an increase in estimated bias error at higher latitudes, reaching up to 50%. Over land, the error estimates also locate regions of potential problems in the tropics and larger cold-season errors at high latitudes that are due to snow. An empirical technique to area average the gridded errors (s) is described that allows one to make error estimates for arbitrary areas and for the tropics and the globe (land and ocean separately, and combined). Over the tropics this calculation leads to a relative error estimate for tropical land and ocean combined of 7%, which is considered to be an upper bound because of the lack of sign-of-the-error canceling when integrating over different areas with a
Ju, Lili; Tian, Li; Wang, Desheng
2009-01-01
In this paper, we present a residual-based a posteriori error estimate for the finite volume discretization of steady convection– diffusion–reaction equations defined on surfaces in R3, which are often implicitly represented as level sets of smooth functions. Reliability and efficiency of the proposed a posteriori error estimator are rigorously proved. Numerical experiments are also conducted to verify the theoretical results and demonstrate the robustness of the error estimator.
Aerial measurement error with a dot planimeter: Some experimental estimates
NASA Technical Reports Server (NTRS)
Yuill, R. S.
1971-01-01
A shape analysis is presented which utilizes a computer to simulate a multiplicity of dot grids mathematically. Results indicate that the number of dots placed over an area to be measured provides the entire correlation with accuracy of measurement, the indices of shape being of little significance. Equations and graphs are provided from which the average expected error, and the maximum range of error, for various numbers of dot points can be read.
NASA Astrophysics Data System (ADS)
Cecinati, Francesca; Moreno Ródenas, Antonio Manuel; Rico-Ramirez, Miguel Angel; ten Veldhuis, Marie-claire; Han, Dawei
2016-04-01
In many research studies rain gauges are used as a reference point measurement for rainfall, because they can reach very good accuracy, especially compared to radar or microwave links, and their use is very widespread. In some applications rain gauge uncertainty is assumed to be small enough to be neglected. This can be done when rain gauges are accurate and their data is correctly managed. Unfortunately, in many operational networks the importance of accurate rainfall data and of data quality control can be underestimated; budget and best practice knowledge can be limiting factors in a correct rain gauge network management. In these cases, the accuracy of rain gauges can drastically drop and the uncertainty associated with the measurements cannot be neglected. This work proposes an approach based on three different kriging methods to integrate rain gauge measurement errors in the overall rainfall uncertainty estimation. In particular, rainfall products of different complexity are derived through 1) block kriging on a single rain gauge 2) ordinary kriging on a network of different rain gauges 3) kriging with external drift to integrate all the available rain gauges with radar rainfall information. The study area is the Eindhoven catchment, contributing to the river Dommel, in the southern part of the Netherlands. The area, 590 km2, is covered by high quality rain gauge measurements by the Royal Netherlands Meteorological Institute (KNMI), which has one rain gauge inside the study area and six around it, and by lower quality rain gauge measurements by the Dommel Water Board and by the Eindhoven Municipality (six rain gauges in total). The integration of the rain gauge measurement error is accomplished in all the cases increasing the nugget of the semivariogram proportionally to the estimated error. Using different semivariogram models for the different networks allows for the separate characterisation of higher and lower quality rain gauges. For the kriging with
Shi, Yun; Xu, Peiliang; Peng, Junhuan; Shi, Chuang; Liu, Jingnan
2014-01-10
Modern observation technology has verified that measurement errors can be proportional to the true values of measurements such as GPS, VLBI baselines and LiDAR. Observational models of this type are called multiplicative error models. This paper is to extend the work of Xu and Shimada published in 2000 on multiplicative error models to analytical error analysis of quantities of practical interest and estimates of the variance of unit weight. We analytically derive the variance-covariance matrices of the three least squares (LS) adjustments, the adjusted measurements and the corrections of measurements in multiplicative error models. For quality evaluation, we construct five estimators for the variance of unit weight in association of the three LS adjustment methods. Although LiDAR measurements are contaminated with multiplicative random errors, LiDAR-based digital elevation models (DEM) have been constructed as if they were of additive random errors. We will simulate a model landslide, which is assumed to be surveyed with LiDAR, and investigate the effect of LiDAR-type multiplicative error measurements on DEM construction and its effect on the estimate of landslide mass volume from the constructed DEM.
Shi, Yun; Xu, Peiliang; Peng, Junhuan; Shi, Chuang; Liu, Jingnan
2014-01-01
Modern observation technology has verified that measurement errors can be proportional to the true values of measurements such as GPS, VLBI baselines and LiDAR. Observational models of this type are called multiplicative error models. This paper is to extend the work of Xu and Shimada published in 2000 on multiplicative error models to analytical error analysis of quantities of practical interest and estimates of the variance of unit weight. We analytically derive the variance-covariance matrices of the three least squares (LS) adjustments, the adjusted measurements and the corrections of measurements in multiplicative error models. For quality evaluation, we construct five estimators for the variance of unit weight in association of the three LS adjustment methods. Although LiDAR measurements are contaminated with multiplicative random errors, LiDAR-based digital elevation models (DEM) have been constructed as if they were of additive random errors. We will simulate a model landslide, which is assumed to be surveyed with LiDAR, and investigate the effect of LiDAR-type multiplicative error measurements on DEM construction and its effect on the estimate of landslide mass volume from the constructed DEM. PMID:24434880
Shi, Yun; Xu, Peiliang; Peng, Junhuan; Shi, Chuang; Liu, Jingnan
2013-01-01
Modern observation technology has verified that measurement errors can be proportional to the true values of measurements such as GPS, VLBI baselines and LiDAR. Observational models of this type are called multiplicative error models. This paper is to extend the work of Xu and Shimada published in 2000 on multiplicative error models to analytical error analysis of quantities of practical interest and estimates of the variance of unit weight. We analytically derive the variance-covariance matrices of the three least squares (LS) adjustments, the adjusted measurements and the corrections of measurements in multiplicative error models. For quality evaluation, we construct five estimators for the variance of unit weight in association of the three LS adjustment methods. Although LiDAR measurements are contaminated with multiplicative random errors, LiDAR-based digital elevation models (DEM) have been constructed as if they were of additive random errors. We will simulate a model landslide, which is assumed to be surveyed with LiDAR, and investigate the effect of LiDAR-type multiplicative error measurements on DEM construction and its effect on the estimate of landslide mass volume from the constructed DEM. PMID:24434880
NASA Astrophysics Data System (ADS)
Honjo, Yasunori; Hasegawa, Hideyuki; Kanai, Hiroshi
2012-07-01
rates estimated using different kernel sizes were examined using the normalized mean-squared error of the estimated strain rate from the actual one obtained by the 1D phase-sensitive method. Compared with conventional kernel sizes, this result shows the possibility of the proposed correlation kernel to enable more accurate measurement of the strain rate. In in vivo measurement, the regional instantaneous velocities and strain rates in the radial direction of the heart wall were analyzed in detail at an extremely high temporal resolution (frame rate of 860 Hz). In this study, transition in contraction and relaxation was able to be detected by 2D tracking. These results indicate the potential of this method in the high-accuracy estimation of the strain rates and detailed analyses of the physiological function of the myocardium.
Joint Estimation of Contamination, Error and Demography for Nuclear DNA from Ancient Humans.
Racimo, Fernando; Renaud, Gabriel; Slatkin, Montgomery
2016-04-01
When sequencing an ancient DNA sample from a hominin fossil, DNA from present-day humans involved in excavation and extraction will be sequenced along with the endogenous material. This type of contamination is problematic for downstream analyses as it will introduce a bias towards the population of the contaminating individual(s). Quantifying the extent of contamination is a crucial step as it allows researchers to account for possible biases that may arise in downstream genetic analyses. Here, we present an MCMC algorithm to co-estimate the contamination rate, sequencing error rate and demographic parameters-including drift times and admixture rates-for an ancient nuclear genome obtained from human remains, when the putative contaminating DNA comes from present-day humans. We assume we have a large panel representing the putative contaminant population (e.g. European, East Asian or African). The method is implemented in a C++ program called 'Demographic Inference with Contamination and Error' (DICE). We applied it to simulations and genome data from ancient Neanderthals and modern humans. With reasonable levels of genome sequence coverage (>3X), we find we can recover accurate estimates of all these parameters, even when the contamination rate is as high as 50%.
Joint Estimation of Contamination, Error and Demography for Nuclear DNA from Ancient Humans.
Racimo, Fernando; Renaud, Gabriel; Slatkin, Montgomery
2016-04-01
When sequencing an ancient DNA sample from a hominin fossil, DNA from present-day humans involved in excavation and extraction will be sequenced along with the endogenous material. This type of contamination is problematic for downstream analyses as it will introduce a bias towards the population of the contaminating individual(s). Quantifying the extent of contamination is a crucial step as it allows researchers to account for possible biases that may arise in downstream genetic analyses. Here, we present an MCMC algorithm to co-estimate the contamination rate, sequencing error rate and demographic parameters-including drift times and admixture rates-for an ancient nuclear genome obtained from human remains, when the putative contaminating DNA comes from present-day humans. We assume we have a large panel representing the putative contaminant population (e.g. European, East Asian or African). The method is implemented in a C++ program called 'Demographic Inference with Contamination and Error' (DICE). We applied it to simulations and genome data from ancient Neanderthals and modern humans. With reasonable levels of genome sequence coverage (>3X), we find we can recover accurate estimates of all these parameters, even when the contamination rate is as high as 50%. PMID:27049965
A microbial clock provides an accurate estimate of the postmortem interval in a mouse model system
Metcalf, Jessica L; Wegener Parfrey, Laura; Gonzalez, Antonio; Lauber, Christian L; Knights, Dan; Ackermann, Gail; Humphrey, Gregory C; Gebert, Matthew J; Van Treuren, Will; Berg-Lyons, Donna; Keepers, Kyle; Guo, Yan; Bullard, James; Fierer, Noah; Carter, David O; Knight, Rob
2013-01-01
Establishing the time since death is critical in every death investigation, yet existing techniques are susceptible to a range of errors and biases. For example, forensic entomology is widely used to assess the postmortem interval (PMI), but errors can range from days to months. Microbes may provide a novel method for estimating PMI that avoids many of these limitations. Here we show that postmortem microbial community changes are dramatic, measurable, and repeatable in a mouse model system, allowing PMI to be estimated within approximately 3 days over 48 days. Our results provide a detailed understanding of bacterial and microbial eukaryotic ecology within a decomposing corpse system and suggest that microbial community data can be developed into a forensic tool for estimating PMI. DOI: http://dx.doi.org/10.7554/eLife.01104.001 PMID:24137541
Development of Classification and Story Building Data for Accurate Earthquake Damage Estimation
NASA Astrophysics Data System (ADS)
Sakai, Yuki; Fukukawa, Noriko; Arai, Kensuke
We investigated the method of developing classification and story building data from census population database in order to estimate earthquake damage more accurately especially in the urban area presuming that there are correlation between numbers of non-wooden or high-rise buildings and the population. We formulated equations of estimating numbers of wooden houses, low-to-mid-rise(1-9 story) and high-rise(over 10 story) non-wooden buildings in the 1km mesh from night and daytime population database based on the building data we investigated and collected in the selected 20 meshs in Kanto area. We could accurately estimate the numbers of three classified buildings by the formulated equations, but in some special cases, such as the apartment block mesh, the estimated values are quite different from actual values.
Fragment-based error estimation in biomolecular modeling
Faver, John C.; Merz, Kenneth M.
2013-01-01
Computer simulations are becoming an increasingly more important component of drug discovery. Computational models are now often able to reproduce and sometimes even predict outcomes of experiments. Still, potential energy models such as force fields contain significant amounts of bias and imprecision. We have shown how even small uncertainties in potential energy models can propagate to yield large errors, and have devised some general error-handling protocols for biomolecular modeling with imprecise energy functions. Herein we discuss those protocols within the contexts of protein–ligand binding and protein folding. PMID:23993915
The Effects of Computational Modeling Errors on the Estimation of Statistical Mechanical Variables.
Faver, John C; Yang, Wei; Merz, Kenneth M
2012-10-01
Computational models used in the estimation of thermodynamic quantities of large chemical systems often require approximate energy models that rely on parameterization and cancellation of errors to yield agreement with experimental measurements. In this work, we show how energy function errors propagate when computing statistical mechanics-derived thermodynamic quantities. Assuming that each microstate included in a statistical ensemble has a measurable amount of error in its calculated energy, we derive low-order expressions for the propagation of these errors in free energy, average energy, and entropy. Through gedanken experiments we show the expected behavior of these error propagation formulas on hypothetical energy surfaces. For very large microstate energy errors, these low-order formulas disagree with estimates from Monte Carlo simulations of error propagation. Hence, such simulations of error propagation may be required when using poor potential energy functions. Propagated systematic errors predicted by these methods can be removed from computed quantities, while propagated random errors yield uncertainty estimates. Importantly, we find that end-point free energy methods maximize random errors and that local sampling of potential energy wells decreases random error significantly. Hence, end-point methods should be avoided in energy computations and should be replaced by methods that incorporate local sampling. The techniques described herein will be used in future work involving the calculation of free energies of biomolecular processes, where error corrections are expected to yield improved agreement with experiment.
Trends and Correlation Estimation in Climate Sciences: Effects of Timescale Errors
NASA Astrophysics Data System (ADS)
Mudelsee, M.; Bermejo, M. A.; Bickert, T.; Chirila, D.; Fohlmeister, J.; Köhler, P.; Lohmann, G.; Olafsdottir, K.; Scholz, D.
2012-12-01
Trend describes time-dependence in the first moment of a stochastic process, and correlation measures the linear relation between two random variables. Accurately estimating the trend and correlation, including uncertainties, from climate time series data in the uni- and bivariate domain, respectively, allows first-order insights into the geophysical process that generated the data. Timescale errors, ubiquitious in paleoclimatology, where archives are sampled for proxy measurements and dated, poses a problem to the estimation. Statistical science and the various applied research fields, including geophysics, have almost completely ignored this problem due to its theoretical almost-intractability. However, computational adaptations or replacements of traditional error formulas have become technically feasible. This contribution gives a short overview of such an adaptation package, bootstrap resampling combined with parametric timescale simulation. We study linear regression, parametric change-point models and nonparametric smoothing for trend estimation. We introduce pairwise-moving block bootstrap resampling for correlation estimation. Both methods share robustness against autocorrelation and non-Gaussian distributional shape. We shortly touch computing-intensive calibration of bootstrap confidence intervals and consider options to parallelize the related computer code. Following examples serve not only to illustrate the methods but tell own climate stories: (1) the search for climate drivers of the Agulhas Current on recent timescales, (2) the comparison of three stalagmite-based proxy series of regional, western German climate over the later part of the Holocene, and (3) trends and transitions in benthic oxygen isotope time series from the Cenozoic. Financial support by Deutsche Forschungsgemeinschaft (FOR 668, FOR 1070, MU 1595/4-1) and the European Commission (MC ITN 238512, MC ITN 289447) is acknowledged.
Reid, Jane M; Keller, Lukas F; Marr, Amy B; Nietlisbach, Pirmin; Sardell, Rebecca J; Arcese, Peter
2014-03-01
Understanding the evolutionary dynamics of inbreeding and inbreeding depression requires unbiased estimation of inbreeding depression across diverse mating systems. However, studies estimating inbreeding depression often measure inbreeding with error, for example, based on pedigree data derived from observed parental behavior that ignore paternity error stemming from multiple mating. Such paternity error causes error in estimated coefficients of inbreeding (f) and reproductive success and could bias estimates of inbreeding depression. We used complete "apparent" pedigree data compiled from observed parental behavior and analogous "actual" pedigree data comprising genetic parentage to quantify effects of paternity error stemming from extra-pair reproduction on estimates of f, reproductive success, and inbreeding depression in free-living song sparrows (Melospiza melodia). Paternity error caused widespread error in estimates of f and male reproductive success, causing inbreeding depression in male and female annual and lifetime reproductive success and juvenile male survival to be substantially underestimated. Conversely, inbreeding depression in adult male survival tended to be overestimated when paternity error was ignored. Pedigree error stemming from extra-pair reproduction therefore caused substantial and divergent bias in estimates of inbreeding depression that could bias tests of evolutionary theories regarding inbreeding and inbreeding depression and their links to variation in mating system.
ERIC Educational Resources Information Center
Kim, ChangHwan; Tamborini, Christopher R.
2012-01-01
Few studies have considered how earnings inequality estimates may be affected by measurement error in self-reported earnings in surveys. Utilizing restricted-use data that links workers in the Survey of Income and Program Participation with their W-2 earnings records, we examine the effect of measurement error on estimates of racial earnings…
Triple collocation: beyond three estimates and separation of structural/non-structural errors
Technology Transfer Automated Retrieval System (TEKTRAN)
This study extends the popular triple collocation method for error assessment from three source estimates to an arbitrary number of source estimates, i.e., to solve the “multiple” collocation problem. The error assessment problem is solved through Pythagorean constraints in Hilbert space, which is s...
Nonparametric Estimation of Standard Errors in Covariance Analysis Using the Infinitesimal Jackknife
ERIC Educational Resources Information Center
Jennrich, Robert I.
2008-01-01
The infinitesimal jackknife provides a simple general method for estimating standard errors in covariance structure analysis. Beyond its simplicity and generality what makes the infinitesimal jackknife method attractive is that essentially no assumptions are required to produce consistent standard error estimates, not even the requirement that the…
Mean-square-error bounds for reduced-order linear state estimators
NASA Technical Reports Server (NTRS)
Baram, Y.; Kalit, G.
1987-01-01
The mean-square error of reduced-order linear state estimators for continuous-time linear systems is investigated. Lower and upper bounds on the minimal mean-square error are presented. The bounds are readily computable at each time-point and at steady state from the solutions to the Ricatti and the Liapunov equations. The usefulness of the error bounds for the analysis and design of reduced-order estimators is illustrated by a practical numerical example.
NASA Astrophysics Data System (ADS)
Punjabi, Alkesh; Ali, Halima
2011-02-01
Any canonical transformation of Hamiltonian equations is symplectic, and any area-preserving transformation in 2D is a symplectomorphism. Based on these, a discrete symplectic map and its continuous symplectic analog are derived for forward magnetic field line trajectories in natural canonical coordinates. The unperturbed axisymmetric Hamiltonian for magnetic field lines is constructed from the experimental data in the DIII-D [J. L. Luxon and L. E. Davis, Fusion Technol. 8, 441 (1985)]. The equilibrium Hamiltonian is a highly accurate, analytic, and realistic representation of the magnetic geometry of the DIII-D. These symplectic mathematical maps are used to calculate the magnetic footprint on the inboard collector plate in the DIII-D. Internal statistical topological noise and field errors are irreducible and ubiquitous in magnetic confinement schemes for fusion. It is important to know the stochasticity and magnetic footprint from noise and error fields. The estimates of the spectrum and mode amplitudes of the spatial topological noise and magnetic errors in the DIII-D are used as magnetic perturbation. The discrete and continuous symplectic maps are used to calculate the magnetic footprint on the inboard collector plate of the DIII-D by inverting the natural coordinates to physical coordinates. The combination of highly accurate equilibrium generating function, natural canonical coordinates, symplecticity, and small step-size together gives a very accurate calculation of magnetic footprint. Radial variation of magnetic perturbation and the response of plasma to perturbation are not included. The inboard footprint from noise and errors are dominated by m =3, n =1 mode. The footprint is in the form of a toroidally winding helical strip. The width of stochastic layer scales as 1/2 power of amplitude. The area of footprint scales as first power of amplitude. The physical parameters such as toroidal angle, length, and poloidal angle covered before striking, and the
NASA Astrophysics Data System (ADS)
Moreira, António H. J.; Queirós, Sandro; Morais, Pedro; Rodrigues, Nuno F.; Correia, André Ricardo; Fernandes, Valter; Pinho, A. C. M.; Fonseca, Jaime C.; Vilaça, João. L.
2015-03-01
The success of dental implant-supported prosthesis is directly linked to the accuracy obtained during implant's pose estimation (position and orientation). Although traditional impression techniques and recent digital acquisition methods are acceptably accurate, a simultaneously fast, accurate and operator-independent methodology is still lacking. Hereto, an image-based framework is proposed to estimate the patient-specific implant's pose using cone-beam computed tomography (CBCT) and prior knowledge of implanted model. The pose estimation is accomplished in a threestep approach: (1) a region-of-interest is extracted from the CBCT data using 2 operator-defined points at the implant's main axis; (2) a simulated CBCT volume of the known implanted model is generated through Feldkamp-Davis-Kress reconstruction and coarsely aligned to the defined axis; and (3) a voxel-based rigid registration is performed to optimally align both patient and simulated CBCT data, extracting the implant's pose from the optimal transformation. Three experiments were performed to evaluate the framework: (1) an in silico study using 48 implants distributed through 12 tridimensional synthetic mandibular models; (2) an in vitro study using an artificial mandible with 2 dental implants acquired with an i-CAT system; and (3) two clinical case studies. The results shown positional errors of 67+/-34μm and 108μm, and angular misfits of 0.15+/-0.08° and 1.4°, for experiment 1 and 2, respectively. Moreover, in experiment 3, visual assessment of clinical data results shown a coherent alignment of the reference implant. Overall, a novel image-based framework for implants' pose estimation from CBCT data was proposed, showing accurate results in agreement with dental prosthesis modelling requirements.
Anderson, K.K.
1994-05-01
Measurement error modeling is a statistical approach to the estimation of unknown model parameters which takes into account the measurement errors in all of the data. Approaches which ignore the measurement errors in so-called independent variables may yield inferior estimates of unknown model parameters. At the same time, experiment-wide variables (such as physical constants) are often treated as known without error, when in fact they were produced from prior experiments. Realistic assessments of the associated uncertainties in the experiment-wide variables can be utilized to improve the estimation of unknown model parameters. A maximum likelihood approach to incorporate measurements of experiment-wide variables and their associated uncertainties is presented here. An iterative algorithm is presented which yields estimates of unknown model parameters and their estimated covariance matrix. Further, the algorithm can be used to assess the sensitivity of the estimates and their estimated covariance matrix to the given experiment-wide variables and their associated uncertainties.
Do We Know Whether Researchers and Reviewers are Estimating Risk and Benefit Accurately?
Hey, Spencer Phillips; Kimmelman, Jonathan
2016-10-01
Accurate estimation of risk and benefit is integral to good clinical research planning, ethical review, and study implementation. Some commentators have argued that various actors in clinical research systems are prone to biased or arbitrary risk/benefit estimation. In this commentary, we suggest the evidence supporting such claims is very limited. Most prior work has imputed risk/benefit beliefs based on past behavior or goals, rather than directly measuring them. We describe an approach - forecast analysis - that would enable direct and effective measure of the quality of risk/benefit estimation. We then consider some objections and limitations to the forecasting approach. PMID:27197044
Do We Know Whether Researchers and Reviewers are Estimating Risk and Benefit Accurately?
Hey, Spencer Phillips; Kimmelman, Jonathan
2016-10-01
Accurate estimation of risk and benefit is integral to good clinical research planning, ethical review, and study implementation. Some commentators have argued that various actors in clinical research systems are prone to biased or arbitrary risk/benefit estimation. In this commentary, we suggest the evidence supporting such claims is very limited. Most prior work has imputed risk/benefit beliefs based on past behavior or goals, rather than directly measuring them. We describe an approach - forecast analysis - that would enable direct and effective measure of the quality of risk/benefit estimation. We then consider some objections and limitations to the forecasting approach.
An hp-adaptivity and error estimation for hyperbolic conservation laws
NASA Technical Reports Server (NTRS)
Bey, Kim S.
1995-01-01
This paper presents an hp-adaptive discontinuous Galerkin method for linear hyperbolic conservation laws. A priori and a posteriori error estimates are derived in mesh-dependent norms which reflect the dependence of the approximate solution on the element size (h) and the degree (p) of the local polynomial approximation. The a posteriori error estimate, based on the element residual method, provides bounds on the actual global error in the approximate solution. The adaptive strategy is designed to deliver an approximate solution with the specified level of error in three steps. The a posteriori estimate is used to assess the accuracy of a given approximate solution and the a priori estimate is used to predict the mesh refinements and polynomial enrichment needed to deliver the desired solution. Numerical examples demonstrate the reliability of the a posteriori error estimates and the effectiveness of the hp-adaptive strategy.
On the accurate estimation of gap fraction during daytime with digital cover photography
NASA Astrophysics Data System (ADS)
Hwang, Y. R.; Ryu, Y.; Kimm, H.; Macfarlane, C.; Lang, M.; Sonnentag, O.
2015-12-01
Digital cover photography (DCP) has emerged as an indirect method to obtain gap fraction accurately. Thus far, however, the intervention of subjectivity, such as determining the camera relative exposure value (REV) and threshold in the histogram, hindered computing accurate gap fraction. Here we propose a novel method that enables us to measure gap fraction accurately during daytime under various sky conditions by DCP. The novel method computes gap fraction using a single DCP unsaturated raw image which is corrected for scattering effects by canopies and a reconstructed sky image from the raw format image. To test the sensitivity of the novel method derived gap fraction to diverse REVs, solar zenith angles and canopy structures, we took photos in one hour interval between sunrise to midday under dense and sparse canopies with REV 0 to -5. The novel method showed little variation of gap fraction across different REVs in both dense and spares canopies across diverse range of solar zenith angles. The perforated panel experiment, which was used to test the accuracy of the estimated gap fraction, confirmed that the novel method resulted in the accurate and consistent gap fractions across different hole sizes, gap fractions and solar zenith angles. These findings highlight that the novel method opens new opportunities to estimate gap fraction accurately during daytime from sparse to dense canopies, which will be useful in monitoring LAI precisely and validating satellite remote sensing LAI products efficiently.
Error estimates for (semi-)empirical dispersion terms and large biomacromolecules.
Korth, Martin
2013-10-14
The first-principles modeling of biomaterials has made tremendous advances over the last few years with the ongoing growth of computing power and impressive developments in the application of density functional theory (DFT) codes to large systems. One important step forward was the development of dispersion corrections for DFT methods, which account for the otherwise neglected dispersive van der Waals (vdW) interactions. Approaches at different levels of theory exist, with the most often used (semi-)empirical ones based on pair-wise interatomic C6R(-6) terms. Similar terms are now also used in connection with semiempirical QM (SQM) methods and density functional tight binding methods (SCC-DFTB). Their basic structure equals the attractive term in Lennard-Jones potentials, common to most force field approaches, but they usually use some type of cutoff function to make the mixing of the (long-range) dispersion term with the already existing (short-range) dispersion and exchange-repulsion effects from the electronic structure theory methods possible. All these dispersion approximations were found to perform accurately for smaller systems, but error estimates for larger systems are very rare and completely missing for really large biomolecules. We derive such estimates for the dispersion terms of DFT, SQM and MM methods using error statistics for smaller systems and dispersion contribution estimates for the PDBbind database of protein-ligand interactions. We find that dispersion terms will usually not be a limiting factor for reaching chemical accuracy, though some force fields and large ligand sizes are problematic. PMID:23963227
Accurate Estimation of the Entropy of Rotation-Translation Probability Distributions.
Fogolari, Federico; Dongmo Foumthuim, Cedrix Jurgal; Fortuna, Sara; Soler, Miguel Angel; Corazza, Alessandra; Esposito, Gennaro
2016-01-12
The estimation of rotational and translational entropies in the context of ligand binding has been the subject of long-time investigations. The high dimensionality (six) of the problem and the limited amount of sampling often prevent the required resolution to provide accurate estimates by the histogram method. Recently, the nearest-neighbor distance method has been applied to the problem, but the solutions provided either address rotation and translation separately, therefore lacking correlations, or use a heuristic approach. Here we address rotational-translational entropy estimation in the context of nearest-neighbor-based entropy estimation, solve the problem numerically, and provide an exact and an approximate method to estimate the full rotational-translational entropy.
Crop area estimation based on remotely-sensed data with an accurate but costly subsample
NASA Technical Reports Server (NTRS)
Gunst, R. F.
1985-01-01
Research activities conducted under the auspices of National Aeronautics and Space Administration Cooperative Agreement NCC 9-9 are discussed. During this contract period research efforts are concentrated in two primary areas. The first are is an investigation of the use of measurement error models as alternatives to least squares regression estimators of crop production or timber biomass. The secondary primary area of investigation is on the estimation of the mixing proportion of two-component mixture models. This report lists publications, technical reports, submitted manuscripts, and oral presentation generated by these research efforts. Possible areas of future research are mentioned.
Allowing for random errors in radiation dose estimates for the atomic bomb survivor data.
Pierce, D A; Stram, D O; Vaeth, M
1990-09-01
The presence of random errors in the individual radiation dose estimates for the A-bomb survivors causes underestimation of radiation effects in dose-response analyses, and also distorts the shape of dose-response curves. Statistical methods are presented which will adjust for these biases, provided that a valid statistical model for the dose estimation errors is used. Emphasis is on clarifying some rather subtle statistical issues. For most of this development the distinction between radiation dose and exposure is not critical. The proposed methods involve downward adjustment of dose estimates, but this does not imply that the dosimetry system is faulty. Rather, this is a part of the dose-response analysis required to remove biases in the risk estimates. The primary focus of this report is on linear dose-response models, but methods for linear-quadratic models are also considered briefly. Some plausible models for the dose estimation errors are considered, which have typical errors in a range of 30-40% of the true values, and sensitivity analysis of the resulting bias corrections is provided. It is found that for these error models the resulting estimates of excess cancer risk based on linear models are about 6-17% greater than estimates that make no allowance for dose estimation errors. This increase in risk estimates is reduced to about 4-11% if, as has often been done recently, survivors with dose estimates above 4 Gy are eliminated from the analysis.
Goal-oriented explicit residual-type error estimates in XFEM
NASA Astrophysics Data System (ADS)
Rüter, Marcus; Gerasimov, Tymofiy; Stein, Erwin
2013-08-01
A goal-oriented a posteriori error estimator is derived to control the error obtained while approximately evaluating a quantity of engineering interest, represented in terms of a given linear or nonlinear functional, using extended finite elements of Q1 type. The same approximation method is used to solve the dual problem as required for the a posteriori error analysis. It is shown that for both problems to be solved numerically the same singular enrichment functions can be used. The goal-oriented error estimator presented can be classified as explicit residual type, i.e. the residuals of the approximations are used directly to compute upper bounds on the error of the quantity of interest. This approach therefore extends the explicit residual-type error estimator for classical energy norm error control as recently presented in Gerasimov et al. (Int J Numer Meth Eng 90:1118-1155, 2012a). Without loss of generality, the a posteriori error estimator is applied to the model problem of linear elastic fracture mechanics. Thus, emphasis is placed on the fracture criterion, here the J-integral, as the chosen quantity of interest. Finally, various illustrative numerical examples are presented where, on the one hand, the error estimator is compared to its finite element counterpart and, on the other hand, improved enrichment functions, as introduced in Gerasimov et al. (2012b), are discussed.
ERIC Educational Resources Information Center
Shoemaker, David M.
Described and listed herein with concomitant sample input and output is the Fortran IV program which estimates parameters and standard errors of estimate per parameters for parameters estimated through multiple matrix sampling. The specific program is an improved and expanded version of an earlier version. (Author/BJG)
Multiclass Bayes error estimation by a feature space sampling technique
NASA Technical Reports Server (NTRS)
Mobasseri, B. G.; Mcgillem, C. D.
1979-01-01
A general Gaussian M-class N-feature classification problem is defined. An algorithm is developed that requires the class statistics as its only input and computes the minimum probability of error through use of a combined analytical and numerical integration over a sequence simplifying transformations of the feature space. The results are compared with those obtained by conventional techniques applied to a 2-class 4-feature discrimination problem with results previously reported and 4-class 4-feature multispectral scanner Landsat data classified by training and testing of the available data.
Estimation of finite population parameters with auxiliary information and response error.
González, L M; Singer, J M; Stanek, E J
2014-10-01
We use a finite population mixed model that accommodates response error in the survey variable of interest and auxiliary information to obtain optimal estimators of population parameters from data collected via simple random sampling. We illustrate the method with the estimation of a regression coefficient and conduct a simulation study to compare the performance of the empirical version of the proposed estimator (obtained by replacing variance components with estimates) with that of the least squares estimator usually employed in such settings. The results suggest that when the auxiliary variable distribution is skewed, the proposed estimator has a smaller mean squared error.
Investigation of error sources in regional inverse estimates of greenhouse gas emissions in Canada
NASA Astrophysics Data System (ADS)
Chan, E.; Chan, D.; Ishizawa, M.; Vogel, F.; Brioude, J.; Delcloo, A.; Wu, Y.; Jin, B.
2015-08-01
Inversion models can use atmospheric concentration measurements to estimate surface fluxes. This study is an evaluation of the errors in a regional flux inversion model for different provinces of Canada, Alberta (AB), Saskatchewan (SK) and Ontario (ON). Using CarbonTracker model results as the target, the synthetic data experiment analyses examined the impacts of the errors from the Bayesian optimisation method, prior flux distribution and the atmospheric transport model, as well as their interactions. The scaling factors for different sub-regions were estimated by the Markov chain Monte Carlo (MCMC) simulation and cost function minimization (CFM) methods. The CFM method results are sensitive to the relative size of the assumed model-observation mismatch and prior flux error variances. Experiment results show that the estimation error increases with the number of sub-regions using the CFM method. For the region definitions that lead to realistic flux estimates, the numbers of sub-regions for the western region of AB/SK combined and the eastern region of ON are 11 and 4 respectively. The corresponding annual flux estimation errors for the western and eastern regions using the MCMC (CFM) method are -7 and -3 % (0 and 8 %) respectively, when there is only prior flux error. The estimation errors increase to 36 and 94 % (40 and 232 %) resulting from transport model error alone. When prior and transport model errors co-exist in the inversions, the estimation errors become 5 and 85 % (29 and 201 %). This result indicates that estimation errors are dominated by the transport model error and can in fact cancel each other and propagate to the flux estimates non-linearly. In addition, it is possible for the posterior flux estimates having larger differences than the prior compared to the target fluxes, and the posterior uncertainty estimates could be unrealistically small that do not cover the target. The systematic evaluation of the different components of the inversion
Damon, Bruce M; Heemskerk, Anneriet M; Ding, Zhaohua
2012-06-01
Fiber curvature is a functionally significant muscle structural property, but its estimation from diffusion-tensor magnetic resonance imaging fiber tracking data may be confounded by noise. The purpose of this study was to investigate the use of polynomial fitting of fiber tracts for improving the accuracy and precision of fiber curvature (κ) measurements. Simulated image data sets were created in order to provide data with known values for κ and pennation angle (θ). Simulations were designed to test the effects of increasing inherent fiber curvature (3.8, 7.9, 11.8 and 15.3 m(-1)), signal-to-noise ratio (50, 75, 100 and 150) and voxel geometry (13.8- and 27.0-mm(3) voxel volume with isotropic resolution; 13.5-mm(3) volume with an aspect ratio of 4.0) on κ and θ measurements. In the originally reconstructed tracts, θ was estimated accurately under most curvature and all imaging conditions studied; however, the estimates of κ were imprecise and inaccurate. Fitting the tracts to second-order polynomial functions provided accurate and precise estimates of κ for all conditions except very high curvature (κ=15.3 m(-1)), while preserving the accuracy of the θ estimates. Similarly, polynomial fitting of in vivo fiber tracking data reduced the κ values of fitted tracts from those of unfitted tracts and did not change the θ values. Polynomial fitting of fiber tracts allows accurate estimation of physiologically reasonable values of κ, while preserving the accuracy of θ estimation.
Error Estimates Derived from the Data for Least-Squares Spline Fitting
Jerome Blair
2007-06-25
The use of least-squares fitting by cubic splines for the purpose of noise reduction in measured data is studied. Splines with variable mesh size are considered. The error, the difference between the input signal and its estimate, is divided into two sources: the R-error, which depends only on the noise and increases with decreasing mesh size, and the Ferror, which depends only on the signal and decreases with decreasing mesh size. The estimation of both errors as a function of time is demonstrated. The R-error estimation requires knowledge of the statistics of the noise and uses well-known methods. The primary contribution of the paper is a method for estimating the F-error that requires no prior knowledge of the signal except that it has four derivatives. It is calculated from the difference between two different spline fits to the data and is illustrated with Monte Carlo simulations and with an example.
Sliding mode output feedback control based on tracking error observer with disturbance estimator.
Xiao, Lingfei; Zhu, Yue
2014-07-01
For a class of systems who suffers from disturbances, an original output feedback sliding mode control method is presented based on a novel tracking error observer with disturbance estimator. The mathematical models of the systems are not required to be with high accuracy, and the disturbances can be vanishing or nonvanishing, while the bounds of disturbances are unknown. By constructing a differential sliding surface and employing reaching law approach, a sliding mode controller is obtained. On the basis of an extended disturbance estimator, a creative tracking error observer is produced. By using the observation of tracking error and the estimation of disturbance, the sliding mode controller is implementable. It is proved that the disturbance estimation error and tracking observation error are bounded, the sliding surface is reachable and the closed-loop system is robustly stable. The simulations on a servomotor positioning system and a five-degree-of-freedom active magnetic bearings system verify the effect of the proposed method.
Space-Time Error Representation and Estimation in Navier-Stokes Calculations
NASA Technical Reports Server (NTRS)
Barth, Timothy J.
2006-01-01
The mathematical framework for a-posteriori error estimation of functionals elucidated by Eriksson et al. [7] and Becker and Rannacher [3] is revisited in a space-time context. Using these theories, a hierarchy of exact and approximate error representation formulas are presented for use in error estimation and mesh adaptivity. Numerical space-time results for simple model problems as well as compressible Navier-Stokes flow at Re = 300 over a 2D circular cylinder are then presented to demonstrate elements of the error representation theory for time-dependent problems.
The effect of errors-in-variables on variance component estimation
NASA Astrophysics Data System (ADS)
Xu, Peiliang
2016-08-01
Although total least squares (TLS) has been widely applied, variance components in an errors-in-variables (EIV) model can be inestimable under certain conditions and unstable in the sense that small random errors can result in very large errors in the estimated variance components. We investigate the effect of the random design matrix on variance component (VC) estimation of MINQUE type by treating the design matrix as if it were errors-free, derive the first-order bias of the VC estimate, and construct bias-corrected VC estimators. As a special case, we obtain a bias-corrected estimate for the variance of unit weight. Although TLS methods are statistically rigorous, they can be computationally too expensive. We directly Taylor-expand the nonlinear weighted LS estimate of parameters up to the second-order approximation in terms of the random errors of the design matrix, derive the bias of the estimate, and use it to construct a bias-corrected weighted LS estimate. Bearing in mind that the random errors of the design matrix will create a bias in the normal matrix of the weighted LS estimate, we propose to calibrate the normal matrix by computing and then removing the bias from the normal matrix. As a result, we can obtain a new parameter estimate, which is called the N-calibrated weighted LS estimate. The simulations have shown that (i) errors-in-variables have a significant effect on VC estimation, if they are large/significant but treated as non-random. The variance components can be incorrectly estimated by more than one order of magnitude, depending on the nature of problems and the sizes of EIV; (ii) the bias-corrected VC estimate can effectively remove the bias of the VC estimate. If the signal-to-noise is small, higher order terms may be necessary. Nevertheless, since we construct the bias-corrected VC estimate by directly removing the estimated bias from the estimate itself, the simulation results have clearly indicated that there is a great risk to obtain
Improved estimates of coordinate error for molecular replacement
Oeffner, Robert D.; Bunkóczi, Gábor; McCoy, Airlie J.; Read, Randy J.
2013-11-01
A function for estimating the effective root-mean-square deviation in coordinates between two proteins has been developed that depends on both the sequence identity and the size of the protein and is optimized for use with molecular replacement in Phaser. A top peak translation-function Z-score of over 8 is found to be a reliable metric of when molecular replacement has succeeded. The estimate of the root-mean-square deviation (r.m.s.d.) in coordinates between the model and the target is an essential parameter for calibrating likelihood functions for molecular replacement (MR). Good estimates of the r.m.s.d. lead to good estimates of the variance term in the likelihood functions, which increases signal to noise and hence success rates in the MR search. Phaser has hitherto used an estimate of the r.m.s.d. that only depends on the sequence identity between the model and target and which was not optimized for the MR likelihood functions. Variance-refinement functionality was added to Phaser to enable determination of the effective r.m.s.d. that optimized the log-likelihood gain (LLG) for a correct MR solution. Variance refinement was subsequently performed on a database of over 21 000 MR problems that sampled a range of sequence identities, protein sizes and protein fold classes. Success was monitored using the translation-function Z-score (TFZ), where a TFZ of 8 or over for the top peak was found to be a reliable indicator that MR had succeeded for these cases with one molecule in the asymmetric unit. Good estimates of the r.m.s.d. are correlated with the sequence identity and the protein size. A new estimate of the r.m.s.d. that uses these two parameters in a function optimized to fit the mean of the refined variance is implemented in Phaser and improves MR outcomes. Perturbing the initial estimate of the r.m.s.d. from the mean of the distribution in steps of standard deviations of the distribution further increases MR success rates.
NASA Astrophysics Data System (ADS)
Hamaker, Henry Chris
1995-12-01
Statistical process control (SPC) techniques often use six times the standard deviation sigma to estimate the range of errors within a process. Two assumptions are inherent in this choice of metric for the range: (1) the normal distribution adequately describes the errors, and (2) the fraction of errors falling within plus or minus 3 sigma, about 99.73%, is sufficiently large that we may consider the fraction occurring outside this range to be negligible. In state-of-the-art photomasks, however, the assumption of normality frequently breaks down, and consequently plus or minus 3 sigma is not a good estimate of the range of errors. In this study, we show that improved estimates for the effective maximum error Em, which is defined as the value for which 99.73% of all errors fall within plus or minus Em of the mean mu, may be obtained by quantifying the deviation from normality of the error distributions using the skewness and kurtosis of the error sampling. Data are presented indicating that in laser reticle- writing tools, Em less than or equal to 3 sigma. We also extend this technique for estimating the range of errors to specifications that are usually described by mu plus 3 sigma. The implications for SPC are examined.
Error estimations and their biases in Monte Carlo eigenvalue calculations
Ueki, Taro; Mori, Takamasa; Nakagawa, Masayuki
1997-01-01
In the Monte Carlo eigenvalue calculation of neutron transport, the eigenvalue is calculated as the average of multiplication factors from cycles, which are called the cycle k{sub eff}`s. Biases in the estimators of the variance and intercycle covariances in Monte Carlo eigenvalue calculations are analyzed. The relations among the real and apparent values of variances and intercycle covariances are derived, where real refers to a true value that is calculated from independently repeated Monte Carlo runs and apparent refers to the expected value of estimates from a single Monte Carlo run. Next, iterative methods based on the foregoing relations are proposed to estimate the standard deviation of the eigenvalue. The methods work well for the cases in which the ratios of the real to apparent values of variances are between 1.4 and 3.1. Even in the case where the foregoing ratio is >5, >70% of the standard deviation estimates fall within 40% from the true value.
Gap filling strategies and error in estimating annual soil respiration
Technology Transfer Automated Retrieval System (TEKTRAN)
Soil respiration (Rsoil) is one of the largest CO2 fluxes in the global carbon (C) cycle. Estimation of annual Rsoil requires extrapolation of survey measurements or gap-filling of automated records to produce a complete time series. While many gap-filling methodologies have been employed, there is ...
EIA Corrects Errors in Its Drilling Activity Estimates Series
1998-01-01
The Energy Information Administration (EIA) has published monthly and annual estimates of oil and gas drilling activity since 1978. These data are key information for many industry analysts, serving as a leading indicator of trends in the industry and a barometer of general industry status.
NASA Astrophysics Data System (ADS)
Montes-Hugo, M.; Bouakba, H.; Arnone, R.
2014-06-01
The understanding of phytoplankton dynamics in the Gulf of the Saint Lawrence (GSL) is critical for managing major fisheries off the Canadian East coast. In this study, the accuracy of two atmospheric correction techniques (NASA standard algorithm, SA, and Kuchinke's spectral optimization, KU) and three ocean color inversion models (Carder's empirical for SeaWiFS (Sea-viewing Wide Field-of-View Sensor), EC, Lee's quasi-analytical, QAA, and Garver- Siegel-Maritorena semi-empirical, GSM) for estimating the phytoplankton absorption coefficient at 443 nm (aph(443)) and the chlorophyll concentration (chl) in the GSL is examined. Each model was validated based on SeaWiFS images and shipboard measurements obtained during May of 2000 and April 2001. In general, aph(443) estimates derived from coupling KU and QAA models presented the smallest differences with respect to in situ determinations as measured by High Pressure liquid Chromatography measurements (median absolute bias per cruise up to 0.005, RMSE up to 0.013). A change on the inversion approach used for estimating aph(443) values produced up to 43.4% increase on prediction error as inferred from the median relative bias per cruise. Likewise, the impact of applying different atmospheric correction schemes was secondary and represented an additive error of up to 24.3%. By using SeaDAS (SeaWiFS Data Analysis System) default values for the optical cross section of phytoplankton (i.e., aph(443) = aph(443)/chl = 0.056 m2mg-1), the median relative bias of our chl estimates as derived from the most accurate spaceborne aph(443) retrievals and with respect to in situ determinations increased up to 29%.
Kerker, Bonnie D.; Owens, Pamela L.; Zigler, Edward; Horwitz, Sarah M.
2004-01-01
OBJECTIVES: The objectives of this literature review were to assess current challenges to estimating the prevalence of mental health disorders among individuals with mental retardation (MR) and to develop recommendations to improve such estimates for this population. METHODS: The authors identified 200 peer-reviewed articles, book chapters, government documents, or reports from national and international organizations on the mental health status of people with MR. Based on the study's inclusion criteria, 52 articles were included in the review. RESULTS: Available data reveal inconsistent estimates of the prevalence of mental health disorders among those with MR, but suggest that some mental health conditions are more common among these individuals than in the general population. Two main challenges to identifying accurate prevalence estimates were found: (1) health care providers have difficulty diagnosing mental health conditions among individuals with MR; and (2) methodological limitations of previous research inhibit confidence in study results. CONCLUSIONS: Accurate prevalence estimates are necessary to ensure the availability of appropriate treatment services. To this end, health care providers should receive more training regarding the mental health treatment of individuals with MR. Further, government officials should discuss mechanisms of collecting nationally representative data, and the research community should utilize consistent methods with representative samples when studying mental health conditions in this population. PMID:15219798
A posteriori error estimates for the Johnson–Nédélec FEM–BEM coupling
Aurada, M.; Feischl, M.; Karkulik, M.; Praetorius, D.
2012-01-01
Only very recently, Sayas [The validity of Johnson–Nédélec's BEM-FEM coupling on polygonal interfaces. SIAM J Numer Anal 2009;47:3451–63] proved that the Johnson–Nédélec one-equation approach from [On the coupling of boundary integral and finite element methods. Math Comput 1980;35:1063–79] provides a stable coupling of finite element method (FEM) and boundary element method (BEM). In our work, we now adapt the analytical results for different a posteriori error estimates developed for the symmetric FEM–BEM coupling to the Johnson–Nédélec coupling. More precisely, we analyze the weighted-residual error estimator, the two-level error estimator, and different versions of (h−h/2)-based error estimators. In numerical experiments, we use these estimators to steer h-adaptive algorithms, and compare the effectivity of the different approaches. PMID:22347772
Zollanvari, Amin; Genton, Marc G
2013-08-01
We provide a fundamental theorem that can be used in conjunction with Kolmogorov asymptotic conditions to derive the first moments of well-known estimators of the actual error rate in linear discriminant analysis of a multivariate Gaussian model under the assumption of a common known covariance matrix. The estimators studied in this paper are plug-in and smoothed resubstitution error estimators, both of which have not been studied before under Kolmogorov asymptotic conditions. As a result of this work, we present an optimal smoothing parameter that makes the smoothed resubstitution an unbiased estimator of the true error. For the sake of completeness, we further show how to utilize the presented fundamental theorem to achieve several previously reported results, namely the first moment of the resubstitution estimator and the actual error rate. We provide numerical examples to show the accuracy of the succeeding finite sample approximations in situations where the number of dimensions is comparable or even larger than the sample size.
Error estimation and adaptive mesh refinement for parallel analysis of shell structures
NASA Technical Reports Server (NTRS)
Keating, Scott C.; Felippa, Carlos A.; Park, K. C.
1994-01-01
The formulation and application of element-level, element-independent error indicators is investigated. This research culminates in the development of an error indicator formulation which is derived based on the projection of element deformation onto the intrinsic element displacement modes. The qualifier 'element-level' means that no information from adjacent elements is used for error estimation. This property is ideally suited for obtaining error values and driving adaptive mesh refinements on parallel computers where access to neighboring elements residing on different processors may incur significant overhead. In addition such estimators are insensitive to the presence of physical interfaces and junctures. An error indicator qualifies as 'element-independent' when only visible quantities such as element stiffness and nodal displacements are used to quantify error. Error evaluation at the element level and element independence for the error indicator are highly desired properties for computing error in production-level finite element codes. Four element-level error indicators have been constructed. Two of the indicators are based on variational formulation of the element stiffness and are element-dependent. Their derivations are retained for developmental purposes. The second two indicators mimic and exceed the first two in performance but require no special formulation of the element stiffness mesh refinement which we demonstrate for two dimensional plane stress problems. The parallelizing of substructures and adaptive mesh refinement is discussed and the final error indicator using two-dimensional plane-stress and three-dimensional shell problems is demonstrated.
Error estimation and adaptive mesh refinement for parallel analysis of shell structures
NASA Astrophysics Data System (ADS)
Keating, Scott C.; Felippa, Carlos A.; Park, K. C.
1994-11-01
The formulation and application of element-level, element-independent error indicators is investigated. This research culminates in the development of an error indicator formulation which is derived based on the projection of element deformation onto the intrinsic element displacement modes. The qualifier 'element-level' means that no information from adjacent elements is used for error estimation. This property is ideally suited for obtaining error values and driving adaptive mesh refinements on parallel computers where access to neighboring elements residing on different processors may incur significant overhead. In addition such estimators are insensitive to the presence of physical interfaces and junctures. An error indicator qualifies as 'element-independent' when only visible quantities such as element stiffness and nodal displacements are used to quantify error. Error evaluation at the element level and element independence for the error indicator are highly desired properties for computing error in production-level finite element codes. Four element-level error indicators have been constructed. Two of the indicators are based on variational formulation of the element stiffness and are element-dependent. Their derivations are retained for developmental purposes. The second two indicators mimic and exceed the first two in performance but require no special formulation of the element stiffness mesh refinement which we demonstrate for two dimensional plane stress problems. The parallelizing of substructures and adaptive mesh refinement is discussed and the final error indicator using two-dimensional plane-stress and three-dimensional shell problems is demonstrated.
A-Posteriori Error Estimation for Hyperbolic Conservation Laws with Constraint
NASA Technical Reports Server (NTRS)
Barth, Timothy
2004-01-01
This lecture considers a-posteriori error estimates for the numerical solution of conservation laws with time invariant constraints such as those arising in magnetohydrodynamics (MHD) and gravitational physics. Using standard duality arguments, a-posteriori error estimates for the discontinuous Galerkin finite element method are then presented for MHD with solenoidal constraint. From these estimates, a procedure for adaptive discretization is outlined. A taxonomy of Green's functions for the linearized MHD operator is given which characterizes the domain of dependence for pointwise errors. The extension to other constrained systems such as the Einstein equations of gravitational physics are then considered. Finally, future directions and open problems are discussed.
A Method to Accurately Estimate the Muscular Torques of Human Wearing Exoskeletons by Torque Sensors
Hwang, Beomsoo; Jeon, Doyoung
2015-01-01
In exoskeletal robots, the quantification of the user’s muscular effort is important to recognize the user’s motion intentions and evaluate motor abilities. In this paper, we attempt to estimate users’ muscular efforts accurately using joint torque sensor which contains the measurements of dynamic effect of human body such as the inertial, Coriolis, and gravitational torques as well as torque by active muscular effort. It is important to extract the dynamic effects of the user’s limb accurately from the measured torque. The user’s limb dynamics are formulated and a convenient method of identifying user-specific parameters is suggested for estimating the user’s muscular torque in robotic exoskeletons. Experiments were carried out on a wheelchair-integrated lower limb exoskeleton, EXOwheel, which was equipped with torque sensors in the hip and knee joints. The proposed methods were evaluated by 10 healthy participants during body weight-supported gait training. The experimental results show that the torque sensors are to estimate the muscular torque accurately in cases of relaxed and activated muscle conditions. PMID:25860074
Hwang, Beomsoo; Jeon, Doyoung
2015-04-09
In exoskeletal robots, the quantification of the user's muscular effort is important to recognize the user's motion intentions and evaluate motor abilities. In this paper, we attempt to estimate users' muscular efforts accurately using joint torque sensor which contains the measurements of dynamic effect of human body such as the inertial, Coriolis, and gravitational torques as well as torque by active muscular effort. It is important to extract the dynamic effects of the user's limb accurately from the measured torque. The user's limb dynamics are formulated and a convenient method of identifying user-specific parameters is suggested for estimating the user's muscular torque in robotic exoskeletons. Experiments were carried out on a wheelchair-integrated lower limb exoskeleton, EXOwheel, which was equipped with torque sensors in the hip and knee joints. The proposed methods were evaluated by 10 healthy participants during body weight-supported gait training. The experimental results show that the torque sensors are to estimate the muscular torque accurately in cases of relaxed and activated muscle conditions.
On-line estimation of error covariance parameters for atmospheric data assimilation
NASA Technical Reports Server (NTRS)
Dee, Dick P.
1995-01-01
A simple scheme is presented for on-line estimation of covariance parameters in statistical data assimilation systems. The scheme is based on a maximum-likelihood approach in which estimates are produced on the basis of a single batch of simultaneous observations. Simple-sample covariance estimation is reasonable as long as the number of available observations exceeds the number of tunable parameters by two or three orders of magnitude. Not much is known at present about model error associated with actual forecast systems. Our scheme can be used to estimate some important statistical model error parameters such as regionally averaged variances or characteristic correlation length scales. The advantage of the single-sample approach is that it does not rely on any assumptions about the temporal behavior of the covariance parameters: time-dependent parameter estimates can be continuously adjusted on the basis of current observations. This is of practical importance since it is likely to be the case that both model error and observation error strongly depend on the actual state of the atmosphere. The single-sample estimation scheme can be incorporated into any four-dimensional statistical data assimilation system that involves explicit calculation of forecast error covariances, including optimal interpolation (OI) and the simplified Kalman filter (SKF). The computational cost of the scheme is high but not prohibitive; on-line estimation of one or two covariance parameters in each analysis box of an operational bozed-OI system is currently feasible. A number of numerical experiments performed with an adaptive SKF and an adaptive version of OI, using a linear two-dimensional shallow-water model and artificially generated model error are described. The performance of the nonadaptive versions of these methods turns out to depend rather strongly on correct specification of model error parameters. These parameters are estimated under a variety of conditions, including
Helb, Danica A.; Tetteh, Kevin K. A.; Felgner, Philip L.; Skinner, Jeff; Hubbard, Alan; Arinaitwe, Emmanuel; Mayanja-Kizza, Harriet; Ssewanyana, Isaac; Kamya, Moses R.; Beeson, James G.; Tappero, Jordan; Smith, David L.; Crompton, Peter D.; Rosenthal, Philip J.; Dorsey, Grant; Drakeley, Christopher J.; Greenhouse, Bryan
2015-01-01
Tools to reliably measure Plasmodium falciparum (Pf) exposure in individuals and communities are needed to guide and evaluate malaria control interventions. Serologic assays can potentially produce precise exposure estimates at low cost; however, current approaches based on responses to a few characterized antigens are not designed to estimate exposure in individuals. Pf-specific antibody responses differ by antigen, suggesting that selection of antigens with defined kinetic profiles will improve estimates of Pf exposure. To identify novel serologic biomarkers of malaria exposure, we evaluated responses to 856 Pf antigens by protein microarray in 186 Ugandan children, for whom detailed Pf exposure data were available. Using data-adaptive statistical methods, we identified combinations of antibody responses that maximized information on an individual’s recent exposure. Responses to three novel Pf antigens accurately classified whether an individual had been infected within the last 30, 90, or 365 d (cross-validated area under the curve = 0.86–0.93), whereas responses to six antigens accurately estimated an individual’s malaria incidence in the prior year. Cross-validated incidence predictions for individuals in different communities provided accurate stratification of exposure between populations and suggest that precise estimates of community exposure can be obtained from sampling a small subset of that community. In addition, serologic incidence predictions from cross-sectional samples characterized heterogeneity within a community similarly to 1 y of continuous passive surveillance. Development of simple ELISA-based assays derived from the successful selection strategy outlined here offers the potential to generate rich epidemiologic surveillance data that will be widely accessible to malaria control programs. PMID:26216993
Joint Estimation of Contamination, Error and Demography for Nuclear DNA from Ancient Humans
Slatkin, Montgomery
2016-01-01
When sequencing an ancient DNA sample from a hominin fossil, DNA from present-day humans involved in excavation and extraction will be sequenced along with the endogenous material. This type of contamination is problematic for downstream analyses as it will introduce a bias towards the population of the contaminating individual(s). Quantifying the extent of contamination is a crucial step as it allows researchers to account for possible biases that may arise in downstream genetic analyses. Here, we present an MCMC algorithm to co-estimate the contamination rate, sequencing error rate and demographic parameters—including drift times and admixture rates—for an ancient nuclear genome obtained from human remains, when the putative contaminating DNA comes from present-day humans. We assume we have a large panel representing the putative contaminant population (e.g. European, East Asian or African). The method is implemented in a C++ program called ‘Demographic Inference with Contamination and Error’ (DICE). We applied it to simulations and genome data from ancient Neanderthals and modern humans. With reasonable levels of genome sequence coverage (>3X), we find we can recover accurate estimates of all these parameters, even when the contamination rate is as high as 50%. PMID:27049965
NASA Technical Reports Server (NTRS)
Barth, Timothy J.; Larson, Mats G.
2000-01-01
We consider a posteriori error estimates for finite volume and finite element methods on arbitrary meshes subject to prescribed error functionals. Error estimates of this type are useful in a number of computational settings: (1) quantitative prediction of the numerical solution error, (2) adaptive meshing, and (3) load balancing of work on parallel computing architectures. Our analysis recasts the class of Godunov finite volumes schemes as a particular form of discontinuous Galerkin method utilizing broken space approximation obtained via reconstruction of cell-averaged data. In this general framework, weighted residual error bounds are readily obtained using duality arguments and Galerkin orthogonality. Additional consideration is given to issues such as nonlinearity, efficiency, and the relationship to other existing methods. Numerical examples are given throughout the talk to demonstrate the sharpness of the estimates and efficiency of the techniques. Additional information is contained in the original.
The estimation error covariance matrix for the ideal state reconstructor with measurement noise
NASA Technical Reports Server (NTRS)
Polites, Michael E.
1988-01-01
A general expression is derived for the state estimation error covariance matrix for the Ideal State Reconstructor when the input measurements are corrupted by measurement noise. An example is presented which shows that the more measurements used in estimating the state at a given time, the better the estimator.
A Posteriori Error Estimation for Discontinuous Galerkin Approximations of Hyperbolic Systems
NASA Technical Reports Server (NTRS)
Larson, Mats G.; Barth, Timothy J.
1999-01-01
This article considers a posteriori error estimation of specified functionals for first-order systems of conservation laws discretized using the discontinuous Galerkin (DG) finite element method. Using duality techniques, we derive exact error representation formulas for both linear and nonlinear functionals given an associated bilinear or nonlinear variational form. Weighted residual approximations of the exact error representation formula are then proposed and numerically evaluated for Ringleb flow, an exact solution of the 2-D Euler equations.
Jubran, Mohammad K; Bansal, Manu; Kondi, Lisimachos P; Grover, Rohan
2009-01-01
In this paper, we propose an optimal strategy for the transmission of scalable video over packet-based multiple-input multiple-output (MIMO) systems. The scalable extension of H.264/AVC that provides a combined temporal, quality and spatial scalability is used. For given channel conditions, we develop a method for the estimation of the distortion of the received video and propose different error concealment schemes. We show the accuracy of our distortion estimation algorithm in comparison with simulated wireless video transmission with packet errors. In the proposed MIMO system, we employ orthogonal space-time block codes (O-STBC) that guarantee independent transmission of different symbols within the block code. In the proposed constrained bandwidth allocation framework, we use the estimated end-to-end decoder distortion to optimally select the application layer parameters, i.e., quantization parameter (QP) and group of pictures (GOP) size, and physical layer parameters, i.e., rate-compatible turbo (RCPT) code rate and symbol constellation. Results show the substantial performance gain by using different symbol constellations across the scalable layers as compared to a fixed constellation.
Effect of geocoding errors on traffic-related air pollutant exposure and concentration estimates
Exposure to traffic-related air pollutants is highest very near roads, and thus exposure estimates are sensitive to positional errors. This study evaluates positional and PM2.5 concentration errors that result from the use of automated geocoding methods and from linearized approx...
Errors and parameter estimation in precipitation-runoff modeling 2. Case study.
Troutman, B.M.
1985-01-01
A case study is presented which illustrates some of the error analysis, sensitivity analysis, and parameter estimation procedures reviewed in the first part of this paper. It is shown that those procedures, most of which come from statistical nonlinear regression theory, are invaluable in interpreting errors in precipitation-runoff modeling and in identifying appropriate calibration strategies. -Author
ERIC Educational Resources Information Center
Keuning, Jos; Hemker, Bas
2014-01-01
The data collection of a cohort study requires making many decisions. Each decision may introduce error in the statistical analyses conducted later on. In the present study, a procedure was developed for estimation of the error made due to the composition of the sample, the item selection procedure, and the test equating process. The math results…
NASA Technical Reports Server (NTRS)
Platnick, Steven; Wind, Galina; Xiong, Xiaoxiong
2011-01-01
MODIS retrievals of cloud optical thickness and effective particle radius employ a well-known VNIR/SWIR solar reflectance technique. For this type of algorithm, we evaluate the uncertainty in simultaneous retrievals of these two parameters to pixel-level (scene-dependent) radiometric error estimates as well as other tractable error sources.
Type I Error Rates and Power Estimates of Selected Parametric and Nonparametric Tests of Scale.
ERIC Educational Resources Information Center
Olejnik, Stephen F.; Algina, James
1987-01-01
Estimated Type I Error rates and power are reported for the Brown-Forsythe, O'Brien, Klotz, and Siegal-Tukey procedures. The effect of aligning the data using deviations from group means or group medians is investigated. (RB)
Logarithmic diagrams in acid-base titrations and estimation of titration errors.
Wänninen, E
1980-01-01
The use of a logarithmic diagram for the estimation of the pH-value at the equivalence point and the titration error when a solution containing one or two acids is titrated with standard alkali is described.
A New Formulation of the Filter-Error Method for Aerodynamic Parameter Estimation in Turbulence
NASA Technical Reports Server (NTRS)
Grauer, Jared A.; Morelli, Eugene A.
2015-01-01
A new formulation of the filter-error method for estimating aerodynamic parameters in nonlinear aircraft dynamic models during turbulence was developed and demonstrated. The approach uses an estimate of the measurement noise covariance to identify the model parameters, their uncertainties, and the process noise covariance, in a relaxation method analogous to the output-error method. Prior information on the model parameters and uncertainties can be supplied, and a post-estimation correction to the uncertainty was included to account for colored residuals not considered in the theory. No tuning parameters, needing adjustment by the analyst, are used in the estimation. The method was demonstrated in simulation using the NASA Generic Transport Model, then applied to the subscale T-2 jet-engine transport aircraft flight. Modeling results in different levels of turbulence were compared with results from time-domain output error and frequency- domain equation error methods to demonstrate the effectiveness of the approach.
The use of neural networks in identifying error sources in satellite-derived tropical SST estimates.
Lee, Yung-Hsiang; Ho, Chung-Ru; Su, Feng-Chun; Kuo, Nan-Jung; Cheng, Yu-Hsin
2011-01-01
An neural network model of data mining is used to identify error sources in satellite-derived tropical sea surface temperature (SST) estimates from thermal infrared sensors onboard the Geostationary Operational Environmental Satellite (GOES). By using the Back Propagation Network (BPN) algorithm, it is found that air temperature, relative humidity, and wind speed variation are the major factors causing the errors of GOES SST products in the tropical Pacific. The accuracy of SST estimates is also improved by the model. The root mean square error (RMSE) for the daily SST estimate is reduced from 0.58 K to 0.38 K and mean absolute percentage error (MAPE) is 1.03%. For the hourly mean SST estimate, its RMSE is also reduced from 0.66 K to 0.44 K and the MAPE is 1.3%.
Estimating the Effective Permittivity for Reconstructing Accurate Microwave-Radar Images.
Lavoie, Benjamin R; Okoniewski, Michal; Fear, Elise C
2016-01-01
We present preliminary results from a method for estimating the optimal effective permittivity for reconstructing microwave-radar images. Using knowledge of how microwave-radar images are formed, we identify characteristics that are typical of good images, and define a fitness function to measure the relative image quality. We build a polynomial interpolant of the fitness function in order to identify the most likely permittivity values of the tissue. To make the estimation process more efficient, the polynomial interpolant is constructed using a locally and dimensionally adaptive sampling method that is a novel combination of stochastic collocation and polynomial chaos. Examples, using a series of simulated, experimental and patient data collected using the Tissue Sensing Adaptive Radar system, which is under development at the University of Calgary, are presented. These examples show how, using our method, accurate images can be reconstructed starting with only a broad estimate of the permittivity range.
Estimating the Effective Permittivity for Reconstructing Accurate Microwave-Radar Images
Lavoie, Benjamin R.; Okoniewski, Michal; Fear, Elise C.
2016-01-01
We present preliminary results from a method for estimating the optimal effective permittivity for reconstructing microwave-radar images. Using knowledge of how microwave-radar images are formed, we identify characteristics that are typical of good images, and define a fitness function to measure the relative image quality. We build a polynomial interpolant of the fitness function in order to identify the most likely permittivity values of the tissue. To make the estimation process more efficient, the polynomial interpolant is constructed using a locally and dimensionally adaptive sampling method that is a novel combination of stochastic collocation and polynomial chaos. Examples, using a series of simulated, experimental and patient data collected using the Tissue Sensing Adaptive Radar system, which is under development at the University of Calgary, are presented. These examples show how, using our method, accurate images can be reconstructed starting with only a broad estimate of the permittivity range. PMID:27611785
Estimating the Effective Permittivity for Reconstructing Accurate Microwave-Radar Images.
Lavoie, Benjamin R; Okoniewski, Michal; Fear, Elise C
2016-01-01
We present preliminary results from a method for estimating the optimal effective permittivity for reconstructing microwave-radar images. Using knowledge of how microwave-radar images are formed, we identify characteristics that are typical of good images, and define a fitness function to measure the relative image quality. We build a polynomial interpolant of the fitness function in order to identify the most likely permittivity values of the tissue. To make the estimation process more efficient, the polynomial interpolant is constructed using a locally and dimensionally adaptive sampling method that is a novel combination of stochastic collocation and polynomial chaos. Examples, using a series of simulated, experimental and patient data collected using the Tissue Sensing Adaptive Radar system, which is under development at the University of Calgary, are presented. These examples show how, using our method, accurate images can be reconstructed starting with only a broad estimate of the permittivity range. PMID:27611785
Accurate estimation of object location in an image sequence using helicopter flight data
NASA Technical Reports Server (NTRS)
Tang, Yuan-Liang; Kasturi, Rangachar
1994-01-01
In autonomous navigation, it is essential to obtain a three-dimensional (3D) description of the static environment in which the vehicle is traveling. For a rotorcraft conducting low-latitude flight, this description is particularly useful for obstacle detection and avoidance. In this paper, we address the problem of 3D position estimation for static objects from a monocular sequence of images captured from a low-latitude flying helicopter. Since the environment is static, it is well known that the optical flow in the image will produce a radiating pattern from the focus of expansion. We propose a motion analysis system which utilizes the epipolar constraint to accurately estimate 3D positions of scene objects in a real world image sequence taken from a low-altitude flying helicopter. Results show that this approach gives good estimates of object positions near the rotorcraft's intended flight-path.
Effective Echo Detection and Accurate Orbit Estimation Algorithms for Space Debris Radar
NASA Astrophysics Data System (ADS)
Isoda, Kentaro; Sakamoto, Takuya; Sato, Toru
Orbit estimation of space debris, objects of no inherent value orbiting the earth, is a task that is important for avoiding collisions with spacecraft. The Kamisaibara Spaceguard Center radar system was built in 2004 as the first radar facility in Japan devoted to the observation of space debris. In order to detect the smaller debris, coherent integration is effective in improving SNR (Signal-to-Noise Ratio). However, it is difficult to apply coherent integration to real data because the motions of the targets are unknown. An effective algorithm is proposed for echo detection and orbit estimation of the faint echoes from space debris. The characteristics of the evaluation function are utilized by the algorithm. Experiments show the proposed algorithm improves SNR by 8.32dB and enables estimation of orbital parameters accurately to allow for re-tracking with a single radar.
Loewe, Axel; Wilhelms, Mathias; Schmid, Jochen; Krause, Mathias J.; Fischer, Fathima; Thomas, Dierk; Scholz, Eberhard P.; Dössel, Olaf; Seemann, Gunnar
2016-01-01
Computational models of cardiac electrophysiology provided insights into arrhythmogenesis and paved the way toward tailored therapies in the last years. To fully leverage in silico models in future research, these models need to be adapted to reflect pathologies, genetic alterations, or pharmacological effects, however. A common approach is to leave the structure of established models unaltered and estimate the values of a set of parameters. Today’s high-throughput patch clamp data acquisition methods require robust, unsupervised algorithms that estimate parameters both accurately and reliably. In this work, two classes of optimization approaches are evaluated: gradient-based trust-region-reflective and derivative-free particle swarm algorithms. Using synthetic input data and different ion current formulations from the Courtemanche et al. electrophysiological model of human atrial myocytes, we show that neither of the two schemes alone succeeds to meet all requirements. Sequential combination of the two algorithms did improve the performance to some extent but not satisfactorily. Thus, we propose a novel hybrid approach coupling the two algorithms in each iteration. This hybrid approach yielded very accurate estimates with minimal dependency on the initial guess using synthetic input data for which a ground truth parameter set exists. When applied to measured data, the hybrid approach yielded the best fit, again with minimal variation. Using the proposed algorithm, a single run is sufficient to estimate the parameters. The degree of superiority over the other investigated algorithms in terms of accuracy and robustness depended on the type of current. In contrast to the non-hybrid approaches, the proposed method proved to be optimal for data of arbitrary signal to noise ratio. The hybrid algorithm proposed in this work provides an important tool to integrate experimental data into computational models both accurately and robustly allowing to assess the often non
ERIC Educational Resources Information Center
Meyvis, Tom; Ratner, Rebecca K.; Levav, Jonathan
2010-01-01
Why do affective forecasting errors persist in the face of repeated disconfirming evidence? Five studies demonstrate that people misremember their forecasts as consistent with their experience and thus fail to perceive the extent of their forecasting error. As a result, people do not learn from past forecasting errors and fail to adjust subsequent…
Error covariance calculation for forecast bias estimation in hydrologic data assimilation
NASA Astrophysics Data System (ADS)
Pauwels, Valentijn R. N.; De Lannoy, Gabriëlle J. M.
2015-12-01
To date, an outstanding issue in hydrologic data assimilation is a proper way of dealing with forecast bias. A frequently used method to bypass this problem is to rescale the observations to the model climatology. While this approach improves the variability in the modeled soil wetness and discharge, it is not designed to correct the results for any bias. Alternatively, attempts have been made towards incorporating dynamic bias estimates into the assimilation algorithm. Persistent bias models are most often used to propagate the bias estimate, where the a priori forecast bias error covariance is calculated as a constant fraction of the unbiased a priori state error covariance. The latter approach is a simplification to the explicit propagation of the bias error covariance. The objective of this paper is to examine to which extent the choice for the propagation of the bias estimate and its error covariance influence the filter performance. An Observation System Simulation Experiment (OSSE) has been performed, in which ground water storage observations are assimilated into a biased conceptual hydrologic model. The magnitudes of the forecast bias and state error covariances are calibrated by optimizing the innovation statistics of groundwater storage. The obtained bias propagation models are found to be identical to persistent bias models. After calibration, both approaches for the estimation of the forecast bias error covariance lead to similar results, with a realistic attribution of error variances to the bias and state estimate, and significant reductions of the bias in both the estimates of groundwater storage and discharge. Overall, the results in this paper justify the use of the traditional approach for online bias estimation with a persistent bias model and a simplified forecast bias error covariance estimation.
ZZ-Type a posteriori error estimators for adaptive boundary element methods on a curve.
Feischl, Michael; Führer, Thomas; Karkulik, Michael; Praetorius, Dirk
2014-01-01
In the context of the adaptive finite element method (FEM), ZZ-error estimators named after Zienkiewicz and Zhu (1987) [52] are mathematically well-established and widely used in practice. In this work, we propose and analyze ZZ-type error estimators for the adaptive boundary element method (BEM). We consider weakly singular and hyper-singular integral equations and prove, in particular, convergence of the related adaptive mesh-refining algorithms. Throughout, the theoretical findings are underlined by numerical experiments.
Ajith, P.; Bose, Sukanta
2009-04-15
We assess the statistical errors in estimating the parameters of nonspinning black hole binaries using ground-based gravitational-wave detectors. While past assessments were based on partial information provided by only the inspiral and/or ring-down pieces of the coalescence signal, the recent progress in analytical and numerical relativity enables us to make more accurate projections using complete inspiral-merger-ring-down waveforms. We employ the Fisher information-matrix formalism to estimate how accurately the source parameters will be measurable using a single interferometric detector as well as a network of interferometers. Those estimates are further vetted by full-fledged Monte Carlo simulations. We find that the parameter accuracies of the complete waveform are, in general, significantly better than those of just the inspiral waveform in the case of binaries with total mass M > or approx. 20M{sub {center_dot}}. In particular, for the case of the Advanced LIGO detector, parameter estimation is the most accurate in the M=100-200M{sub {center_dot}} range. For an M=100M{sub {center_dot}} system, the errors in measuring the total mass and the symmetric mass-ratio are reduced by an order of magnitude or more compared to inspiral waveforms. Furthermore, for binaries located at a fixed luminosity distance d{sub L}, and observed with the Advanced LIGO-Advanced Virgo network, the sky-position error is expected to vary widely across the sky: For M=100M{sub {center_dot}} systems at d{sub L}=1 Gpc, this variation ranges mostly from about a hundredth of a square degree to about a square degree, with an average value of nearly a tenth of a square degree. This is more than 40 times better than the average sky-position accuracy of inspiral waveforms at this mass range. For the mass parameters as well as the sky position, this improvement in accuracy is due partly to the increased signal-to-noise ratio and partly to the information about these parameters harnessed through
Accurate Time-Dependent Traveling-Wave Tube Model Developed for Computational Bit-Error-Rate Testing
NASA Technical Reports Server (NTRS)
Kory, Carol L.
2001-01-01
The phenomenal growth of the satellite communications industry has created a large demand for traveling-wave tubes (TWT's) operating with unprecedented specifications requiring the design and production of many novel devices in record time. To achieve this, the TWT industry heavily relies on computational modeling. However, the TWT industry's computational modeling capabilities need to be improved because there are often discrepancies between measured TWT data and that predicted by conventional two-dimensional helical TWT interaction codes. This limits the analysis and design of novel devices or TWT's with parameters differing from what is conventionally manufactured. In addition, the inaccuracy of current computational tools limits achievable TWT performance because optimized designs require highly accurate models. To address these concerns, a fully three-dimensional, time-dependent, helical TWT interaction model was developed using the electromagnetic particle-in-cell code MAFIA (Solution of MAxwell's equations by the Finite-Integration-Algorithm). The model includes a short section of helical slow-wave circuit with excitation fed by radiofrequency input/output couplers, and an electron beam contained by periodic permanent magnet focusing. A cutaway view of several turns of the three-dimensional helical slow-wave circuit with input/output couplers is shown. This has been shown to be more accurate than conventionally used two-dimensional models. The growth of the communications industry has also imposed a demand for increased data rates for the transmission of large volumes of data. To achieve increased data rates, complex modulation and multiple access techniques are employed requiring minimum distortion of the signal as it is passed through the TWT. Thus, intersymbol interference (ISI) becomes a major consideration, as well as suspected causes such as reflections within the TWT. To experimentally investigate effects of the physical TWT on ISI would be
Estep, Donald
2015-11-30
This project addressed the challenge of predictive computational analysis of strongly coupled, highly nonlinear multiphysics systems characterized by multiple physical phenomena that span a large range of length- and time-scales. Specifically, the project was focused on computational estimation of numerical error and sensitivity analysis of computational solutions with respect to variations in parameters and data. In addition, the project investigated the use of accurate computational estimates to guide efficient adaptive discretization. The project developed, analyzed and evaluated new variational adjoint-based techniques for integration, model, and data error estimation/control and sensitivity analysis, in evolutionary multiphysics multiscale simulations.
Reward prediction error signals associated with a modified time estimation task.
Holroyd, Clay B; Krigolson, Olave E
2007-11-01
The feedback error-related negativity (fERN) is a component of the human event-related brain potential (ERP) elicited by feedback stimuli. A recent theory holds that the fERN indexes a reward prediction error signal associated with the adaptive modification of behavior. Here we present behavioral and ERP data recorded from participants engaged in a modified time estimation task. As predicted by the theory, our results indicate that fERN amplitude reflects a reward prediction error signal and that the size of this error signal is correlated across participants with changes in task performance.
Lipnikov, Konstantin; Agouzal, Abdellatif; Vassilevski, Yuri
2009-01-01
We present a new technology for generating meshes minimizing the interpolation and discretization errors or their gradients. The key element of this methodology is construction of a space metric from edge-based error estimates. For a mesh with N{sub h} triangles, the error is proportional to N{sub h}{sup -1} and the gradient of error is proportional to N{sub h}{sup -1/2} which are optimal asymptotics. The methodology is verified with numerical experiments.
Solving large tomographic linear systems: size reduction and error estimation
NASA Astrophysics Data System (ADS)
Voronin, Sergey; Mikesell, Dylan; Slezak, Inna; Nolet, Guust
2014-10-01
We present a new approach to reduce a sparse, linear system of equations associated with tomographic inverse problems. We begin by making a modification to the commonly used compressed sparse-row format, whereby our format is tailored to the sparse structure of finite-frequency (volume) sensitivity kernels in seismic tomography. Next, we cluster the sparse matrix rows to divide a large matrix into smaller subsets representing ray paths that are geographically close. Singular value decomposition of each subset allows us to project the data onto a subspace associated with the largest eigenvalues of the subset. After projection we reject those data that have a signal-to-noise ratio (SNR) below a chosen threshold. Clustering in this way assures that the sparse nature of the system is minimally affected by the projection. Moreover, our approach allows for a precise estimation of the noise affecting the data while also giving us the ability to identify outliers. We illustrate the method by reducing large matrices computed for global tomographic systems with cross-correlation body wave delays, as well as with surface wave phase velocity anomalies. For a massive matrix computed for 3.7 million Rayleigh wave phase velocity measurements, imposing a threshold of 1 for the SNR, we condensed the matrix size from 1103 to 63 Gbyte. For a global data set of multiple-frequency P wave delays from 60 well-distributed deep earthquakes we obtain a reduction to 5.9 per cent. This type of reduction allows one to avoid loss of information due to underparametrizing models. Alternatively, if data have to be rejected to fit the system into computer memory, it assures that the most important data are preserved.
NASA Astrophysics Data System (ADS)
Kasaragod, Deepa; Sugiyama, Satoshi; Ikuno, Yasushi; Alonso-Caneiro, David; Yamanari, Masahiro; Fukuda, Shinichi; Oshika, Tetsuro; Hong, Young-Joo; Li, En; Makita, Shuichi; Miura, Masahiro; Yasuno, Yoshiaki
2016-03-01
Polarization sensitive optical coherence tomography (PS-OCT) is a functional extension of OCT that contrasts the polarization properties of tissues. It has been applied to ophthalmology, cardiology, etc. Proper quantitative imaging is required for a widespread clinical utility. However, the conventional method of averaging to improve the signal to noise ratio (SNR) and the contrast of the phase retardation (or birefringence) images introduce a noise bias offset from the true value. This bias reduces the effectiveness of birefringence contrast for a quantitative study. Although coherent averaging of Jones matrix tomography has been widely utilized and has improved the image quality, the fundamental limitation of nonlinear dependency of phase retardation and birefringence to the SNR was not overcome. So the birefringence obtained by PS-OCT was still not accurate for a quantitative imaging. The nonlinear effect of SNR to phase retardation and birefringence measurement was previously formulated in detail for a Jones matrix OCT (JM-OCT) [1]. Based on this, we had developed a maximum a-posteriori (MAP) estimator and quantitative birefringence imaging was demonstrated [2]. However, this first version of estimator had a theoretical shortcoming. It did not take into account the stochastic nature of SNR of OCT signal. In this paper, we present an improved version of the MAP estimator which takes into account the stochastic property of SNR. This estimator uses a probability distribution function (PDF) of true local retardation, which is proportional to birefringence, under a specific set of measurements of the birefringence and SNR. The PDF was pre-computed by a Monte-Carlo (MC) simulation based on the mathematical model of JM-OCT before the measurement. A comparison between this new MAP estimator, our previous MAP estimator [2], and the standard mean estimator is presented. The comparisons are performed both by numerical simulation and in vivo measurements of anterior and
Rashid, Mamoon; Pain, Arnab
2013-01-01
Summary: READSCAN is a highly scalable parallel program to identify non-host sequences (of potential pathogen origin) and estimate their genome relative abundance in high-throughput sequence datasets. READSCAN accurately classified human and viral sequences on a 20.1 million reads simulated dataset in <27 min using a small Beowulf compute cluster with 16 nodes (Supplementary Material). Availability: http://cbrc.kaust.edu.sa/readscan Contact: arnab.pain@kaust.edu.sa or raeece.naeem@gmail.com Supplementary information: Supplementary data are available at Bioinformatics online. PMID:23193222
Error analysis of empirical ocean tide models estimated from TOPEX/POSEIDON altimetry
NASA Astrophysics Data System (ADS)
Desai, Shailen D.; Wahr, John M.; Chao, Yi
1997-11-01
An error budget is proposed for the TOPEX/POSEIDON (T/P) empirical ocean tide models estimated during the primary mission. The error budget evaluates the individual contribution of errors in each of the altimetric range corrections, orbit errors caused by errors in the background ocean tide potential, and errors caused by the general circulation of the oceans, to errors in the ocean tide models of the eight principal diurnal and semidiurnal tidal components, and the two principal long-period tidal components. The effect of continually updating the T/P empirical ocean tide models during the primary T/P mission is illustrated through tide gauge comparisons and then used to predict the impact of further updates during the extended mission. Both the tide gauge comparisons and the error analysis predict errors in the tide models for the eight principal diurnal and semidiurnal constituents to be of the order of 2-3 cm root-sum-square. The dominant source of errors in the T/P ocean tide models appears to be caused by the general circulation of the oceans observed by the T/P altimeter. Further updates of the T/P empirical ocean tide models during the extended mission should not provide significant improvements in the diurnal and semidiurnal ocean tide models but should provide significant improvements in the long-period ocean tide models, particularly in the monthly (Mm) tidal component.
Sansone, Giuseppe; Maschio, Lorenzo; Usvyat, Denis; Schütz, Martin; Karttunen, Antti
2016-01-01
The black phosphorus (black-P) crystal is formed of covalently bound layers of phosphorene stacked together by weak van der Waals interactions. An experimental measurement of the exfoliation energy of black-P is not available presently, making theoretical studies the most important source of information for the optimization of phosphorene production. Here, we provide an accurate estimate of the exfoliation energy of black-P on the basis of multilevel quantum chemical calculations, which include the periodic local Møller-Plesset perturbation theory of second order, augmented by higher-order corrections, which are evaluated with finite clusters mimicking the crystal. Very similar results are also obtained by density functional theory with the D3-version of Grimme's empirical dispersion correction. Our estimate of the exfoliation energy for black-P of -151 meV/atom is substantially larger than that of graphite, suggesting the need for different strategies to generate isolated layers for these two systems. PMID:26651397
Sansone, Giuseppe; Maschio, Lorenzo; Usvyat, Denis; Schütz, Martin; Karttunen, Antti
2016-01-01
The black phosphorus (black-P) crystal is formed of covalently bound layers of phosphorene stacked together by weak van der Waals interactions. An experimental measurement of the exfoliation energy of black-P is not available presently, making theoretical studies the most important source of information for the optimization of phosphorene production. Here, we provide an accurate estimate of the exfoliation energy of black-P on the basis of multilevel quantum chemical calculations, which include the periodic local Møller-Plesset perturbation theory of second order, augmented by higher-order corrections, which are evaluated with finite clusters mimicking the crystal. Very similar results are also obtained by density functional theory with the D3-version of Grimme's empirical dispersion correction. Our estimate of the exfoliation energy for black-P of -151 meV/atom is substantially larger than that of graphite, suggesting the need for different strategies to generate isolated layers for these two systems.
Accurate Estimation of Carotid Luminal Surface Roughness Using Ultrasonic Radio-Frequency Echo
NASA Astrophysics Data System (ADS)
Kitamura, Kosuke; Hasegawa, Hideyuki; Kanai, Hiroshi
2012-07-01
It would be useful to measure the minute surface roughness of the carotid arterial wall to detect the early stage of atherosclerosis. In conventional ultrasonography, the axial resolution of a B-mode image depends on the ultrasonic wavelength of 150 µm at 10 MHz because a B-mode image is constructed using the amplitude of the radio-frequency (RF) echo. Therefore, the surface roughness caused by atherosclerosis in an early stage cannot be measured using a conventional B-mode image obtained by ultrasonography because the roughness is 10-20 µm. We have realized accurate transcutaneous estimation of such a minute surface profile using the lateral motion of the carotid arterial wall, which is estimated by block matching of received ultrasonic signals. However, the width of the region where the surface profile is estimated depends on the magnitude of the lateral displacement of the carotid arterial wall (i.e., if the lateral displacement of the arterial wall is 1 mm, the surface profile is estimated in a region of 1 mm in width). In this study, the width was increased by combining surface profiles estimated using several ultrasonic beams. In the present study, we first measured a fine wire, whose diameter was 13 µm, using ultrasonic equipment to obtain an ultrasonic beam profile for determination of the optimal kernel size for block matching based on the correlation between RF echoes. Second, we estimated the lateral displacement and surface profile of a phantom, which had a saw tooth profile on its surface, and compared the surface profile measured by ultrasound with that measured by a laser profilometer. Finally, we estimated the lateral displacement and surface roughness of the carotid arterial wall of three healthy subjects (24-, 23-, and 23-year-old males) using the proposed method.
NASA Astrophysics Data System (ADS)
Behmanesh, Iman; Moaveni, Babak
2016-07-01
This paper presents a Hierarchical Bayesian model updating framework to account for the effects of ambient temperature and excitation amplitude. The proposed approach is applied for model calibration, response prediction and damage identification of a footbridge under changing environmental/ambient conditions. The concrete Young's modulus of the footbridge deck is the considered updating structural parameter with its mean and variance modeled as functions of temperature and excitation amplitude. The identified modal parameters over 27 months of continuous monitoring of the footbridge are used to calibrate the updating parameters. One of the objectives of this study is to show that by increasing the levels of information in the updating process, the posterior variation of the updating structural parameter (concrete Young's modulus) is reduced. To this end, the calibration is performed at three information levels using (1) the identified modal parameters, (2) modal parameters and ambient temperatures, and (3) modal parameters, ambient temperatures, and excitation amplitudes. The calibrated model is then validated by comparing the model-predicted natural frequencies and those identified from measured data after deliberate change to the structural mass. It is shown that accounting for modeling error uncertainties is crucial for reliable response prediction, and accounting only the estimated variability of the updating structural parameter is not sufficient for accurate response predictions. Finally, the calibrated model is used for damage identification of the footbridge.
Effect of random errors in planar PIV data on pressure estimation in vortex dominated flows
NASA Astrophysics Data System (ADS)
McClure, Jeffrey; Yarusevych, Serhiy
2015-11-01
The sensitivity of pressure estimation techniques from Particle Image Velocimetry (PIV) measurements to random errors in measured velocity data is investigated using the flow over a circular cylinder as a test case. Direct numerical simulations are performed for ReD = 100, 300 and 1575, spanning laminar, transitional, and turbulent wake regimes, respectively. A range of random errors typical for PIV measurements is applied to synthetic PIV data extracted from numerical results. A parametric study is then performed using a number of common pressure estimation techniques. Optimal temporal and spatial resolutions are derived based on the sensitivity of the estimated pressure fields to the simulated random error in velocity measurements, and the results are compared to an optimization model derived from error propagation theory. It is shown that the reductions in spatial and temporal scales at higher Reynolds numbers leads to notable changes in the optimal pressure evaluation parameters. The effect of smaller scale wake structures is also quantified. The errors in the estimated pressure fields are shown to depend significantly on the pressure estimation technique employed. The results are used to provide recommendations for the use of pressure and force estimation techniques from experimental PIV measurements in vortex dominated laminar and turbulent wake flows.
Measurement Error in Nonparametric Item Response Curve Estimation. Research Report. ETS RR-11-28
ERIC Educational Resources Information Center
Guo, Hongwen; Sinharay, Sandip
2011-01-01
Nonparametric, or kernel, estimation of item response curve (IRC) is a concern theoretically and operationally. Accuracy of this estimation, often used in item analysis in testing programs, is biased when the observed scores are used as the regressor because the observed scores are contaminated by measurement error. In this study, we investigate…
Lamb mode selection for accurate wall loss estimation via guided wave tomography
Huthwaite, P.; Ribichini, R.; Lowe, M. J. S.; Cawley, P.
2014-02-18
Guided wave tomography offers a method to accurately quantify wall thickness losses in pipes and vessels caused by corrosion. This is achieved using ultrasonic waves transmitted over distances of approximately 1–2m, which are measured by an array of transducers and then used to reconstruct a map of wall thickness throughout the inspected region. To achieve accurate estimations of remnant wall thickness, it is vital that a suitable Lamb mode is chosen. This paper presents a detailed evaluation of the fundamental modes, S{sub 0} and A{sub 0}, which are of primary interest in guided wave tomography thickness estimates since the higher order modes do not exist at all thicknesses, to compare their performance using both numerical and experimental data while considering a range of challenging phenomena. The sensitivity of A{sub 0} to thickness variations was shown to be superior to S{sub 0}, however, the attenuation from A{sub 0} when a liquid loading was present was much higher than S{sub 0}. A{sub 0} was less sensitive to the presence of coatings on the surface of than S{sub 0}.
A novel data-driven approach to model error estimation in Data Assimilation
NASA Astrophysics Data System (ADS)
Pathiraja, Sahani; Moradkhani, Hamid; Marshall, Lucy; Sharma, Ashish
2016-04-01
Error characterisation is a fundamental component of Data Assimilation (DA) studies. Effectively describing model error statistics has been a challenging area, with many traditional methods requiring some level of subjectivity (for instance in defining the error covariance structure). Recent advances have focused on removing the need for tuning of error parameters, although there are still some outstanding issues. Many methods focus only on the first and second moments, and rely on assuming multivariate Gaussian statistics. We propose a non-parametric, data-driven framework to estimate the full distributional form of model error, ie. the transition density p(xt|xt-1). All sources of uncertainty associated with the model simulations are considered, without needing to assign error characteristics/devise stochastic perturbations for individual components of model uncertainty (eg. input, parameter and structural). A training period is used to derive the error distribution of observed variables, conditioned on (potentially hidden) states. Errors in hidden states are estimated from the conditional distribution of observed variables using non-linear optimization. The framework is discussed in detail, and an application to a hydrologic case study with hidden states for one-day ahead streamflow prediction is presented. Results demonstrate improved predictions and more realistic uncertainty bounds compared to a standard tuning approach.
NASA Astrophysics Data System (ADS)
Granata, Daniele; Carnevale, Vincenzo
2016-08-01
The collective behavior of a large number of degrees of freedom can be often described by a handful of variables. This observation justifies the use of dimensionality reduction approaches to model complex systems and motivates the search for a small set of relevant “collective” variables. Here, we analyze this issue by focusing on the optimal number of variable needed to capture the salient features of a generic dataset and develop a novel estimator for the intrinsic dimension (ID). By approximating geodesics with minimum distance paths on a graph, we analyze the distribution of pairwise distances around the maximum and exploit its dependency on the dimensionality to obtain an ID estimate. We show that the estimator does not depend on the shape of the intrinsic manifold and is highly accurate, even for exceedingly small sample sizes. We apply the method to several relevant datasets from image recognition databases and protein multiple sequence alignments and discuss possible interpretations for the estimated dimension in light of the correlations among input variables and of the information content of the dataset.
Granata, Daniele; Carnevale, Vincenzo
2016-01-01
The collective behavior of a large number of degrees of freedom can be often described by a handful of variables. This observation justifies the use of dimensionality reduction approaches to model complex systems and motivates the search for a small set of relevant “collective” variables. Here, we analyze this issue by focusing on the optimal number of variable needed to capture the salient features of a generic dataset and develop a novel estimator for the intrinsic dimension (ID). By approximating geodesics with minimum distance paths on a graph, we analyze the distribution of pairwise distances around the maximum and exploit its dependency on the dimensionality to obtain an ID estimate. We show that the estimator does not depend on the shape of the intrinsic manifold and is highly accurate, even for exceedingly small sample sizes. We apply the method to several relevant datasets from image recognition databases and protein multiple sequence alignments and discuss possible interpretations for the estimated dimension in light of the correlations among input variables and of the information content of the dataset. PMID:27510265
Granata, Daniele; Carnevale, Vincenzo
2016-01-01
The collective behavior of a large number of degrees of freedom can be often described by a handful of variables. This observation justifies the use of dimensionality reduction approaches to model complex systems and motivates the search for a small set of relevant "collective" variables. Here, we analyze this issue by focusing on the optimal number of variable needed to capture the salient features of a generic dataset and develop a novel estimator for the intrinsic dimension (ID). By approximating geodesics with minimum distance paths on a graph, we analyze the distribution of pairwise distances around the maximum and exploit its dependency on the dimensionality to obtain an ID estimate. We show that the estimator does not depend on the shape of the intrinsic manifold and is highly accurate, even for exceedingly small sample sizes. We apply the method to several relevant datasets from image recognition databases and protein multiple sequence alignments and discuss possible interpretations for the estimated dimension in light of the correlations among input variables and of the information content of the dataset. PMID:27510265
Estimating Model Prediction Error: Should You Treat Predictions as Fixed or Random?
NASA Technical Reports Server (NTRS)
Wallach, Daniel; Thorburn, Peter; Asseng, Senthold; Challinor, Andrew J.; Ewert, Frank; Jones, James W.; Rotter, Reimund; Ruane, Alexander
2016-01-01
Crop models are important tools for impact assessment of climate change, as well as for exploring management options under current climate. It is essential to evaluate the uncertainty associated with predictions of these models. We compare two criteria of prediction error; MSEP fixed, which evaluates mean squared error of prediction for a model with fixed structure, parameters and inputs, and MSEP uncertain( X), which evaluates mean squared error averaged over the distributions of model structure, inputs and parameters. Comparison of model outputs with data can be used to estimate the former. The latter has a squared bias term, which can be estimated using hindcasts, and a model variance term, which can be estimated from a simulation experiment. The separate contributions to MSEP uncertain (X) can be estimated using a random effects ANOVA. It is argued that MSEP uncertain (X) is the more informative uncertainty criterion, because it is specific to each prediction situation.
MIDAS robust trend estimator for accurate GPS station velocities without step detection
NASA Astrophysics Data System (ADS)
Blewitt, Geoffrey; Kreemer, Corné; Hammond, William C.; Gazeaux, Julien
2016-03-01
Automatic estimation of velocities from GPS coordinate time series is becoming required to cope with the exponentially increasing flood of available data, but problems detectable to the human eye are often overlooked. This motivates us to find an automatic and accurate estimator of trend that is resistant to common problems such as step discontinuities, outliers, seasonality, skewness, and heteroscedasticity. Developed here, Median Interannual Difference Adjusted for Skewness (MIDAS) is a variant of the Theil-Sen median trend estimator, for which the ordinary version is the median of slopes vij = (xj-xi)/(tj-ti) computed between all data pairs i > j. For normally distributed data, Theil-Sen and least squares trend estimates are statistically identical, but unlike least squares, Theil-Sen is resistant to undetected data problems. To mitigate both seasonality and step discontinuities, MIDAS selects data pairs separated by 1 year. This condition is relaxed for time series with gaps so that all data are used. Slopes from data pairs spanning a step function produce one-sided outliers that can bias the median. To reduce bias, MIDAS removes outliers and recomputes the median. MIDAS also computes a robust and realistic estimate of trend uncertainty. Statistical tests using GPS data in the rigid North American plate interior show ±0.23 mm/yr root-mean-square (RMS) accuracy in horizontal velocity. In blind tests using synthetic data, MIDAS velocities have an RMS accuracy of ±0.33 mm/yr horizontal, ±1.1 mm/yr up, with a 5th percentile range smaller than all 20 automatic estimators tested. Considering its general nature, MIDAS has the potential for broader application in the geosciences.
MIDAS robust trend estimator for accurate GPS station velocities without step detection
Kreemer, Corné; Hammond, William C.; Gazeaux, Julien
2016-01-01
Abstract Automatic estimation of velocities from GPS coordinate time series is becoming required to cope with the exponentially increasing flood of available data, but problems detectable to the human eye are often overlooked. This motivates us to find an automatic and accurate estimator of trend that is resistant to common problems such as step discontinuities, outliers, seasonality, skewness, and heteroscedasticity. Developed here, Median Interannual Difference Adjusted for Skewness (MIDAS) is a variant of the Theil‐Sen median trend estimator, for which the ordinary version is the median of slopes vij = (xj–xi)/(tj–ti) computed between all data pairs i > j. For normally distributed data, Theil‐Sen and least squares trend estimates are statistically identical, but unlike least squares, Theil‐Sen is resistant to undetected data problems. To mitigate both seasonality and step discontinuities, MIDAS selects data pairs separated by 1 year. This condition is relaxed for time series with gaps so that all data are used. Slopes from data pairs spanning a step function produce one‐sided outliers that can bias the median. To reduce bias, MIDAS removes outliers and recomputes the median. MIDAS also computes a robust and realistic estimate of trend uncertainty. Statistical tests using GPS data in the rigid North American plate interior show ±0.23 mm/yr root‐mean‐square (RMS) accuracy in horizontal velocity. In blind tests using synthetic data, MIDAS velocities have an RMS accuracy of ±0.33 mm/yr horizontal, ±1.1 mm/yr up, with a 5th percentile range smaller than all 20 automatic estimators tested. Considering its general nature, MIDAS has the potential for broader application in the geosciences. PMID:27668140
MIDAS robust trend estimator for accurate GPS station velocities without step detection
Kreemer, Corné; Hammond, William C.; Gazeaux, Julien
2016-01-01
Abstract Automatic estimation of velocities from GPS coordinate time series is becoming required to cope with the exponentially increasing flood of available data, but problems detectable to the human eye are often overlooked. This motivates us to find an automatic and accurate estimator of trend that is resistant to common problems such as step discontinuities, outliers, seasonality, skewness, and heteroscedasticity. Developed here, Median Interannual Difference Adjusted for Skewness (MIDAS) is a variant of the Theil‐Sen median trend estimator, for which the ordinary version is the median of slopes vij = (xj–xi)/(tj–ti) computed between all data pairs i > j. For normally distributed data, Theil‐Sen and least squares trend estimates are statistically identical, but unlike least squares, Theil‐Sen is resistant to undetected data problems. To mitigate both seasonality and step discontinuities, MIDAS selects data pairs separated by 1 year. This condition is relaxed for time series with gaps so that all data are used. Slopes from data pairs spanning a step function produce one‐sided outliers that can bias the median. To reduce bias, MIDAS removes outliers and recomputes the median. MIDAS also computes a robust and realistic estimate of trend uncertainty. Statistical tests using GPS data in the rigid North American plate interior show ±0.23 mm/yr root‐mean‐square (RMS) accuracy in horizontal velocity. In blind tests using synthetic data, MIDAS velocities have an RMS accuracy of ±0.33 mm/yr horizontal, ±1.1 mm/yr up, with a 5th percentile range smaller than all 20 automatic estimators tested. Considering its general nature, MIDAS has the potential for broader application in the geosciences.
NASA Astrophysics Data System (ADS)
Gibbons, S. J.; Pabian, F.; Näsholm, S. P.; Kværna', T.; Mykkeltveit, S.
2016-10-01
modified velocity gradients reduce the residuals, the relative location uncertainties, and the sensitivity to the combination of stations used. The traveltime gradients appear to be overestimated for the regional phases, and teleseismic relative location estimates are likely to be more accurate despite an apparent lower precision. Calibrations for regional phases are essential given that smaller magnitude events are likely not to be recorded teleseismically. We discuss the implications for the absolute event locations. Placing the 2006 event under a local maximum of overburden at 41.293°N, 129.105°E would imply a location of 41.299°N, 129.075°E for the January 2016 event, providing almost optimal overburden for the later four events.
Raman spectroscopy for highly accurate estimation of the age of refrigerated porcine muscle
NASA Astrophysics Data System (ADS)
Timinis, Constantinos; Pitris, Costas
2016-03-01
The high water content of meat, combined with all the nutrients it contains, make it vulnerable to spoilage at all stages of production and storage even when refrigerated at 5 °C. A non-destructive and in situ tool for meat sample testing, which could provide an accurate indication of the storage time of meat, would be very useful for the control of meat quality as well as for consumer safety. The proposed solution is based on Raman spectroscopy which is non-invasive and can be applied in situ. For the purposes of this project, 42 meat samples from 14 animals were obtained and three Raman spectra per sample were collected every two days for two weeks. The spectra were subsequently processed and the sample age was calculated using a set of linear differential equations. In addition, the samples were classified in categories corresponding to the age in 2-day steps (i.e., 0, 2, 4, 6, 8, 10, 12 or 14 days old), using linear discriminant analysis and cross-validation. Contrary to other studies, where the samples were simply grouped into two categories (higher or lower quality, suitable or unsuitable for human consumption, etc.), in this study, the age was predicted with a mean error of ~ 1 day (20%) or classified, in 2-day steps, with 100% accuracy. Although Raman spectroscopy has been used in the past for the analysis of meat samples, the proposed methodology has resulted in a prediction of the sample age far more accurately than any report in the literature.
Creel, Scott; Spong, Goran; Sands, Jennifer L; Rotella, Jay; Zeigle, Janet; Joe, Lawrence; Murphy, Kerry M; Smith, Douglas
2003-07-01
Determining population sizes can be difficult, but is essential for conservation. By counting distinct microsatellite genotypes, DNA from noninvasive samples (hair, faeces) allows estimation of population size. Problems arise because genotypes from noninvasive samples are error-prone, but genotyping errors can be reduced by multiple polymerase chain reaction (PCR). For faecal genotypes from wolves in Yellowstone National Park, error rates varied substantially among samples, often above the 'worst-case threshold' suggested by simulation. Consequently, a substantial proportion of multilocus genotypes held one or more errors, despite multiple PCR. These genotyping errors created several genotypes per individual and caused overestimation (up to 5.5-fold) of population size. We propose a 'matching approach' to eliminate this overestimation bias. PMID:12803649
Effect of Numerical Error on Gravity Field Estimation for GRACE and Future Gravity Missions
NASA Astrophysics Data System (ADS)
McCullough, Christopher; Bettadpur, Srinivas
2015-04-01
In recent decades, gravity field determination from low Earth orbiting satellites, such as the Gravity Recovery and Climate Experiment (GRACE), has become increasingly more effective due to the incorporation of high accuracy measurement devices. Since instrumentation quality will only increase in the near future and the gravity field determination process is computationally and numerically intensive, numerical error from the use of double precision arithmetic will eventually become a prominent error source. While using double-extended or quadruple precision arithmetic will reduce these errors, the numerical limitations of current orbit determination algorithms and processes must be accurately identified and quantified in order to adequately inform the science data processing techniques of future gravity missions. The most obvious numerical limitation in the orbit determination process is evident in the comparison of measured observables with computed values, derived from mathematical models relating the satellites' numerically integrated state to the observable. Significant error in the computed trajectory will corrupt this comparison and induce error in the least squares solution of the gravitational field. In addition, errors in the numerically computed trajectory propagate into the evaluation of the mathematical measurement model's partial derivatives. These errors amalgamate in turn with numerical error from the computation of the state transition matrix, computed using the variational equations of motion, in the least squares mapping matrix. Finally, the solution of the linearized least squares system, computed using a QR factorization, is also susceptible to numerical error. Certain interesting combinations of each of these numerical errors are examined in the framework of GRACE gravity field determination to analyze and quantify their effects on gravity field recovery.
Are satellite based rainfall estimates accurate enough for crop modelling under Sahelian climate?
NASA Astrophysics Data System (ADS)
Ramarohetra, J.; Sultan, B.
2012-04-01
EPSAT-SG) are integrated using a crop model, then compared and tested against simulations obtained using in situ data. As in situ data, kriged rain gauge measurements are computed from about 50 rain gauges within the square degree. We show that direct use of SRFE does not reproduce the yield variability obtained from in situ observations. In a second time, different satellite products errors (e.g. annual bias, accuracy at the beginning of the rainy season) are corrected before yield modelling to assess their impact on crop yield simulation and to be able to know which improvement in SRFE will be useful for crop yield estimation. We show that corrected satellite products enable a better yield variability representation and that error correction does not have the same impact on the different varieties computed. Finally, simulated yield quality versus precipitations temporal resolution is assessed - as well as SRFE accuracy versus SRFE temporal resolution - in order to sort out the best agreement between temporal resolution and SRFE accuracy.
Estimating error cross-correlations in soil moisture data sets using extended collocation analysis
NASA Astrophysics Data System (ADS)
Gruber, A.; Su, C.-H.; Crow, W. T.; Zwieback, S.; Dorigo, W. A.; Wagner, W.
2016-02-01
Global soil moisture records are essential for studying the role of hydrologic processes within the larger earth system. Various studies have shown the benefit of assimilating satellite-based soil moisture data into water balance models or merging multisource soil moisture retrievals into a unified data set. However, this requires an appropriate parameterization of the error structures of the underlying data sets. While triple collocation (TC) analysis has been widely recognized as a powerful tool for estimating random error variances of coarse-resolution soil moisture data sets, the estimation of error cross covariances remains an unresolved challenge. Here we propose a method—referred to as extended collocation (EC) analysis—for estimating error cross-correlations by generalizing the TC method to an arbitrary number of data sets and relaxing the therein made assumption of zero error cross-correlation for certain data set combinations. A synthetic experiment shows that EC analysis is able to reliably recover true error cross-correlation levels. Applied to real soil moisture retrievals from Advanced Microwave Scanning Radiometer-EOS (AMSR-E) C-band and X-band observations together with advanced scatterometer (ASCAT) retrievals, modeled data from Global Land Data Assimilation System (GLDAS)-Noah and in situ measurements drawn from the International Soil Moisture Network, EC yields reasonable and strong nonzero error cross-correlations between the two AMSR-E products. Against expectation, nonzero error cross-correlations are also found between ASCAT and AMSR-E. We conclude that the proposed EC method represents an important step toward a fully parameterized error covariance matrix for coarse-resolution soil moisture data sets, which is vital for any rigorous data assimilation framework or data merging scheme.
Effect of geocoding errors on traffic-related air pollutant exposure and concentration estimates.
Ganguly, Rajiv; Batterman, Stuart; Isakov, Vlad; Snyder, Michelle; Breen, Michael; Brakefield-Caldwell, Wilma
2015-01-01
Exposure to traffic-related air pollutants is highest very near roads, and thus exposure estimates are sensitive to positional errors. This study evaluates positional and PM2.5 concentration errors that result from the use of automated geocoding methods and from linearized approximations of roads in link-based emission inventories. Two automated geocoders (Bing Map and ArcGIS) along with handheld GPS instruments were used to geocode 160 home locations of children enrolled in an air pollution study investigating effects of traffic-related pollutants in Detroit, Michigan. The average and maximum positional errors using the automated geocoders were 35 and 196 m, respectively. Comparing road edge and road centerline, differences in house-to-highway distances averaged 23 m and reached 82 m. These differences were attributable to road curvature, road width and the presence of ramps, factors that should be considered in proximity measures used either directly as an exposure metric or as inputs to dispersion or other models. Effects of positional errors for the 160 homes on PM2.5 concentrations resulting from traffic-related emissions were predicted using a detailed road network and the RLINE dispersion model. Concentration errors averaged only 9%, but maximum errors reached 54% for annual averages and 87% for maximum 24-h averages. Whereas most geocoding errors appear modest in magnitude, 5% to 20% of residences are expected to have positional errors exceeding 100 m. Such errors can substantially alter exposure estimates near roads because of the dramatic spatial gradients of traffic-related pollutant concentrations. To ensure the accuracy of exposure estimates for traffic-related air pollutants, especially near roads, confirmation of geocoordinates is recommended.
Effect of geocoding errors on traffic-related air pollutant exposure and concentration estimates
Ganguly, Rajiv; Batterman, Stuart; Isakov, Vlad; Snyder, Michelle; Breen, Michael; Brakefield-Caldwell, Wilma
2015-01-01
Exposure to traffic-related air pollutants is highest very near roads, and thus exposure estimates are sensitive to positional errors. This study evaluates positional and PM2.5 concentration errors that result from the use of automated geocoding methods and from linearized approximations of roads in link-based emission inventories. Two automated geocoders (Bing Map and ArcGIS) along with handheld GPS instruments were used to geocode 160 home locations of children enrolled in an air pollution study investigating effects of traffic-related pollutants in Detroit, Michigan. The average and maximum positional errors using the automated geocoders were 35 and 196 m, respectively. Comparing road edge and road centerline, differences in house-to-highway distances averaged 23 m and reached 82 m. These differences were attributable to road curvature, road width and the presence of ramps, factors that should be considered in proximity measures used either directly as an exposure metric or as inputs to dispersion or other models. Effects of positional errors for the 160 homes on PM2.5 concentrations resulting from traffic-related emissions were predicted using a detailed road network and the RLINE dispersion model. Concentration errors averaged only 9%, but maximum errors reached 54% for annual averages and 87% for maximum 24-h averages. Whereas most geocoding errors appear modest in magnitude, 5% to 20% of residences are expected to have positional errors exceeding 100 m. Such errors can substantially alter exposure estimates near roads because of the dramatic spatial gradients of traffic-related pollutant concentrations. To ensure the accuracy of exposure estimates for traffic-related air pollutants, especially near roads, confirmation of geocoordinates is recommended. PMID:25670023
Estimation of 3D reconstruction errors in a stereo-vision system
NASA Astrophysics Data System (ADS)
Belhaoua, A.; Kohler, S.; Hirsch, E.
2009-06-01
The paper presents an approach for error estimation for the various steps of an automated 3D vision-based reconstruction procedure of manufactured workpieces. The process is based on a priori planning of the task and built around a cognitive intelligent sensory system using so-called Situation Graph Trees (SGT) as a planning tool. Such an automated quality control system requires the coordination of a set of complex processes performing sequentially data acquisition, its quantitative evaluation and the comparison with a reference model (e.g., CAD object model) in order to evaluate quantitatively the object. To ensure efficient quality control, the aim is to be able to state if reconstruction results fulfill tolerance rules or not. Thus, the goal is to evaluate independently the error for each step of the stereo-vision based 3D reconstruction (e.g., for calibration, contour segmentation, matching and reconstruction) and then to estimate the error for the whole system. In this contribution, we analyze particularly the segmentation error due to localization errors for extracted edge points supposed to belong to lines and curves composing the outline of the workpiece under evaluation. The fitting parameters describing these geometric features are used as quality measure to determine confidence intervals and finally to estimate the segmentation errors. These errors are then propagated through the whole reconstruction procedure, enabling to evaluate their effect on the final 3D reconstruction result, specifically on position uncertainties. Lastly, analysis of these error estimates enables to evaluate the quality of the 3D reconstruction, as illustrated by the shown experimental results.
Jensen, Jonas; Olesen, Jacob Bjerring; Stuart, Matthias Bo; Hansen, Peter Møller; Nielsen, Michael Bachmann; Jensen, Jørgen Arendt
2016-08-01
A method for vector velocity volume flow estimation is presented, along with an investigation of its sources of error and correction of actual volume flow measurements. Volume flow errors are quantified theoretically by numerical modeling, through flow phantom measurements, and studied in vivo. This paper investigates errors from estimating volumetric flow using a commercial ultrasound scanner and the common assumptions made in the literature. The theoretical model shows, e.g. that volume flow is underestimated by 15%, when the scan plane is off-axis with the vessel center by 28% of the vessel radius. The error sources were also studied in vivo under realistic clinical conditions, and the theoretical results were applied for correcting the volume flow errors. Twenty dialysis patients with arteriovenous fistulas were scanned to obtain vector flow maps of fistulas. When fitting an ellipsis to cross-sectional scans of the fistulas, the major axis was on average 10.2mm, which is 8.6% larger than the minor axis. The ultrasound beam was on average 1.5mm from the vessel center, corresponding to 28% of the semi-major axis in an average fistula. Estimating volume flow with an elliptical, rather than circular, vessel area and correcting the ultrasound beam for being off-axis, gave a significant (p=0.008) reduction in error from 31.2% to 24.3%. The error is relative to the Ultrasound Dilution Technique, which is considered the gold standard for volume flow estimation for dialysis patients. The study shows the importance of correcting for volume flow errors, which are often made in clinical practice.
Accurate estimation of human body orientation from RGB-D sensors.
Liu, Wu; Zhang, Yongdong; Tang, Sheng; Tang, Jinhui; Hong, Richang; Li, Jintao
2013-10-01
Accurate estimation of human body orientation can significantly enhance the analysis of human behavior, which is a fundamental task in the field of computer vision. However, existing orientation estimation methods cannot handle the various body poses and appearances. In this paper, we propose an innovative RGB-D-based orientation estimation method to address these challenges. By utilizing the RGB-D information, which can be real time acquired by RGB-D sensors, our method is robust to cluttered environment, illumination change and partial occlusions. Specifically, efficient static and motion cue extraction methods are proposed based on the RGB-D superpixels to reduce the noise of depth data. Since it is hard to discriminate all the 360 (°) orientation using static cues or motion cues independently, we propose to utilize a dynamic Bayesian network system (DBNS) to effectively employ the complementary nature of both static and motion cues. In order to verify our proposed method, we build a RGB-D-based human body orientation dataset that covers a wide diversity of poses and appearances. Our intensive experimental evaluations on this dataset demonstrate the effectiveness and efficiency of the proposed method. PMID:23893759
Efficient and accurate estimation of relative order tensors from λ- maps
NASA Astrophysics Data System (ADS)
Mukhopadhyay, Rishi; Miao, Xijiang; Shealy, Paul; Valafar, Homayoun
2009-06-01
The rapid increase in the availability of RDC data from multiple alignment media in recent years has necessitated the development of more sophisticated analyses that extract the RDC data's full information content. This article presents an analysis of the distribution of RDCs from two media (2D-RDC data), using the information obtained from a λ-map. This article also introduces an efficient algorithm, which leverages these findings to extract the order tensors for each alignment medium using unassigned RDC data in the absence of any structural information. The results of applying this 2D-RDC analysis method to synthetic and experimental data are reported in this article. The relative order tensor estimates obtained from the 2D-RDC analysis are compared to order tensors obtained from the program REDCAT after using assignment and structural information. The final comparisons indicate that the relative order tensors estimated from the unassigned 2D-RDC method very closely match the results from methods that require assignment and structural information. The presented method is successful even in cases with small datasets. The results of analyzing experimental RDC data for the protein 1P7E are presented to demonstrate the potential of the presented work in accurately estimating the principal order parameters from RDC data that incompletely sample the RDC space. In addition to the new algorithm, a discussion of the uniqueness of the solutions is presented; no more than two clusters of distinct solutions have been shown to satisfy each λ-map.
Accurate estimation of human body orientation from RGB-D sensors.
Liu, Wu; Zhang, Yongdong; Tang, Sheng; Tang, Jinhui; Hong, Richang; Li, Jintao
2013-10-01
Accurate estimation of human body orientation can significantly enhance the analysis of human behavior, which is a fundamental task in the field of computer vision. However, existing orientation estimation methods cannot handle the various body poses and appearances. In this paper, we propose an innovative RGB-D-based orientation estimation method to address these challenges. By utilizing the RGB-D information, which can be real time acquired by RGB-D sensors, our method is robust to cluttered environment, illumination change and partial occlusions. Specifically, efficient static and motion cue extraction methods are proposed based on the RGB-D superpixels to reduce the noise of depth data. Since it is hard to discriminate all the 360 (°) orientation using static cues or motion cues independently, we propose to utilize a dynamic Bayesian network system (DBNS) to effectively employ the complementary nature of both static and motion cues. In order to verify our proposed method, we build a RGB-D-based human body orientation dataset that covers a wide diversity of poses and appearances. Our intensive experimental evaluations on this dataset demonstrate the effectiveness and efficiency of the proposed method.
Accurate estimation of the RMS emittance from single current amplifier data
Stockli, Martin P.; Welton, R.F.; Keller, R.; Letchford, A.P.; Thomae, R.W.; Thomason, J.W.G.
2002-05-31
This paper presents the SCUBEEx rms emittance analysis, a self-consistent, unbiased elliptical exclusion method, which combines traditional data-reduction methods with statistical methods to obtain accurate estimates for the rms emittance. Rather than considering individual data, the method tracks the average current density outside a well-selected, variable boundary to separate the measured beam halo from the background. The average outside current density is assumed to be part of a uniform background and not part of the particle beam. Therefore the average outside current is subtracted from the data before evaluating the rms emittance within the boundary. As the boundary area is increased, the average outside current and the inside rms emittance form plateaus when all data containing part of the particle beam are inside the boundary. These plateaus mark the smallest acceptable exclusion boundary and provide unbiased estimates for the average background and the rms emittance. Small, trendless variations within the plateaus allow for determining the uncertainties of the estimates caused by variations of the measured background outside the smallest acceptable exclusion boundary. The robustness of the method is established with complementary variations of the exclusion boundary. This paper presents a detailed comparison between traditional data reduction methods and SCUBEEx by analyzing two complementary sets of emittance data obtained with a Lawrence Berkeley National Laboratory and an ISIS H{sup -} ion source.
An a posteriori error estimator for shape optimization: application to EIT
NASA Astrophysics Data System (ADS)
Giacomini, M.; Pantz, O.; Trabelsi, K.
2015-11-01
In this paper we account for the numerical error introduced by the Finite Element approximation of the shape gradient to construct a guaranteed shape optimization method. We present a goal-oriented strategy inspired by the complementary energy principle to construct a constant-free, fully-computable a posteriori error estimator and to derive a certified upper bound of the error in the shape gradient. The resulting Adaptive Boundary Variation Algorithm (ABVA) is able to identify a genuine descent direction at each iteration and features a reliable stopping criterion for the optimization loop. Some preliminary numerical results for the inverse identification problem of Electrical Impedance Tomography are presented.
Burr, T; Croft, S; Krieger, T; Martin, K; Norman, C; Walsh, S
2016-02-01
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
Burr, T; Croft, S; Krieger, T; Martin, K; Norman, C; Walsh, S
2016-02-01
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
Multilevel Error Estimation and Adaptive h-Refinement for Cartesian Meshes with Embedded Boundaries
NASA Technical Reports Server (NTRS)
Aftosmis, M. J.; Berger, M. J.; Kwak, Dochan (Technical Monitor)
2002-01-01
This paper presents the development of a mesh adaptation module for a multilevel Cartesian solver. While the module allows mesh refinement to be driven by a variety of different refinement parameters, a central feature in its design is the incorporation of a multilevel error estimator based upon direct estimates of the local truncation error using tau-extrapolation. This error indicator exploits the fact that in regions of uniform Cartesian mesh, the spatial operator is exactly the same on the fine and coarse grids, and local truncation error estimates can be constructed by evaluating the residual on the coarse grid of the restricted solution from the fine grid. A new strategy for adaptive h-refinement is also developed to prevent errors in smooth regions of the flow from being masked by shocks and other discontinuous features. For certain classes of error histograms, this strategy is optimal for achieving equidistribution of the refinement parameters on hierarchical meshes, and therefore ensures grid converged solutions will be achieved for appropriately chosen refinement parameters. The robustness and accuracy of the adaptation module is demonstrated using both simple model problems and complex three dimensional examples using meshes with from 10(exp 6), to 10(exp 7) cells.
NASA Astrophysics Data System (ADS)
Hernandez-Pajares, M.; Juan, J.; Sanz, J.; Aragon-Angel, A.
2007-05-01
, estimated from the GNSS data only, and which reduces the errors associated to non-tomographic approaches, taking care of electron density vertical distribution.[-4mm] b) A simple GNSS model for the "Medium Scale Traveling Ionospheric Disturbances", being this one of the main ionospheric perturbations affecting the carrier phase ambiguity fixing, and hence the capability of accurate navigation.[-4mm] The corresponding models and performances will be illustrated with several experiments involving actual data. And finally the existing and new WARTK applications will be briefly mentioned, in the context of different projects. References: Hernandez-Pajares, M.; Juan, J. M.; Sanz, J.; Colombo, O. L., Improving the real-time ionospheric determination from GPS sites at very long distances over the equator, Journal of Geophysical Research (Space Physics), Volume 107, Issue A10, pp. SIA 10-1, CiteID 1296, DOI 10.1029/2001JA009203, 10/2002. Hernandez-Pajares, M.; Juan, J. M.; Sanz, J., Medium-scale traveling ionospheric disturbances affecting GPS measurements: Spatial and temporal analysis, Journal of Geophysical Research, Volume 111, Issue A7, CiteID A07S11, DOI 10.1029/2005JA011474, 06/2006.
Global Warming Estimation from MSU: Correction for Drift and Calibration Errors
NASA Technical Reports Server (NTRS)
Prabhakara, C.; Iacovazzi, R., Jr.; Yoo, J.-M.
2000-01-01
Microwave Sounding Unit (MSU) radiometer observations in Ch 2 (53.74 GHz), made in the nadir direction from sequential, sun-synchronous, polar-orbiting NOAA morning satellites (NOAA 6, 10 and 12 that have approximately 7am/7pm orbital geometry) and. afternoon satellites (NOAA 7, 9, 11 and 14 that have approximately 2am/2pm orbital geometry) are analyzed in this study to derive global temperature trend from 1980 to 1998. In order to remove the discontinuities between the data of the successive satellites and to get a continuous time series, first we have used shortest possible time record of each satellite. In this way we get a preliminary estimate of the global temperature trend of 0.21 K/decade. However, this estimate is affected by systematic time-dependent errors. One such error is the instrument calibration error eo. This error can be inferred whenever there are overlapping measurements made by two satellites over an extended period of time. From the available successive satellite data we have taken the longest possible time record of each satellite to form the time series during the period 1980 to 1998 to this error eo. We find eo can decrease the global temperature trend by approximately 0.07 K/decade. In addition there are systematic time dependent errors ed and ec present in the data that are introduced by the drift in the satellite orbital geometry. ed arises from the diurnal cycle in temperature and ec is the drift related change in the calibration of the MSU. In order to analyze the nature of these drift related errors the multi-satellite Ch 2 data set is partitioned into am and pm subsets to create two independent time series. The error ed can be assessed in the am and pm data of Ch 2 on land and can be eliminated. Observation made in the MSU Ch 1 (50.3 GHz) support this approach. The error ec is obvious only in the difference between the pm and am observations of Ch 2 over the ocean. We have followed two different paths to assess the impact of the
Global Warming Estimation from MSU: Correction for Drift and Calibration Errors
NASA Technical Reports Server (NTRS)
Prabhakara, C.; Iacovazzi, R., Jr.; Yoo, J.-M.; Einaudi, Franco (Technical Monitor)
2000-01-01
Microwave Sounding Unit (MSU) radiometer observations in Ch 2 (53.74 GHz), made in the nadir direction from sequential, sun-synchronous, polar-orbiting NOAA morning satellites (NOAA 6, 10 and 12 that have about 7am/7pm orbital geometry) and afternoon satellites (NOAA 7, 9, 11 and 14 that have about 2am/2pm orbital geometry) are analyzed in this study to derive global temperature trend from 1980 to 1998. In order to remove the discontinuities between the data of the successive satellites and to get a continuous time series, first we have used shortest possible time record of each satellite. In this way we get a preliminary estimate of the global temperature trend of 0.21 K/decade. However, this estimate is affected by systematic time-dependent errors. One such error is the instrument calibration error. This error can be inferred whenever there are overlapping measurements made by two satellites over an extended period of time. From the available successive satellite data we have taken the longest possible time record of each satellite to form the time series during the period 1980 to 1998 to this error. We find we can decrease the global temperature trend by about 0.07 K/decade. In addition there are systematic time dependent errors present in the data that are introduced by the drift in the satellite orbital geometry arises from the diurnal cycle in temperature which is the drift related change in the calibration of the MSU. In order to analyze the nature of these drift related errors the multi-satellite Ch 2 data set is partitioned into am and pm subsets to create two independent time series. The error can be assessed in the am and pm data of Ch 2 on land and can be eliminated. Observations made in the MSU Ch 1 (50.3 GHz) support this approach. The error is obvious only in the difference between the pm and am observations of Ch 2 over the ocean. We have followed two different paths to assess the impact of the errors on the global temperature trend. In one path the
Computation of the factorized error covariance of the difference between correlated estimators
NASA Technical Reports Server (NTRS)
Wolff, Peter J.; Mohan, Srinivas N.; Stienon, Francis M.; Bierman, Gerald J.
1990-01-01
A state estimation problem where some of the measurements may be common to two or more data sets is considered. Two approaches for computing the error covariance of the difference between filtered estimates (for each data set) are discussed. The first algorithm is based on postprocessing of the Kalman gain profiles of two correlated estimators. It uses UD factors of the covariance of the relative error. The second algorithm uses a square root information filter applied to relative error analysis. In the absence of process noise, the square root information filter is computationally more efficient and more flexible than the Kalman gain (covariance update) method. Both the algorithms (covariance and information matrix based) are applied to a Venus orbiter simulation, and their performances are compared.
Use of an OSSE to Evaluate Background Error Covariances Estimated by the 'NMC Method'
NASA Technical Reports Server (NTRS)
Errico, Ronald M.; Prive, Nikki C.; Gu, Wei
2014-01-01
The NMC method has proven utility for prescribing approximate background-error covariances required by variational data assimilation systems. Here, untunedNMCmethod estimates are compared with explicitly determined error covariances produced within an OSSE context by exploiting availability of the true simulated states. Such a comparison provides insights into what kind of rescaling is required to render the NMC method estimates usable. It is shown that rescaling of variances and directional correlation lengths depends greatly on both pressure and latitude. In particular, some scaling coefficients appropriate in the Tropics are the reciprocal of those in the Extratropics. Also, the degree of dynamic balance is grossly overestimated by the NMC method. These results agree with previous examinations of the NMC method which used ensembles as an alternative for estimating background-error statistics.
Quick and accurate estimation of the elastic constants using the minimum image method
NASA Astrophysics Data System (ADS)
Tretiakov, Konstantin V.; Wojciechowski, Krzysztof W.
2015-04-01
A method for determining the elastic properties using the minimum image method (MIM) is proposed and tested on a model system of particles interacting by the Lennard-Jones (LJ) potential. The elastic constants of the LJ system are determined in the thermodynamic limit, N → ∞, using the Monte Carlo (MC) method in the NVT and NPT ensembles. The simulation results show that when determining the elastic constants, the contribution of long-range interactions cannot be ignored, because that would lead to erroneous results. In addition, the simulations have revealed that the inclusion of further interactions of each particle with all its minimum image neighbors even in case of small systems leads to results which are very close to the values of elastic constants in the thermodynamic limit. This enables one for a quick and accurate estimation of the elastic constants using very small samples.
Pitfalls in accurate estimation of overdiagnosis: implications for screening policy and compliance.
Feig, Stephen A
2013-01-01
Stories in the public media that 30 to 50% of screen-detected breast cancers are overdiagnosed dissuade women from being screened because overdiagnosed cancers would never result in death if undetected yet do result in unnecessary treatment. However, such concerns are unwarranted because the frequency of overdiagnosis, when properly calculated, is only 0 to 5%. In the previous issue of Breast Cancer Research, Duffy and Parmar report that accurate estimation of the rate of overdiagnosis recognizes the effect of lead time on detection rates and the consequent requirement for an adequate number of years of follow-up. These indispensable elements were absent from highly publicized studies that overestimated the frequency of overdiagnosis.
Guadagnoli, M A; Kohl, R M
2001-06-01
The authors of the present study investigated the apparent contradiction between early and more recent views of knowledge of results (KR), the idea that how one is engaged before receiving KR may not be independent of how one uses that KR. In a 2 ×: 2 factorial design, participants (N = 64) practiced a simple force-production task and (a) were required, or not required, to estimate error about their previous response and (b) were provided KR either after every response (100%) or after every 5th response (20%) during acquisition. A no-KR retention test revealed an interaction between acquisition error estimation and KR frequencies. The group that received 100% KR and was required to error estimate during acquisition performed the best during retention. The 2 groups that received 20% KR performed less well. Finally, the group that received 100% KR and was not required to error estimate during acquisition performed the poorest during retention. One general interpretation of that pattern of results is that motor learning is an increasing function of the degree to which participants use KR to test response hypotheses (J. A. Adams, 1971; R. A. Schmidt, 1975). Practicing simple responses coupled with error estimation may embody response hypotheses that can be tested with KR, thus benefiting motor learning most under a 100% KR condition. Practicing simple responses without error estimation is less likely to embody response hypothesis, however, which may increase the probability that participants will use KR to guide upcoming responses, thus attenuating motor learning under a 100% KR condition. The authors conclude, therefore, that how one is engaged before receiving KR may not be independent of how one uses KR. PMID:11404216
A Simple yet Accurate Method for the Estimation of the Biovolume of Planktonic Microorganisms.
Saccà, Alessandro
2016-01-01
Determining the biomass of microbial plankton is central to the study of fluxes of energy and materials in aquatic ecosystems. This is typically accomplished by applying proper volume-to-carbon conversion factors to group-specific abundances and biovolumes. A critical step in this approach is the accurate estimation of biovolume from two-dimensional (2D) data such as those available through conventional microscopy techniques or flow-through imaging systems. This paper describes a simple yet accurate method for the assessment of the biovolume of planktonic microorganisms, which works with any image analysis system allowing for the measurement of linear distances and the estimation of the cross sectional area of an object from a 2D digital image. The proposed method is based on Archimedes' principle about the relationship between the volume of a sphere and that of a cylinder in which the sphere is inscribed, plus a coefficient of 'unellipticity' introduced here. Validation and careful evaluation of the method are provided using a variety of approaches. The new method proved to be highly precise with all convex shapes characterised by approximate rotational symmetry, and combining it with an existing method specific for highly concave or branched shapes allows covering the great majority of cases with good reliability. Thanks to its accuracy, consistency, and low resources demand, the new method can conveniently be used in substitution of any extant method designed for convex shapes, and can readily be coupled with automated cell imaging technologies, including state-of-the-art flow-through imaging devices. PMID:27195667
A Simple yet Accurate Method for the Estimation of the Biovolume of Planktonic Microorganisms
2016-01-01
Determining the biomass of microbial plankton is central to the study of fluxes of energy and materials in aquatic ecosystems. This is typically accomplished by applying proper volume-to-carbon conversion factors to group-specific abundances and biovolumes. A critical step in this approach is the accurate estimation of biovolume from two-dimensional (2D) data such as those available through conventional microscopy techniques or flow-through imaging systems. This paper describes a simple yet accurate method for the assessment of the biovolume of planktonic microorganisms, which works with any image analysis system allowing for the measurement of linear distances and the estimation of the cross sectional area of an object from a 2D digital image. The proposed method is based on Archimedes’ principle about the relationship between the volume of a sphere and that of a cylinder in which the sphere is inscribed, plus a coefficient of ‘unellipticity’ introduced here. Validation and careful evaluation of the method are provided using a variety of approaches. The new method proved to be highly precise with all convex shapes characterised by approximate rotational symmetry, and combining it with an existing method specific for highly concave or branched shapes allows covering the great majority of cases with good reliability. Thanks to its accuracy, consistency, and low resources demand, the new method can conveniently be used in substitution of any extant method designed for convex shapes, and can readily be coupled with automated cell imaging technologies, including state-of-the-art flow-through imaging devices. PMID:27195667
Accurate Estimation of the Fine Layering Effect on the Wave Propagation in the Carbonate Rocks
NASA Astrophysics Data System (ADS)
Bouchaala, F.; Ali, M. Y.
2014-12-01
The attenuation caused to the seismic wave during its propagation can be mainly divided into two parts, the scattering and the intrinsic attenuation. The scattering is an elastic redistribution of the energy due to the medium heterogeneities. However the intrinsic attenuation is an inelastic phenomenon, mainly due to the fluid-grain friction during the wave passage. The intrinsic attenuation is directly related to the physical characteristics of the medium, so this parameter is very can be used for media characterization and fluid detection, which is beneficial for the oil and gas industry. The intrinsic attenuation is estimated by subtracting the scattering from the total attenuation, therefore the accuracy of the intrinsic attenuation is directly dependent on the accuracy of the total attenuation and the scattering. The total attenuation can be estimated from the recorded waves, by using in-situ methods as the spectral ratio and frequency shift methods. The scattering is estimated by assuming the heterogeneities as a succession of stacked layers, each layer is characterized by a single density and velocity. The accuracy of the scattering is strongly dependent on the layer thicknesses, especially in the case of the media composed of carbonate rocks, such media are known for their strong heterogeneity. Previous studies gave some assumptions for the choice of the layer thickness, but they showed some limitations especially in the case of carbonate rocks. In this study we established a relationship between the layer thicknesses and the frequency of the propagation, after certain mathematical development of the Generalized O'Doherty-Anstey formula. We validated this relationship through some synthetic tests and real data provided from a VSP carried out over an onshore oilfield in the emirate of Abu Dhabi in the United Arab Emirates, primarily composed of carbonate rocks. The results showed the utility of our relationship for an accurate estimation of the scattering
Stenroos, Matti; Hauk, Olaf
2013-11-01
The conductivity profile of the head has a major effect on EEG signals, but unfortunately the conductivity for the most important compartment, skull, is only poorly known. In dipole modeling studies, errors in modeled skull conductivity have been considered to have a detrimental effect on EEG source estimation. However, as dipole models are very restrictive, those results cannot be generalized to other source estimation methods. In this work, we studied the sensitivity of EEG and combined MEG+EEG source estimation to errors in skull conductivity using a distributed source model and minimum-norm (MN) estimation. We used a MEG/EEG modeling set-up that reflected state-of-the-art practices of experimental research. Cortical surfaces were segmented and realistically-shaped three-layer anatomical head models were constructed, and forward models were built with Galerkin boundary element method while varying the skull conductivity. Lead-field topographies and MN spatial filter vectors were compared across conductivities, and the localization and spatial spread of the MN estimators were assessed using intuitive resolution metrics. The results showed that the MN estimator is robust against errors in skull conductivity: the conductivity had a moderate effect on amplitudes of lead fields and spatial filter vectors, but the effect on corresponding morphologies was small. The localization performance of the EEG or combined MEG+EEG MN estimator was only minimally affected by the conductivity error, while the spread of the estimate varied slightly. Thus, the uncertainty with respect to skull conductivity should not prevent researchers from applying minimum norm estimation to EEG or combined MEG+EEG data. Comparing our results to those obtained earlier with dipole models shows that general judgment on the performance of an imaging modality should not be based on analysis with one source estimation method only.
ZZ-Type a posteriori error estimators for adaptive boundary element methods on a curve☆
Feischl, Michael; Führer, Thomas; Karkulik, Michael; Praetorius, Dirk
2014-01-01
In the context of the adaptive finite element method (FEM), ZZ-error estimators named after Zienkiewicz and Zhu (1987) [52] are mathematically well-established and widely used in practice. In this work, we propose and analyze ZZ-type error estimators for the adaptive boundary element method (BEM). We consider weakly singular and hyper-singular integral equations and prove, in particular, convergence of the related adaptive mesh-refining algorithms. Throughout, the theoretical findings are underlined by numerical experiments. PMID:24748725
Christodoulou, Christos George (University of New Mexico, Albuquerque, NM); Abdallah, Chaouki T. (University of New Mexico, Albuquerque, NM); Rohwer, Judd Andrew
2003-02-01
The paper presents a multiclass, multilabel implementation of least squares support vector machines (LS-SVM) for direction of arrival (DOA) estimation in a CDMA system. For any estimation or classification system, the algorithm's capabilities and performance must be evaluated. Specifically, for classification algorithms, a high confidence level must exist along with a technique to tag misclassifications automatically. The presented learning algorithm includes error control and validation steps for generating statistics on the multiclass evaluation path and the signal subspace dimension. The error statistics provide a confidence level for the classification accuracy.
Upper bounds on position error of a single location estimate in wireless sensor networks
NASA Astrophysics Data System (ADS)
Gholami, Mohammad Reza; Ström, Erik G.; Wymeersch, Henk; Gezici, Sinan
2014-12-01
This paper studies upper bounds on the position error for a single estimate of an unknown target node position based on distance estimates in wireless sensor networks. In this study, we investigate a number of approaches to confine the target node position to bounded sets for different scenarios. Firstly, if at least one distance estimate error is positive, we derive a simple, but potentially loose upper bound, which is always valid. In addition assuming that the probability density of measurement noise is nonzero for positive values and a sufficiently large number of distance estimates are available, we propose an upper bound, which is valid with high probability. Secondly, if a reasonable lower bound on negative measurement errors is known a priori, we manipulate the distance estimates to obtain a new set with positive measurement errors. In general, we formulate bounds as nonconvex optimization problems. To solve the problems, we employ a relaxation technique and obtain semidefinite programs. We also propose a simple approach to find the bounds in closed forms. Simulation results show reasonable tightness for different bounds in various situations.
NASA Astrophysics Data System (ADS)
Hu, Yongxiang; Behrenfeld, Mike; Hostetler, Chris; Pelon, Jacques; Trepte, Charles; Hair, John; Slade, Wayne; Cetinic, Ivona; Vaughan, Mark; Lu, Xiaomei; Zhai, Pengwang; Weimer, Carl; Winker, David; Verhappen, Carolus C.; Butler, Carolyn; Liu, Zhaoyan; Hunt, Bill; Omar, Ali; Rodier, Sharon; Lifermann, Anne; Josset, Damien; Hou, Weilin; MacDonnell, David; Rhew, Ray
2016-06-01
Beam attenuation coefficient, c, provides an important optical index of plankton standing stocks, such as phytoplankton biomass and total particulate carbon concentration. Unfortunately, c has proven difficult to quantify through remote sensing. Here, we introduce an innovative approach for estimating c using lidar depolarization measurements and diffuse attenuation coefficients from ocean color products or lidar measurements of Brillouin scattering. The new approach is based on a theoretical formula established from Monte Carlo simulations that links the depolarization ratio of sea water to the ratio of diffuse attenuation Kd and beam attenuation C (i.e., a multiple scattering factor). On July 17, 2014, the CALIPSO satellite was tilted 30° off-nadir for one nighttime orbit in order to minimize ocean surface backscatter and demonstrate the lidar ocean subsurface measurement concept from space. Depolarization ratios of ocean subsurface backscatter are measured accurately. Beam attenuation coefficients computed from the depolarization ratio measurements compare well with empirical estimates from ocean color measurements. We further verify the beam attenuation coefficient retrievals using aircraft-based high spectral resolution lidar (HSRL) data that are collocated with in-water optical measurements.
Greater contrast in Martian hydrological history from more accurate estimates of paleodischarge
NASA Astrophysics Data System (ADS)
Jacobsen, R. E.; Burr, D. M.
2016-09-01
Correlative width-discharge relationships from the Missouri River Basin are commonly used to estimate fluvial paleodischarge on Mars. However, hydraulic geometry provides alternative, and causal, width-discharge relationships derived from broader samples of channels, including those in reduced-gravity (submarine) environments. Comparison of these relationships implies that causal relationships from hydraulic geometry should yield more accurate and more precise discharge estimates. Our remote analysis of a Martian-terrestrial analog channel, combined with in situ discharge data, substantiates this implication. Applied to Martian features, these results imply that paleodischarges of interior channels of Noachian-Hesperian (~3.7 Ga) valley networks have been underestimated by a factor of several, whereas paleodischarges for smaller fluvial deposits of the Late Hesperian-Early Amazonian (~3.0 Ga) have been overestimated. Thus, these new paleodischarges significantly magnify the contrast between early and late Martian hydrologic activity. Width-discharge relationships from hydraulic geometry represent validated tools for quantifying fluvial input near candidate landing sites of upcoming missions.
Estimation of the minimum mRNA splicing error rate in vertebrates.
Skandalis, A
2016-01-01
The majority of protein coding genes in vertebrates contain several introns that are removed by the mRNA splicing machinery. Errors during splicing can generate aberrant transcripts and degrade the transmission of genetic information thus contributing to genomic instability and disease. However, estimating the error rate of constitutive splicing is complicated by the process of alternative splicing which can generate multiple alternative transcripts per locus and is particularly active in humans. In order to estimate the error frequency of constitutive mRNA splicing and avoid bias by alternative splicing we have characterized the frequency of splice variants at three loci, HPRT, POLB, and TRPV1 in multiple tissues of six vertebrate species. Our analysis revealed that the frequency of splice variants varied widely among loci, tissues, and species. However, the lowest observed frequency is quite constant among loci and approximately 0.1% aberrant transcripts per intron. Arguably this reflects the "irreducible" error rate of splicing, which consists primarily of the combination of replication errors by RNA polymerase II in splice consensus sequences and spliceosome errors in correctly pairing exons. PMID:26811995
Audit of the global carbon budget: estimate errors and their impact on uptake uncertainty
NASA Astrophysics Data System (ADS)
Ballantyne, A. P.; Andres, R.; Houghton, R.; Stocker, B. D.; Wanninkhof, R.; Anderegg, W.; Cooper, L. A.; DeGrandpre, M.; Tans, P. P.; Miller, J. C.; Alden, C.; White, J. W. C.
2014-10-01
Over the last 5 decades monitoring systems have been developed to detect changes in the accumulation of C in the atmosphere, ocean, and land; 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 error 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 2 σ error of the atmospheric growth rate has decreased from 1.2 Pg C yr-1 in the 1960s to 0.3 Pg C yr-1 in the 2000s, leading to a ~20% reduction in the over-all uncertainty of net global C uptake by the biosphere. While fossil fuel emissions have increased by a factor of 4 over the last 5 decades, 2 σ errors in fossil fuel emissions due to national reporting errors and differences in energy reporting practices have increased from 0.3 Pg C yr-1 in the 1960s to almost 1.0 Pg C yr-1 during the 2000s. At the same time land use emissions have declined slightly over the last 5 decades, but their relative errors remain high. Notably, errors associated with fossil fuel emissions have come to dominate uncertainty in the global C budget and are now comparable to the total emissions from land use, thus efforts to reduce errors in fossil fuel emissions are necessary. Given all the major sources of error in the global C budget that we could identify, we are 93% confident that C uptake has increased and 97% confident that C uptake by the terrestrial biosphere has increased over the last 5 decades. Although the persistence of future C sinks remains unknown and some ecosystem services may be compromised by this continued C uptake (e.g. ocean acidification), it is clear that arguably the greatest ecosystem service currently provided by the biosphere is the
NASA Technical Reports Server (NTRS)
Huynh, Loc C.; Duval, R. W.
1986-01-01
The use of Redundant Asynchronous Multiprocessor System to achieve ultrareliable Fault Tolerant Control Systems shows great promise. The development has been hampered by the inability to determine whether differences in the outputs of redundant CPU's are due to failures or to accrued error built up by slight differences in CPU clock intervals. This study derives an analytical dynamic model of the difference between redundant CPU's due to differences in their clock intervals and uses this model with on-line parameter identification to idenitify the differences in the clock intervals. The ability of this methodology to accurately track errors due to asynchronisity generate an error signal with the effect of asynchronisity removed and this signal may be used to detect and isolate actual system failures.
Estimation of Smoothing Error in SBUV Profile and Total Ozone Retrieval
NASA Technical Reports Server (NTRS)
Kramarova, N. A.; Bhartia, P. K.; Frith, S. M.; Fisher, B. L.; McPeters, R. D.; Taylor, S.; Labow, G. J.
2011-01-01
Data from the Nimbus-4, Nimbus-7 Solar Backscatter Ultra Violet (SBUV) and seven of the NOAA series of SBUV/2 instruments spanning 41 years are being reprocessed using V8.6 algorithm. The data are scheduled to be released by the end of August 2011. An important focus of the new algorithm is to estimate various sources of errors in the SBUV profiles and total ozone retrievals. We discuss here the smoothing errors that describe the components of the profile variability that the SBUV observing system can not measure. The SBUV(/2) instruments have a vertical resolution of 5 km in the middle stratosphere, decreasing to 8 to 10 km below the ozone peak and above 0.5 hPa. To estimate the smoothing effect of the SBUV algorithm, the actual statistics of the fine vertical structure of ozone profiles must be known. The covariance matrix of the ensemble of measured ozone profiles with the high vertical resolution would be a formal representation of the actual ozone variability. We merged the MLS (version 3) and sonde ozone profiles to calculate the covariance matrix, which in general case, for single profile retrieval, might be a function of the latitude and month. Using the averaging kernels of the SBUV(/2) measurements and calculated total covariance matrix one can estimate the smoothing errors for the SBUV ozone profiles. A method to estimate the smoothing effect of the SBUV algorithm is described and the covariance matrixes and averaging kernels are provided along with the SBUV(/2) ozone profiles. The magnitude of the smoothing error varies with altitude, latitude, season and solar zenith angle. The analysis of the smoothing errors, based on the SBUV(/2) monthly zonal mean time series, shows that the largest smoothing errors were detected in the troposphere and might be as large as 15-20% and rapidly decrease with the altitude. In the stratosphere above 40 hPa the smoothing errors are less than 5% and between 10 and 1 hPa the smoothing errors are on the order of 1%. We
Estimating genotype error rates from high-coverage next-generation sequence data.
Wall, Jeffrey D; Tang, Ling Fung; Zerbe, Brandon; Kvale, Mark N; Kwok, Pui-Yan; Schaefer, Catherine; Risch, Neil
2014-11-01
Exome and whole-genome sequencing studies are becoming increasingly common, but little is known about the accuracy of the genotype calls made by the commonly used platforms. Here we use replicate high-coverage sequencing of blood and saliva DNA samples from four European-American individuals to estimate lower bounds on the error rates of Complete Genomics and Illumina HiSeq whole-genome and whole-exome sequencing. Error rates for nonreference genotype calls range from 0.1% to 0.6%, depending on the platform and the depth of coverage. Additionally, we found (1) no difference in the error profiles or rates between blood and saliva samples; (2) Complete Genomics sequences had substantially higher error rates than Illumina sequences had; (3) error rates were higher (up to 6%) for rare or unique variants; (4) error rates generally declined with genotype quality (GQ) score, but in a nonlinear fashion for the Illumina data, likely due to loss of specificity of GQ scores greater than 60; and (5) error rates increased with increasing depth of coverage for the Illumina data. These findings, especially (3)-(5), suggest that caution should be taken in interpreting the results of next-generation sequencing-based association studies, and even more so in clinical application of this technology in the absence of validation by other more robust sequencing or genotyping methods.
Error estimates of triangular finite elements under a weak angle condition
NASA Astrophysics Data System (ADS)
Mao, Shipeng; Shi, Zhongci
2009-08-01
In this note, by analyzing the interpolation operator of Girault and Raviart given in [V. Girault, P.A. Raviart, Finite element methods for Navier-Stokes equations, Theory and algorithms, in: Springer Series in Computational Mathematics, Springer-Verlag, Berlin,1986] over triangular meshes, we prove optimal interpolation error estimates for Lagrange triangular finite elements of arbitrary order under the maximal angle condition in a unified and simple way. The key estimate is only an application of the Bramble-Hilbert lemma.
A Refined Algorithm On The Estimation Of Residual Motion Errors In Airborne SAR Images
NASA Astrophysics Data System (ADS)
Zhong, Xuelian; Xiang, Maosheng; Yue, Huanyin; Guo, Huadong
2010-10-01
Due to the lack of accuracy in the navigation system, residual motion errors (RMEs) frequently appear in the airborne SAR image. For very high resolution SAR imaging and repeat-pass SAR interferometry, the residual motion errors must be estimated and compensated. We have proposed a new algorithm before to estimate the residual motion errors for an individual SAR image. It exploits point-like targets distributed along the azimuth direction, and not only corrects the phase, but also improves the azimuth focusing. But the required point targets are selected by hand, which is time- and labor-consuming. In addition, the algorithm is sensitive to noises. In this paper, a refined algorithm is proposed aiming at these two shortcomings. With real X-band airborne SAR data, the feasibility and accuracy of the refined algorithm are demonstrated.
NASA Astrophysics Data System (ADS)
Li, Guofa; Huang, Wei; Zheng, Hao; Zhang, Baoqing
2016-02-01
The spectral ratio method (SRM) is widely used to estimate quality factor Q via the linear regression of seismic attenuation under the assumption of a constant Q. However, the estimate error will be introduced when this assumption is violated. For the frequency-dependent Q described by a power-law function, we derived the analytical expression of estimate error as a function of the power-law exponent γ and the ratio of the bandwidth to the central frequency σ . Based on the theoretical analysis, we found that the estimate errors are mainly dominated by the exponent γ , and less affected by the ratio σ . This phenomenon implies that the accuracy of the Q estimate can hardly be improved by adjusting the width and range of the frequency band. Hence, we proposed a two-parameter regression method to estimate the frequency-dependent Q from the nonlinear seismic attenuation. The proposed method was tested using the direct waves acquired by a near-surface cross-hole survey, and its reliability was evaluated in comparison with the result of SRM.
Mapping the Origins of Time: Scalar Errors in Infant Time Estimation
ERIC Educational Resources Information Center
Addyman, Caspar; Rocha, Sinead; Mareschal, Denis
2014-01-01
Time is central to any understanding of the world. In adults, estimation errors grow linearly with the length of the interval, much faster than would be expected of a clock-like mechanism. Here we present the first direct demonstration that this is also true in human infants. Using an eye-tracking paradigm, we examined 4-, 6-, 10-, and…
A Sandwich-Type Standard Error Estimator of SEM Models with Multivariate Time Series
ERIC Educational Resources Information Center
Zhang, Guangjian; Chow, Sy-Miin; Ong, Anthony D.
2011-01-01
Structural equation models are increasingly used as a modeling tool for multivariate time series data in the social and behavioral sciences. Standard error estimators of SEM models, originally developed for independent data, require modifications to accommodate the fact that time series data are inherently dependent. In this article, we extend a…
Application of a posteriori error estimates for the steady Stokes-Brinkman equation in 2D
NASA Astrophysics Data System (ADS)
Hasal, Martin; Burda, Pavel
2016-06-01
The paper deals with the Stokes-Brinkman equation. We investigate a posteriori error estimates for the Stokes-Brinkman equation on two-dimensional polygonal domains. Special attention is paid to the value of the hydraulic conductivity coefficients. We present numerical results for an incompressible flow problem in a domain with corners.
A Derivation of the Unbiased Standard Error of Estimate: The General Case.
ERIC Educational Resources Information Center
O'Brien, Francis J., Jr.
This paper is part of a series of applied statistics monographs intended to provide supplementary reading for applied statistics students. In the present paper, derivations of the unbiased standard error of estimate for both the raw score and standard score linear models are presented. The derivations for raw score linear models are presented in…
Archibald, Richard K; Deiterding, Ralf; Hauck, Cory D; Jakeman, John D; Xiu, Dongbin
2012-01-01
We have develop a fast method that can capture piecewise smooth functions in high dimensions with high order and low computational cost. This method can be used for both approximation and error estimation of stochastic simulations where the computations can either be guided or come from a legacy database.
Discretization error estimation and exact solution generation using the method of nearby problems.
Sinclair, Andrew J.; Raju, Anil; Kurzen, Matthew J.; Roy, Christopher John; Phillips, Tyrone S.
2011-10-01
The Method of Nearby Problems (MNP), a form of defect correction, is examined as a method for generating exact solutions to partial differential equations and as a discretization error estimator. For generating exact solutions, four-dimensional spline fitting procedures were developed and implemented into a MATLAB code for generating spline fits on structured domains with arbitrary levels of continuity between spline zones. For discretization error estimation, MNP/defect correction only requires a single additional numerical solution on the same grid (as compared to Richardson extrapolation which requires additional numerical solutions on systematically-refined grids). When used for error estimation, it was found that continuity between spline zones was not required. A number of cases were examined including 1D and 2D Burgers equation, the 2D compressible Euler equations, and the 2D incompressible Navier-Stokes equations. The discretization error estimation results compared favorably to Richardson extrapolation and had the advantage of only requiring a single grid to be generated.
ERIC Educational Resources Information Center
Bond, William Glenn
2012-01-01
In this paper, I propose to demonstrate a means of error estimation preprocessing in the assembly of overlapping aerial image mosaics. The mosaic program automatically assembles several hundred aerial images from a data set by aligning them, via image registration using a pattern search method, onto a GIS grid. The method presented first locates…
Standard Error Estimation of 3PL IRT True Score Equating with an MCMC Method
ERIC Educational Resources Information Center
Liu, Yuming; Schulz, E. Matthew; Yu, Lei
2008-01-01
A Markov chain Monte Carlo (MCMC) method and a bootstrap method were compared in the estimation of standard errors of item response theory (IRT) true score equating. Three test form relationships were examined: parallel, tau-equivalent, and congeneric. Data were simulated based on Reading Comprehension and Vocabulary tests of the Iowa Tests of…
A Generalizability Theory Approach to Standard Error Estimates for Bookmark Standard Settings
ERIC Educational Resources Information Center
Lee, Guemin; Lewis, Daniel M.
2008-01-01
The bookmark standard-setting procedure is an item response theory-based method that is widely implemented in state testing programs. This study estimates standard errors for cut scores resulting from bookmark standard settings under a generalizability theory model and investigates the effects of different universes of generalization and error…
Estimation of chromatic errors from broadband images for high contrast imaging
NASA Astrophysics Data System (ADS)
Sirbu, Dan; Belikov, Ruslan
2015-09-01
Usage of an internal coronagraph with an adaptive optical system for wavefront correction for direct imaging of exoplanets is currently being considered for many mission concepts, including as an instrument addition to the WFIRST-AFTA mission to follow the James Web Space Telescope. The main technical challenge associated with direct imaging of exoplanets with an internal coronagraph is to effectively control both the diffraction and scattered light from the star so that the dim planetary companion can be seen. For the deformable mirror (DM) to recover a dark hole region with sufficiently high contrast in the image plane, wavefront errors are usually estimated using probes on the DM. To date, most broadband lab demonstrations use narrowband filters to estimate the chromaticity of the wavefront error, but this reduces the photon flux per filter and requires a filter system. Here, we propose a method to estimate the chromaticity of wavefront errors using only a broadband image. This is achieved by using special DM probes that have sufficient chromatic diversity. As a case example, we simulate the retrieval of the spectrum of the central wavelength from broadband images for a simple shaped- pupil coronagraph with a conjugate DM and compute the resulting estimation error.
Interval Estimation for True Raw and Scale Scores under the Binomial Error Model
ERIC Educational Resources Information Center
Lee, Won-Chan; Brennan, Robert L.; Kolen, Michael J.
2006-01-01
Assuming errors of measurement are distributed binomially, this article reviews various procedures for constructing an interval for an individual's true number-correct score; presents two general interval estimation procedures for an individual's true scale score (i.e., normal approximation and endpoints conversion methods); compares various…
ERIC Educational Resources Information Center
Cui, Zhongmin; Kolen, Michael J.
2008-01-01
This article considers two methods of estimating standard errors of equipercentile equating: the parametric bootstrap method and the nonparametric bootstrap method. Using a simulation study, these two methods are compared under three sample sizes (300, 1,000, and 3,000), for two test content areas (the Iowa Tests of Basic Skills Maps and Diagrams…
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.; et al
2015-04-30
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 concludemore » 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
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.
2015-04-30
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
Audit of the global carbon budget: estimate errors and their impact on uptake uncertainty
NASA Astrophysics Data System (ADS)
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
Accurate Visual Heading Estimation at High Rotation Rate Without Oculomotor or Static-Depth Cues
NASA Technical Reports Server (NTRS)
Stone, Leland S.; Perrone, John A.; Null, Cynthia H. (Technical Monitor)
1995-01-01
It has been claimed that either oculomotor or static depth cues provide the signals about self-rotation necessary approx.-1 deg/s. We tested this hypothesis by simulating self-motion along a curved path with the eyes fixed in the head (plus or minus 16 deg/s of rotation). Curvilinear motion offers two advantages: 1) heading remains constant in retinotopic coordinates, and 2) there is no visual-oculomotor conflict (both actual and simulated eye position remain stationary). We simulated 400 ms of rotation combined with 16 m/s of translation at fixed angles with respect to gaze towards two vertical planes of random dots initially 12 and 24 m away, with a field of view of 45 degrees. Four subjects were asked to fixate a central cross and to respond whether they were translating to the left or right of straight-ahead gaze. From the psychometric curves, heading bias (mean) and precision (semi-interquartile) were derived. The mean bias over 2-5 runs was 3.0, 4.0, -2.0, -0.4 deg for the first author and three naive subjects, respectively (positive indicating towards the rotation direction). The mean precision was 2.0, 1.9, 3.1, 1.6 deg. respectively. The ability of observers to make relatively accurate and precise heading judgments, despite the large rotational flow component, refutes the view that extra-flow-field information is necessary for human visual heading estimation at high rotation rates. Our results support models that process combined translational/rotational flow to estimate heading, but should not be construed to suggest that other cues do not play an important role when they are available to the observer.
Estimation of sampling error uncertainties in observed surface air temperature change in China
NASA Astrophysics Data System (ADS)
Hua, Wei; Shen, Samuel S. P.; Weithmann, Alexander; Wang, Huijun
2016-06-01
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.
Ginting, Victor
2014-03-15
it was demonstrated that a posteriori analyses in general and in particular one that uses adjoint methods can accurately and efficiently compute numerical error estimates and sensitivity for critical Quantities of Interest (QoIs) that depend on a large number of parameters. Activities include: analysis and implementation of several time integration techniques for solving system of ODEs as typically obtained from spatial discretization of PDE systems; multirate integration methods for ordinary differential equations; formulation and analysis of an iterative multi-discretization Galerkin finite element method for multi-scale reaction-diffusion equations; investigation of an inexpensive postprocessing technique to estimate the error of finite element solution of the second-order quasi-linear elliptic problems measured in some global metrics; investigation of an application of the residual-based a posteriori error estimates to symmetric interior penalty discontinuous Galerkin method for solving a class of second order quasi-linear elliptic problems; a posteriori analysis of explicit time integrations for system of linear ordinary differential equations; derivation of accurate a posteriori goal oriented error estimates for a user-defined quantity of interest for two classes of first and second order IMEX schemes for advection-diffusion-reaction problems; Postprocessing finite element solution; and A Bayesian Framework for Uncertain Quantification of Porous Media Flows.
Test models for improving filtering with model errors through stochastic parameter estimation
Gershgorin, B.; Harlim, J. Majda, A.J.
2010-01-01
The filtering skill for turbulent signals from nature is often limited by model errors created by utilizing an imperfect model for filtering. Updating the parameters in the imperfect model through stochastic parameter estimation is one way to increase filtering skill and model performance. Here a suite of stringent test models for filtering with stochastic parameter estimation is developed based on the Stochastic Parameterization Extended Kalman Filter (SPEKF). These new SPEKF-algorithms systematically correct both multiplicative and additive biases and involve exact formulas for propagating the mean and covariance including the parameters in the test model. A comprehensive study is presented of robust parameter regimes for increasing filtering skill through stochastic parameter estimation for turbulent signals as the observation time and observation noise are varied and even when the forcing is incorrectly specified. The results here provide useful guidelines for filtering turbulent signals in more complex systems with significant model errors.
Error Estimates for Approximate Solutions of the Riccati Equation with Real or Complex Potentials
NASA Astrophysics Data System (ADS)
Finster, Felix; Smoller, Joel
2010-09-01
A method is presented for obtaining rigorous error estimates for approximate solutions of the Riccati equation, with real or complex potentials. Our main tool is to derive invariant region estimates for complex solutions of the Riccati equation. We explain the general strategy for applying these estimates and illustrate the method in typical examples, where the approximate solutions are obtained by gluing together WKB and Airy solutions of corresponding one-dimensional Schrödinger equations. Our method is motivated by, and has applications to, the analysis of linear wave equations in the geometry of a rotating black hole.
Estimated Cost Savings from Reducing Errors in the Preparation of Sterile Doses of Medications
Schneider, Philip J.
2014-01-01
Abstract Background: Preventing intravenous (IV) preparation errors will improve patient safety and reduce costs by an unknown amount. Objective: To estimate the financial benefit of robotic preparation of sterile medication doses compared to traditional manual preparation techniques. Methods: A probability pathway model based on published rates of errors in the preparation of sterile doses of medications was developed. Literature reports of adverse events were used to project the array of medical outcomes that might result from these errors. These parameters were used as inputs to a customized simulation model that generated a distribution of possible outcomes, their probability, and associated costs. Results: By varying the important parameters across ranges found in published studies, the simulation model produced a range of outcomes for all likely possibilities. Thus it provided a reliable projection of the errors avoided and the cost savings of an automated sterile preparation technology. The average of 1,000 simulations resulted in the prevention of 5,420 medication errors and associated savings of $288,350 per year. The simulation results can be narrowed to specific scenarios by fixing model parameters that are known and allowing the unknown parameters to range across values found in previously published studies. Conclusions: The use of a robotic device can reduce health care costs by preventing errors that can cause adverse drug events. PMID:25477598
Estimates of Mode-S EHS aircraft-derived wind observation errors using triple collocation
NASA Astrophysics Data System (ADS)
de Haan, Siebren
2016-08-01
Information on the accuracy of meteorological observation is essential to assess the applicability of the measurements. In general, accuracy information is difficult to obtain in operational situations, since the truth is unknown. One method to determine this accuracy is by comparison with the model equivalent of the observation. The advantage of this method is that all measured parameters can be evaluated, from 2 m temperature observation to satellite radiances. The drawback is that these comparisons also contain the (unknown) model error. By applying the so-called triple-collocation method , on two independent observations at the same location in space and time, combined with model output, and assuming uncorrelated observations, the three error variances can be estimated. This method is applied in this study to estimate wind observation errors from aircraft, obtained utilizing information from air traffic control surveillance radar with Selective Mode Enhanced Surveillance capabilities Mode-S EHS, see. Radial wind measurements from Doppler weather radar and wind vector measurements from sodar, together with equivalents from a non-hydrostatic numerical weather prediction model, are used to assess the accuracy of the Mode-S EHS wind observations. The Mode-S EHS wind (zonal and meridional) observation error is estimated to be less than 1.4 ± 0.1 m s-1 near the surface and around 1.1 ± 0.3 m s-1 at 500 hPa.
Entropy-Based TOA Estimation and SVM-Based Ranging Error Mitigation in UWB Ranging Systems
Yin, Zhendong; Cui, Kai; Wu, Zhilu; Yin, Liang
2015-01-01
The major challenges for Ultra-wide Band (UWB) indoor ranging systems are the dense multipath and non-line-of-sight (NLOS) problems of the indoor environment. To precisely estimate the time of arrival (TOA) of the first path (FP) in such a poor environment, a novel approach of entropy-based TOA estimation and support vector machine (SVM) regression-based ranging error mitigation is proposed in this paper. The proposed method can estimate the TOA precisely by measuring the randomness of the received signals and mitigate the ranging error without the recognition of the channel conditions. The entropy is used to measure the randomness of the received signals and the FP can be determined by the decision of the sample which is followed by a great entropy decrease. The SVM regression is employed to perform the ranging-error mitigation by the modeling of the regressor between the characteristics of received signals and the ranging error. The presented numerical simulation results show that the proposed approach achieves significant performance improvements in the CM1 to CM4 channels of the IEEE 802.15.4a standard, as compared to conventional approaches. PMID:26007726
Entropy-Based TOA Estimation and SVM-Based Ranging Error Mitigation in UWB Ranging Systems.
Yin, Zhendong; Cui, Kai; Wu, Zhilu; Yin, Liang
2015-01-01
The major challenges for Ultra-wide Band (UWB) indoor ranging systems are the dense multipath and non-line-of-sight (NLOS) problems of the indoor environment. To precisely estimate the time of arrival (TOA) of the first path (FP) in such a poor environment, a novel approach of entropy-based TOA estimation and support vector machine (SVM) regression-based ranging error mitigation is proposed in this paper. The proposed method can estimate the TOA precisely by measuring the randomness of the received signals and mitigate the ranging error without the recognition of the channel conditions. The entropy is used to measure the randomness of the received signals and the FP can be determined by the decision of the sample which is followed by a great entropy decrease. The SVM regression is employed to perform the ranging-error mitigation by the modeling of the regressor between the characteristics of received signals and the ranging error. The presented numerical simulation results show that the proposed approach achieves significant performance improvements in the CM1 to CM4 channels of the IEEE 802.15.4a standard, as compared to conventional approaches.
Estimating pole/zero errors in GSN-IRIS/USGS network calibration metadata
Ringler, A.T.; Hutt, C.R.; Aster, R.; Bolton, H.; Gee, L.S.; Storm, T.
2012-01-01
Mapping the digital record of a seismograph into true ground motion requires the correction of the data by some description of the instrument's response. For the Global Seismographic Network (Butler et al., 2004), as well as many other networks, this instrument response is represented as a Laplace domain pole–zero model and published in the Standard for the Exchange of Earthquake Data (SEED) format. This Laplace representation assumes that the seismometer behaves as a linear system, with any abrupt changes described adequately via multiple time-invariant epochs. The SEED format allows for published instrument response errors as well, but these typically have not been estimated or provided to users. We present an iterative three-step method to estimate the instrument response parameters (poles and zeros) and their associated errors using random calibration signals. First, we solve a coarse nonlinear inverse problem using a least-squares grid search to yield a first approximation to the solution. This approach reduces the likelihood of poorly estimated parameters (a local-minimum solution) caused by noise in the calibration records and enhances algorithm convergence. Second, we iteratively solve a nonlinear parameter estimation problem to obtain the least-squares best-fit Laplace pole–zero–gain model. Third, by applying the central limit theorem, we estimate the errors in this pole–zero model by solving the inverse problem at each frequency in a two-thirds octave band centered at each best-fit pole–zero frequency. This procedure yields error estimates of the 99% confidence interval. We demonstrate the method by applying it to a number of recent Incorporated Research Institutions in Seismology/United States Geological Survey (IRIS/USGS) network calibrations (network code IU).
How accurately can we estimate energetic costs in a marine top predator, the king penguin?
Halsey, Lewis G; Fahlman, Andreas; Handrich, Yves; Schmidt, Alexander; Woakes, Anthony J; Butler, Patrick J
2007-01-01
King penguins (Aptenodytes patagonicus) are one of the greatest consumers of marine resources. However, while their influence on the marine ecosystem is likely to be significant, only an accurate knowledge of their energy demands will indicate their true food requirements. Energy consumption has been estimated for many marine species using the heart rate-rate of oxygen consumption (f(H) - V(O2)) technique, and the technique has been applied successfully to answer eco-physiological questions. However, previous studies on the energetics of king penguins, based on developing or applying this technique, have raised a number of issues about the degree of validity of the technique for this species. These include the predictive validity of the present f(H) - V(O2) equations across different seasons and individuals and during different modes of locomotion. In many cases, these issues also apply to other species for which the f(H) - V(O2) technique has been applied. In the present study, the accuracy of three prediction equations for king penguins was investigated based on validity studies and on estimates of V(O2) from published, field f(H) data. The major conclusions from the present study are: (1) in contrast to that for walking, the f(H) - V(O2) relationship for swimming king penguins is not affected by body mass; (2) prediction equation (1), log(V(O2) = -0.279 + 1.24log(f(H) + 0.0237t - 0.0157log(f(H)t, derived in a previous study, is the most suitable equation presently available for estimating V(O2) in king penguins for all locomotory and nutritional states. A number of possible problems associated with producing an f(H) - V(O2) relationship are discussed in the present study. Finally, a statistical method to include easy-to-measure morphometric characteristics, which may improve the accuracy of f(H) - V(O2) prediction equations, is explained. PMID:17363231
A variational method for finite element stress recovery and error estimation
NASA Technical Reports Server (NTRS)
Tessler, A.; Riggs, H. R.; Macy, S. C.
1993-01-01
A variational method for obtaining smoothed stresses from a finite element derived nonsmooth stress field is presented. The method is based on minimizing a functional involving discrete least-squares error plus a penalty constraint that ensures smoothness of the stress field. An equivalent accuracy criterion is developed for the smoothing analysis which results in a C sup 1-continuous smoothed stress field possessing the same order of accuracy as that found at the superconvergent optimal stress points of the original finite element analysis. Application of the smoothing analysis to residual error estimation is also demonstrated.
First order error propagation of the procrustes method for 3D attitude estimation.
Dorst, Leo
2005-02-01
The well-known Procrustes method determines the optimal rigid body motion that registers two point clouds by minimizing the square distances of the residuals. In this paper, we perform the first order error analysis of this method for the 3D case, fully specifying how directional noise in the point clouds affects the estimated parameters of the rigid body motion. These results are much more specific than the error bounds which have been established in numerical analysis. We provide an intuitive understanding of the outcome to facilitate direct use in applications.
Macroscale water fluxes 1. Quantifying errors in the estimation of basin mean precipitation
NASA Astrophysics Data System (ADS)
Milly, P. C. D.; Dunne, K. A.
2002-10-01
Developments in analysis and modeling of continental water and energy balances are hindered by the limited availability and quality of observational data. The lack of information on error characteristics of basin water supply is an especially serious limitation. Here we describe the development and testing of methods for quantifying several errors in basin mean precipitation, both in the long-term mean and in the monthly and annual anomalies. To quantify errors in the long-term mean, two error indices are developed and tested with positive results. The first provides an estimate of the variance of the spatial sampling error of long-term basin mean precipitation obtained from a gauge network, in the absence of orographic effects; this estimate is obtained by use only of the gauge records. The second gives a simple estimate of the basin mean orographic bias as a function of the topographic structure of the basin and the locations of gauges therein. Neither index requires restrictive statistical assumptions (such as spatial homogeneity) about the precipitation process. Adjustments of precipitation for gauge bias and estimates of the adjustment errors are made by applying results of a previous study. Additionally, standard correlation-based methods are applied for the quantification of spatial sampling errors in the estimation of monthly and annual values of basin mean precipitation. These methods also perform well, as indicated by network subsampling tests in densely gauged basins. The methods are developed and applied with data for 175 large (median area of 51,000 km2) river basins of the world for which contemporaneous, continuous (missing fewer than 2% of data values), long-term (median record length of 54 years) river discharge records are also available. Spatial coverage of the resulting river basin data set is greatest in the middle latitudes, though many basins are located in the tropics and the high latitudes, and the data set spans the major climatic and
Estimates of ocean forecast error covariance derived from Hessian Singular Vectors
NASA Astrophysics Data System (ADS)
Smith, Kevin D.; Moore, Andrew M.; Arango, Hernan G.
2015-05-01
Experience in numerical weather prediction suggests that singular value decomposition (SVD) of a forecast can yield useful a priori information about the growth of forecast errors. It has been shown formally that SVD using the inverse of the expected analysis error covariance matrix to define the norm at initial time yields the Empirical Orthogonal Functions (EOFs) of the forecast error covariance matrix at the final time. Because of their connection to the 2nd derivative of the cost function in 4-dimensional variational (4D-Var) data assimilation, the initial time singular vectors defined in this way are often referred to as the Hessian Singular Vectors (HSVs). In the present study, estimates of ocean forecast errors and forecast error covariance were computed using SVD applied to a baroclinically unstable temperature front in a re-entrant channel using the Regional Ocean Modeling System (ROMS). An identical twin approach was used in which a truth run of the model was sampled to generate synthetic hydrographic observations that were then assimilated into the same model started from an incorrect initial condition using 4D-Var. The 4D-Var system was run sequentially, and forecasts were initialized from each ocean analysis. SVD was performed on the resulting forecasts to compute the HSVs and corresponding EOFs of the expected forecast error covariance matrix. In this study, a reduced rank approximation of the inverse expected analysis error covariance matrix was used to compute the HSVs and EOFs based on the Lanczos vectors computed during the 4D-Var minimization of the cost function. This has the advantage that the entire spectrum of HSVs and EOFs in the reduced space can be computed. The associated singular value spectrum is found to yield consistent and reliable estimates of forecast error variance in the space spanned by the EOFs. In addition, at long forecast lead times the resulting HSVs and companion EOFs are able to capture many features of the actual
DTI quality control assessment via error estimation from Monte Carlo simulations
NASA Astrophysics Data System (ADS)
Farzinfar, Mahshid; Li, Yin; Verde, Audrey R.; Oguz, Ipek; Gerig, Guido; Styner, Martin A.
2013-03-01
Diffusion Tensor Imaging (DTI) is currently the state of the art method for characterizing the microscopic tissue structure of white matter in normal or diseased brain in vivo. DTI is estimated from a series of Diffusion Weighted Imaging (DWI) volumes. DWIs suffer from a number of artifacts which mandate stringent Quality Control (QC) schemes to eliminate lower quality images for optimal tensor estimation. Conventionally, QC procedures exclude artifact-affected DWIs from subsequent computations leading to a cleaned, reduced set of DWIs, called DWI-QC. Often, a rejection threshold is heuristically/empirically chosen above which the entire DWI-QC data is rendered unacceptable and thus no DTI is computed. In this work, we have devised a more sophisticated, Monte-Carlo (MC) simulation based method for the assessment of resulting tensor properties. This allows for a consistent, error-based threshold definition in order to reject/accept the DWI-QC data. Specifically, we propose the estimation of two error metrics related to directional distribution bias of Fractional Anisotropy (FA) and the Principal Direction (PD). The bias is modeled from the DWI-QC gradient information and a Rician noise model incorporating the loss of signal due to the DWI exclusions. Our simulations further show that the estimated bias can be substantially different with respect to magnitude and directional distribution depending on the degree of spatial clustering of the excluded DWIs. Thus, determination of diffusion properties with minimal error requires an evenly distributed sampling of the gradient directions before and after QC.
Mass load estimation errors utilizing grab sampling strategies in a karst watershed
Fogle, A.W.; Taraba, J.L.; Dinger, J.S.
2003-01-01
Developing a mass load estimation method appropriate for a given stream and constituent is difficult due to inconsistencies in hydrologic and constituent characteristics. The difficulty may be increased in flashy flow conditions such as karst. Many projects undertaken are constrained by budget and manpower and do not have the luxury of sophisticated sampling strategies. The objectives of this study were to: (1) examine two grab sampling strategies with varying sampling intervals and determine the error in mass load estimates, and (2) determine the error that can be expected when a grab sample is collected at a time of day when the diurnal variation is most divergent from the daily mean. Results show grab sampling with continuous flow to be a viable data collection method for estimating mass load in the study watershed. Comparing weekly, biweekly, and monthly grab sampling, monthly sampling produces the best results with this method. However, the time of day the sample is collected is important. Failure to account for diurnal variability when collecting a grab sample may produce unacceptable error in mass load estimates. The best time to collect a sample is when the diurnal cycle is nearest the daily mean.
On the error in crop acreage estimation using satellite (LANDSAT) data
NASA Technical Reports Server (NTRS)
Chhikara, R. (Principal Investigator)
1983-01-01
The problem of crop acreage estimation using satellite data is discussed. Bias and variance of a crop proportion estimate in an area segment obtained from the classification of its multispectral sensor data are derived as functions of the means, variances, and covariance of error rates. The linear discriminant analysis and the class proportion estimation for the two class case are extended to include a third class of measurement units, where these units are mixed on ground. Special attention is given to the investigation of mislabeling in training samples and its effect on crop proportion estimation. It is shown that the bias and variance of the estimate of a specific crop acreage proportion increase as the disparity in mislabeling rates between two classes increases. Some interaction is shown to take place, causing the bias and the variance to decrease at first and then to increase, as the mixed unit class varies in size from 0 to 50 percent of the total area segment.
NASA Astrophysics Data System (ADS)
Zheng, Wei; Wang, Zhaokui; Ding, Yanwei; Li, Zhaowei
2016-05-01
Firstly, the new single and combined error models applied to estimate the cumulative geoid height error are efficiently produced by the dominating error sources consisting of the gravity gradient of the satellite-equipped gradiometer and the orbital position of the space-borne GPS/GLONASS receiver using the power spectral principle. At degree 250, the cumulative geoid height error is 1.769 × 10-1 m based on the new combined error model, which preferably accords with a recovery accuracy of 1.760 × 10-1 m from the GOCE-only Earth gravity field model GO_CONS_GCF_2_TIM_R2 released in Germany. Therefore, the new combined error model of the cumulative geoid height is correct and reliable in this study. Secondly, the requirements analysis for the future GOCE Follow-On satellite system is carried out in respect of the preferred design of the matching measurement accuracy of key payloads comprising the gravity gradient and orbital position and the optimal selection of the orbital altitude of the satellite. We recommend the gravity gradient with an accuracy of 10-13-10-15/s2, the orbital position with a precision of 1-0.1 cm and the orbital altitude of 200-250 km in the future GOCE Follow-On mission.
Robust Estimator for Non-Line-of-Sight Error Mitigation in Indoor Localization
NASA Astrophysics Data System (ADS)
Casas, R.; Marco, A.; Guerrero, J. J.; Falcó, J.
2006-12-01
Indoor localization systems are undoubtedly of interest in many application fields. Like outdoor systems, they suffer from non-line-of-sight (NLOS) errors which hinder their robustness and accuracy. Though many ad hoc techniques have been developed to deal with this problem, unfortunately most of them are not applicable indoors due to the high variability of the environment (movement of furniture and of people, etc.). In this paper, we describe the use of robust regression techniques to detect and reject NLOS measures in a location estimation using multilateration. We show how the least-median-of-squares technique can be used to overcome the effects of NLOS errors, even in environments with little infrastructure, and validate its suitability by comparing it to other methods described in the bibliography. We obtained remarkable results when using it in a real indoor positioning system that works with Bluetooth and ultrasound (BLUPS), even when nearly half the measures suffered from NLOS or other coarse errors.
Imani, Farsad; Karimi Rouzbahani, Hamid Reza; Goudarzi, Mehrdad; Tarrahi, Mohammad Javad; Ebrahim Soltani, Alireza
2016-01-01
Background: During anesthesia, continuous body temperature monitoring is essential, especially in children. Anesthesia can increase the risk of loss of body temperature by three to four times. Hypothermia in children results in increased morbidity and mortality. Since the measurement points of the core body temperature are not easily accessible, near core sites, like rectum, are used. Objectives: The purpose of this study was to measure skin temperature over the carotid artery and compare it with the rectum temperature, in order to propose a model for accurate estimation of near core body temperature. Patients and Methods: Totally, 124 patients within the age range of 2 - 6 years, undergoing elective surgery, were selected. Temperature of rectum and skin over the carotid artery was measured. Then, the patients were randomly divided into two groups (each including 62 subjects), namely modeling (MG) and validation groups (VG). First, in the modeling group, the average temperature of the rectum and skin over the carotid artery were measured separately. The appropriate model was determined, according to the significance of the model’s coefficients. The obtained model was used to predict the rectum temperature in the second group (VG group). Correlation of the predicted values with the real values (the measured rectum temperature) in the second group was investigated. Also, the difference in the average values of these two groups was examined in terms of significance. Results: In the modeling group, the average rectum and carotid temperatures were 36.47 ± 0.54°C and 35.45 ± 0.62°C, respectively. The final model was obtained, as follows: Carotid temperature × 0.561 + 16.583 = Rectum temperature. The predicted value was calculated based on the regression model and then compared with the measured rectum value, which showed no significant difference (P = 0.361). Conclusions: The present study was the first research, in which rectum temperature was compared with that
Time-to-contact estimation errors among older drivers with useful field of view impairments.
Rusch, Michelle L; Schall, Mark C; Lee, John D; Dawson, Jeffrey D; Edwards, Samantha V; Rizzo, Matthew
2016-10-01
Previous research indicates that useful field of view (UFOV) decline affects older driver performance. In particular, elderly drivers have difficulty estimating oncoming vehicle time-to-contact (TTC). The objective of this study was to evaluate how UFOV impairments affect TTC estimates in elderly drivers deciding when to make a left turn across oncoming traffic. TTC estimates were obtained from 64 middle-aged (n=17, age=46±6years) and older (n=37, age=75±6years) licensed drivers with a range of UFOV abilities using interactive scenarios in a fixed-base driving simulator. Each driver was situated in an intersection to turn left across oncoming traffic approaching and disappearing at differing distances (1.5, 3, or 5s) and speeds (45, 55, or 65mph). Drivers judged when each oncoming vehicle would collide with them if they were to turn left. Findings showed that TTC estimates across all drivers, on average, were most accurate for oncoming vehicles travelling at the highest velocities and least accurate for those travelling at the slowest velocities. Drivers with the worst UFOV scores had the least accurate TTC estimates, especially for slower oncoming vehicles. Results suggest age-related UFOV decline impairs older driver judgment of TTC with oncoming vehicles in safety-critical left-turn situations. Our results are compatible with national statistics on older driver crash proclivity at intersections.
Time-to-contact estimation errors among older drivers with useful field of view impairments.
Rusch, Michelle L; Schall, Mark C; Lee, John D; Dawson, Jeffrey D; Edwards, Samantha V; Rizzo, Matthew
2016-10-01
Previous research indicates that useful field of view (UFOV) decline affects older driver performance. In particular, elderly drivers have difficulty estimating oncoming vehicle time-to-contact (TTC). The objective of this study was to evaluate how UFOV impairments affect TTC estimates in elderly drivers deciding when to make a left turn across oncoming traffic. TTC estimates were obtained from 64 middle-aged (n=17, age=46±6years) and older (n=37, age=75±6years) licensed drivers with a range of UFOV abilities using interactive scenarios in a fixed-base driving simulator. Each driver was situated in an intersection to turn left across oncoming traffic approaching and disappearing at differing distances (1.5, 3, or 5s) and speeds (45, 55, or 65mph). Drivers judged when each oncoming vehicle would collide with them if they were to turn left. Findings showed that TTC estimates across all drivers, on average, were most accurate for oncoming vehicles travelling at the highest velocities and least accurate for those travelling at the slowest velocities. Drivers with the worst UFOV scores had the least accurate TTC estimates, especially for slower oncoming vehicles. Results suggest age-related UFOV decline impairs older driver judgment of TTC with oncoming vehicles in safety-critical left-turn situations. Our results are compatible with national statistics on older driver crash proclivity at intersections. PMID:27472816
Hellander, Andreas; Lawson, Michael J; Drawert, Brian; Petzold, Linda
2015-01-01
The efficiency of exact simulation methods for the reaction-diffusion master equation (RDME) is severely limited by the large number of diffusion events if the mesh is fine or if diffusion constants are large. Furthermore, inherent properties of exact kinetic-Monte Carlo simulation methods limit the efficiency of parallel implementations. Several approximate and hybrid methods have appeared that enable more efficient simulation of the RDME. A common feature to most of them is that they rely on splitting the system into its reaction and diffusion parts and updating them sequentially over a discrete timestep. This use of operator splitting enables more efficient simulation but it comes at the price of a temporal discretization error that depends on the size of the timestep. So far, existing methods have not attempted to estimate or control this error in a systematic manner. This makes the solvers hard to use for practitioners since they must guess an appropriate timestep. It also makes the solvers potentially less efficient than if the timesteps are adapted to control the error. Here, we derive estimates of the local error and propose a strategy to adaptively select the timestep when the RDME is simulated via a first order operator splitting. While the strategy is general and applicable to a wide range of approximate and hybrid methods, we exemplify it here by extending a previously published approximate method, the Diffusive Finite-State Projection (DFSP) method, to incorporate temporal adaptivity. PMID:26865735
NASA Astrophysics Data System (ADS)
Hellander, Andreas; Lawson, Michael J.; Drawert, Brian; Petzold, Linda
2014-06-01
The efficiency of exact simulation methods for the reaction-diffusion master equation (RDME) is severely limited by the large number of diffusion events if the mesh is fine or if diffusion constants are large. Furthermore, inherent properties of exact kinetic-Monte Carlo simulation methods limit the efficiency of parallel implementations. Several approximate and hybrid methods have appeared that enable more efficient simulation of the RDME. A common feature to most of them is that they rely on splitting the system into its reaction and diffusion parts and updating them sequentially over a discrete timestep. This use of operator splitting enables more efficient simulation but it comes at the price of a temporal discretization error that depends on the size of the timestep. So far, existing methods have not attempted to estimate or control this error in a systematic manner. This makes the solvers hard to use for practitioners since they must guess an appropriate timestep. It also makes the solvers potentially less efficient than if the timesteps were adapted to control the error. Here, we derive estimates of the local error and propose a strategy to adaptively select the timestep when the RDME is simulated via a first order operator splitting. While the strategy is general and applicable to a wide range of approximate and hybrid methods, we exemplify it here by extending a previously published approximate method, the diffusive finite-state projection (DFSP) method, to incorporate temporal adaptivity.
Lu, Dan; Ye, Ming; Meyer, Philip D.; Curtis, Gary P.; Shi, Xiaoqing; Niu, Xu-Feng; Yabusaki, Steve B.
2013-01-01
When conducting model averaging for assessing groundwater conceptual model uncertainty, the averaging weights are often evaluated using model selection criteria such as AIC, AICc, BIC, and KIC (Akaike Information Criterion, Corrected Akaike Information Criterion, Bayesian Information Criterion, and Kashyap Information Criterion, respectively). However, this method often leads to an unrealistic situation in which the best model receives overwhelmingly large averaging weight (close to 100%), which cannot be justified by available data and knowledge. It was found in this study that this problem was caused by using the covariance matrix, CE, of measurement errors for estimating the negative log likelihood function common to all the model selection criteria. This problem can be resolved by using the covariance matrix, Cek, of total errors (including model errors and measurement errors) to account for the correlation between the total errors. An iterative two-stage method was developed in the context of maximum likelihood inverse modeling to iteratively infer the unknown Cek from the residuals during model calibration. The inferred Cek was then used in the evaluation of model selection criteria and model averaging weights. While this method was limited to serial data using time series techniques in this study, it can be extended to spatial data using geostatistical techniques. The method was first evaluated in a synthetic study and then applied to an experimental study, in which alternative surface complexation models were developed to simulate column experiments of uranium reactive transport. It was found that the total errors of the alternative models were temporally correlated due to the model errors. The iterative two-stage method using Cekresolved the problem that the best model receives 100% model averaging weight, and the resulting model averaging weights were supported by the calibration results and physical understanding of the alternative models. Using Cek
NASA Astrophysics Data System (ADS)
Bogner, K.; Pappenberger, F.
2011-07-01
River discharge predictions often show errors that degrade the quality of forecasts. Three different methods of error correction are compared, namely, an autoregressive model with and without exogenous input (ARX and AR, respectively), and a method based on wavelet transforms. For the wavelet method, a Vector-Autoregressive model with exogenous input (VARX) is simultaneously fitted for the different levels of wavelet decomposition; after predicting the next time steps for each scale, a reconstruction formula is applied to transform the predictions in the wavelet domain back to the original time domain. The error correction methods are combined with the Hydrological Uncertainty Processor (HUP) in order to estimate the predictive conditional distribution. For three stations along the Danube catchment, and using output from the European Flood Alert System (EFAS), we demonstrate that the method based on wavelets outperforms simpler methods and uncorrected predictions with respect to mean absolute error, Nash-Sutcliffe efficiency coefficient (and its decomposed performance criteria), informativeness score, and in particular forecast reliability. The wavelet approach efficiently accounts for forecast errors with scale properties of unknown source and statistical structure.
Estimation of random errors for lidar based on noise scale factor
NASA Astrophysics Data System (ADS)
Wang, Huan-Xue; Liu, Jian-Guo; Zhang, Tian-Shu
2015-08-01
Estimation of random errors, which are due to shot noise of photomultiplier tube (PMT) or avalanche photodiode (APD) detectors, is very necessary in lidar observation. Due to the Poisson distribution of incident electrons, there still exists a proportional relationship between standard deviation and square root of its mean value. Based on this relationship, noise scale factor (NSF) is introduced into the estimation, which only needs a single data sample. This method overcomes the distractions of atmospheric fluctuations during calculation of random errors. The results show that this method is feasible and reliable. Project supported by the Strategic Priority Research Program of the Chinese Academy of Sciences (Grant No. XDB05040300) and the National Natural Science Foundation of China (Grant No. 41205119).
A Novel Four-Node Quadrilateral Smoothing Element for Stress Enhancement and Error Estimation
NASA Technical Reports Server (NTRS)
Tessler, A.; Riggs, H. R.; Dambach, M.
1998-01-01
A four-node, quadrilateral smoothing element is developed based upon a penalized-discrete-least-squares variational formulation. The smoothing methodology recovers C1-continuous stresses, thus enabling effective a posteriori error estimation and automatic adaptive mesh refinement. The element formulation is originated with a five-node macro-element configuration consisting of four triangular anisoparametric smoothing elements in a cross-diagonal pattern. This element pattern enables a convenient closed-form solution for the degrees of freedom of the interior node, resulting from enforcing explicitly a set of natural edge-wise penalty constraints. The degree-of-freedom reduction scheme leads to a very efficient formulation of a four-node quadrilateral smoothing element without any compromise in robustness and accuracy of the smoothing analysis. The application examples include stress recovery and error estimation in adaptive mesh refinement solutions for an elasticity problem and an aerospace structural component.
NASA Astrophysics Data System (ADS)
Bai, YanHong; Wu, YongKe; Xie, XiaoPing
2016-09-01
Superconvergence and a posteriori error estimators of recovery type are analyzed for the 4-node hybrid stress quadrilateral finite element method proposed by Pian and Sumihara (Int. J. Numer. Meth. Engrg., 1984, 20: 1685-1695) for linear elasticity problems. Uniform superconvergence of order $O(h^{1+\\min\\{\\alpha,1\\}})$ with respect to the Lam\\'{e} constant $\\lambda$ is established for both the recovered gradients of the displacement vector and the stress tensor under a mesh assumption, where $\\alpha>0$ is a parameter characterizing the distortion of meshes from parallelograms to quadrilaterals. A posteriori error estimators based on the recovered quantities are shown to be asymptotically exact. Numerical experiments confirm the theoretical results.
Error estimation and adaptive order nodal method for solving multidimensional transport problems
Zamonsky, O.M.; Gho, C.J.; Azmy, Y.Y.
1998-01-01
The authors propose a modification of the Arbitrarily High Order Transport Nodal method whereby they solve each node and each direction using different expansion order. With this feature and a previously proposed a posteriori error estimator they develop an adaptive order scheme to automatically improve the accuracy of the solution of the transport equation. They implemented the modified nodal method, the error estimator and the adaptive order scheme into a discrete-ordinates code for solving monoenergetic, fixed source, isotropic scattering problems in two-dimensional Cartesian geometry. They solve two test problems with large homogeneous regions to test the adaptive order scheme. The results show that using the adaptive process the storage requirements are reduced while preserving the accuracy of the results.
NASA Technical Reports Server (NTRS)
Bell, Thomas L.; Kundu, Prasun K.; Kummerow, Christian D.; Einaudi, Franco (Technical Monitor)
2000-01-01
Quantitative use of satellite-derived maps of monthly rainfall requires some measure of the accuracy of the satellite estimates. The rainfall estimate for a given map grid box is subject to both remote-sensing error and, in the case of low-orbiting satellites, sampling error due to the limited number of observations of the grid box provided by the satellite. A simple model of rain behavior predicts that Root-mean-square (RMS) random error in grid-box averages should depend in a simple way on the local average rain rate, and the predicted behavior has been seen in simulations using surface rain-gauge and radar data. This relationship was examined using satellite SSM/I data obtained over the western equatorial Pacific during TOGA COARE. RMS error inferred directly from SSM/I rainfall estimates was found to be larger than predicted from surface data, and to depend less on local rain rate than was predicted. Preliminary examination of TRMM microwave estimates shows better agreement with surface data. A simple method of estimating rms error in satellite rainfall estimates is suggested, based on quantities that can be directly computed from the satellite data.
Houle, D; Meyer, K
2015-08-01
We explore the estimation of uncertainty in evolutionary parameters using a recently devised approach for resampling entire additive genetic variance-covariance matrices (G). Large-sample theory shows that maximum-likelihood estimates (including restricted maximum likelihood, REML) asymptotically have a multivariate normal distribution, with covariance matrix derived from the inverse of the information matrix, and mean equal to the estimated G. This suggests that sampling estimates of G from this distribution can be used to assess the variability of estimates of G, and of functions of G. We refer to this as the REML-MVN method. This has been implemented in the mixed-model program WOMBAT. Estimates of sampling variances from REML-MVN were compared to those from the parametric bootstrap and from a Bayesian Markov chain Monte Carlo (MCMC) approach (implemented in the R package MCMCglmm). We apply each approach to evolvability statistics previously estimated for a large, 20-dimensional data set for Drosophila wings. REML-MVN and MCMC sampling variances are close to those estimated with the parametric bootstrap. Both slightly underestimate the error in the best-estimated aspects of the G matrix. REML analysis supports the previous conclusion that the G matrix for this population is full rank. REML-MVN is computationally very efficient, making it an attractive alternative to both data resampling and MCMC approaches to assessing confidence in parameters of evolutionary interest. PMID:26079756
Brown, Judith A.; Bishop, Joseph E.
2016-07-20
An a posteriori error-estimation framework is introduced to quantify and reduce modeling errors resulting from approximating complex mesoscale material behavior with a simpler macroscale model. Such errors may be prevalent when modeling welds and additively manufactured structures, where spatial variations and material textures may be present in the microstructure. We consider a case where a <100> fiber texture develops in the longitudinal scanning direction of a weld. Transversely isotropic elastic properties are obtained through homogenization of a microstructural model with this texture and are considered the reference weld properties within the error-estimation framework. Conversely, isotropic elastic properties are considered approximatemore » weld properties since they contain no representation of texture. Errors introduced by using isotropic material properties to represent a weld are assessed through a quantified error bound in the elastic regime. Lastly, an adaptive error reduction scheme is used to determine the optimal spatial variation of the isotropic weld properties to reduce the error bound.« less
Error Estimates of the Ares I Computed Turbulent Ascent Longitudinal Aerodynamic Analysis
NASA Technical Reports Server (NTRS)
Abdol-Hamid, Khaled S.; Ghaffari, Farhad
2012-01-01
Numerical predictions of the longitudinal aerodynamic characteristics for the Ares I class of vehicles, along with the associated error estimate derived from an iterative convergence grid refinement, are presented. Computational results are based on an unstructured grid, Reynolds-averaged Navier-Stokes analysis. The validity of the approach to compute the associated error estimates, derived from a base grid to an extrapolated infinite-size grid, was first demonstrated on a sub-scaled wind tunnel model at representative ascent flow conditions for which the experimental data existed. Such analysis at the transonic flow conditions revealed a maximum deviation of about 23% between the computed longitudinal aerodynamic coefficients with the base grid and the measured data across the entire roll angles. This maximum deviation from the wind tunnel data was associated with the computed normal force coefficient at the transonic flow condition and was reduced to approximately 16% based on the infinite-size grid. However, all the computed aerodynamic coefficients with the base grid at the supersonic flow conditions showed a maximum deviation of only about 8% with that level being improved to approximately 5% for the infinite-size grid. The results and the error estimates based on the established procedure are also presented for the flight flow conditions.
Estimate of procession and polar motion errors from planetary encounter station location solutions
NASA Technical Reports Server (NTRS)
Pease, G. E.
1978-01-01
Jet Propulsion Laboratory Deep Space Station (DSS) location solutions based on two JPL planetary ephemerides, DE 84 and DE 96, at eight planetary encounters were used to obtain weighted least squares estimates of precession and polar motion errors. The solution for precession error in right ascension yields a value of 0.3 X 10 to the minus 5 power plus or minus 0.8 X 10 to the minus 6 power deg/year. This maps to a right ascension error of 1.3 X 10 to the minus 5 power plus or minus 0.4 X 10 to the minus 5 power deg at the first Voyager 1979 Jupiter encounter if the current JPL DSS location set is used. Solutions for precession and polar motion using station locations based on DE 84 agree well with the solution using station locations referenced to DE 96. The precession solution removes the apparent drift in station longitude and spin axis distance estimates, while the encounter polar motion solutions consistently decrease the scatter in station spin axis distance estimates.
García-Donas, Julieta G; Dyke, Jeffrey; Paine, Robert R; Nathena, Despoina; Kranioti, Elena F
2016-02-01
Most age estimation methods are proven problematic when applied in highly fragmented skeletal remains. Rib histomorphometry is advantageous in such cases; yet it is vital to test and revise existing techniques particularly when used in legal settings (Crowder and Rosella, 2007). This study tested Stout & Paine (1992) and Stout et al. (1994) histological age estimation methods on a Modern Greek sample using different sampling sites. Six left 4th ribs of known age and sex were selected from a modern skeletal collection. Each rib was cut into three equal segments. Two thin sections were acquired from each segment. A total of 36 thin sections were prepared and analysed. Four variables (cortical area, intact and fragmented osteon density and osteon population density) were calculated for each section and age was estimated according to Stout & Paine (1992) and Stout et al. (1994). The results showed that both methods produced a systemic underestimation of the individuals (to a maximum of 43 years) although a general improvement in accuracy levels was observed when applying the Stout et al. (1994) formula. There is an increase of error rates with increasing age with the oldest individual showing extreme differences between real age and estimated age. Comparison of the different sampling sites showed small differences between the estimated ages suggesting that any fragment of the rib could be used without introducing significant error. Yet, a larger sample should be used to confirm these results.
García-Donas, Julieta G; Dyke, Jeffrey; Paine, Robert R; Nathena, Despoina; Kranioti, Elena F
2016-02-01
Most age estimation methods are proven problematic when applied in highly fragmented skeletal remains. Rib histomorphometry is advantageous in such cases; yet it is vital to test and revise existing techniques particularly when used in legal settings (Crowder and Rosella, 2007). This study tested Stout & Paine (1992) and Stout et al. (1994) histological age estimation methods on a Modern Greek sample using different sampling sites. Six left 4th ribs of known age and sex were selected from a modern skeletal collection. Each rib was cut into three equal segments. Two thin sections were acquired from each segment. A total of 36 thin sections were prepared and analysed. Four variables (cortical area, intact and fragmented osteon density and osteon population density) were calculated for each section and age was estimated according to Stout & Paine (1992) and Stout et al. (1994). The results showed that both methods produced a systemic underestimation of the individuals (to a maximum of 43 years) although a general improvement in accuracy levels was observed when applying the Stout et al. (1994) formula. There is an increase of error rates with increasing age with the oldest individual showing extreme differences between real age and estimated age. Comparison of the different sampling sites showed small differences between the estimated ages suggesting that any fragment of the rib could be used without introducing significant error. Yet, a larger sample should be used to confirm these results. PMID:26698389
A semiempirical error estimation technique for PWV derived from atmospheric radiosonde data
NASA Astrophysics Data System (ADS)
Castro-Almazán, Julio A.; Pérez-Jordán, Gabriel; Muñoz-Tuñón, Casiana
2016-09-01
A semiempirical method for estimating the error and optimum number of sampled levels in precipitable water vapour (PWV) determinations from atmospheric radiosoundings is proposed. Two terms have been considered: the uncertainties in the measurements and the sampling error. Also, the uncertainty has been separated in the variance and covariance components. The sampling and covariance components have been modelled from an empirical dataset of 205 high-vertical-resolution radiosounding profiles, equipped with Vaisala RS80 and RS92 sondes at four different locations: Güímar (GUI) in Tenerife, at sea level, and the astronomical observatory at Roque de los Muchachos (ORM, 2300 m a.s.l.) on La Palma (both on the Canary Islands, Spain), Lindenberg (LIN) in continental Germany, and Ny-Ålesund (NYA) in the Svalbard Islands, within the Arctic Circle. The balloons at the ORM were launched during intensive and unique site-testing runs carried out in 1990 and 1995, while the data for the other sites were obtained from radiosounding stations operating for a period of 1 year (2013-2014). The PWV values ranged between ˜ 0.9 and ˜ 41 mm. The method sub-samples the profile for error minimization. The result is the minimum error and the optimum number of levels. The results obtained in the four sites studied showed that the ORM is the driest of the four locations and the one with the fastest vertical decay of PWV. The exponential autocorrelation pressure lags ranged from 175 hPa (ORM) to 500 hPa (LIN). The results show a coherent behaviour with no biases as a function of the profile. The final error is roughly proportional to PWV whereas the optimum number of levels (N0) is the reverse. The value of N0 is less than 400 for 77 % of the profiles and the absolute errors are always < 0.6 mm. The median relative error is 2.0 ± 0.7 % and the 90th percentile P90 = 4.6 %. Therefore, whereas a radiosounding samples at least N0 uniform vertical levels, depending on the water
Ignatova, Irina; French, Andrew S; Immonen, Esa-Ville; Frolov, Roman; Weckström, Matti
2014-06-01
Shannon's seminal approach to estimating information capacity is widely used to quantify information processing by biological systems. However, the Shannon information theory, which is based on power spectrum estimation, necessarily contains two sources of error: time delay bias error and random error. These errors are particularly important for systems with relatively large time delay values and for responses of limited duration, as is often the case in experimental work. The window function type and size chosen, as well as the values of inherent delays cause changes in both the delay bias and random errors, with possibly strong effect on the estimates of system properties. Here, we investigated the properties of these errors using white-noise simulations and analysis of experimental photoreceptor responses to naturalistic and white-noise light contrasts. Photoreceptors were used from several insect species, each characterized by different visual performance, behavior, and ecology. We show that the effect of random error on the spectral estimates of photoreceptor performance (gain, coherence, signal-to-noise ratio, Shannon information rate) is opposite to that of the time delay bias error: the former overestimates information rate, while the latter underestimates it. We propose a new algorithm for reducing the impact of time delay bias error and random error, based on discovering, and then using that size of window, at which the absolute values of these errors are equal and opposite, thus cancelling each other, allowing minimally biased measurement of neural coding.
Determining Sample Size for Accurate Estimation of the Squared Multiple Correlation Coefficient.
ERIC Educational Resources Information Center
Algina, James; Olejnik, Stephen
2000-01-01
Discusses determining sample size for estimation of the squared multiple correlation coefficient and presents regression equations that permit determination of the sample size for estimating this parameter for up to 20 predictor variables. (SLD)
Xue, Hongqi; Miao, Hongyu; Wu, Hulin
2010-01-01
This article considers estimation of constant and time-varying coefficients in nonlinear ordinary differential equation (ODE) models where analytic closed-form solutions are not available. The numerical solution-based nonlinear least squares (NLS) estimator is investigated in this study. A numerical algorithm such as the Runge–Kutta method is used to approximate the ODE solution. The asymptotic properties are established for the proposed estimators considering both numerical error and measurement error. The B-spline is used to approximate the time-varying coefficients, and the corresponding asymptotic theories in this case are investigated under the framework of the sieve approach. Our results show that if the maximum step size of the p-order numerical algorithm goes to zero at a rate faster than n−1/(p∧4), the numerical error is negligible compared to the measurement error. This result provides a theoretical guidance in selection of the step size for numerical evaluations of ODEs. Moreover, we have shown that the numerical solution-based NLS estimator and the sieve NLS estimator are strongly consistent. The sieve estimator of constant parameters is asymptotically normal with the same asymptotic co-variance as that of the case where the true ODE solution is exactly known, while the estimator of the time-varying parameter has the optimal convergence rate under some regularity conditions. The theoretical results are also developed for the case when the step size of the ODE numerical solver does not go to zero fast enough or the numerical error is comparable to the measurement error. We illustrate our approach with both simulation studies and clinical data on HIV viral dynamics. PMID:21132064
Estimating and comparing microbial diversity in the presence of sequencing errors
Chiu, Chun-Huo
2016-01-01
Estimating and comparing microbial diversity are statistically challenging due to limited sampling and possible sequencing errors for low-frequency counts, producing spurious singletons. The inflated singleton count seriously affects statistical analysis and inferences about microbial diversity. Previous statistical approaches to tackle the sequencing errors generally require different parametric assumptions about the sampling model or about the functional form of frequency counts. Different parametric assumptions may lead to drastically different diversity estimates. We focus on nonparametric methods which are universally valid for all parametric assumptions and can be used to compare diversity across communities. We develop here a nonparametric estimator of the true singleton count to replace the spurious singleton count in all methods/approaches. Our estimator of the true singleton count is in terms of the frequency counts of doubletons, tripletons and quadrupletons, provided these three frequency counts are reliable. To quantify microbial alpha diversity for an individual community, we adopt the measure of Hill numbers (effective number of taxa) under a nonparametric framework. Hill numbers, parameterized by an order q that determines the measures’ emphasis on rare or common species, include taxa richness (q = 0), Shannon diversity (q = 1, the exponential of Shannon entropy), and Simpson diversity (q = 2, the inverse of Simpson index). A diversity profile which depicts the Hill number as a function of order q conveys all information contained in a taxa abundance distribution. Based on the estimated singleton count and the original non-singleton frequency counts, two statistical approaches (non-asymptotic and asymptotic) are developed to compare microbial diversity for multiple communities. (1) A non-asymptotic approach refers to the comparison of estimated diversities of standardized samples with a common finite sample size or sample completeness. This
Estimating and comparing microbial diversity in the presence of sequencing errors.
Chiu, Chun-Huo; Chao, Anne
2016-01-01
Estimating and comparing microbial diversity are statistically challenging due to limited sampling and possible sequencing errors for low-frequency counts, producing spurious singletons. The inflated singleton count seriously affects statistical analysis and inferences about microbial diversity. Previous statistical approaches to tackle the sequencing errors generally require different parametric assumptions about the sampling model or about the functional form of frequency counts. Different parametric assumptions may lead to drastically different diversity estimates. We focus on nonparametric methods which are universally valid for all parametric assumptions and can be used to compare diversity across communities. We develop here a nonparametric estimator of the true singleton count to replace the spurious singleton count in all methods/approaches. Our estimator of the true singleton count is in terms of the frequency counts of doubletons, tripletons and quadrupletons, provided these three frequency counts are reliable. To quantify microbial alpha diversity for an individual community, we adopt the measure of Hill numbers (effective number of taxa) under a nonparametric framework. Hill numbers, parameterized by an order q that determines the measures' emphasis on rare or common species, include taxa richness (q = 0), Shannon diversity (q = 1, the exponential of Shannon entropy), and Simpson diversity (q = 2, the inverse of Simpson index). A diversity profile which depicts the Hill number as a function of order q conveys all information contained in a taxa abundance distribution. Based on the estimated singleton count and the original non-singleton frequency counts, two statistical approaches (non-asymptotic and asymptotic) are developed to compare microbial diversity for multiple communities. (1) A non-asymptotic approach refers to the comparison of estimated diversities of standardized samples with a common finite sample size or sample completeness. This approach
Estimating and comparing microbial diversity in the presence of sequencing errors.
Chiu, Chun-Huo; Chao, Anne
2016-01-01
Estimating and comparing microbial diversity are statistically challenging due to limited sampling and possible sequencing errors for low-frequency counts, producing spurious singletons. The inflated singleton count seriously affects statistical analysis and inferences about microbial diversity. Previous statistical approaches to tackle the sequencing errors generally require different parametric assumptions about the sampling model or about the functional form of frequency counts. Different parametric assumptions may lead to drastically different diversity estimates. We focus on nonparametric methods which are universally valid for all parametric assumptions and can be used to compare diversity across communities. We develop here a nonparametric estimator of the true singleton count to replace the spurious singleton count in all methods/approaches. Our estimator of the true singleton count is in terms of the frequency counts of doubletons, tripletons and quadrupletons, provided these three frequency counts are reliable. To quantify microbial alpha diversity for an individual community, we adopt the measure of Hill numbers (effective number of taxa) under a nonparametric framework. Hill numbers, parameterized by an order q that determines the measures' emphasis on rare or common species, include taxa richness (q = 0), Shannon diversity (q = 1, the exponential of Shannon entropy), and Simpson diversity (q = 2, the inverse of Simpson index). A diversity profile which depicts the Hill number as a function of order q conveys all information contained in a taxa abundance distribution. Based on the estimated singleton count and the original non-singleton frequency counts, two statistical approaches (non-asymptotic and asymptotic) are developed to compare microbial diversity for multiple communities. (1) A non-asymptotic approach refers to the comparison of estimated diversities of standardized samples with a common finite sample size or sample completeness. This approach
NASA Technical Reports Server (NTRS)
Sparks, Lawrence
2013-01-01
Current satellite-based augmentation systems estimate ionospheric delay using algorithms that assume the electron density of the ionosphere is non-negligible only in a thin shell located near the peak of the actual profile. In its initial operating capability, for example, the Wide Area Augmentation System incorporated the thin shell model into an estimation algorithm that calculates vertical delay using a planar fit. Under disturbed conditions or at low latitude where ionospheric structure is complex, however, the thin shell approximation can serve as a significant source of estimation error. A recent upgrade of the system replaced the planar fit algorithm with an algorithm based upon kriging. The upgrade owes its success, in part, to the ability of kriging to mitigate the error due to this approximation. Previously, alternative delay estimation algorithms have been proposed that eliminate the need for invoking the thin shell model altogether. Prior analyses have compared the accuracy achieved by these methods to the accuracy achieved by the planar fit algorithm. This paper extends these analyses to include a comparison with the accuracy achieved by kriging. It concludes by examining how a satellite-based augmentation system might be implemented without recourse to the thin shell approximation.
NASA Astrophysics Data System (ADS)
Taki, Hirofumi; Yamakawa, Makoto; Shiina, Tsuyoshi; Sato, Toru
2015-07-01
High-accuracy ultrasound motion estimation has become an essential technique in blood flow imaging, elastography, and motion imaging of the heart wall. Speckle tracking has been one of the best motion estimators; however, conventional speckle-tracking methods neglect the effect of out-of-plane motion and deformation. Our proposed method assumes that the cross-correlation between a reference signal and a comparison signal depends on the spatio-temporal distance between the two signals. The proposed method uses the decrease in the cross-correlation value in a reference frame to compensate for the intrinsic error caused by out-of-plane motion and deformation without a priori information. The root-mean-square error of the estimated lateral tissue motion velocity calculated by the proposed method ranged from 6.4 to 34% of that using a conventional speckle-tracking method. This study demonstrates the high potential of the proposed method for improving the estimation of tissue motion using an ultrasound speckle-tracking method in medical diagnosis.
Miller, David A.; Nichols, J.D.; McClintock, B.T.; Grant, E.H.C.; Bailey, L.L.; Weir, L.A.
2011-01-01
Efforts to draw inferences about species occurrence frequently account for false negatives, the common situation when individuals of a species are not detected even when a site is occupied. However, recent studies suggest the need to also deal with false positives, which occur when species are misidentified so that a species is recorded as detected when a site is unoccupied. Bias in estimators of occupancy, colonization, and extinction can be severe when false positives occur. Accordingly, we propose models that simultaneously account for both types of error. Our approach can be used to improve estimates of occupancy for study designs where a subset of detections is of a type or method for which false positives can be assumed to not occur. We illustrate properties of the estimators with simulations and data for three species of frogs. We show that models that account for possible misidentification have greater support (lower AIC for two species) and can yield substantially different occupancy estimates than those that do not. When the potential for misidentification exists, researchers should consider analytical techniques that can account for this source of error, such as those presented here. ?? 2011 by the Ecological Society of America..
NASA Astrophysics Data System (ADS)
Panasyuk, Svetlana V.; Hager, Bradford H.
2000-12-01
We perform a joint inversion of Earth's geoid and dynamic topography for radial mantle viscosity structure using a number of models of interior density heterogeneities, including an assessment of the error budget. We identify three classes of errors: those related to the density perturbations used as input, those due to insufficiently constrained observables, and those due to the limitations of our analytical model. We estimate the amplitudes of these errors in the spectral domain. Our minimization function weights the squared deviations of the compared quantities with the corresponding errors, so that the components with more reliability contribute to the solution more strongly than less certain ones. We develop a quasi-analytical solution for mantle flow in a compressible, spherical shell with Newtonian rheology, allowing for continuous radial variations of viscosity, together with a possible reduction of viscosity within the phase change regions due to the effects of transformational superplasticity. The inversion reveals three distinct families of viscosity profiles, all of which have an order of magnitude stiffening within the lower mantle, with a soft D'' layer below. The main distinction among the families is the location of the lowest-viscosity region-directly beneath the lithosphere, just above 400km depth or just above 670km depth. All profiles have a reduction of viscosity within one or more of the major phase transformations, leading to reduced dynamic topography, so that whole-mantle convection is consistent with small surface topography.
SANG-a kernel density estimator incorporating information about the measurement error
NASA Astrophysics Data System (ADS)
Hayes, Robert
Analyzing nominally large data sets having a measurement error unique to each entry is evaluated with a novel technique. This work begins with a review of modern analytical methodologies such as histograming data, ANOVA, regression (weighted and unweighted) along with various error propagation and estimation techniques. It is shown that by assuming the errors obey a functional distribution (such as normal or Poisson), a superposition of the assumed forms then provides the most comprehensive and informative graphical depiction of the data set's statistical information. The resultant approach is evaluated only for normally distributed errors so that the method is effectively a Superposition Analysis of Normalized Gaussians (SANG). SANG is shown to be easily calculated and highly informative in a single graph from what would otherwise require multiple analysis and figures to accomplish the same result. The work is demonstrated using historical radiochemistry measurements from a transuranic waste geological repository's environmental monitoring program. This work paid for under NRC-HQ-84-14-G-0059.
Liu, Hong; Wang, Jie; Xu, Xiangyang; Song, Enmin; Wang, Qian; Jin, Renchao; Hung, Chih-Cheng; Fei, Baowei
2014-11-01
A robust and accurate center-frequency (CF) estimation (RACE) algorithm for improving the performance of the local sine-wave modeling (SinMod) method, which is a good motion estimation method for tagged cardiac magnetic resonance (MR) images, is proposed in this study. The RACE algorithm can automatically, effectively and efficiently produce a very appropriate CF estimate for the SinMod method, under the circumstance that the specified tagging parameters are unknown, on account of the following two key techniques: (1) the well-known mean-shift algorithm, which can provide accurate and rapid CF estimation; and (2) an original two-direction-combination strategy, which can further enhance the accuracy and robustness of CF estimation. Some other available CF estimation algorithms are brought out for comparison. Several validation approaches that can work on the real data without ground truths are specially designed. Experimental results on human body in vivo cardiac data demonstrate the significance of accurate CF estimation for SinMod, and validate the effectiveness of RACE in facilitating the motion estimation performance of SinMod.
Optimum data weighting and error calibration for estimation of gravitational parameters
NASA Technical Reports Server (NTRS)
Lerch, F. J.
1989-01-01
A new technique was developed for the weighting of data from satellite tracking systems in order to obtain an optimum least squares solution and an error calibration for the solution parameters. Data sets from optical, electronic, and laser systems on 17 satellites in GEM-T1 (Goddard Earth Model, 36x36 spherical harmonic field) were employed toward application of this technique for gravity field parameters. Also, GEM-T2 (31 satellites) was recently computed as a direct application of the method and is summarized here. The method employs subset solutions of the data associated with the complete solution and uses an algorithm to adjust the data weights by requiring the differences of parameters between solutions to agree with their error estimates. With the adjusted weights the process provides for an automatic calibration of the error estimates for the solution parameters. The data weights derived are generally much smaller than corresponding weights obtained from nominal values of observation accuracy or residuals. Independent tests show significant improvement for solutions with optimal weighting as compared to the nominal weighting. The technique is general and may be applied to orbit parameters, station coordinates, or other parameters than the gravity model.
PEET: a Matlab tool for estimating physical gate errors in quantum information processing systems
NASA Astrophysics Data System (ADS)
Hocker, David; Kosut, Robert; Rabitz, Herschel
2016-09-01
A Physical Error Estimation Tool (PEET) is introduced in Matlab for predicting physical gate errors of quantum information processing (QIP) operations by constructing and then simulating gate sequences for a wide variety of user-defined, Hamiltonian-based physical systems. PEET is designed to accommodate the interdisciplinary needs of quantum computing design by assessing gate performance for users familiar with the underlying physics of QIP, as well as those interested in higher-level computing operations. The structure of PEET separates the bulk of the physical details of a system into Gate objects, while the construction of quantum computing gate operations are contained in GateSequence objects. Gate errors are estimated by Monte Carlo sampling of noisy gate operations. The main utility of PEET, though, is the implementation of QuantumControl methods that act to generate and then test gate sequence and pulse-shaping techniques for QIP performance. This work details the structure of PEET and gives instructive examples for its operation.
NASA Astrophysics Data System (ADS)
Shi, Lei; Wang, Z. J.
2015-08-01
Adjoint-based mesh adaptive methods are capable of distributing computational resources to areas which are important for predicting an engineering output. In this paper, we develop an adjoint-based h-adaptation approach based on the high-order correction procedure via reconstruction formulation (CPR) to minimize the output or functional error. A dual-consistent CPR formulation of hyperbolic conservation laws is developed and its dual consistency is analyzed. Super-convergent functional and error estimate for the output with the CPR method are obtained. Factors affecting the dual consistency, such as the solution point distribution, correction functions, boundary conditions and the discretization approach for the non-linear flux divergence term, are studied. The presented method is then used to perform simulations for the 2D Euler and Navier-Stokes equations with mesh adaptation driven by the adjoint-based error estimate. Several numerical examples demonstrate the ability of the presented method to dramatically reduce the computational cost comparing with uniform grid refinement.
NASA Technical Reports Server (NTRS)
Kalton, G.
1983-01-01
A number of surveys were conducted to study the relationship between the level of aircraft or traffic noise exposure experienced by people living in a particular area and their annoyance with it. These surveys generally employ a clustered sample design which affects the precision of the survey estimates. Regression analysis of annoyance on noise measures and other variables is often an important component of the survey analysis. Formulae are presented for estimating the standard errors of regression coefficients and ratio of regression coefficients that are applicable with a two- or three-stage clustered sample design. Using a simple cost function, they also determine the optimum allocation of the sample across the stages of the sample design for the estimation of a regression coefficient.
NASA Astrophysics Data System (ADS)
Li, Yuan; Ryu, Dongryeol; Western, Andrew W.; Wang, Q. J.; Robertson, David E.; Crow, Wade T.
2014-11-01
For operational flood forecasting, discharge observations may be assimilated into a hydrologic model to improve forecasts. However, the performance of conventional filtering schemes can be degraded by ignoring the time lag between soil moisture and discharge responses. This has led to ongoing development of more appropriate ways to implement sequential data assimilation. In this paper, an ensemble Kalman smoother (EnKS) with fixed time window is implemented for the GR4H hydrologic model (modèle du Génie Rural à 4 paramètres Horaire) to update current and antecedent model states. Model and observation error parameters are estimated through the maximum a posteriori method constrained by prior information drawn from flow gauging data. When evaluated in a hypothetical forecasting mode using observed rainfall, the EnKS is found to be more stable and produce more accurate discharge forecasts than a standard ensemble Kalman filter (EnKF) by reducing the mean of the ensemble root mean squared error (MRMSE) by 13-17%. The latter tends to over-correct current model states and leads to spurious peaks and oscillations in discharge forecasts. When evaluated in a real-time forecasting mode using rainfall forecasts from a numerical weather prediction model, the benefit of the EnKS is reduced as uncertainty in rainfall forecasts becomes dominant, especially at large forecast lead time.
mBEEF-vdW: Robust fitting of error estimation density functionals
NASA Astrophysics Data System (ADS)
Lundgaard, Keld T.; Wellendorff, Jess; Voss, Johannes; Jacobsen, Karsten W.; Bligaard, Thomas
2016-06-01
We propose a general-purpose semilocal/nonlocal exchange-correlation functional approximation, named mBEEF-vdW. The exchange is a meta generalized gradient approximation, and the correlation is a semilocal and nonlocal mixture, with the Rutgers-Chalmers approximation for van der Waals (vdW) forces. The functional is fitted within the Bayesian error estimation functional (BEEF) framework [J. Wellendorff et al., Phys. Rev. B 85, 235149 (2012), 10.1103/PhysRevB.85.235149; J. Wellendorff et al., J. Chem. Phys. 140, 144107 (2014), 10.1063/1.4870397]. We improve the previously used fitting procedures by introducing a robust MM-estimator based loss function, reducing the sensitivity to outliers in the datasets. To more reliably determine the optimal model complexity, we furthermore introduce a generalization of the bootstrap 0.632 estimator with hierarchical bootstrap sampling and geometric mean estimator over the training datasets. Using this estimator, we show that the robust loss function leads to a 10 % improvement in the estimated prediction error over the previously used least-squares loss function. The mBEEF-vdW functional is benchmarked against popular density functional approximations over a wide range of datasets relevant for heterogeneous catalysis, including datasets that were not used for its training. Overall, we find that mBEEF-vdW has a higher general accuracy than competing popular functionals, and it is one of the best performing functionals on chemisorption systems, surface energies, lattice constants, and dispersion. We also show the potential-energy curve of graphene on the nickel(111) surface, where mBEEF-vdW matches the experimental binding length. mBEEF-vdW is currently available in gpaw and other density functional theory codes through Libxc, version 3.0.0.
An analytic technique for statistically modeling random atomic clock errors in estimation
NASA Technical Reports Server (NTRS)
Fell, P. J.
1981-01-01
Minimum variance estimation requires that the statistics of random observation errors be modeled properly. If measurements are derived through the use of atomic frequency standards, then one source of error affecting the observable is random fluctuation in frequency. This is the case, for example, with range and integrated Doppler measurements from satellites of the Global Positioning and baseline determination for geodynamic applications. An analytic method is presented which approximates the statistics of this random process. The procedure starts with a model of the Allan variance for a particular oscillator and develops the statistics of range and integrated Doppler measurements. A series of five first order Markov processes is used to approximate the power spectral density obtained from the Allan variance.
NASA Technical Reports Server (NTRS)
Mobasseri, B. G.; Mcgillem, C. D.; Anuta, P. E. (Principal Investigator)
1978-01-01
The author has identified the following significant results. The probability of correct classification of various populations in data was defined as the primary performance index. The multispectral data being of multiclass nature as well, required a Bayes error estimation procedure that was dependent on a set of class statistics alone. The classification error was expressed in terms of an N dimensional integral, where N was the dimensionality of the feature space. The multispectral scanner spatial model was represented by a linear shift, invariant multiple, port system where the N spectral bands comprised the input processes. The scanner characteristic function, the relationship governing the transformation of the input spatial, and hence, spectral correlation matrices through the systems, was developed.
Feuerstein, Marco; Reichl, Tobias; Vogel, Jakob; Traub, Joerg; Navab, Nassir
2008-09-01
In abdominal surgery, a laparoscopic ultrasound transducer is commonly used to detect lesions such as metastases. The determination and visualization of the position and orientation of its flexible tip in relation to the patient or other surgical instruments can be a great support for surgeons using the transducer intraoperatively. This difficult subject has recently received attention from the scientific community. Electromagnetic tracking systems can be applied to track the flexible tip; however, current limitations of electromagnetic tracking include its accuracy and sensibility, i.e., the magnetic field can be distorted by ferromagnetic material. This paper presents two novel methods for estimation of electromagnetic tracking error. Based on optical tracking of the laparoscope, as well as on magneto-optic and visual tracking of the transducer, these methods automatically detect in 85% of all cases whether tracking is erroneous or not, and reduce tracking errors by up to 2.5 mm.
Wu, Jibo
2016-01-01
In this article, a generalized difference-based ridge estimator is proposed for the vector parameter in a partial linear model when the errors are dependent. It is supposed that some additional linear constraints may hold to the whole parameter space. Its mean-squared error matrix is compared with the generalized restricted difference-based estimator. Finally, the performance of the new estimator is explained by a simulation study and a numerical example.
NASA Astrophysics Data System (ADS)
Lajohn, L. A.; Pratt, R. H.
2015-05-01
There is no simple parameter that can be used to predict when impulse approximation (IA) can yield accurate Compton scattering doubly differential cross sections (DDCS) in relativistic regimes. When Z is low, a small value of the parameter /q (where is the average initial electron momentum and q is the momentum transfer) suffices. For small Z the photon electron kinematic contribution described in relativistic S-matrix (SM) theory reduces to an expression, Xrel, which is present in the relativistic impulse approximation (RIA) formula for Compton DDCS. When Z is high, the S-Matrix photon electron kinematics no longer reduces to Xrel, and this along with the error characterized by the magnitude of /q contribute to the RIA error Δ. We demonstrate and illustrate in the form of contour plots that there are regimes of incident photon energy ωi and scattering angle θ in which the two types of errors at least partially cancel. Our calculations show that when θ is about 65° for Uranium K-shell scattering, Δ is less than 1% over an ωi range of 300 to 900 keV.
Shen, Yan; Lou, Shuqin; Wang, Xin
2014-03-20
The evaluation accuracy of real optical properties of photonic crystal fibers (PCFs) is determined by the accurate extraction of air hole edges from microscope images of cross sections of practical PCFs. A novel estimation method of point spread function (PSF) based on Kalman filter is presented to rebuild the micrograph image of the PCF cross-section and thus evaluate real optical properties for practical PCFs. Through tests on both artificially degraded images and microscope images of cross sections of practical PCFs, we prove that the proposed method can achieve more accurate PSF estimation and lower PSF variance than the traditional Bayesian estimation method, and thus also reduce the defocus effect. With this method, we rebuild the microscope images of two kinds of commercial PCFs produced by Crystal Fiber and analyze the real optical properties of these PCFs. Numerical results are in accord with the product parameters.
Hussain, Zahra; Svensson, Carl-Magnus; Besle, Julien; Webb, Ben S.; Barrett, Brendan T.; McGraw, Paul V.
2015-01-01
We describe a method for deriving the linear cortical magnification factor from positional error across the visual field. We compared magnification obtained from this method between normally sighted individuals and amblyopic individuals, who receive atypical visual input during development. The cortical magnification factor was derived for each subject from positional error at 32 locations in the visual field, using an established model of conformal mapping between retinal and cortical coordinates. Magnification of the normally sighted group matched estimates from previous physiological and neuroimaging studies in humans, confirming the validity of the approach. The estimate of magnification for the amblyopic group was significantly lower than the normal group: by 4.4 mm deg−1 at 1° eccentricity, assuming a constant scaling factor for both groups. These estimates, if correct, suggest a role for early visual experience in establishing retinotopic mapping in cortex. We discuss the implications of altered cortical magnification for cortical size, and consider other neural changes that may account for the amblyopic results. PMID:25761341
Analysis of open-loop conical scan pointing error and variance estimators
NASA Technical Reports Server (NTRS)
Alvarez, L. S.
1993-01-01
General pointing error and variance estimators for an open-loop conical scan (conscan) system are derived and analyzed. The conscan algorithm is modeled as a weighted least-squares estimator whose inputs are samples of receiver carrier power and its associated measurement uncertainty. When the assumptions of constant measurement noise and zero pointing error estimation are applied, the variance equation is then strictly a function of the carrier power to uncertainty ratio and the operator selectable radius and period input to the algorithm. The performance equation is applied to a 34-m mirror-based beam-waveguide conscan system interfaced with the Block V Receiver Subsystem tracking a Ka-band (32-GHz) downlink. It is shown that for a carrier-to-noise power ratio greater than or equal to 30 dB-Hz, the conscan period for Ka-band operation may be chosen well below the current DSN minimum of 32 sec. The analysis presented forms the basis of future conscan work in both research and development as well as for the upcoming DSN antenna controller upgrade for the new DSS-24 34-m beam-waveguide antenna.
Estimating Random Errors Due to Shot Noise in Backscatter Lidar Observations
NASA Technical Reports Server (NTRS)
Liu, Zhaoyan; Hunt, William; Vaughan, Mark A.; Hostetler, Chris A.; McGill, Matthew J.; Powell, Kathy; Winker, David M.; Hu, Yongxiang
2006-01-01
In this paper, we discuss the estimation of random errors due to shot noise in backscatter lidar observations that use either photomultiplier tube (PMT) or avalanche photodiode (APD) detectors. The statistical characteristics of photodetection are reviewed, and photon count distributions of solar background signals and laser backscatter signals are examined using airborne lidar observations at 532 nm using a photon-counting mode APD. Both distributions appear to be Poisson, indicating that the arrival at the photodetector of photons for these signals is a Poisson stochastic process. For Poisson-distributed signals, a proportional, one-to-one relationship is known to exist between the mean of a distribution and its variance. Although the multiplied photocurrent no longer follows a strict Poisson distribution in analog-mode APD and PMT detectors, the proportionality still exists between the mean and the variance of the multiplied photocurrent. We make use of this relationship by introducing the noise scale factor (NSF), which quantifies the constant of proportionality that exists between the root-mean-square of the random noise in a measurement and the square root of the mean signal. Using the NSF to estimate random errors in lidar measurements due to shot noise provides a significant advantage over the conventional error estimation techniques, in that with the NSF uncertainties can be reliably calculated from/for a single data sample. Methods for evaluating the NSF are presented. Algorithms to compute the NSF are developed for the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) lidar and tested using data from the Lidar In-space Technology Experiment (LITE). OCIS Codes:
Estimating random errors due to shot noise in backscatter lidar observations.
Liu, Zhaoyan; Hunt, William; Vaughan, Mark; Hostetler, Chris; McGill, Matthew; Powell, Kathleen; Winker, David; Hu, Yongxiang
2006-06-20
We discuss the estimation of random errors due to shot noise in backscatter lidar observations that use either photomultiplier tube (PMT) or avalanche photodiode (APD) detectors. The statistical characteristics of photodetection are reviewed, and photon count distributions of solar background signals and laser backscatter signals are examined using airborne lidar observations at 532 nm using a photon-counting mode APD. Both distributions appear to be Poisson, indicating that the arrival at the photodetector of photons for these signals is a Poisson stochastic process. For Poisson- distributed signals, a proportional, one-to-one relationship is known to exist between the mean of a distribution and its variance. Although the multiplied photocurrent no longer follows a strict Poisson distribution in analog-mode APD and PMT detectors, the proportionality still exists between the mean and the variance of the multiplied photocurrent. We make use of this relationship by introducing the noise scale factor (NSF), which quantifies the constant of proportionality that exists between the root mean square of the random noise in a measurement and the square root of the mean signal. Using the NSF to estimate random errors in lidar measurements due to shot noise provides a significant advantage over the conventional error estimation techniques, in that with the NSF, uncertainties can be reliably calculated from or for a single data sample. Methods for evaluating the NSF are presented. Algorithms to compute the NSF are developed for the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations lidar and tested using data from the Lidar In-space Technology Experiment.
Optimal error estimates for high order Runge-Kutta methods applied to evolutionary equations
McKinney, W.R.
1989-01-01
Fully discrete approximations to 1-periodic solutions of the Generalized Korteweg de-Vries and the Cahn-Hilliard equations are analyzed. These approximations are generated by an Implicit Runge-Kutta method for the temporal discretization and a Galerkin Finite Element method for the spatial discretization. Furthermore, these approximations may be of arbitrarily high order. In particular, it is shown that the well-known order reduction phenomenon afflicting Implicit Runge Kutta methods does not occur. Numerical results supporting these optimal error estimates for the Korteweg-de Vries equation and indicating the existence of a slow motion manifold for the Cahn-Hilliard equation are also provided.
Error in Estimates of Tissue Material Properties from Shear Wave Dispersion Ultrasound Vibrometry
Urban, Matthew W.; Chen, Shigao; Greenleaf, James F.
2009-01-01
Shear wave velocity measurements are used in elasticity imaging to find the shear elasticity and viscosity of tissue. A technique called shear wave dispersion ultrasound vibrometry (SDUV) has been introduced to use the dispersive nature of shear wave velocity to locally estimate the material properties of tissue. Shear waves are created using a multifrequency ultrasound radiation force, and the propagating shear waves are measured a few millimeters away from the excitation point. The shear wave velocity is measured using a repetitive pulse-echo method and Kalman filtering to find the phase of the harmonic shear wave at 2 different locations. A viscoelastic Voigt model and the shear wave velocity measurements at different frequencies are used to find the shear elasticity (μ1) and viscosity (μ2) of the tissue. The purpose of this paper is to report the accuracy of the SDUV method over a range of different values of μ1 and μ2. A motion detection model of a vibrating scattering medium was used to analyze measurement errors of vibration phase in a scattering medium. To assess the accuracy of the SDUV method, we modeled the effects of phase errors on estimates of shear wave velocity and material properties while varying parameters such as shear stiffness and viscosity, shear wave amplitude, the distance between shear wave measurements (Δr), signal-to-noise ratio (SNR) of the ultrasound pulse-echo method, and the frequency range of the measurements. We performed an experiment in a section of porcine muscle to evaluate variation of the aforementioned parameters on the estimated shear wave velocity and material property measurements and to validate the error prediction model. The model showed that errors in the shear wave velocity and material property estimates were minimized by maximizing shear wave amplitude, pulse-echo SNR, Δr, and the bandwidth used for shear wave measurements. The experimental model showed optimum performance could be obtained for Δr = 3-6 mm
NASA Astrophysics Data System (ADS)
Plotnikov, M. Yu.; Shkarupa, E. V.
2015-11-01
Presently, the direct simulation Monte Carlo (DSMC) method is widely used for solving rarefied gas dynamics problems. As applied to steady-state problems, a feature of this method is the use of dependent sample values of random variables for the calculation of macroparameters of gas flows. A new combined approach to estimating the statistical error of the method is proposed that does not practically require additional computations, and it is applicable for any degree of probabilistic dependence of sample values. Features of the proposed approach are analyzed theoretically and numerically. The approach is tested using the classical Fourier problem and the problem of supersonic flow of rarefied gas through permeable obstacle.
Errors in Expected Human Losses Due to Incorrect Seismic Hazard Estimates
NASA Astrophysics Data System (ADS)
Wyss, M.; Nekrasova, A.; Kossobokov, V. G.
2011-12-01
The probability of strong ground motion is presented in seismic hazard maps, in which peak ground accelerations (PGA) with 10% probability of exceedance in 50 years are shown by color codes. It has become evident that these maps do not correctly give the seismic hazard. On the seismic hazard map of Japan, the epicenters of the recent large earthquakes are located in the regions of relatively low hazard. The errors of the GSHAP maps have been measured by the difference between observed and expected intensities due to large earthquakes. Here, we estimate how the errors in seismic hazard estimates propagate into errors in estimating the potential fatalities and affected population. We calculated the numbers of fatalities that would have to be expected in the regions of the nine earthquakes with more than 1,000 fatalities during the last 10 years with relatively reliable estimates of fatalities, assuming a magnitude which generates as a maximum intensity the one given by the GSHAP maps. This value is the number of fatalities to be exceeded with probability of 10% during 50 years. In most regions of devastating earthquakes, there are no instruments to measure ground accelerations. Therefore, we converted the PGA expected as a likely maximum based on the GSHAP maps to intensity. The magnitude of the earthquake that would cause the intensity expected by GSHAP as a likely maximum was calculated by M(GSHAP) = (I0 +1.5)/1.5. The numbers of fatalities, which were expected, based on earthquakes with M(GSHAP), were calculated using the loss estimating program QLARM. We calibrated this tool for each case by calculating the theoretical damage and numbers of fatalities (Festim) for the disastrous test earthquakes, generating a match with the observe numbers of fatalities (Fobs=Festim) by adjusting the attenuation relationship within the bounds of commonly observed laws. Calculating the numbers of fatalities expected for the earthquakes with M(GSHAP) will thus yield results that
Accurate state estimation for a hydraulic actuator via a SDRE nonlinear filter
NASA Astrophysics Data System (ADS)
Strano, Salvatore; Terzo, Mario
2016-06-01
The state estimation in hydraulic actuators is a fundamental tool for the detection of faults or a valid alternative to the installation of sensors. Due to the hard nonlinearities that characterize the hydraulic actuators, the performances of the linear/linearization based techniques for the state estimation are strongly limited. In order to overcome these limits, this paper focuses on an alternative nonlinear estimation method based on the State-Dependent-Riccati-Equation (SDRE). The technique is able to fully take into account the system nonlinearities and the measurement noise. A fifth order nonlinear model is derived and employed for the synthesis of the estimator. Simulations and experimental tests have been conducted and comparisons with the largely used Extended Kalman Filter (EKF) are illustrated. The results show the effectiveness of the SDRE based technique for applications characterized by not negligible nonlinearities such as dead zone and frictions.
Estimation of the extrapolation error in the calibration of type S thermocouples
NASA Astrophysics Data System (ADS)
Giorgio, P.; Garrity, K. M.; Rebagliati, M. Jiménez; García Skabar, J.
2013-09-01
Measurement results from the calibration performed at NIST of ten new type S thermocouples have been analyzed to estimate the extrapolation error. Thermocouples have been calibrated at the fixed points of Zn, Al, Ag and Au and calibration curves were calculated using different numbers of FPs. It was found for these thermocouples that the absolute value of the extrapolation error, evaluated by measurement at the Au freezing-point temperature, is at most 0.10 °C and 0.27 °C when the fixed-points of Zn, Al and Ag, or the fixed-points of Zn and Al, are respectively used to calculate the calibration curve. It is also shown that absolute value of the extrapolation error, evaluated by measurement at the Ag freezing-point temperature is at most 0.25 °C when the fixed-points of Zn and Al, are used to calculate the calibration curve. This study is oriented to help those labs that lack a direct mechanism to achieve a high temperature calibration. It supports, up to 1064 °C, the application of a similar procedure to that used by Burns and Scroger in NIST SP-250-35 for calibrating a new type S thermocouple. The uncertainty amounts a few tenths of a degree Celsius.
The GFR and GFR decline cannot be accurately estimated in type 2 diabetics.
Gaspari, Flavio; Ruggenenti, Piero; Porrini, Esteban; Motterlini, Nicola; Cannata, Antonio; Carrara, Fabiola; Jiménez Sosa, Alejandro; Cella, Claudia; Ferrari, Silvia; Stucchi, Nadia; Parvanova, Aneliya; Iliev, Ilian; Trevisan, Roberto; Bossi, Antonio; Zaletel, Jelka; Remuzzi, Giuseppe
2013-07-01
There are no adequate studies that have formally tested the performance of different estimating formulas in patients with type 2 diabetes both with and without overt nephropathy. Here we evaluated the agreement between baseline GFRs, GFR changes at month 6, and long-term GFR decline measured by iohexol plasma clearance or estimated by 15 creatinine-based formulas in 600 type 2 diabetics followed for a median of 4.0 years. Ninety patients were hyperfiltering. The number of those identified by estimation formulas ranged from 0 to 24:58 were not identified by any formula. Baseline GFR was significantly underestimated and a 6-month GFR reduction was missed in hyperfiltering patients. Long-term GFR decline was also underestimated by all formulas in the whole study group and in hyper-, normo-, and hypofiltering patients considered separately. Five formulas generated positive slopes in hyperfiltering patients. Baseline concordance correlation coefficients and total deviation indexes ranged from 32.1% to 92.6% and from 0.21 to 0.53, respectively. Concordance correlation coefficients between estimated and measured long-term GFR decline ranged from -0.21 to 0.35. The agreement between estimated and measured values was also poor within each subgroup considered separately. Thus, our study questions the use of any estimation formula to identify hyperfiltering patients and monitor renal disease progression and response to treatment in type 2 diabetics without overt nephropathy.
Geomechanical Analysis with Rigorous Error Estimates for a Double-Porosity Reservoir Model
Berryman, J G
2005-04-11
A model of random polycrystals of porous laminates is introduced to provide a means for studying geomechanical properties of double-porosity reservoirs. Calculations on the resulting earth reservoir model can proceed semi-analytically for studies of either the poroelastic or transport coefficients. Rigorous bounds of the Hashin-Shtrikman type provide estimates of overall bulk and shear moduli, and thereby also provide rigorous error estimates for geomechanical constants obtained from up-scaling based on a self-consistent effective medium method. The influence of hidden (or presumed unknown) microstructure on the final results can then be evaluated quantitatively. Detailed descriptions of the use of the model and some numerical examples showing typical results for the double-porosity poroelastic coefficients of a heterogeneous reservoir are presented.
Prediction and standard error estimation for a finite universe total when a stratum is not sampled
Wright, T.
1994-01-01
In the context of a universe of trucks operating in the United States in 1990, this paper presents statistical methodology for estimating a finite universe total on a second occasion when a part of the universe is sampled and the remainder of the universe is not sampled. Prediction is used to compensate for the lack of data from the unsampled portion of the universe. The sample is assumed to be a subsample of an earlier sample where stratification is used on both occasions before sample selection. Accounting for births and deaths in the universe between the two points in time, the detailed sampling plan, estimator, standard error, and optimal sample allocation, are presented with a focus on the second occasion. If prior auxiliary information is available, the methodology is also applicable to a first occasion.
NASA Technical Reports Server (NTRS)
Borovikov, Anna; Rienecker, Michele M.; Keppenne, Christian; Johnson, Gregory C.
2004-01-01
One of the most difficult aspects of ocean state estimation is the prescription of the model forecast error covariances. The paucity of ocean observations limits our ability to estimate the covariance structures from model-observation differences. In most practical applications, simple covariances are usually prescribed. Rarely are cross-covariances between different model variables used. Here a comparison is made between a univariate Optimal Interpolation (UOI) scheme and a multivariate OI algorithm (MvOI) in the assimilation of ocean temperature. In the UOI case only temperature is updated using a Gaussian covariance function and in the MvOI salinity, zonal and meridional velocities as well as temperature, are updated using an empirically estimated multivariate covariance matrix. Earlier studies have shown that a univariate OI has a detrimental effect on the salinity and velocity fields of the model. Apparently, in a sequential framework it is important to analyze temperature and salinity together. For the MvOI an estimation of the model error statistics is made by Monte-Carlo techniques from an ensemble of model integrations. An important advantage of using an ensemble of ocean states is that it provides a natural way to estimate cross-covariances between the fields of different physical variables constituting the model state vector, at the same time incorporating the model's dynamical and thermodynamical constraints as well as the effects of physical boundaries. Only temperature observations from the Tropical Atmosphere-Ocean array have been assimilated in this study. In order to investigate the efficacy of the multivariate scheme two data assimilation experiments are validated with a large independent set of recently published subsurface observations of salinity, zonal velocity and temperature. For reference, a third control run with no data assimilation is used to check how the data assimilation affects systematic model errors. While the performance of the
Gilbert, E.S.; Fix, J.J.
1996-08-01
This report addresses laboratory measurement error in estimates of external doses obtained from personnel dosimeters, and investigates the effects of these errors on linear dose-response analyses of data from epidemiologic studies of nuclear workers. These errors have the distinguishing feature that they are independent across time and across workers. Although the calculations made for this report were based on Hanford data, the overall conclusions are likely to be relevant for other epidemiologic studies of workers exposed to external radiation.
NASA Astrophysics Data System (ADS)
Lee, Y.; Keehm, Y.
2011-12-01
Estimating the degree of weathering in stone cultural heritage, such as pagodas and statues is very important to plan conservation and restoration. The ultrasonic measurement is one of commonly-used techniques to evaluate weathering index of stone cultual properties, since it is easy to use and non-destructive. Typically we use a portable ultrasonic device, PUNDIT with exponential sensors. However, there are many factors to cause errors in measurements such as operators, sensor layouts or measurement directions. In this study, we carried out variety of measurements with different operators (male and female), different sensor layouts (direct and indirect), and sensor directions (anisotropy). For operators bias, we found that there were not significant differences by the operator's sex, while the pressure an operator exerts can create larger error in measurements. Calibrating with a standard sample for each operator is very essential in this case. For the sensor layout, we found that the indirect measurement (commonly used for cultural properties, since the direct measurement is difficult in most cases) gives lower velocity than the real one. We found that the correction coefficient is slightly different for different types of rocks: 1.50 for granite and sandstone and 1.46 for marble. From the sensor directions, we found that many rocks have slight anisotropy in their ultrasonic velocity measurement, though they are considered isotropic in macroscopic scale. Thus averaging four different directional measurement (0°, 45°, 90°, 135°) gives much less errors in measurements (the variance is 2-3 times smaller). In conclusion, we reported the error in ultrasonic meaurement of stone cultural properties by various sources quantitatively and suggested the amount of correction and procedures to calibrate the measurements. Acknowledgement: This study, which forms a part of the project, has been achieved with the support of national R&D project, which has been hosted by
Measurement error affects risk estimates for recruitment to the Hudson River stock of striped bass.
Dunning, Dennis J; Ross, Quentin E; Munch, Stephan B; Ginzburg, Lev R
2002-06-01
We examined the consequences of ignoring the distinction between measurement error and natural variability in an assessment of risk to the Hudson River stock of striped bass posed by entrainment at the Bowline Point, Indian Point, and Roseton power plants. Risk was defined as the probability that recruitment of age-1+ striped bass would decline by 80% or more, relative to the equilibrium value, at least once during the time periods examined (1, 5, 10, and 15 years). Measurement error, estimated using two abundance indices from independent beach seine surveys conducted on the Hudson River, accounted for 50% of the variability in one index and 56% of the variability in the other. If a measurement error of 50% was ignored and all of the variability in abundance was attributed to natural causes, the risk that recruitment of age-1+ striped bass would decline by 80% or more after 15 years was 0.308 at the current level of entrainment mortality (11%). However, the risk decreased almost tenfold (0.032) if a measurement error of 50% was considered. The change in risk attributable to decreasing the entrainment mortality rate from 11 to 0% was very small (0.009) and similar in magnitude to the change in risk associated with an action proposed in Amendment #5 to the Interstate Fishery Management Plan for Atlantic striped bass (0.006)--an increase in the instantaneous fishing mortality rate from 0.33 to 0.4. The proposed increase in fishing mortality was not considered an adverse environmental impact, which suggests that potentially costly efforts to reduce entrainment mortality on the Hudson River stock of striped bass are not warranted.
Practical error estimates for Reynolds' lubrication approximation and its higher order corrections
Wilkening, Jon
2008-12-10
Reynolds lubrication approximation is used extensively to study flows between moving machine parts, in narrow channels, and in thin films. The solution of Reynolds equation may be thought of as the zeroth order term in an expansion of the solution of the Stokes equations in powers of the aspect ratio {var_epsilon} of the domain. In this paper, we show how to compute the terms in this expansion to arbitrary order on a two-dimensional, x-periodic domain and derive rigorous, a-priori error bounds for the difference between the exact solution and the truncated expansion solution. Unlike previous studies of this sort, the constants in our error bounds are either independent of the function h(x) describing the geometry, or depend on h and its derivatives in an explicit, intuitive way. Specifically, if the expansion is truncated at order 2k, the error is O({var_epsilon}{sup 2k+2}) and h enters into the error bound only through its first and third inverse moments {integral}{sub 0}{sup 1} h(x){sup -m} dx, m = 1,3 and via the max norms {parallel} 1/{ell}! h{sup {ell}-1}{partial_derivative}{sub x}{sup {ell}}h{parallel}{sub {infinity}}, 1 {le} {ell} {le} 2k + 2. We validate our estimates by comparing with finite element solutions and present numerical evidence that suggests that even when h is real analytic and periodic, the expansion solution forms an asymptotic series rather than a convergent series.
NASA Technical Reports Server (NTRS)
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.
Uncertainty estimation in form error evaluation of freeform surfaces for precision metrology
NASA Astrophysics Data System (ADS)
Zhang, Xiangchao; Xiao, Hong; Zhang, Hao; He, Xiaoying; Xu, Min
2016-01-01
Freeform surfaces are widely used in precision components to realize novel functionalities. In order to evaluate the form qualities of the manufactured freeform parts, surface matching/fitting is required. The uncertainty of the obtained form deviations needs to be estimated to assess the reliability of form error evaluation. The GUM approach is extensively adopted for uncertainty assessment in precision metrology, but it is not suited for assessing the nonlinear matching/fitting problems of freeform models. In this paper a Monte-Carlo method is developed to estimate the uncertainty of the fitted position, shape and form error metrics. Based on the correlation analysis, the effects of objective functions in numerical optimization, noise amplitudes in measurement, shapes of freeform surfaces and so on are determined. Then the significant factors dominating the reliability of the fitted results can be identified. Henceforth the matching/fitting procedures can be arranged appropriately to reduce the uncertainty of the evaluation results and improve the reliability of freeform surface characterization.
FAST TRACK COMMUNICATION Accurate estimate of α variation and isotope shift parameters in Na and Mg+
NASA Astrophysics Data System (ADS)
Sahoo, B. K.
2010-12-01
We present accurate calculations of fine-structure constant variation coefficients and isotope shifts in Na and Mg+ using the relativistic coupled-cluster method. In our approach, we are able to discover the roles of various correlation effects explicitly to all orders in these calculations. Most of the results, especially for the excited states, are reported for the first time. It is possible to ascertain suitable anchor and probe lines for the studies of possible variation in the fine-structure constant by using the above results in the considered systems.
GPS receivers timing data processing using neural networks: optimal estimation and errors modeling.
Mosavi, M R
2007-10-01
The Global Positioning System (GPS) is a network of satellites, whose original purpose was to provide accurate navigation, guidance, and time transfer to military users. The past decade has also seen rapid concurrent growth in civilian GPS applications, including farming, mining, surveying, marine, and outdoor recreation. One of the most significant of these civilian applications is commercial aviation. A stand-alone civilian user enjoys an accuracy of 100 meters and 300 nanoseconds, 25 meters and 200 nanoseconds, before and after Selective Availability (SA) was turned off. In some applications, high accuracy is required. In this paper, five Neural Networks (NNs) are proposed for acceptable noise reduction of GPS receivers timing data. The paper uses from an actual data collection for evaluating the performance of the methods. An experimental test setup is designed and implemented for this purpose. The obtained experimental results from a Coarse Acquisition (C/A)-code single-frequency GPS receiver strongly support the potential of methods to give high accurate timing. Quality of the obtained results is very good, so that GPS timing RMS error reduce to less than 120 and 40 nanoseconds, with and without SA. PMID:18098370
GPS receivers timing data processing using neural networks: optimal estimation and errors modeling.
Mosavi, M R
2007-10-01
The Global Positioning System (GPS) is a network of satellites, whose original purpose was to provide accurate navigation, guidance, and time transfer to military users. The past decade has also seen rapid concurrent growth in civilian GPS applications, including farming, mining, surveying, marine, and outdoor recreation. One of the most significant of these civilian applications is commercial aviation. A stand-alone civilian user enjoys an accuracy of 100 meters and 300 nanoseconds, 25 meters and 200 nanoseconds, before and after Selective Availability (SA) was turned off. In some applications, high accuracy is required. In this paper, five Neural Networks (NNs) are proposed for acceptable noise reduction of GPS receivers timing data. The paper uses from an actual data collection for evaluating the performance of the methods. An experimental test setup is designed and implemented for this purpose. The obtained experimental results from a Coarse Acquisition (C/A)-code single-frequency GPS receiver strongly support the potential of methods to give high accurate timing. Quality of the obtained results is very good, so that GPS timing RMS error reduce to less than 120 and 40 nanoseconds, with and without SA.
NASA Astrophysics Data System (ADS)
Ciocca, Francesco; Sharma, Varun; Lunati, Ivan; Parlange, Marc B.
2013-04-01
Ground heat flux plays a crucial role in surface energy budget: an incorrect estimation of energy storage and heat fluxes in soils occur when probes such as heat flux plates are adopted, and these mistakes can account for up to 90% of the residual variance (Higgins, GRL, 2012). A promising alternative to heat flux plates is represented by Multi Function Heat Pulse Probes (MFHPP). They have proven to be accurate in thermal properties and heat fluxes estimation (e.g. Cobos, VZJ, 2003) and can be used to monitor and quantify subsurface evaporation in field experiments (Xiao et al., VZJ, 2011). We perform a laboratory experiment with controlled temperature in a small Plexiglas column (20cm diameter and 40cm height). The column is packed with homogeneously saturated sandy soil and equipped with three MFHPPs in the upper 4cm and thermocouples and dielectric soil moisture probes deeper. This configuration allows for accurate and simultaneous ground heat flux, soil moisture and subsurface evaporation measurements. Total evaporation is monitored using a precision scale, while an infrared gun and a long wave radiometer measure the soil skin temperature and the outgoing long-short wave radiation, respectively. A fan and a heat lamp placed above the column allow to mimick on a smaller and more controlled scale the field conditions induced by the diurnal cycle. At a reference height above the column relative humidity, wind speed and air temperature are collected. Results are interpreted by means of numerical simulations performed with an ad-hoc-developed numerical model that simulates coupled heat and moisture transfer in soils and is used to match and interpolate the temperature and soil moisture values got at finite depths within the column. Ground heat fluxes are then estimated by integrating over almost continuous, numerically simulated temperature profiles, which avoids errors due to use of discrete data (Lunati et al., WRR, 2012) and leads to a more reliable estimate of
Temkin, Nancy R
2004-10-01
Different authors have used different estimates of variability in the denominator of the Reliable Change Index (RCI). Maassen attempts to clarify some of the differences and the assumptions underlying them. In particular he compares the 'classical' approach using an estimate S(Ed) supposedly based on measurement error alone with an estimate S(Diff) based on the variability of observed differences in a population that should have no true change. Maassen concludes that not only is S(Ed) based on classical theory, but it properly estimates variability due to measurement error and practice effect while S(Diff) overestimates variability by accounting twice for the variability due to practice. Simulations show Maassen to be wrong on both accounts. With an error rate nominally set to 10%, RCI estimates using S(Diff) wrongly declare change in 10.4% and 9.4% of simulated cases without true change while estimates using S(Ed) wrongly declare change in 17.5% and 12.3% of the simulated cases (p < .000000001 and p < .008, respectively). In the simulation that separates measurement error and practice effects, SEd estimates the variability of change due to measurement error to be .34, when the true variability due to measurement error was .014. Neuropsychologists should not use SEd in the denominator of the RCI. PMID:15637781
Precision Pointing Control to and Accurate Target Estimation of a Non-Cooperative Vehicle
NASA Technical Reports Server (NTRS)
VanEepoel, John; Thienel, Julie; Sanner, Robert M.
2006-01-01
In 2004, NASA began investigating a robotic servicing mission for the Hubble Space Telescope (HST). Such a mission would not only require estimates of the HST attitude and rates in order to achieve capture by the proposed Hubble Robotic Vehicle (HRV), but also precision control to achieve the desired rate and maintain the orientation to successfully dock with HST. To generalize the situation, HST is the target vehicle and HRV is the chaser. This work presents a nonlinear approach for estimating the body rates of a non-cooperative target vehicle, and coupling this estimation to a control scheme. Non-cooperative in this context relates to the target vehicle no longer having the ability to maintain attitude control or transmit attitude knowledge.
Some recommendations for an accurate estimation of Lanice conchilega density based on tube counts
NASA Astrophysics Data System (ADS)
van Hoey, Gert; Vincx, Magda; Degraer, Steven
2006-12-01
The tube building polychaete Lanice conchilega is a common and ecologically important species in intertidal and shallow subtidal sands. It builds a characteristic tube with ragged fringes and can retract rapidly into its tube to depths of more than 20 cm. Therefore, it is very difficult to sample L. conchilega individuals, especially with a Van Veen grab. Consequently, many studies have used tube counts as estimates of real densities. This study reports on some aspects to be considered when using tube counts as a density estimate of L. conchilega, based on intertidal and subtidal samples. Due to its accuracy and independence of sampling depth, the tube method is considered the prime method to estimate the density of L. conchilega. However, caution is needed when analyzing samples with fragile young individuals and samples from areas where temporary physical disturbance is likely to occur.
Accurate State Estimation and Tracking of a Non-Cooperative Target Vehicle
NASA Technical Reports Server (NTRS)
Thienel, Julie K.; Sanner, Robert M.
2006-01-01
Autonomous space rendezvous scenarios require knowledge of the target vehicle state in order to safely dock with the chaser vehicle. Ideally, the target vehicle state information is derived from telemetered data, or with the use of known tracking points on the target vehicle. However, if the target vehicle is non-cooperative and does not have the ability to maintain attitude control, or transmit attitude knowledge, the docking becomes more challenging. This work presents a nonlinear approach for estimating the body rates of a non-cooperative target vehicle, and coupling this estimation to a tracking control scheme. The approach is tested with the robotic servicing mission concept for the Hubble Space Telescope (HST). Such a mission would not only require estimates of the HST attitude and rates, but also precision control to achieve the desired rate and maintain the orientation to successfully dock with HST.
Fast and accurate probability density estimation in large high dimensional astronomical datasets
NASA Astrophysics Data System (ADS)
Gupta, Pramod; Connolly, Andrew J.; Gardner, Jeffrey P.
2015-01-01
Astronomical surveys will generate measurements of hundreds of attributes (e.g. color, size, shape) on hundreds of millions of sources. Analyzing these large, high dimensional data sets will require efficient algorithms for data analysis. An example of this is probability density estimation that is at the heart of many classification problems such as the separation of stars and quasars based on their colors. Popular density estimation techniques use binning or kernel density estimation. Kernel density estimation has a small memory footprint but often requires large computational resources. Binning has small computational requirements but usually binning is implemented with multi-dimensional arrays which leads to memory requirements which scale exponentially with the number of dimensions. Hence both techniques do not scale well to large data sets in high dimensions. We present an alternative approach of binning implemented with hash tables (BASH tables). This approach uses the sparseness of data in the high dimensional space to ensure that the memory requirements are small. However hashing requires some extra computation so a priori it is not clear if the reduction in memory requirements will lead to increased computational requirements. Through an implementation of BASH tables in C++ we show that the additional computational requirements of hashing are negligible. Hence this approach has small memory and computational requirements. We apply our density estimation technique to photometric selection of quasars using non-parametric Bayesian classification and show that the accuracy of the classification is same as the accuracy of earlier approaches. Since the BASH table approach is one to three orders of magnitude faster than the earlier approaches it may be useful in various other applications of density estimation in astrostatistics.
ERIC Educational Resources Information Center
Paek, Insu; Cai, Li
2014-01-01
The present study was motivated by the recognition that standard errors (SEs) of item response theory (IRT) model parameters are often of immediate interest to practitioners and that there is currently a lack of comparative research on different SE (or error variance-covariance matrix) estimation procedures. The present study investigated item…
Edge-based a posteriori error estimators for generation of d-dimensional quasi-optimal meshes
Lipnikov, Konstantin; Agouzal, Abdellatif; Vassilevski, Yuri
2009-01-01
We present a new method of metric recovery for minimization of L{sub p}-norms of the interpolation error or its gradient. The method uses edge-based a posteriori error estimates. The method is analyzed for conformal simplicial meshes in spaces of arbitrary dimension d.
van Tuinen, Marcel; Hadly, Elizabeth A
2004-08-01
The best reconstructions of the history of life will use both molecular time estimates and fossil data. Errors in molecular rate estimation typically are unaccounted for and no attempts have been made to quantify this uncertainty comprehensively. Here, focus is primarily on fossil calibration error because this error is least well understood and nearly universally disregarded. Our quantification of errors in the synapsid-diapsid calibration illustrates that although some error can derive from geological dating of sedimentary rocks, the absence of good stem fossils makes phylogenetic error the most critical. We therefore propose the use of calibration ages that are based on the first undisputed synapsid and diapsid. This approach yields minimum age estimates and standard errors of 306.1 +/- 8.5 MYR for the divergence leading to birds and mammals. Because this upper bound overlaps with the recent use of 310 MYR, we do not support the notion that several metazoan divergence times are significantly overestimated because of serious miscalibration (sensuLee 1999). However, the propagation of relevant errors reduces the statistical significance of the pre-K-T boundary diversification of many bird lineages despite retaining similar point time estimates. Our results demand renewed investigation into suitable loci and fossil calibrations for constructing evolutionary timescales.
Spectral estimation from laser scanner data for accurate color rendering of objects
NASA Astrophysics Data System (ADS)
Baribeau, Rejean
2002-06-01
Estimation methods are studied for the recovery of the spectral reflectance across the visible range from the sensing at just three discrete laser wavelengths. Methods based on principal component analysis and on spline interpolation are judged based on the CIE94 color differences for some reference data sets. These include the Macbeth color checker, the OSA-UCS color charts, some artist pigments, and a collection of miscellaneous surface colors. The optimal three sampling wavelengths are also investigated. It is found that color can be estimated with average accuracy ΔE94 = 2.3 when optimal wavelengths 455 nm, 540 n, and 610 nm are used.
Ur Rehman, Yasar Abbas; Tariq, Muhammad; Khan, Omar Usman
2015-01-01
Object localization plays a key role in many popular applications of Wireless Multimedia Sensor Networks (WMSN) and as a result, it has acquired a significant status for the research community. A significant body of research performs this task without considering node orientation, object geometry and environmental variations. As a result, the localized object does not reflect the real world scenarios. In this paper, a novel object localization scheme for WMSN has been proposed that utilizes range free localization, computer vision, and principle component analysis based algorithms. The proposed approach provides the best possible approximation of distance between a wmsn sink and an object, and the orientation of the object using image based information. Simulation results report 99% efficiency and an error ratio of 0.01 (around 1 ft) when compared to other popular techniques. PMID:26528919
NASA Astrophysics Data System (ADS)
Omori, Takayuki; Sano, Katsuhiro; Yoneda, Minoru
2014-05-01
This paper presents new correction approaches for "early" radiocarbon ages to reconstruct the Paleolithic absolute chronology. In order to discuss time-space distribution about the replacement of archaic humans, including Neanderthals in Europe, by the modern humans, a massive data, which covers a wide-area, would be needed. Today, some radiocarbon databases focused on the Paleolithic have been published and used for chronological studies. From a viewpoint of current analytical technology, however, the any database have unreliable results that make interpretation of radiocarbon dates difficult. Most of these unreliable ages had been published in the early days of radiocarbon analysis. In recent years, new analytical methods to determine highly-accurate dates have been developed. Ultrafiltration and ABOx-SC methods, as new sample pretreatments for bone and charcoal respectively, have attracted attention because they could remove imperceptible contaminates and derive reliable accurately ages. In order to evaluate the reliability of "early" data, we investigated the differences and variabilities of radiocarbon ages on different pretreatments, and attempted to develop correction functions for the assessment of the reliability. It can be expected that reliability of the corrected age is increased and the age applied to chronological research together with recent ages. Here, we introduce the methodological frameworks and archaeological applications.
Application of parameter estimation to aircraft stability and control: The output-error approach
NASA Technical Reports Server (NTRS)
Maine, Richard E.; Iliff, Kenneth W.
1986-01-01
The practical application of parameter estimation methodology to the problem of estimating aircraft stability and control derivatives from flight test data is examined. The primary purpose of the document is to present a comprehensive and unified picture of the entire parameter estimation process and its integration into a flight test program. The document concentrates on the output-error method to provide a focus for detailed examination and to allow us to give specific examples of situations that have arisen. The document first derives the aircraft equations of motion in a form suitable for application to estimation of stability and control derivatives. It then discusses the issues that arise in adapting the equations to the limitations of analysis programs, using a specific program for an example. The roles and issues relating to mass distribution data, preflight predictions, maneuver design, flight scheduling, instrumentation sensors, data acquisition systems, and data processing are then addressed. Finally, the document discusses evaluation and the use of the analysis results.
Saviane, Chiara; Silver, R Angus
2006-06-15
Synapses play a crucial role in information processing in the brain. Amplitude fluctuations of synaptic responses can be used to extract information about the mechanisms underlying synaptic transmission and its modulation. In particular, multiple-probability fluctuation analysis can be used to estimate the number of functional release sites, the mean probability of release and the amplitude of the mean quantal response from fits of the relationship between the variance and mean amplitude of postsynaptic responses, recorded at different probabilities. To determine these quantal parameters, calculate their uncertainties and the goodness-of-fit of the model, it is important to weight the contribution of each data point in the fitting procedure. We therefore investigated the errors associated with measuring the variance by determining the best estimators of the variance of the variance and have used simulations of synaptic transmission to test their accuracy and reliability under different experimental conditions. For central synapses, which generally have a low number of release sites, the amplitude distribution of synaptic responses is not normal, thus the use of a theoretical variance of the variance based on the normal assumption is not a good approximation. However, appropriate estimators can be derived for the population and for limited sample sizes using a more general expression that involves higher moments and introducing unbiased estimators based on the h-statistics. Our results are likely to be relevant for various applications of fluctuation analysis when few channels or release sites are present.
Results and Error Estimates from GRACE Forward Modeling over Greenland, Canada, and Alaska
NASA Astrophysics Data System (ADS)
Bonin, J. A.; Chambers, D. P.
2012-12-01
Forward modeling using a weighted least squares technique allows GRACE information to be projected onto a pre-determined collection of local basins. This decreases the impact of spatial leakage, allowing estimates of mass change to be better localized. The technique is especially valuable where models of current-day mass change are poor, such as over Greenland and Antarctica. However, the accuracy of the forward model technique has not been determined, nor is it known how the distribution of the local basins affects the results. We use a "truth" model composed of hydrology and ice-melt slopes as an example case, to estimate the uncertainties of this forward modeling method and expose those design parameters which may result in an incorrect high-resolution mass distribution. We then apply these optimal parameters in a forward model estimate created from RL05 GRACE data. We compare the resulting mass slopes with the expected systematic errors from the simulation, as well as GIA and basic trend-fitting uncertainties. We also consider whether specific regions (such as Ellesmere Island and Baffin Island) can be estimated reliably using our optimal basin layout.
How Accurate and Robust Are the Phylogenetic Estimates of Austronesian Language Relationships?
Greenhill, Simon J.; Drummond, Alexei J.; Gray, Russell D.
2010-01-01
We recently used computational phylogenetic methods on lexical data to test between two scenarios for the peopling of the Pacific. Our analyses of lexical data supported a pulse-pause scenario of Pacific settlement in which the Austronesian speakers originated in Taiwan around 5,200 years ago and rapidly spread through the Pacific in a series of expansion pulses and settlement pauses. We claimed that there was high congruence between traditional language subgroups and those observed in the language phylogenies, and that the estimated age of the Austronesian expansion at 5,200 years ago was consistent with the archaeological evidence. However, the congruence between the language phylogenies and the evidence from historical linguistics was not quantitatively assessed using tree comparison metrics. The robustness of the divergence time estimates to different calibration points was also not investigated exhaustively. Here we address these limitations by using a systematic tree comparison metric to calculate the similarity between the Bayesian phylogenetic trees and the subgroups proposed by historical linguistics, and by re-estimating the age of the Austronesian expansion using only the most robust calibrations. The results show that the Austronesian language phylogenies are highly congruent with the traditional subgroupings, and the date estimates are robust even when calculated using a restricted set of historical calibrations. PMID:20224774
NASA Astrophysics Data System (ADS)
Xue, Haile; Shen, Xueshun; Chou, Jifan
2015-10-01
Errors inevitably exist in numerical weather prediction (NWP) due to imperfect numeric and physical parameterizations. To eliminate these errors, by considering NWP as an inverse problem, an unknown term in the prediction equations can be estimated inversely by using the past data, which are presumed to represent the imperfection of the NWP model (model error, denoted as ME). In this first paper of a two-part series, an iteration method for obtaining the MEs in past intervals is presented, and the results from testing its convergence in idealized experiments are reported. Moreover, two batches of iteration tests were applied in the global forecast system of the Global and Regional Assimilation and Prediction System (GRAPES-GFS) for July-August 2009 and January-February 2010. The datasets associated with the initial conditions and sea surface temperature (SST) were both based on NCEP (National Centers for Environmental Prediction) FNL (final) data. The results showed that 6th h forecast errors were reduced to 10% of their original value after a 20-step iteration. Then, off-line forecast error corrections were estimated linearly based on the 2-month mean MEs and compared with forecast errors. The estimated error corrections agreed well with the forecast errors, but the linear growth rate of the estimation was steeper than the forecast error. The advantage of this iteration method is that the MEs can provide the foundation for online correction. A larger proportion of the forecast errors can be expected to be canceled out by properly introducing the model error correction into GRAPES-GFS.
Error and bias in size estimates of whale sharks: implications for understanding demography
Sequeira, Ana M. M.; Thums, Michele; Brooks, Kim; Meekan, Mark G.
2016-01-01
Body size and age at maturity are indicative of the vulnerability of a species to extinction. However, they are both difficult to estimate for large animals that cannot be restrained for measurement. For very large species such as whale sharks, body size is commonly estimated visually, potentially resulting in the addition of errors and bias. Here, we investigate the errors and bias associated with total lengths of whale sharks estimated visually by comparing them with measurements collected using a stereo-video camera system at Ningaloo Reef, Western Australia. Using linear mixed-effects models, we found that visual lengths were biased towards underestimation with increasing size of the shark. When using the stereo-video camera, the number of larger individuals that were possibly mature (or close to maturity) that were detected increased by approximately 10%. Mean lengths calculated by each method were, however, comparable (5.002 ± 1.194 and 6.128 ± 1.609 m, s.d.), confirming that the population at Ningaloo is mostly composed of immature sharks based on published lengths at maturity. We then collated data sets of total lengths sampled from aggregations of whale sharks worldwide between 1995 and 2013. Except for locations in the East Pacific where large females have been reported, these aggregations also largely consisted of juveniles (mean lengths less than 7 m). Sightings of the largest individuals were limited and occurred mostly prior to 2006. This result highlights the urgent need to locate and quantify the numbers of mature male and female whale sharks in order to ascertain the conservation status and ensure persistence of the species. PMID:27069656
Error and bias in size estimates of whale sharks: implications for understanding demography.
Sequeira, Ana M M; Thums, Michele; Brooks, Kim; Meekan, Mark G
2016-03-01
Body size and age at maturity are indicative of the vulnerability of a species to extinction. However, they are both difficult to estimate for large animals that cannot be restrained for measurement. For very large species such as whale sharks, body size is commonly estimated visually, potentially resulting in the addition of errors and bias. Here, we investigate the errors and bias associated with total lengths of whale sharks estimated visually by comparing them with measurements collected using a stereo-video camera system at Ningaloo Reef, Western Australia. Using linear mixed-effects models, we found that visual lengths were biased towards underestimation with increasing size of the shark. When using the stereo-video camera, the number of larger individuals that were possibly mature (or close to maturity) that were detected increased by approximately 10%. Mean lengths calculated by each method were, however, comparable (5.002 ± 1.194 and 6.128 ± 1.609 m, s.d.), confirming that the population at Ningaloo is mostly composed of immature sharks based on published lengths at maturity. We then collated data sets of total lengths sampled from aggregations of whale sharks worldwide between 1995 and 2013. Except for locations in the East Pacific where large females have been reported, these aggregations also largely consisted of juveniles (mean lengths less than 7 m). Sightings of the largest individuals were limited and occurred mostly prior to 2006. This result highlights the urgent need to locate and quantify the numbers of mature male and female whale sharks in order to ascertain the conservation status and ensure persistence of the species. PMID:27069656
Error and bias in size estimates of whale sharks: implications for understanding demography.
Sequeira, Ana M M; Thums, Michele; Brooks, Kim; Meekan, Mark G
2016-03-01
Body size and age at maturity are indicative of the vulnerability of a species to extinction. However, they are both difficult to estimate for large animals that cannot be restrained for measurement. For very large species such as whale sharks, body size is commonly estimated visually, potentially resulting in the addition of errors and bias. Here, we investigate the errors and bias associated with total lengths of whale sharks estimated visually by comparing them with measurements collected using a stereo-video camera system at Ningaloo Reef, Western Australia. Using linear mixed-effects models, we found that visual lengths were biased towards underestimation with increasing size of the shark. When using the stereo-video camera, the number of larger individuals that were possibly mature (or close to maturity) that were detected increased by approximately 10%. Mean lengths calculated by each method were, however, comparable (5.002 ± 1.194 and 6.128 ± 1.609 m, s.d.), confirming that the population at Ningaloo is mostly composed of immature sharks based on published lengths at maturity. We then collated data sets of total lengths sampled from aggregations of whale sharks worldwide between 1995 and 2013. Except for locations in the East Pacific where large females have been reported, these aggregations also largely consisted of juveniles (mean lengths less than 7 m). Sightings of the largest individuals were limited and occurred mostly prior to 2006. This result highlights the urgent need to locate and quantify the numbers of mature male and female whale sharks in order to ascertain the conservation status and ensure persistence of the species.
Accurate estimation of influenza epidemics using Google search data via ARGO.
Yang, Shihao; Santillana, Mauricio; Kou, S C
2015-11-24
Accurate real-time tracking of influenza outbreaks helps public health officials make timely and meaningful decisions that could save lives. We propose an influenza tracking model, ARGO (AutoRegression with GOogle search data), that uses publicly available online search data. In addition to having a rigorous statistical foundation, ARGO outperforms all previously available Google-search-based tracking models, including the latest version of Google Flu Trends, even though it uses only low-quality search data as input from publicly available Google Trends and Google Correlate websites. ARGO not only incorporates the seasonality in influenza epidemics but also captures changes in people's online search behavior over time. ARGO is also flexible, self-correcting, robust, and scalable, making it a potentially powerful tool that can be used for real-time tracking of other social events at multiple temporal and spatial resolutions.
Accurate estimation of influenza epidemics using Google search data via ARGO.
Yang, Shihao; Santillana, Mauricio; Kou, S C
2015-11-24
Accurate real-time tracking of influenza outbreaks helps public health officials make timely and meaningful decisions that could save lives. We propose an influenza tracking model, ARGO (AutoRegression with GOogle search data), that uses publicly available online search data. In addition to having a rigorous statistical foundation, ARGO outperforms all previously available Google-search-based tracking models, including the latest version of Google Flu Trends, even though it uses only low-quality search data as input from publicly available Google Trends and Google Correlate websites. ARGO not only incorporates the seasonality in influenza epidemics but also captures changes in people's online search behavior over time. ARGO is also flexible, self-correcting, robust, and scalable, making it a potentially powerful tool that can be used for real-time tracking of other social events at multiple temporal and spatial resolutions. PMID:26553980
Accurate estimation of influenza epidemics using Google search data via ARGO
Yang, Shihao; Santillana, Mauricio; Kou, S. C.
2015-01-01
Accurate real-time tracking of influenza outbreaks helps public health officials make timely and meaningful decisions that could save lives. We propose an influenza tracking model, ARGO (AutoRegression with GOogle search data), that uses publicly available online search data. In addition to having a rigorous statistical foundation, ARGO outperforms all previously available Google-search–based tracking models, including the latest version of Google Flu Trends, even though it uses only low-quality search data as input from publicly available Google Trends and Google Correlate websites. ARGO not only incorporates the seasonality in influenza epidemics but also captures changes in people’s online search behavior over time. ARGO is also flexible, self-correcting, robust, and scalable, making it a potentially powerful tool that can be used for real-time tracking of other social events at multiple temporal and spatial resolutions. PMID:26553980
Shimansky, Y P
2011-05-01
It is well known from numerous studies that perception can be significantly affected by intended action in many everyday situations, indicating that perception and related decision-making is not a simple, one-way sequence, but a complex iterative cognitive process. However, the underlying functional mechanisms are yet unclear. Based on an optimality approach, a quantitative computational model of one such mechanism has been developed in this study. It is assumed in the model that significant uncertainty about task-related parameters of the environment results in parameter estimation errors and an optimal control system should minimize the cost of such errors in terms of the optimality criterion. It is demonstrated that, if the cost of a parameter estimation error is significantly asymmetrical with respect to error direction, the tendency to minimize error cost creates a systematic deviation of the optimal parameter estimate from its maximum likelihood value. Consequently, optimization of parameter estimate and optimization of control action cannot be performed separately from each other under parameter uncertainty combined with asymmetry of estimation error cost, thus making the certainty equivalence principle non-applicable under those conditions. A hypothesis that not only the action, but also perception itself is biased by the above deviation of parameter estimate is supported by ample experimental evidence. The results provide important insights into the cognitive mechanisms of interaction between sensory perception and planning an action under realistic conditions. Implications for understanding related functional mechanisms of optimal control in the CNS are discussed.
Plant DNA Barcodes Can Accurately Estimate Species Richness in Poorly Known Floras
Costion, Craig; Ford, Andrew; Cross, Hugh; Crayn, Darren; Harrington, Mark; Lowe, Andrew
2011-01-01
Background Widespread uptake of DNA barcoding technology for vascular plants has been slow due to the relatively poor resolution of species discrimination (∼70%) and low sequencing and amplification success of one of the two official barcoding loci, matK. Studies to date have mostly focused on finding a solution to these intrinsic limitations of the markers, rather than posing questions that can maximize the utility of DNA barcodes for plants with the current technology. Methodology/Principal Findings Here we test the ability of plant DNA barcodes using the two official barcoding loci, rbcLa and matK, plus an alternative barcoding locus, trnH-psbA, to estimate the species diversity of trees in a tropical rainforest plot. Species discrimination accuracy was similar to findings from previous studies but species richness estimation accuracy proved higher, up to 89%. All combinations which included the trnH-psbA locus performed better at both species discrimination and richness estimation than matK, which showed little enhanced species discriminatory power when concatenated with rbcLa. The utility of the trnH-psbA locus is limited however, by the occurrence of intraspecific variation observed in some angiosperm families to occur as an inversion that obscures the monophyly of species. Conclusions/Significance We demonstrate for the first time, using a case study, the potential of plant DNA barcodes for the rapid estimation of species richness in taxonomically poorly known areas or cryptic populations revealing a powerful new tool for rapid biodiversity assessment. The combination of the rbcLa and trnH-psbA loci performed better for this purpose than any two-locus combination that included matK. We show that although DNA barcodes fail to discriminate all species of plants, new perspectives and methods on biodiversity value and quantification may overshadow some of these shortcomings by applying barcode data in new ways. PMID:22096501
Chon, K H; Cohen, R J; Holstein-Rathlou, N H
1997-01-01
A linear and nonlinear autoregressive moving average (ARMA) identification algorithm is developed for modeling time series data. The algorithm uses Laguerre expansion of kernals (LEK) to estimate Volterra-Wiener kernals. However, instead of estimating linear and nonlinear system dynamics via moving average models, as is the case for the Volterra-Wiener analysis, we propose an ARMA model-based approach. The proposed algorithm is essentially the same as LEK, but this algorithm is extended to include past values of the output as well. Thus, all of the advantages associated with using the Laguerre function remain with our algorithm; but, by extending the algorithm to the linear and nonlinear ARMA model, a significant reduction in the number of Laguerre functions can be made, compared with the Volterra-Wiener approach. This translates into a more compact system representation and makes the physiological interpretation of higher order kernels easier. Furthermore, simulation results show better performance of the proposed approach in estimating the system dynamics than LEK in certain cases, and it remains effective in the presence of significant additive measurement noise. PMID:9236985
Endres, M I; Lobeck-Luchterhand, K M; Espejo, L A; Tucker, C B
2014-01-01
Dairy welfare assessment programs are becoming more common on US farms. Outcome-based measurements, such as locomotion, hock lesion, hygiene, and body condition scores (BCS), are included in these assessments. The objective of the current study was to investigate the proportion of cows in the pen or subsamples of pens on a farm needed to provide an accurate estimate of the previously mentioned measurements. In experiment 1, we evaluated cows in 52 high pens (50 farms) for lameness using a 1- to 5-scale locomotion scoring system (1 = normal and 5 = severely lame; 24.4 and 6% of animals were scored ≥ 3 or ≥ 4, respectively). Cows were also given a BCS using a 1- to 5-scale, where 1 = emaciated and 5 = obese; cows were rarely thin (BCS ≤ 2; 0.10% of cows) or fat (BCS ≥ 4; 0.11% of cows). Hygiene scores were assessed on a 1- to 5-scale with 1 = clean and 5 = severely dirty; 54.9% of cows had a hygiene score ≥ 3. Hock injuries were classified as 1 = no lesion, 2 = mild lesion, and 3 = severe lesion; 10.6% of cows had a score of 3. Subsets of data were created with 10 replicates of random sampling that represented 100, 90, 80, 70, 60, 50, 40, 30, 20, 15, 10, 5, and 3% of the cows measured/pen. In experiment 2, we scored the same outcome measures on all cows in lactating pens from 12 farms and evaluated using pen subsamples: high; high and fresh; high, fresh, and hospital; and high, low, and hospital. For both experiments, the association between the estimates derived from all subsamples and entire pen (experiment 1) or herd (experiment 2) prevalence was evaluated using linear regression. To be considered a good estimate, 3 criteria must be met: R(2)>0.9, slope = 1, and intercept = 0. In experiment 1, on average, recording 15% of the pen represented the percentage of clinically lame cows (score ≥ 3), whereas 30% needed to be measured to estimate severe lameness (score ≥ 4). Only 15% of the pen was needed to estimate the percentage of the herd with a hygiene
Song, Yunpeng; Wu, Sen; Xu, Linyan; Fu, Xing
2015-03-10
Measurement of force on a micro- or nano-Newton scale is important when exploring the mechanical properties of materials in the biophysics and nanomechanical fields. The atomic force microscope (AFM) is widely used in microforce measurement. The cantilever probe works as an AFM force sensor, and the spring constant of the cantilever is of great significance to the accuracy of the measurement results. This paper presents a normal spring constant calibration method with the combined use of an electromagnetic balance and a homemade AFM head. When the cantilever presses the balance, its deflection is detected through an optical lever integrated in the AFM head. Meanwhile, the corresponding bending force is recorded by the balance. Then the spring constant can be simply calculated using Hooke's law. During the calibration, a feedback loop is applied to control the deflection of the cantilever. Errors that may affect the stability of the cantilever could be compensated rapidly. Five types of commercial cantilevers with different shapes, stiffness, and operating modes were chosen to evaluate the performance of our system. Based on the uncertainty analysis, the expanded relative standard uncertainties of the normal spring constant of most measured cantilevers are believed to be better than 2%.
Intermediate-mass-ratio inspirals in the Einstein Telescope. II. Parameter estimation errors
Huerta, E. A.; Gair, Jonathan R.
2011-02-15
We explore the precision with which the Einstein Telescope will be able to measure the parameters of intermediate-mass-ratio inspirals, i.e., the inspirals of stellar mass compact objects into intermediate-mass black holes (IMBHs). We calculate the parameter estimation errors using the Fisher Matrix formalism and present results of Monte Carlo simulations of these errors over choices for the extrinsic parameters of the source. These results are obtained using two different models for the gravitational waveform which were introduced in paper I of this series. These two waveform models include the inspiral, merger, and ringdown phases in a consistent way. One of the models, based on the transition scheme of Ori and Thorne [A. Ori and K. S. Thorne, Phys. Rev. D 62, 124022 (2000)], is valid for IMBHs of arbitrary spin; whereas, the second model, based on the effective-one-body approach, has been developed to cross-check our results in the nonspinning limit. In paper I of this series, we demonstrated the excellent agreement in both phase and amplitude between these two models for nonspinning black holes, and that their predictions for signal-to-noise ratios are consistent to within 10%. We now use these waveform models to estimate parameter estimation errors for binary systems with masses 1.4M{sub {circle_dot}}+100M{sub {circle_dot}}, 10M{sub {circle_dot}}+100M{sub {circle_dot}}, 1.4M{sub {circle_dot}}+500M{sub {circle_dot}}, and 10M{sub {circle_dot}}+500M{sub {circle_dot}} and various choices for the spin of the central IMBH. Assuming a detector network of three Einstein Telescopes, the analysis shows that for a 10M{sub {circle_dot}} compact object inspiralling into a 100M{sub {circle_dot}} IMBH with spin q=0.3, detected with a signal-to-noise ratio of 30, we should be able to determine the compact object and IMBH masses, and the IMBH spin magnitude to fractional accuracies of {approx}10{sup -3}, {approx}10{sup -3.5}, and {approx}10{sup -3}, respectively. We also
Samuel, Michael D.; Storm, Daniel J.; Rolley, Robert E.; Beissel, Thomas; Richards, Bryan J.; Van Deelen, Timothy R.
2014-01-01
The age structure of harvested animals provides the basis for many demographic analyses. Ages of harvested white-tailed deer (Odocoileus virginianus) and other ungulates often are estimated by evaluating replacement and wear patterns of teeth, which is subjective and error-prone. Few previous studies however, examined age- and sex-specific error rates. Counting cementum annuli of incisors is an alternative, more accurate method of estimating age, but factors that influence consistency of cementum annuli counts are poorly known. We estimated age of 1,261 adult (≥1.5 yr old) white-tailed deer harvested in Wisconsin and Illinois (USA; 2005–2008) using both wear-and-replacement and cementum annuli. We compared cementum annuli with wear-and-replacement estimates to assess misclassification rates by sex and age. Wear-and-replacement for estimating ages of white-tailed deer resulted in substantial misclassification compared with cementum annuli. Age classes of females were consistently underestimated, while those of males were underestimated for younger age classes but overestimated for older age classes. Misclassification resulted in an impression of a younger age-structure than actually was the case. Additionally, we obtained paired age-estimates from cementum annuli for 295 deer. Consistency of paired cementum annuli age-estimates decreased with age, was lower in females than males, and decreased as age estimates became less certain. Our results indicated that errors in the wear-and-replacement techniques are substantial and could impact demographic analyses that use age-structure information.
Yuan, Xuebing; Yu, Shuai; Zhang, Shengzhi; Wang, Guoping; Liu, Sheng
2015-01-01
Inertial navigation based on micro-electromechanical system (MEMS) inertial measurement units (IMUs) has attracted numerous researchers due to its high reliability and independence. The heading estimation, as one of the most important parts of inertial navigation, has been a research focus in this field. Heading estimation using magnetometers is perturbed by magnetic disturbances, such as indoor concrete structures and electronic equipment. The MEMS gyroscope is also used for heading estimation. However, the accuracy of gyroscope is unreliable with time. In this paper, a wearable multi-sensor system has been designed to obtain the high-accuracy indoor heading estimation, according to a quaternion-based unscented Kalman filter (UKF) algorithm. The proposed multi-sensor system including one three-axis accelerometer, three single-axis gyroscopes, one three-axis magnetometer and one microprocessor minimizes the size and cost. The wearable multi-sensor system was fixed on waist of pedestrian and the quadrotor unmanned aerial vehicle (UAV) for heading estimation experiments in our college building. The results show that the mean heading estimation errors are less 10° and 5° to multi-sensor system fixed on waist of pedestrian and the quadrotor UAV, respectively, compared to the reference path. PMID:25961384
Yuan, Xuebing; Yu, Shuai; Zhang, Shengzhi; Wang, Guoping; Liu, Sheng
2015-01-01
Inertial navigation based on micro-electromechanical system (MEMS) inertial measurement units (IMUs) has attracted numerous researchers due to its high reliability and independence. The heading estimation, as one of the most important parts of inertial navigation, has been a research focus in this field. Heading estimation using magnetometers is perturbed by magnetic disturbances, such as indoor concrete structures and electronic equipment. The MEMS gyroscope is also used for heading estimation. However, the accuracy of gyroscope is unreliable with time. In this paper, a wearable multi-sensor system has been designed to obtain the high-accuracy indoor heading estimation, according to a quaternion-based unscented Kalman filter (UKF) algorithm. The proposed multi-sensor system including one three-axis accelerometer, three single-axis gyroscopes, one three-axis magnetometer and one microprocessor minimizes the size and cost. The wearable multi-sensor system was fixed on waist of pedestrian and the quadrotor unmanned aerial vehicle (UAV) for heading estimation experiments in our college building. The results show that the mean heading estimation errors are less 10° and 5° to multi-sensor system fixed on waist of pedestrian and the quadrotor UAV, respectively, compared to the reference path. PMID:25961384
Yuan, Xuebing; Yu, Shuai; Zhang, Shengzhi; Wang, Guoping; Liu, Sheng
2015-05-07
Inertial navigation based on micro-electromechanical system (MEMS) inertial measurement units (IMUs) has attracted numerous researchers due to its high reliability and independence. The heading estimation, as one of the most important parts of inertial navigation, has been a research focus in this field. Heading estimation using magnetometers is perturbed by magnetic disturbances, such as indoor concrete structures and electronic equipment. The MEMS gyroscope is also used for heading estimation. However, the accuracy of gyroscope is unreliable with time. In this paper, a wearable multi-sensor system has been designed to obtain the high-accuracy indoor heading estimation, according to a quaternion-based unscented Kalman filter (UKF) algorithm. The proposed multi-sensor system including one three-axis accelerometer, three single-axis gyroscopes, one three-axis magnetometer and one microprocessor minimizes the size and cost. The wearable multi-sensor system was fixed on waist of pedestrian and the quadrotor unmanned aerial vehicle (UAV) for heading estimation experiments in our college building. The results show that the mean heading estimation errors are less 10° and 5° to multi-sensor system fixed on waist of pedestrian and the quadrotor UAV, respectively, compared to the reference path.
NASA Astrophysics Data System (ADS)
Littenberg, Tyson B.; Farr, Ben; Coughlin, Scott; Kalogera, Vicky
2016-03-01
Among the most eagerly anticipated opportunities made possible by Advanced LIGO/Virgo are multimessenger observations of compact mergers. Optical counterparts may be short-lived so rapid characterization of gravitational wave (GW) events is paramount for discovering electromagnetic signatures. One way to meet the demand for rapid GW parameter estimation is to trade off accuracy for speed, using waveform models with simplified treatment of the compact objects’ spin. We report on the systematic errors in GW parameter estimation suffered when using different spin approximations to recover generic signals. Component mass measurements can be biased by \\gt 5σ using simple-precession waveforms and in excess of 20σ when non-spinning templates are employed. This suggests that electromagnetic observing campaigns should not take a strict approach to selecting which LIGO/Virgo candidates warrant follow-up observations based on low-latency mass estimates. For sky localization, we find that searched areas are up to a factor of ∼ 2 larger for non-spinning analyses, and are systematically larger for any of the simplified waveforms considered in our analysis. Distance biases for the non-precessing waveforms can be in excess of 100% and are largest when the spin angular momenta are in the orbital plane of the binary. We confirm that spin-aligned waveforms should be used for low-latency parameter estimation at the minimum. Including simple precession, though more computationally costly, mitigates biases except for signals with extreme precession effects. Our results shine a spotlight on the critical need for development of computationally inexpensive precessing waveforms and/or massively parallel algorithms for parameter estimation.
NASA Astrophysics Data System (ADS)
Hansen, Scott K.; Vesselinov, Velimir V.
2016-10-01
We develop empirically-grounded error envelopes for localization of a point contamination release event in the saturated zone of a previously uncharacterized heterogeneous aquifer into which a number of plume-intercepting wells have been drilled. We assume that flow direction in the aquifer is known exactly and velocity is known to within a factor of two of our best guess from well observations prior to source identification. Other aquifer and source parameters must be estimated by interpretation of well breakthrough data via the advection-dispersion equation. We employ high performance computing to generate numerous random realizations of aquifer parameters and well locations, simulate well breakthrough data, and then employ unsupervised machine optimization techniques to estimate the most likely spatial (or space-time) location of the source. Tabulating the accuracy of these estimates from the multiple realizations, we relate the size of 90% and 95% confidence envelopes to the data quantity (number of wells) and model quality (fidelity of ADE interpretation model to actual concentrations in a heterogeneous aquifer with channelized flow). We find that for purely spatial localization of the contaminant source, increased data quantities can make up for reduced model quality. For space-time localization, we find similar qualitative behavior, but significantly degraded spatial localization reliability and less improvement from extra data collection. Since the space-time source localization problem is much more challenging, we also tried a multiple-initial-guess optimization strategy. This greatly enhanced performance, but gains from additional data collection remained limited.
Liang, Hua
2008-01-01
Differential equation (DE) models are widely used in many scientific fields that include engineering, physics and biomedical sciences. The so-called “forward problem”, the problem of simulations and predictions of state variables for given parameter values in the DE models, has been extensively studied by mathematicians, physicists, engineers and other scientists. However, the “inverse problem”, the problem of parameter estimation based on the measurements of output variables, has not been well explored using modern statistical methods, although some least squares-based approaches have been proposed and studied. In this paper, we propose parameter estimation methods for ordinary differential equation models (ODE) based on the local smoothing approach and a pseudo-least squares (PsLS) principle under a framework of measurement error in regression models. The asymptotic properties of the proposed PsLS estimator are established. We also compare the PsLS method to the corresponding SIMEX method and evaluate their finite sample performances via simulation studies. We illustrate the proposed approach using an application example from an HIV dynamic study. PMID:19956350
NASA Astrophysics Data System (ADS)
Tregoning, P.; McClusky, S.; Purcell, A. P.; McQueen, H.
2015-12-01
Non-gravitational accelerations acting on each of the GRACE satellites are measured in the along-track, cross-track and radial directions by the accelerometers onboard each satellite. The calibration of the observed non-gravitational accelerations requires determining a scaling factor and (at least) an offset for the observations in each of the three directions. We demonstrate in this presentation how small errors in the scale factors used to calibrate the accelerometer observations affect the noise level in the estimated temporal gravity field. We performed a parameter space search to find the optimal scale factors that generated the smallest prefit range-rate residuals and found that we can model the satellite orbits without the use of any empirical parameters, whilst still being able to identify the temporal gravity field signal in the prefit KBRR residuals. We will describe some physical conditions when the satellites are in the shadow of the Earth that we use to constrain the estimates of calibration biases and scales and show how the noise level of the estimated temporal gravity field varies with and without the use of the optimal calibration values for the accelerometer observations. A similar approach will be both required and effective to calibrate the accelerometers onboard the GRACE Follow-On mission.
Al-lela, Omer Qutaiba B; Bahari, Mohd Baidi; Al-abbassi, Mustafa G; Salih, Muhannad R M; Basher, Amena Y
2012-06-01
The immunization status of children is improved by interventions that increase community demand for compulsory and non-compulsory vaccines, one of the most important interventions related to immunization providers. The aim of this study is to evaluate the activities of immunization providers in terms of activities time and cost, to calculate the immunization doses cost, and to determine the immunization dose errors cost. Time-motion and cost analysis study design was used. Five public health clinics in Mosul-Iraq participated in the study. Fifty (50) vaccine doses were required to estimate activities time and cost. Micro-costing method was used; time and cost data were collected for each immunization-related activity performed by the clinic staff. A stopwatch was used to measure the duration of activity interactions between the parents and clinic staff. The immunization service cost was calculated by multiplying the average salary/min by activity time per minute. 528 immunization cards of Iraqi children were scanned to determine the number and the cost of immunization doses errors (extraimmunization doses and invalid doses). The average time for child registration was 6.7 min per each immunization dose, and the physician spent more than 10 min per dose. Nurses needed more than 5 min to complete child vaccination. The total cost of immunization activities was 1.67 US$ per each immunization dose. Measles vaccine (fifth dose) has a lower price (0.42 US$) than all other immunization doses. The cost of a total of 288 invalid doses was 744.55 US$ and the cost of a total of 195 extra immunization doses was 503.85 US$. The time spent on physicians' activities was longer than that spent on registrars' and nurses' activities. Physician total cost was higher than registrar cost and nurse cost. The total immunization cost will increase by about 13.3% owing to dose errors.
Efficient Solar Scene Wavefront Estimation with Reduced Systematic and RMS Errors: Summary
NASA Astrophysics Data System (ADS)
Anugu, N.; Garcia, P.
2016-04-01
Wave front sensing for solar telescopes is commonly implemented with the Shack-Hartmann sensors. Correlation algorithms are usually used to estimate the extended scene Shack-Hartmann sub-aperture image shifts or slopes. The image shift is computed by correlating a reference sub-aperture image with the target distorted sub-aperture image. The pixel position where the maximum correlation is located gives the image shift in integer pixel coordinates. Sub-pixel precision image shifts are computed by applying a peak-finding algorithm to the correlation peak Poyneer (2003); Löfdahl (2010). However, the peak-finding algorithm results are usually biased towards the integer pixels, these errors are called as systematic bias errors Sjödahl (1994). These errors are caused due to the low pixel sampling of the images. The amplitude of these errors depends on the type of correlation algorithm and the type of peak-finding algorithm being used. To study the systematic errors in detail, solar sub-aperture synthetic images are constructed by using a Swedish Solar Telescope solar granulation image1. The performance of cross-correlation algorithm in combination with different peak-finding algorithms is investigated. The studied peak-finding algorithms are: parabola Poyneer (2003); quadratic polynomial Löfdahl (2010); threshold center of gravity Bailey (2003); Gaussian Nobach & Honkanen (2005) and Pyramid Bailey (2003). The systematic error study reveals that that the pyramid fit is the most robust to pixel locking effects. The RMS error analysis study reveals that the threshold centre of gravity behaves better in low SNR, although the systematic errors in the measurement are large. It is found that no algorithm is best for both the systematic and the RMS error reduction. To overcome the above problem, a new solution is proposed. In this solution, the image sampling is increased prior to the actual correlation matching. The method is realized in two steps to improve its
Accurate Estimation of Airborne Ultrasonic Time-of-Flight for Overlapping Echoes
Sarabia, Esther G.; Llata, Jose R.; Robla, Sandra; Torre-Ferrero, Carlos; Oria, Juan P.
2013-01-01
In this work, an analysis of the transmission of ultrasonic signals generated by piezoelectric sensors for air applications is presented. Based on this analysis, an ultrasonic response model is obtained for its application to the recognition of objects and structured environments for navigation by autonomous mobile robots. This model enables the analysis of the ultrasonic response that is generated using a pair of sensors in transmitter-receiver configuration using the pulse-echo technique. This is very interesting for recognizing surfaces that simultaneously generate a multiple echo response. This model takes into account the effect of the radiation pattern, the resonant frequency of the sensor, the number of cycles of the excitation pulse, the dynamics of the sensor and the attenuation with distance in the medium. This model has been developed, programmed and verified through a battery of experimental tests. Using this model a new procedure for obtaining accurate time of flight is proposed. This new method is compared with traditional ones, such as threshold or correlation, to highlight its advantages and drawbacks. Finally the advantages of this method are demonstrated for calculating multiple times of flight when the echo is formed by several overlapping echoes. PMID:24284774
Accurate estimation of airborne ultrasonic time-of-flight for overlapping echoes.
Sarabia, Esther G; Llata, Jose R; Robla, Sandra; Torre-Ferrero, Carlos; Oria, Juan P
2013-01-01
In this work, an analysis of the transmission of ultrasonic signals generated by piezoelectric sensors for air applications is presented. Based on this analysis, an ultrasonic response model is obtained for its application to the recognition of objects and structured environments for navigation by autonomous mobile robots. This model enables the analysis of the ultrasonic response that is generated using a pair of sensors in transmitter-receiver configuration using the pulse-echo technique. This is very interesting for recognizing surfaces that simultaneously generate a multiple echo response. This model takes into account the effect of the radiation pattern, the resonant frequency of the sensor, the number of cycles of the excitation pulse, the dynamics of the sensor and the attenuation with distance in the medium. This model has been developed, programmed and verified through a battery of experimental tests. Using this model a new procedure for obtaining accurate time of flight is proposed. This new method is compared with traditional ones, such as threshold or correlation, to highlight its advantages and drawbacks. Finally the advantages of this method are demonstrated for calculating multiple times of flight when the echo is formed by several overlapping echoes. PMID:24284774
Krauss, Ken W.; Duberstein, Jamie A.; Conner, William H.
2015-01-01
Forests comprise approximately 37% of the terrestrial land surface and influence global water cycling. However, very little attention has been directed towards understanding environmental impacts on stand water use (S) or in identifying rates of S from specific forested wetlands. Here, we use sapflow techniques to address two separate but linked objectives: (1) determine S in four, hydrologically distinctive South Carolina (USA) wetland forests from 2009–2010 and (2) describe potential error, uncertainty and stand-level variation associated with these assessments. Sapflow measurements were made from a number of tree species for approximately 2–8 months over 2 years to initiate the model, which was applied to canopy trees (DBH > 10–20 cm). We determined that S in three healthy forested wetlands varied from 1.97–3.97 mm day−1 or 355–687 mm year−1 when scaled. In contrast, saltwater intrusion impacted individual tree physiology and size class distributions on a fourth site, which decreased S to 0.61–1.13 mm day−1 or 110–196 mm year−1. The primary sources of error in estimations using sapflow probes would relate to calibration of probes and standardization relative to no flow periods and accounting for accurate sapflow attenuation with radial depth into the sapwood by species and site. Such inherent variation in water use among wetland forest stands makes small differences in S (<200 mm year−1) difficult to detect statistically through modelling, even though small differences may be important to local water cycling. These data also represent some of the first assessments of S from temperate, coastal forested wetlands along the Atlantic coast of the USA.
Bahşı, Ayşe Kurt; Yalçınbaş, Salih
2016-01-01
In this study, the Fibonacci collocation method based on the Fibonacci polynomials are presented to solve for the fractional diffusion equations with variable coefficients. The fractional derivatives are described in the Caputo sense. This method is derived by expanding the approximate solution with Fibonacci polynomials. Using this method of the fractional derivative this equation can be reduced to a set of linear algebraic equations. Also, an error estimation algorithm which is based on the residual functions is presented for this method. The approximate solutions are improved by using this error estimation algorithm. If the exact solution of the problem is not known, the absolute error function of the problems can be approximately computed by using the Fibonacci polynomial solution. By using this error estimation function, we can find improved solutions which are more efficient than direct numerical solutions. Numerical examples, figures, tables are comparisons have been presented to show efficiency and usable of proposed method. PMID:27610294
Random weighting error estimation for the inversion result of finite-fault rupture history
NASA Astrophysics Data System (ADS)
Ai, Yin-Shuang; Zheng, Tian-Yu; He, Yu-Mei
1999-07-01
Since the non-unique solution exists in the inversion for finite-fault rupture history, the random weighting method has been used to estimate error of the inversion results in this paper. The resolution distributions of slip amplitude, rake, rupture time and rise time on the finite fault were deduced quantitatively by model calculation. By using the random weighting method, the inversion results of Taiwan Strait earthquake and Myanmar-China boundary earthquake show that the parameters related to the rupture centers of two events have the highest resolution, and the solution are the most reliable; otherwise the resolution of the slip amplitudes and rise time on the finite-fault boundary is low.
Shirley, Natalie R; Ramirez Montes, Paula Andrea
2015-01-01
The purpose of this study was to assess observer error in phase versus component-based scoring systems used to develop age estimation methods in forensic anthropology. A method preferred by forensic anthropologists in the AAFS was selected for this evaluation (the Suchey-Brooks method for the pubic symphysis). The Suchey-Brooks descriptions were used to develop a corresponding component-based scoring system for comparison. Several commonly used reliability statistics (kappa, weighted kappa, and the intraclass correlation coefficient) were calculated to assess observer agreement between two observers and to evaluate the efficacy of each of these statistics for this study. The linear weighted kappa was determined to be the most suitable measure of observer agreement. The results show that a component-based system offers the possibility for more objective scoring than a phase system as long as the coding possibilities for each trait do not exceed three states of expression, each with as little overlap as possible.
Dynamic Programming and Error Estimates for Stochastic Control Problems with Maximum Cost
Bokanowski, Olivier; Picarelli, Athena; Zidani, Hasnaa
2015-02-15
This work is concerned with stochastic optimal control for a running maximum cost. A direct approach based on dynamic programming techniques is studied leading to the characterization of the value function as the unique viscosity solution of a second order Hamilton–Jacobi–Bellman (HJB) equation with an oblique derivative boundary condition. A general numerical scheme is proposed and a convergence result is provided. Error estimates are obtained for the semi-Lagrangian scheme. These results can apply to the case of lookback options in finance. Moreover, optimal control problems with maximum cost arise in the characterization of the reachable sets for a system of controlled stochastic differential equations. Some numerical simulations on examples of reachable analysis are included to illustrate our approach.
Thermal hydraulic simulations, error estimation and parameter sensitivity studies in Drekar::CFD
Smith, Thomas Michael; Shadid, John N.; Pawlowski, Roger P.; Cyr, Eric C.; Wildey, Timothy Michael
2014-01-01
This report describes work directed towards completion of the Thermal Hydraulics Methods (THM) CFD Level 3 Milestone THM.CFD.P7.05 for the Consortium for Advanced Simulation of Light Water Reactors (CASL) Nuclear Hub effort. The focus of this milestone was to demonstrate the thermal hydraulics and adjoint based error estimation and parameter sensitivity capabilities in the CFD code called Drekar::CFD. This milestone builds upon the capabilities demonstrated in three earlier milestones; THM.CFD.P4.02 [12], completed March, 31, 2012, THM.CFD.P5.01 [15] completed June 30, 2012 and THM.CFD.P5.01 [11] completed on October 31, 2012.
NASA Technical Reports Server (NTRS)
Todling, Ricardo
2015-01-01
Recently, this author studied an approach to the estimation of system error based on combining observation residuals derived from a sequential filter and fixed lag-1 smoother. While extending the methodology to a variational formulation, experimenting with simple models and making sure consistency was found between the sequential and variational formulations, the limitations of the residual-based approach came clearly to the surface. This note uses the sequential assimilation application to simple nonlinear dynamics to highlight the issue. Only when some of the underlying error statistics are assumed known is it possible to estimate the unknown component. In general, when considerable uncertainties exist in the underlying statistics as a whole, attempts to obtain separate estimates of the various error covariances are bound to lead to misrepresentation of errors. The conclusions are particularly relevant to present-day attempts to estimate observation-error correlations from observation residual statistics. A brief illustration of the issue is also provided by comparing estimates of error correlations derived from a quasi-operational assimilation system and a corresponding Observing System Simulation Experiments framework.
Menon, R.K.; Bloch, C.A.; Sperling, M.A. )
1990-06-01
We investigated whether errors occur in the estimation of ovine maternal-fetal glucose (Glc) kinetics using the isotope dilution technique when the Glc pool is rapidly expanded by exogenous (protocol A) or endogenous (protocol C) Glc entry and sought possible solutions (protocol B). In protocol A (n = 8), after attaining steady-state Glc specific activity (SA) by (U-14C)glucose (period 1), infusion of Glc (period 2) predictably decreased Glc SA, whereas. (U-14C)glucose concentration unexpectedly rose from 7,208 +/- 367 (means +/- SE) in period 1 to 8,558 +/- 308 disintegrations/min (dpm) per ml in period 2 (P less than 0.01). Fetal endogenous Glc production (EGP) was negligible during period 1 (0.44 +/- 1.0), but yielded a physiologically impossible negative value of -2.1 +/- 0.72 mg.kg-1.min-1 during period 2. When the fall in Glc SA during Glc infusion was prevented by addition of (U-14C)glucose admixed with the exogenous Glc (protocol B; n = 7), EGP was no longer negative. In protocol C (n = 6), sequential infusions of four increasing doses of epinephrine serially decreased SA, whereas tracer Glc increased from 7,483 +/- 608 to 11,525 +/- 992 dpm/ml plasma (P less than 0.05), imposing an obligatory underestimation of EGP. Thus a tracer mixing problem leads to erroneous estimations of fetal Glc utilization and Glc production via the three-compartment model in sheep when the Glc pool is expanded exogenously or endogenously. These errors can be minimized by maintaining the Glc SA relatively constant.
Eisele, Thomas P; Rhoda, Dale A; Cutts, Felicity T; Keating, Joseph; Ren, Ruilin; Barros, Aluisio J D; Arnold, Fred
2013-01-01
Nationally representative household surveys are increasingly relied upon to measure maternal, newborn, and child health (MNCH) intervention coverage at the population level in low- and middle-income countries. Surveys are the best tool we have for this purpose and are central to national and global decision making. However, all survey point estimates have a certain level of error (total survey error) comprising sampling and non-sampling error, both of which must be considered when interpreting survey results for decision making. In this review, we discuss the importance of considering these errors when interpreting MNCH intervention coverage estimates derived from household surveys, using relevant examples from national surveys to provide context. Sampling error is usually thought of as the precision of a point estimate and is represented by 95% confidence intervals, which are measurable. Confidence intervals can inform judgments about whether estimated parameters are likely to be different from the real value of a parameter. We recommend, therefore, that confidence intervals for key coverage indicators should always be provided in survey reports. By contrast, the direction and magnitude of non-sampling error is almost always unmeasurable, and therefore unknown. Information error and bias are the most common sources of non-sampling error in household survey estimates and we recommend that they should always be carefully considered when interpreting MNCH intervention coverage based on survey data. Overall, we recommend that future research on measuring MNCH intervention coverage should focus on refining and improving survey-based coverage estimates to develop a better understanding of how results should be interpreted and used.
NASA Astrophysics Data System (ADS)
Hacker, Joshua; Lee, Jared; Lei, Lili
2014-05-01
Numerical weather prediction (NWP) models have deficiencies in surface and boundary layer parameterizations, which may be particularly acute over complex terrain. Structural and physical model deficiencies are often poorly understood, and can be difficult to identify. Uncertain model parameters can lead to one class of model deficiencies when they are mis-specified. Augmenting the model state variables with parameters, data assimilation can be used to estimate the parameter distributions as long as the forecasts for observed variables is linearly dependent on the parameters. Reduced forecast (background) error shows that the parameter is accounting for some component of model error. Ensemble data assimilation has the favorable characteristic of providing ensemble-mean parameter estimates, eliminating some noise in the estimates when additional constraints on the error dynamics are unknown. This study focuses on coupling the Weather Research and Forecasting (WRF) NWP model with the Data Assimilation Research Testbed (DART) to estimate the Zilitinkevich parameter (CZIL). CZIL controls the thermal 'roughness length' for a given momentum roughness, thereby controlling heat and moisture fluxes through the surface layer by specifying the (unobservable) aerodynamic surface temperature. Month-long data assimilation experiments with 96 ensemble members, and grid spacing down to 3.3 km, provide a data set for interpreting parametric model errors in complex terrain. Experiments are during fall 2012 over the western U.S., and radiosonde, aircraft, satellite wind, surface, and mesonet observations are assimilated every 3 hours. One ensemble has a globally constant value of CZIL=0.1 (the WRF default value), while a second ensemble allows CZIL to vary over the range [0.01, 0.99], with distributions updated via the assimilation. Results show that the CZIL estimates do vary in time and space. Most often, forecasts are more skillful with the updated parameter values, compared to the
An Energy-Efficient Strategy for Accurate Distance Estimation in Wireless Sensor Networks
Tarrío, Paula; Bernardos, Ana M.; Casar, José R.
2012-01-01
In line with recent research efforts made to conceive energy saving protocols and algorithms and power sensitive network architectures, in this paper we propose a transmission strategy to minimize the energy consumption in a sensor network when using a localization technique based on the measurement of the strength (RSS) or the time of arrival (TOA) of the received signal. In particular, we find the transmission power and the packet transmission rate that jointly minimize the total consumed energy, while ensuring at the same time a desired accuracy in the RSS or TOA measurements. We also propose some corrections to these theoretical results to take into account the effects of shadowing and packet loss in the propagation channel. The proposed strategy is shown to be effective in realistic scenarios providing energy savings with respect to other transmission strategies, and also guaranteeing a given accuracy in the distance estimations, which will serve to guarantee a desired accuracy in the localization result. PMID:23202218
Accurate automatic estimation of total intracranial volume: a nuisance variable with less nuisance.
Malone, Ian B; Leung, Kelvin K; Clegg, Shona; Barnes, Josephine; Whitwell, Jennifer L; Ashburner, John; Fox, Nick C; Ridgway, Gerard R
2015-01-01
Total intracranial volume (TIV/ICV) is an important covariate for volumetric analyses of the brain and brain regions, especially in the study of neurodegenerative diseases, where it can provide a proxy of maximum pre-morbid brain volume. The gold-standard method is manual delineation of brain scans, but this requires careful work by trained operators. We evaluated Statistical Parametric Mapping 12 (SPM12) automated segmentation for TIV measurement in place of manual segmentation and also compared it with SPM8 and FreeSurfer 5.3.0. For T1-weighted MRI acquired from 288 participants in a multi-centre clinical trial in Alzheimer's disease we find a high correlation between SPM12 TIV and manual TIV (R(2)=0.940, 95% Confidence Interval (0.924, 0.953)), with a small mean difference (SPM12 40.4±35.4ml lower than manual, amounting to 2.8% of the overall mean TIV in the study). The correlation with manual measurements (the key aspect when using TIV as a covariate) for SPM12 was significantly higher (p<0.001) than for either SPM8 (R(2)=0.577 CI (0.500, 0.644)) or FreeSurfer (R(2)=0.801 CI (0.744, 0.843)). These results suggest that SPM12 TIV estimates are an acceptable substitute for labour-intensive manual estimates even in the challenging context of multiple centres and the presence of neurodegenerative pathology. We also briefly discuss some aspects of the statistical modelling approaches to adjust for TIV. PMID:25255942
Gershgorin, B.; Harlim, J. Majda, A.J.
2010-01-01
The filtering and predictive skill for turbulent signals is often limited by the lack of information about the true dynamics of the system and by our inability to resolve the assumed dynamics with sufficiently high resolution using the current computing power. The standard approach is to use a simple yet rich family of constant parameters to account for model errors through parameterization. This approach can have significant skill by fitting the parameters to some statistical feature of the true signal; however in the context of real-time prediction, such a strategy performs poorly when intermittent transitions to instability occur. Alternatively, we need a set of dynamic parameters. One strategy for estimating parameters on the fly is a stochastic parameter estimation through partial observations of the true signal. In this paper, we extend our newly developed stochastic parameter estimation strategy, the Stochastic Parameterization Extended Kalman Filter (SPEKF), to filtering sparsely observed spatially extended turbulent systems which exhibit abrupt stability transition from time to time despite a stable average behavior. For our primary numerical example, we consider a turbulent system of externally forced barotropic Rossby waves with instability introduced through intermittent negative damping. We find high filtering skill of SPEKF applied to this toy model even in the case of very sparse observations (with only 15 out of the 105 grid points observed) and with unspecified external forcing and damping. Additive and multiplicative bias corrections are used to learn the unknown features of the true dynamics from observations. We also present a comprehensive study of predictive skill in the one-mode context including the robustness toward variation of stochastic parameters, imperfect initial conditions and finite ensemble effect. Furthermore, the proposed stochastic parameter estimation scheme applied to the same spatially extended Rossby wave system demonstrates
[Research on maize multispectral image accurate segmentation and chlorophyll index estimation].
Wu, Qian; Sun, Hong; Li, Min-zan; Song, Yuan-yuan; Zhang, Yan-e
2015-01-01
In order to rapidly acquire maize growing information in the field, a non-destructive method of maize chlorophyll content index measurement was conducted based on multi-spectral imaging technique and imaging processing technology. The experiment was conducted at Yangling in Shaanxi province of China and the crop was Zheng-dan 958 planted in about 1 000 m X 600 m experiment field. Firstly, a 2-CCD multi-spectral image monitoring system was available to acquire the canopy images. The system was based on a dichroic prism, allowing precise separation of the visible (Blue (B), Green (G), Red (R): 400-700 nm) and near-infrared (NIR, 760-1 000 nm) band. The multispectral images were output as RGB and NIR images via the system vertically fixed to the ground with vertical distance of 2 m and angular field of 50°. SPAD index of each sample was'measured synchronously to show the chlorophyll content index. Secondly, after the image smoothing using adaptive smooth filtering algorithm, the NIR maize image was selected to segment the maize leaves from background, because there was a big difference showed in gray histogram between plant and soil background. The NIR image segmentation algorithm was conducted following steps of preliminary and accuracy segmentation: (1) The results of OTSU image segmentation method and the variable threshold algorithm were discussed. It was revealed that the latter was better one in corn plant and weed segmentation. As a result, the variable threshold algorithm based on local statistics was selected for the preliminary image segmentation. The expansion and corrosion were used to optimize the segmented image. (2) The region labeling algorithm was used to segment corn plants from soil and weed background with an accuracy of 95. 59 %. And then, the multi-spectral image of maize canopy was accurately segmented in R, G and B band separately. Thirdly, the image parameters were abstracted based on the segmented visible and NIR images. The average gray
NASA Astrophysics Data System (ADS)
Wu, A. G.; Chen, S.; Jia, D. L.
2016-05-01
In this paper, the bias-compensation-based recursive least-squares (LS) estimation algorithm with a forgetting factor is proposed for output error models. First, for the unknown white noise, the so-called weighted average variance is introduced. With this weighted average variance, a bias-compensation term is first formulated to achieve the bias-eliminated estimates of the system parameters. Then, the weighted average variance is estimated. Finally, the final estimation algorithm is obtained by combining the estimation of the weighted average variance and the recursive LS estimation algorithm with a forgetting factor. The effectiveness of the proposed identification algorithm is verified by a numerical example.
Lin, T.; Wang, H.
1995-12-31
The swift improvement of computational capabilities enables us to apply finite element methods to simulate more and more problems arising from various applications. A fundamental question associated with finite element simulations is their accuracy. In other words, before we can make any decisions based on the numerical solutions, we must be sure that they are acceptable in the sense that their errors are within the given tolerances. Various estimators have been developed to assess the accuracy of finite element solutions, and they can be classified basically into two types: a priori error estimates and a posteriori error estimates. While a priori error estimates can give us asymptotic convergence rates of numerical solutions in terms of the grid size before the computations, they depend on certain Sobolev norms of the true solutions which are not known, in general. Therefore, it is difficult, if not impossible, to use a priori estimates directly to decide whether a numerical solution is acceptable or a finer partition (and so a new numerical solution) is needed. In contrast, a posteriori error estimates depends only on the numerical solutions, and they usually give computable quantities about the accuracy of the numerical solutions.
Thermal Conductivities in Solids from First Principles: Accurate Computations and Rapid Estimates
NASA Astrophysics Data System (ADS)
Carbogno, Christian; Scheffler, Matthias
In spite of significant research efforts, a first-principles determination of the thermal conductivity κ at high temperatures has remained elusive. Boltzmann transport techniques that account for anharmonicity perturbatively become inaccurate under such conditions. Ab initio molecular dynamics (MD) techniques using the Green-Kubo (GK) formalism capture the full anharmonicity, but can become prohibitively costly to converge in time and size. We developed a formalism that accelerates such GK simulations by several orders of magnitude and that thus enables its application within the limited time and length scales accessible in ab initio MD. For this purpose, we determine the effective harmonic potential occurring during the MD, the associated temperature-dependent phonon properties and lifetimes. Interpolation in reciprocal and frequency space then allows to extrapolate to the macroscopic scale. For both force-field and ab initio MD, we validate this approach by computing κ for Si and ZrO2, two materials known for their particularly harmonic and anharmonic character. Eventually, we demonstrate how these techniques facilitate reasonable estimates of κ from existing MD calculations at virtually no additional computational cost.
Accurate Estimation of Protein Folding and Unfolding Times: Beyond Markov State Models.
Suárez, Ernesto; Adelman, Joshua L; Zuckerman, Daniel M
2016-08-01
Because standard molecular dynamics (MD) simulations are unable to access time scales of interest in complex biomolecular systems, it is common to "stitch together" information from multiple shorter trajectories using approximate Markov state model (MSM) analysis. However, MSMs may require significant tuning and can yield biased results. Here, by analyzing some of the longest protein MD data sets available (>100 μs per protein), we show that estimators constructed based on exact non-Markovian (NM) principles can yield significantly improved mean first-passage times (MFPTs) for protein folding and unfolding. In some cases, MSM bias of more than an order of magnitude can be corrected when identical trajectory data are reanalyzed by non-Markovian approaches. The NM analysis includes "history" information, higher order time correlations compared to MSMs, that is available in every MD trajectory. The NM strategy is insensitive to fine details of the states used and works well when a fine time-discretization (i.e., small "lag time") is used. PMID:27340835
NASA Astrophysics Data System (ADS)
Vuye, Cedric; Vanlanduit, Steve; Guillaume, Patrick
2009-06-01
When using optical measurements of the sound fields inside a glass tube, near the material under test, to estimate the reflection and absorption coefficients, not only these acoustical parameters but also confidence intervals can be determined. The sound fields are visualized using a scanning laser Doppler vibrometer (SLDV). In this paper the influence of different test signals on the quality of the results, obtained with this technique, is examined. The amount of data gathered during one measurement scan makes a thorough statistical analysis possible leading to the knowledge of confidence intervals. The use of a multi-sine, constructed on the resonance frequencies of the test tube, shows to be a very good alternative for the traditional periodic chirp. This signal offers the ability to obtain data for multiple frequencies in one measurement, without the danger of a low signal-to-noise ratio. The variability analysis in this paper clearly shows the advantages of the proposed multi-sine compared to the periodic chirp. The measurement procedure and the statistical analysis are validated by measuring the reflection ratio at a closed end and comparing the results with the theoretical value. Results of the testing of two building materials (an acoustic ceiling tile and linoleum) are presented and compared to supplier data.
Macbeth, Gilbert M; Broderick, Damien; Ovenden, Jennifer R; Buckworth, Rik C
2011-11-01
Genotypes produced from samples collected non-invasively in harsh field conditions often lack the full complement of data from the selected microsatellite loci. The application to genetic mark-recapture methodology in wildlife species can therefore be prone to misidentifications leading to both 'true non-recaptures' being falsely accepted as recaptures (Type I errors) and 'true recaptures' being undetected (Type II errors). Here we present a new likelihood method that allows every pairwise genotype comparison to be evaluated independently. We apply this method to determine the total number of recaptures by estimating and optimising the balance between Type I errors and Type II errors. We show through simulation that the standard error of recapture estimates can be minimised through our algorithms. Interestingly, the precision of our recapture estimates actually improved when we included individuals with missing genotypes, as this increased the number of pairwise comparisons potentially uncovering more recaptures. Simulations suggest that the method is tolerant to per locus error rates of up to 5% per locus and can theoretically work in datasets with as little as 60% of loci genotyped. Our methods can be implemented in datasets where standard mismatch analyses fail to distinguish recaptures. Finally, we show that by assigning a low Type I error rate to our matching algorithms we can generate a dataset of individuals of known capture histories that is suitable for the downstream analysis with traditional mark-recapture methods.
Surface loading effects for precise geodetic observations: models and error estimates
NASA Astrophysics Data System (ADS)
Boy, J. P.
2015-12-01
The precision reached by modern geodetic techniques requires an accurate modeling of surface loading processes in order to reach the millimeter-level for displacements, the nanogal-level for surface gravity observations. Over the past decade, many operational loading services have been established, allowing researchers to access atmospheric, tidal and non-tidal oceanic, hydrological loading models and correct geodetic observations. We present here an overview of the EOST loading service (http://loading.u-strasbg.fr) providing different products of atmospheric, non-tidal oceanic and hydrological loading effects on displacements and surface gravity. We also investigate and assess the different sources of errors in loading computations: The choice of the reference frame for displacement computations (Center-of-Figure versus Center-of-Mass). The differences between different atmospheric (reanalysis versus operational models), non-tidal oceanic (low resolution versus eddy-resolving models) and hydrological models. The model of ocean response to pressure forcing (inverted barometer versus a dynamic model). The resolution of the land/sea mask used for the loading computations. The choice of an Earth model to compute Green's functions. The differences between interpolated loading grids and station computations.
Limongi, Roberto; Pérez, Francisco J.; Modroño, Cristián; González-Mora, José L.
2016-01-01
Action update, substituting a prepotent behavior with a new action, allows the organism to counteract surprising environmental demands. However, action update fails when the organism is uncertain about when to release the substituting behavior, when it faces temporal uncertainty. Predictive coding states that accurate perception demands minimization of precise prediction errors. Activity of the right anterior insula (rAI) is associated with temporal uncertainty. Therefore, we hypothesize that temporal uncertainty during action update would cause the AI to decrease the sensitivity to ascending prediction errors. Moreover, action update requires response inhibition which recruits the frontostriatal indirect pathway associated with motor control. Therefore, we also hypothesize that temporal estimation errors modulate frontostriatal connections. To test these hypotheses, we collected fMRI data when participants performed an action-update paradigm within the context of temporal estimation. We fit dynamic causal models to the imaging data. Competing models comprised the inferior occipital gyrus (IOG), right supramarginal gyrus (rSMG), rAI, right presupplementary motor area (rPreSMA), and the right striatum (rSTR). The winning model showed that temporal uncertainty drove activity into the rAI and decreased insular sensitivity to ascending prediction errors, as shown by weak connectivity strength of rSMG→rAI connections. Moreover, temporal estimation errors weakened rPreSMA→rSTR connections and also modulated rAI→rSTR connections, causing the disruption of action update. Results provide information about the neurophysiological implementation of the so-called horse-race model of action control. We suggest that, contrary to what might be believed, unsuccessful action update could be a homeostatic process that represents a Bayes optimal encoding of uncertainty. PMID:27445737
DETECTABILITY AND ERROR ESTIMATION IN ORBITAL FITS OF RESONANT EXTRASOLAR PLANETS
Giuppone, C. A.; Beauge, C.; Tadeu dos Santos, M.; Ferraz-Mello, S.; Michtchenko, T. A.
2009-07-10
We estimate the conditions for detectability of two planets in a 2/1 mean-motion resonance from radial velocity data, as a function of their masses, number of observations and the signal-to-noise ratio. Even for a data set of the order of 100 observations and standard deviations of the order of a few meters per second, we find that Jovian-size resonant planets are difficult to detect if the masses of the planets differ by a factor larger than {approx}4. This is consistent with the present population of real exosystems in the 2/1 commensurability, most of which have resonant pairs with similar minimum masses, and could indicate that many other resonant systems exist, but are currently beyond the detectability limit. Furthermore, we analyze the error distribution in masses and orbital elements of orbital fits from synthetic data sets for resonant planets in the 2/1 commensurability. For various mass ratios and number of data points we find that the eccentricity of the outer planet is systematically overestimated, although the inner planet's eccentricity suffers a much smaller effect. If the initial conditions correspond to small-amplitude oscillations around stable apsidal corotation resonances, the amplitudes estimated from the orbital fits are biased toward larger amplitudes, in accordance to results found in real resonant extrasolar systems.
Wind effect on PV module temperature: Analysis of different techniques for an accurate estimation.
NASA Astrophysics Data System (ADS)
Schwingshackl, Clemens; Petitta, Marcello; Ernst Wagner, Jochen; Belluardo, Giorgio; Moser, David; Castelli, Mariapina; Zebisch, Marc; Tetzlaff, Anke
2013-04-01
temperature estimation using meteorological parameters. References: [1] Skoplaki, E. et al., 2008: A simple correlation for the operating temperature of photovoltaic modules of arbitrary mounting, Solar Energy Materials & Solar Cells 92, 1393-1402 [2] Skoplaki, E. et al., 2008: Operating temperature of photovoltaic modules: A survey of pertinent correlations, Renewable Energy 34, 23-29 [3] Koehl, M. et al., 2011: Modeling of the nominal operating cell temperature based on outdoor weathering, Solar Energy Materials & Solar Cells 95, 1638-1646 [4] Mattei, M. et al., 2005: Calculation of the polycrystalline PV module temperature using a simple method of energy balance, Renewable Energy 31, 553-567 [5] Kurtz, S. et al.: Evaluation of high-temperature exposure of rack-mounted photovoltaic modules
Hansen, Scott K.; Vesselinov, Velimir Valentinov
2016-09-09
We develop empirically-grounded error envelopes for localization of a point contamination release event in the saturated zone of a previously uncharacterized heterogeneous aquifer into which a number of plume-intercepting wells have been drilled. We assume that flow direction in the aquifer is known exactly and velocity is known to within a factor of two of our best guess from well observations prior to source identification. Other aquifer and source parameters must be estimated by interpretation of well breakthrough data via the advection-dispersion equation. We employ high performance computing to generate numerous random realizations of aquifer parameters and well locations, simulatemore » well breakthrough data, and then employ unsupervised machine optimization techniques to estimate the most likely spatial (or space-time) location of the source. Tabulating the accuracy of these estimates from the multiple realizations, we relate the size of 90% and 95% confidence envelopes to the data quantity (number of wells) and model quality (fidelity of ADE interpretation model to actual concentrations in a heterogeneous aquifer with channelized flow). We find that for purely spatial localization of the contaminant source, increased data quantities can make up for reduced model quality. For space-time localization, we find similar qualitative behavior, but significantly degraded spatial localization reliability and less improvement from extra data collection. Since the space-time source localization problem is much more challenging, we also tried a multiple-initial-guess optimization strategy. Furthermore, this greatly enhanced performance, but gains from additional data collection remained limited.« less
NASA Astrophysics Data System (ADS)
Cao, Lu; Li, Hengnian
2016-10-01
For the satellite attitude estimation problem, the serious model errors always exist and hider the estimation performance of the Attitude Determination and Control System (ACDS), especially for a small satellite with low precision sensors. To deal with this problem, a new algorithm for the attitude estimation, referred to as the unscented predictive variable structure filter (UPVSF) is presented. This strategy is proposed based on the variable structure control concept and unscented transform (UT) sampling method. It can be implemented in real time with an ability to estimate the model errors on-line, in order to improve the state estimation precision. In addition, the model errors in this filter are not restricted only to the Gaussian noises; therefore, it has the advantages to deal with the various kinds of model errors or noises. It is anticipated that the UT sampling strategy can further enhance the robustness and accuracy of the novel UPVSF. Numerical simulations show that the proposed UPVSF is more effective and robustness in dealing with the model errors and low precision sensors compared with the traditional unscented Kalman filter (UKF).
NASA Technical Reports Server (NTRS)
Nishimura, T.
1975-01-01
This paper proposes a worst-error analysis for dealing with problems of estimation of spacecraft trajectories in deep space missions. Navigation filters in use assume either constant or stochastic (Markov) models for their estimated parameters. When the actual behavior of these parameters does not follow the pattern of the assumed model, the filters sometimes result in very poor performance. To prepare for such pathological cases, the worst errors of both batch and sequential filters are investigated based on the incremental sensitivity studies of these filters. By finding critical switching instances of non-gravitational accelerations, intensive tracking can be carried out around those instances. Also the worst errors in the target plane provide a measure in assignment of the propellant budget for trajectory corrections. Thus the worst-error study presents useful information as well as practical criteria in establishing the maneuver and tracking strategy of spacecraft's missions.
NASA Astrophysics Data System (ADS)
Assous, Franck; Chaskalovic, Joël
2013-03-01
In this Note, we propose a new methodology based on exploratory data mining techniques to evaluate the errors due to the description of a given real system. First, we decompose this description error into four types of sources. Then, we construct databases of the entire information produced by different numerical approximation methods, to assess and compare the significant differences between these methods, using techniques like decision trees, Kohonen's cards, or neural networks. As an example, we characterize specific states of the real system for which we can locally appreciate the accuracy between two kinds of finite elements methods. In this case, this allowed us to precise the classical Bramble-Hilbert theorem that gives a global error estimate, whereas our approach gives a local error estimate.