Systematic Error Modeling and Bias Estimation
Zhang, Feihu; Knoll, Alois
2016-01-01
This paper analyzes the statistic properties of the systematic error in terms of range and bearing during the transformation process. Furthermore, we rely on a weighted nonlinear least square method to calculate the biases based on the proposed models. The results show the high performance of the proposed approach for error modeling and bias estimation. PMID:27213386
Systematic Error Modeling and Bias Estimation.
Zhang, Feihu; Knoll, Alois
2016-01-01
This paper analyzes the statistic properties of the systematic error in terms of range and bearing during the transformation process. Furthermore, we rely on a weighted nonlinear least square method to calculate the biases based on the proposed models. The results show the high performance of the proposed approach for error modeling and bias estimation. PMID:27213386
Statistical errors in Monte Carlo estimates of systematic errors
NASA Astrophysics Data System (ADS)
Roe, Byron P.
2007-01-01
For estimating the effects of a number of systematic errors on a data sample, one can generate Monte Carlo (MC) runs with systematic parameters varied and examine the change in the desired observed result. Two methods are often used. In the unisim method, the systematic parameters are varied one at a time by one standard deviation, each parameter corresponding to a MC run. In the multisim method (see ), each MC run has all of the parameters varied; the amount of variation is chosen from the expected distribution of each systematic parameter, usually assumed to be a normal distribution. The variance of the overall systematic error determination is derived for each of the two methods and comparisons are made between them. If one focuses not on the error in the prediction of an individual systematic error, but on the overall error due to all systematic errors in the error matrix element in data bin m, the number of events needed is strongly reduced because of the averaging effect over all of the errors. For simple models presented here the multisim model was far better if the statistical error in the MC samples was larger than an individual systematic error, while for the reverse case, the unisim model was better. Exact formulas and formulas for the simple toy models are presented so that realistic calculations can be made. The calculations in the present note are valid if the errors are in a linear region. If that region extends sufficiently far, one can have the unisims or multisims correspond to k standard deviations instead of one. This reduces the number of events required by a factor of k2. The specific terms unisim and multisim were coined by Peter Meyers and Steve Brice, respectively, for the MiniBooNE experiment. However, the concepts have been developed over time and have been in general use for some time.
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. PMID:27159007
Sampling of systematic errors to estimate likelihood weights in nuclear data uncertainty propagation
NASA Astrophysics Data System (ADS)
Helgesson, P.; Sjöstrand, H.; Koning, A. J.; Rydén, J.; Rochman, D.; Alhassan, E.; Pomp, S.
2016-01-01
In methodologies for nuclear data (ND) uncertainty assessment and propagation based on random sampling, likelihood weights can be used to infer experimental information into the distributions for the ND. As the included number of correlated experimental points grows large, the computational time for the matrix inversion involved in obtaining the likelihood can become a practical problem. There are also other problems related to the conventional computation of the likelihood, e.g., the assumption that all experimental uncertainties are Gaussian. In this study, a way to estimate the likelihood which avoids matrix inversion is investigated; instead, the experimental correlations are included by sampling of systematic errors. It is shown that the model underlying the sampling methodology (using univariate normal distributions for random and systematic errors) implies a multivariate Gaussian for the experimental points (i.e., the conventional model). It is also shown that the likelihood estimates obtained through sampling of systematic errors approach the likelihood obtained with matrix inversion as the sample size for the systematic errors grows large. In studied practical cases, it is seen that the estimates for the likelihood weights converge impractically slowly with the sample size, compared to matrix inversion. The computational time is estimated to be greater than for matrix inversion in cases with more experimental points, too. Hence, the sampling of systematic errors has little potential to compete with matrix inversion in cases where the latter is applicable. Nevertheless, the underlying model and the likelihood estimates can be easier to intuitively interpret than the conventional model and the likelihood function involving the inverted covariance matrix. Therefore, this work can both have pedagogical value and be used to help motivating the conventional assumption of a multivariate Gaussian for experimental data. The sampling of systematic errors could also
Statistical uncertainties and systematic errors in weak lensing mass estimates of galaxy clusters
NASA Astrophysics Data System (ADS)
Köhlinger, F.; Hoekstra, H.; Eriksen, M.
2015-11-01
Upcoming and ongoing large area weak lensing surveys will also discover large samples of galaxy clusters. Accurate and precise masses of galaxy clusters are of major importance for cosmology, for example, in establishing well-calibrated observational halo mass functions for comparison with cosmological predictions. We investigate the level of statistical uncertainties and sources of systematic errors expected for weak lensing mass estimates. Future surveys that will cover large areas on the sky, such as Euclid or LSST and to lesser extent DES, will provide the largest weak lensing cluster samples with the lowest level of statistical noise regarding ensembles of galaxy clusters. However, the expected low level of statistical uncertainties requires us to scrutinize various sources of systematic errors. In particular, we investigate the bias due to cluster member galaxies which are erroneously treated as background source galaxies due to wrongly assigned photometric redshifts. We find that this effect is significant when referring to stacks of galaxy clusters. Finally, we study the bias due to miscentring, i.e. the displacement between any observationally defined cluster centre and the true minimum of its gravitational potential. The impact of this bias might be significant with respect to the statistical uncertainties. However, complementary future missions such as eROSITA will allow us to define stringent priors on miscentring parameters which will mitigate this bias significantly.
Yang, Jun; Liang, Bin; Zhang, Tao; Song, Jingyan
2011-01-01
The star centroid estimation is the most important operation, which directly affects the precision of attitude determination for star sensors. This paper presents a theoretical study of the systematic error introduced by the star centroid estimation algorithm. The systematic error is analyzed through a frequency domain approach and numerical simulations. It is shown that the systematic error consists of the approximation error and truncation error which resulted from the discretization approximation and sampling window limitations, respectively. A criterion for choosing the size of the sampling window to reduce the truncation error is given in this paper. The systematic error can be evaluated as a function of the actual star centroid positions under different Gaussian widths of star intensity distribution. In order to eliminate the systematic error, a novel compensation algorithm based on the least squares support vector regression (LSSVR) with Radial Basis Function (RBF) kernel is proposed. Simulation results show that when the compensation algorithm is applied to the 5-pixel star sampling window, the accuracy of star centroid estimation is improved from 0.06 to 6 × 10−5 pixels. PMID:22164021
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
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.
GREAT3 results - I. Systematic errors in shear estimation and the impact of real galaxy morphology
NASA Astrophysics Data System (ADS)
Mandelbaum, Rachel; Rowe, Barnaby; Armstrong, Robert; Bard, Deborah; Bertin, Emmanuel; Bosch, James; Boutigny, Dominique; Courbin, Frederic; Dawson, William A.; Donnarumma, Annamaria; Fenech Conti, Ian; Gavazzi, Raphaël; Gentile, Marc; Gill, Mandeep S. S.; Hogg, David W.; Huff, Eric M.; Jee, M. James; Kacprzak, Tomasz; Kilbinger, Martin; Kuntzer, Thibault; Lang, Dustin; Luo, Wentao; March, Marisa C.; Marshall, Philip J.; Meyers, Joshua E.; Miller, Lance; Miyatake, Hironao; Nakajima, Reiko; Ngolé Mboula, Fred Maurice; Nurbaeva, Guldariya; Okura, Yuki; Paulin-Henriksson, Stéphane; Rhodes, Jason; Schneider, Michael D.; Shan, Huanyuan; Sheldon, Erin S.; Simet, Melanie; Starck, Jean-Luc; Sureau, Florent; Tewes, Malte; Zarb Adami, Kristian; Zhang, Jun; Zuntz, Joe
2015-07-01
We present first results from the third GRavitational lEnsing Accuracy Testing (GREAT3) challenge, the third in a sequence of challenges for testing methods of inferring weak gravitational lensing shear distortions from simulated galaxy images. GREAT3 was divided into experiments to test three specific questions, and included simulated space- and ground-based data with constant or cosmologically varying shear fields. The simplest (control) experiment included parametric galaxies with a realistic distribution of signal-to-noise, size, and ellipticity, and a complex point spread function (PSF). The other experiments tested the additional impact of realistic galaxy morphology, multiple exposure imaging, and the uncertainty about a spatially varying PSF; the last two questions will be explored in Paper II. The 24 participating teams competed to estimate lensing shears to within systematic error tolerances for upcoming Stage-IV dark energy surveys, making 1525 submissions overall. GREAT3 saw considerable variety and innovation in the types of methods applied. Several teams now meet or exceed the targets in many of the tests conducted (to within the statistical errors). We conclude that the presence of realistic galaxy morphology in simulations changes shear calibration biases by ˜1 per cent for a wide range of methods. Other effects such as truncation biases due to finite galaxy postage stamps, and the impact of galaxy type as measured by the Sérsic index, are quantified for the first time. Our results generalize previous studies regarding sensitivities to galaxy size and signal-to-noise, and to PSF properties such as seeing and defocus. Almost all methods' results support the simple model in which additive shear biases depend linearly on PSF ellipticity.
GREAT3 results - I. Systematic errors in shear estimation and the impact of real galaxy morphology
Mandelbaum, Rachel; Rowe, Barnaby; Armstrong, Robert; Bard, Deborah; Bertin, Emmanuel; Bosch, James; Boutigny, Dominique; Courbin, Frederic; Dawson, William A.; Donnarumma, Annamaria; Fenech Conti, Ian; Gavazzi, Raphael; Gentile, Marc; Gill, Mandeep S. S.; Hogg, David W.; Huff, Eric M.; Jee, M. James; Kacprzak, Tomasz; Kilbinger, Martin; Kuntzer, Thibault; Lang, Dustin; Luo, Wentao; March, Marisa C.; Marshall, Philip J.; Meyers, Joshua E.; Miller, Lance; Miyatake, Hironao; Nakajima, Reiko; Ngole Mboula, Fred Maurice; Nurbaeva, Guldariya; Okura, Yuki; Paulin-Henriksson, Stephane; Rhodes, Jason; Schneider, Michael D.; Shan, Huanyuan; Sheldon, Erin S.; Simet, Melanie; Starck, Jean -Luc; Sureau, Florent; Tewes, Malte; Zarb Adami, Kristian; Zhang, Jun; Zuntz, Joe
2015-05-11
The study present first results from the third GRavitational lEnsing Accuracy Testing (GREAT3) challenge, the third in a sequence of challenges for testing methods of inferring weak gravitational lensing shear distortions from simulated galaxy images. GREAT3 was divided into experiments to test three specific questions, and included simulated space- and ground-based data with constant or cosmologically varying shear fields. The simplest (control) experiment included parametric galaxies with a realistic distribution of signal-to-noise, size, and ellipticity, and a complex point spread function (PSF). The other experiments tested the additional impact of realistic galaxy morphology, multiple exposure imaging, and the uncertainty about a spatially varying PSF; the last two questions will be explored in Paper II. The 24 participating teams competed to estimate lensing shears to within systematic error tolerances for upcoming Stage-IV dark energy surveys, making 1525 submissions overall. GREAT3 saw considerable variety and innovation in the types of methods applied. Several teams now meet or exceed the targets in many of the tests conducted (to within the statistical errors). We conclude that the presence of realistic galaxy morphology in simulations changes shear calibration biases by ~1 per cent for a wide range of methods. Other effects such as truncation biases due to finite galaxy postage stamps, and the impact of galaxy type as measured by the Sérsic index, are quantified for the first time. Our results generalize previous studies regarding sensitivities to galaxy size and signal-to-noise, and to PSF properties such as seeing and defocus. Almost all methods’ results support the simple model in which additive shear biases depend linearly on PSF ellipticity.
GREAT3 results - I. Systematic errors in shear estimation and the impact of real galaxy morphology
Mandelbaum, Rachel; Rowe, Barnaby; Armstrong, Robert; Bard, Deborah; Bertin, Emmanuel; Bosch, James; Boutigny, Dominique; Courbin, Frederic; Dawson, William A.; Donnarumma, Annamaria; et al
2015-05-11
The study present first results from the third GRavitational lEnsing Accuracy Testing (GREAT3) challenge, the third in a sequence of challenges for testing methods of inferring weak gravitational lensing shear distortions from simulated galaxy images. GREAT3 was divided into experiments to test three specific questions, and included simulated space- and ground-based data with constant or cosmologically varying shear fields. The simplest (control) experiment included parametric galaxies with a realistic distribution of signal-to-noise, size, and ellipticity, and a complex point spread function (PSF). The other experiments tested the additional impact of realistic galaxy morphology, multiple exposure imaging, and the uncertainty aboutmore » a spatially varying PSF; the last two questions will be explored in Paper II. The 24 participating teams competed to estimate lensing shears to within systematic error tolerances for upcoming Stage-IV dark energy surveys, making 1525 submissions overall. GREAT3 saw considerable variety and innovation in the types of methods applied. Several teams now meet or exceed the targets in many of the tests conducted (to within the statistical errors). We conclude that the presence of realistic galaxy morphology in simulations changes shear calibration biases by ~1 per cent for a wide range of methods. Other effects such as truncation biases due to finite galaxy postage stamps, and the impact of galaxy type as measured by the Sérsic index, are quantified for the first time. Our results generalize previous studies regarding sensitivities to galaxy size and signal-to-noise, and to PSF properties such as seeing and defocus. Almost all methods’ results support the simple model in which additive shear biases depend linearly on PSF ellipticity.« less
Estimation of Systematic Errors for Deuteron Electric Dipole Moment Search at COSY
NASA Astrophysics Data System (ADS)
Chekmenev, Stanislav
2016-02-01
An experimental method which is aimed to find a permanent EDM of a charged particle was proposed by the JEDI (Jülich Electric Dipole moment Investigations) collaboration. EDMs can be observed by their influence on spin motion. The only possible way to perform a direct measurement is to use a storage ring. For this purpose, it was decided to carry out the first precursor experiment at the Cooler Synchrotron (COSY). Since the EDM of a particle violates CP invariance it is expected to be tiny, treatment of all various sources of systematic errors should be done with a great level of precision. One should clearly understand how misalignments of the magnets affects the beam and the spin motion. It is planned to use a RF Wien filter for the precusor experiment. In this paper the simulations of the systematic effects for the RF Wien filter device method will be discussed.
NASA Astrophysics Data System (ADS)
Appleby, Graham; Rodríguez, José; Altamimi, Zuheir
2016-06-01
Satellite laser ranging (SLR) to the geodetic satellites LAGEOS and LAGEOS-2 uniquely determines the origin of the terrestrial reference frame and, jointly with very long baseline interferometry, its scale. Given such a fundamental role in satellite geodesy, it is crucial that any systematic errors in either technique are at an absolute minimum as efforts continue to realise the reference frame at millimetre levels of accuracy to meet the present and future science requirements. Here, we examine the intrinsic accuracy of SLR measurements made by tracking stations of the International Laser Ranging Service using normal point observations of the two LAGEOS satellites in the period 1993 to 2014. The approach we investigate in this paper is to compute weekly reference frame solutions solving for satellite initial state vectors, station coordinates and daily Earth orientation parameters, estimating along with these weekly average range errors for each and every one of the observing stations. Potential issues in any of the large number of SLR stations assumed to have been free of error in previous realisations of the ITRF may have been absorbed in the reference frame, primarily in station height. Likewise, systematic range errors estimated against a fixed frame that may itself suffer from accuracy issues will absorb network-wide problems into station-specific results. Our results suggest that in the past two decades, the scale of the ITRF derived from the SLR technique has been close to 0.7 ppb too small, due to systematic errors either or both in the range measurements and their treatment. We discuss these results in the context of preparations for ITRF2014 and additionally consider the impact of this work on the currently adopted value of the geocentric gravitational constant, GM.
Protecting weak measurements against systematic errors
NASA Astrophysics Data System (ADS)
Pang, Shengshi; Alonso, Jose Raul Gonzalez; Brun, Todd A.; Jordan, Andrew N.
2016-07-01
In this work, we consider the systematic error of quantum metrology by weak measurements under decoherence. We derive the systematic error of maximum likelihood estimation in general to the first-order approximation of a small deviation in the probability distribution and study the robustness of standard weak measurement and postselected weak measurements against systematic errors. We show that, with a large weak value, the systematic error of a postselected weak measurement when the probe undergoes decoherence can be significantly lower than that of a standard weak measurement. This indicates another advantage of weak-value amplification in improving the performance of parameter estimation. We illustrate the results by an exact numerical simulation of decoherence arising from a bosonic mode and compare it to the first-order analytical result we obtain.
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.
Bayesian Error Estimation Functionals
NASA Astrophysics Data System (ADS)
Jacobsen, Karsten W.
The challenge of approximating the exchange-correlation functional in Density Functional Theory (DFT) has led to the development of numerous different approximations of varying accuracy on different calculated properties. There is therefore a need for reliable estimation of prediction errors within the different approximation schemes to DFT. The Bayesian Error Estimation Functionals (BEEF) have been developed with this in mind. The functionals are constructed by fitting to experimental and high-quality computational databases for molecules and solids including chemisorption and van der Waals systems. This leads to reasonably accurate general-purpose functionals with particual focus on surface science. The fitting procedure involves considerations on how to combine different types of data, and applies Tikhonov regularization and bootstrap cross validation. The methodology has been applied to construct GGA and metaGGA functionals with and without inclusion of long-ranged van der Waals contributions. The error estimation is made possible by the generation of not only a single functional but through the construction of a probability distribution of functionals represented by a functional ensemble. The use of the functional ensemble is illustrated on compound heat of formation and by investigations of the reliability of calculated catalytic ammonia synthesis rates.
Alonso-Carné, Jorge; García-Martín, Alberto; Estrada-Peña, Agustin
2013-11-01
The modelling of habitat suitability for parasites is a growing area of research due to its association with climate change and ensuing shifts in the distribution of infectious diseases. Such models depend on remote sensing data and require accurate, high-resolution temperature measurements. The temperature is critical for accurate estimation of development rates and potential habitat ranges for a given parasite. The MODIS sensors aboard the Aqua and Terra satellites provide high-resolution temperature data for remote sensing applications. This paper describes comparative analysis of MODIS-derived temperatures relative to ground records of surface temperature in the western Palaearctic. The results show that MODIS overestimated maximum temperature values and underestimated minimum temperatures by up to 5-6 °C. The combined use of both Aqua and Terra datasets provided the most accurate temperature estimates around latitude 35-44° N, with an overestimation during spring-summer months and an underestimation in autumn-winter. Errors in temperature estimation were associated with specific ecological regions within the target area as well as technical limitations in the temporal and orbital coverage of the satellites (e.g. sensor limitations and satellite transit times). We estimated error propagation of temperature uncertainties in parasite habitat suitability models by comparing outcomes of published models. Error estimates reached 36% of annual respective measurements depending on the model used. Our analysis demonstrates the importance of adequate image processing and points out the limitations of MODIS temperature data as inputs into predictive models concerning parasite lifecycles. PMID:24258878
Klenzing, J. H.; Earle, G. D.; Heelis, R. A.; Coley, W. R.
2009-05-15
The use of biased grids as energy filters for charged particles is common in satellite-borne instruments such as a planar retarding potential analyzer (RPA). Planar RPAs are currently flown on missions such as the Communications/Navigation Outage Forecast System and the Defense Meteorological Satellites Program to obtain estimates of geophysical parameters including ion velocity and temperature. It has been shown previously that the use of biased grids in such instruments creates a nonuniform potential in the grid plane, which leads to inherent errors in the inferred parameters. A simulation of ion interactions with various configurations of biased grids has been developed using a commercial finite-element analysis software package. Using a statistical approach, the simulation calculates collected flux from Maxwellian ion distributions with three-dimensional drift relative to the instrument. Perturbations in the performance of flight instrumentation relative to expectations from the idealized RPA flux equation are discussed. Both single grid and dual-grid systems are modeled to investigate design considerations. Relative errors in the inferred parameters for each geometry are characterized as functions of ion temperature and drift velocity.
Simulation of Systematic Errors in Phase-Referenced VLBI Astrometry
NASA Astrophysics Data System (ADS)
Pradel, N.; Charlot, P.; Lestrade, J.-F.
2005-12-01
The astrometric accuracy in the relative coordinates of two angularly-close radio sources observed with the phase-referencing VLBI technique is limited by systematic errors. These include geometric errors and atmospheric errors. Based on simulation with the SPRINT software, we evaluate the impact of these errors in the estimated relative source coordinates for standard VLBA observations. Such evaluations are useful to estimate the actual accuracy of phase-referenced VLBI astrometry.
Measuring Systematic Error with Curve Fits
ERIC Educational Resources Information Center
Rupright, Mark E.
2011-01-01
Systematic errors are often unavoidable in the introductory physics laboratory. As has been demonstrated in many papers in this journal, such errors can present a fundamental problem for data analysis, particularly when comparing the data to a given model. In this paper I give three examples in which my students use popular curve-fitting software…
Antenna pointing systematic error model derivations
NASA Technical Reports Server (NTRS)
Guiar, C. N.; Lansing, F. L.; Riggs, R.
1987-01-01
The pointing model used to represent and correct systematic errors for the Deep Space Network (DSN) antennas is presented. Analytical expressions are given in both azimuth-elevation (az-el) and hour angle-declination (ha-dec) mounts for RF axis collimation error, encoder offset, nonorthogonality of axes, axis plane tilt, and structural flexure due to gravity loading. While the residual pointing errors (rms) after correction appear to be within the ten percent of the half-power beamwidth criterion commonly set for good pointing accuracy, the DSN has embarked on an extensive pointing improvement and modeling program aiming toward an order of magnitude higher pointing precision.
Systematic errors in strong lens modeling
NASA Astrophysics Data System (ADS)
Johnson, Traci Lin; Sharon, Keren; Bayliss, Matthew B.
2015-08-01
The lensing community has made great strides in quantifying the statistical errors associated with strong lens modeling. However, we are just now beginning to understand the systematic errors. Quantifying these errors is pertinent to Frontier Fields science, as number counts and luminosity functions are highly sensitive to the value of the magnifications of background sources across the entire field of view. We are aware that models can be very different when modelers change their assumptions about the parameterization of the lensing potential (i.e., parametric vs. non-parametric models). However, models built while utilizing a single methodology can lead to inconsistent outcomes for different quantities, distributions, and qualities of redshift information regarding the multiple images used as constraints in the lens model. We investigate how varying the number of multiple image constraints and available redshift information of those constraints (ex., spectroscopic vs. photometric vs. no redshift) can influence the outputs of our parametric strong lens models, specifically, the mass distribution and magnifications of background sources. We make use of the simulated clusters by M. Meneghetti et al. and the first two Frontier Fields clusters, which have a high number of multiply imaged galaxies with spectroscopically-measured redshifts (or input redshifts, in the case of simulated clusters). This work will not only inform upon Frontier Field science, but also for work on the growing collection of strong lensing galaxy clusters, most of which are less massive and are capable of lensing a handful of galaxies, and are more prone to these systematic errors.
A posteriori error estimator and error control for contact problems
NASA Astrophysics Data System (ADS)
Weiss, Alexander; Wohlmuth, Barbara I.
2009-09-01
In this paper, we consider two error estimators for one-body contact problems. The first error estimator is defined in terms of H( div ) -conforming stress approximations and equilibrated fluxes while the second is a standard edge-based residual error estimator without any modification with respect to the contact. We show reliability and efficiency for both estimators. Moreover, the error is bounded by the first estimator with a constant one plus a higher order data oscillation term plus a term arising from the contact that is shown numerically to be of higher order. The second estimator is used in a control-based AFEM refinement strategy, and the decay of the error in the energy is shown. Several numerical tests demonstrate the performance of both estimators.
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).
Reducing systematic error in weak lensing cluster surveys
Utsumi, Yousuke; Miyazaki, Satoshi; Hamana, Takashi; Geller, Margaret J.; Kurtz, Michael J.; Fabricant, Daniel G.; Dell'Antonio, Ian P.; Oguri, Masamune
2014-05-10
Weak lensing provides an important route toward collecting samples of clusters of galaxies selected by mass. Subtle systematic errors in image reduction can compromise the power of this technique. We use the B-mode signal to quantify this systematic error and to test methods for reducing this error. We show that two procedures are efficient in suppressing systematic error in the B-mode: (1) refinement of the mosaic CCD warping procedure to conform to absolute celestial coordinates and (2) truncation of the smoothing procedure on a scale of 10'. Application of these procedures reduces the systematic error to 20% of its original amplitude. We provide an analytic expression for the distribution of the highest peaks in noise maps that can be used to estimate the fraction of false peaks in the weak-lensing κ-signal-to-noise ratio (S/N) maps as a function of the detection threshold. Based on this analysis, we select a threshold S/N = 4.56 for identifying an uncontaminated set of weak-lensing peaks in two test fields covering a total area of ∼3 deg{sup 2}. Taken together these fields contain seven peaks above the threshold. Among these, six are probable systems of galaxies and one is a superposition. We confirm the reliability of these peaks with dense redshift surveys, X-ray, and imaging observations. The systematic error reduction procedures we apply are general and can be applied to future large-area weak-lensing surveys. Our high-peak analysis suggests that with an S/N threshold of 4.5, there should be only 2.7 spurious weak-lensing peaks even in an area of 1000 deg{sup 2}, where we expect ∼2000 peaks based on our Subaru fields.
Medication Errors in the Southeast Asian Countries: A Systematic Review
Salmasi, Shahrzad; Khan, Tahir Mehmood; Hong, Yet Hoi; Ming, Long Chiau; Wong, Tin Wui
2015-01-01
Background Medication error (ME) is a worldwide issue, but most studies on ME have been undertaken in developed countries and very little is known about ME in Southeast Asian countries. This study aimed systematically to identify and review research done on ME in Southeast Asian countries in order to identify common types of ME and estimate its prevalence in this region. Methods The literature relating to MEs in Southeast Asian countries was systematically reviewed in December 2014 by using; Embase, Medline, Pubmed, ProQuest Central and the CINAHL. Inclusion criteria were studies (in any languages) that investigated the incidence and the contributing factors of ME in patients of all ages. Results The 17 included studies reported data from six of the eleven Southeast Asian countries: five studies in Singapore, four in Malaysia, three in Thailand, three in Vietnam, one in the Philippines and one in Indonesia. There was no data on MEs in Brunei, Laos, Cambodia, Myanmar and Timor. Of the seventeen included studies, eleven measured administration errors, four focused on prescribing errors, three were done on preparation errors, three on dispensing errors and two on transcribing errors. There was only one study of reconciliation error. Three studies were interventional. Discussion The most frequently reported types of administration error were incorrect time, omission error and incorrect dose. Staff shortages, and hence heavy workload for nurses, doctor/nurse distraction, and misinterpretation of the prescription/medication chart, were identified as contributing factors of ME. There is a serious lack of studies on this topic in this region which needs to be addressed if the issue of ME is to be fully understood and addressed. PMID:26340679
More on Systematic Error in a Boyle's Law Experiment
ERIC Educational Resources Information Center
McCall, Richard P.
2012-01-01
A recent article in "The Physics Teacher" describes a method for analyzing a systematic error in a Boyle's law laboratory activity. Systematic errors are important to consider in physics labs because they tend to bias the results of measurements. There are numerous laboratory examples and resources that discuss this common source of error.
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.
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. PMID:26000444
Effects of Structural Errors on Parameter Estimates
NASA Technical Reports Server (NTRS)
Hadaegh, F. Y.; Bekey, G. A.
1987-01-01
Paper introduces concept of near equivalence in probability between different parameters or mathematical models of physical system. One in series of papers, each establishes different part of rigorous theory of mathematical modeling based on concepts of structural error, identifiability, and equivalence. This installment focuses upon effects of additive structural errors on degree of bias in estimates parameters.
Error Estimates for Numerical Integration Rules
ERIC Educational Resources Information Center
Mercer, Peter R.
2005-01-01
The starting point for this discussion of error estimates is the fact that integrals that arise in Fourier series have properties that can be used to get improved bounds. This idea is extended to more general situations.
Systematic errors in precipitation measurements with different rain gauge sensors
NASA Astrophysics Data System (ADS)
Sungmin, O.; Foelsche, Ulrich
2015-04-01
Ground-level rain gauges provide the most direct measurement of precipitation and therefore such precipitation measurement datasets are often utilized for the evaluation of precipitation estimates via remote sensing and in climate model simulations. However, measured precipitation by means of national standard gauge networks is constrained by their spatial density. For this reason, in order to accurately measure precipitation it is of essential importance to understand the performance and reliability of rain gauges. This study is aimed to assess the systematic errors between measurements taken with different rain gauge sensors. We will mainly address extreme precipitation events as these are connected with high uncertainties in the measurements. Precipitation datasets for the study are available from WegenerNet, a dense network of 151 meteorological stations within an area of about 20 km × 15 km centred near the city of Feldbach in the southeast of Austria. The WegenerNet has a horizontal resolution of about 1.4-km and employs 'tripping bucket' rain gauges for precipitation measurements with three different types of sensors; a reference station provides measurements from all types of sensors. The results will illustrate systematic errors via the comparison of the precipitation datasets gained with different types of sensors. The analyses will be carried out by direct comparison between the datasets from the reference station. In addition, the dependence of the systematic errors on meteorological conditions, e.g. precipitation intensity and wind speed, will be investigated to assess the feasibility of applying the WegenerNet datasets for the study of extreme precipitation events. The study can be regarded as a pre-processing research to further studies in hydro-meteorological applications, which require high-resolution precipitation datasets, such as satellite/radar-derived precipitation validation and hydrodynamic modelling.
Identifying and Reducing Systematic Errors in Chromosome Conformation Capture Data
Hahn, Seungsoo; Kim, Dongsup
2015-01-01
Chromosome conformation capture (3C)-based techniques have recently been used to uncover the mystic genomic architecture in the nucleus. These techniques yield indirect data on the distances between genomic loci in the form of contact frequencies that must be normalized to remove various errors. This normalization process determines the quality of data analysis. In this study, we describe two systematic errors that result from the heterogeneous local density of restriction sites and different local chromatin states, methods to identify and remove those artifacts, and three previously described sources of systematic errors in 3C-based data: fragment length, mappability, and local DNA composition. To explain the effect of systematic errors on the results, we used three different published data sets to show the dependence of the results on restriction enzymes and experimental methods. Comparison of the results from different restriction enzymes shows a higher correlation after removing systematic errors. In contrast, using different methods with the same restriction enzymes shows a lower correlation after removing systematic errors. Notably, the improved correlation of the latter case caused by systematic errors indicates that a higher correlation between results does not ensure the validity of the normalization methods. Finally, we suggest a method to analyze random error and provide guidance for the maximum reproducibility of contact frequency maps. PMID:26717152
Identifying and Reducing Systematic Errors in Chromosome Conformation Capture Data.
Hahn, Seungsoo; Kim, Dongsup
2015-01-01
Chromosome conformation capture (3C)-based techniques have recently been used to uncover the mystic genomic architecture in the nucleus. These techniques yield indirect data on the distances between genomic loci in the form of contact frequencies that must be normalized to remove various errors. This normalization process determines the quality of data analysis. In this study, we describe two systematic errors that result from the heterogeneous local density of restriction sites and different local chromatin states, methods to identify and remove those artifacts, and three previously described sources of systematic errors in 3C-based data: fragment length, mappability, and local DNA composition. To explain the effect of systematic errors on the results, we used three different published data sets to show the dependence of the results on restriction enzymes and experimental methods. Comparison of the results from different restriction enzymes shows a higher correlation after removing systematic errors. In contrast, using different methods with the same restriction enzymes shows a lower correlation after removing systematic errors. Notably, the improved correlation of the latter case caused by systematic errors indicates that a higher correlation between results does not ensure the validity of the normalization methods. Finally, we suggest a method to analyze random error and provide guidance for the maximum reproducibility of contact frequency maps. PMID:26717152
Improved Systematic Pointing Error Model for the DSN Antennas
NASA Technical Reports Server (NTRS)
Rochblatt, David J.; Withington, Philip M.; Richter, Paul H.
2011-01-01
New pointing models have been developed for large reflector antennas whose construction is founded on elevation over azimuth mount. At JPL, the new models were applied to the Deep Space Network (DSN) 34-meter antenna s subnet for corrections of their systematic pointing errors; it achieved significant improvement in performance at Ka-band (32-GHz) and X-band (8.4-GHz). The new models provide pointing improvements relative to the traditional models by a factor of two to three, which translate to approximately 3-dB performance improvement at Ka-band. For radio science experiments where blind pointing performance is critical, the new innovation provides a new enabling technology. The model extends the traditional physical models with higher-order mathematical terms, thereby increasing the resolution of the model for a better fit to the underlying systematic imperfections that are the cause of antenna pointing errors. The philosophy of the traditional model was that all mathematical terms in the model must be traced to a physical phenomenon causing antenna pointing errors. The traditional physical terms are: antenna axis tilts, gravitational flexure, azimuth collimation, azimuth encoder fixed offset, azimuth and elevation skew, elevation encoder fixed offset, residual refraction, azimuth encoder scale error, and antenna pointing de-rotation terms for beam waveguide (BWG) antennas. Besides the addition of spherical harmonics terms, the new models differ from the traditional ones in that the coefficients for the cross-elevation and elevation corrections are completely independent and may be different, while in the traditional model, some of the terms are identical. In addition, the new software allows for all-sky or mission-specific model development, and can utilize the previously used model as an a priori estimate for the development of the updated models.
Systematic errors for a Mueller matrix dual rotating compensator ellipsometer.
Broch, Laurent; En Naciri, Aotmane; Johann, Luc
2008-06-01
The characterization of anisotropic materials and complex systems by ellipsometry has pushed the design of instruments to require the measurement of the full reflection Mueller matrix of the sample with a great precision. Therefore Mueller matrix ellipsometers have emerged over the past twenty years. The values of some coefficients of the matrix can be very small and errors due to noise or systematic errors can induce distored analysis. We present a detailed characterization of the systematic errors for a Mueller Matrix Ellipsometer in the dual-rotating compensator configuration. Starting from a general formalism, we derive explicit first-order expressions for the errors on all the coefficients of the Mueller matrix of the sample. The errors caused by inaccuracy of the azimuthal arrangement of the optical components and residual ellipticity introduced by imperfect optical elements are shown. A new method based on a four-zone averaging measurement is proposed to vanish the systematic errors. PMID:18545594
Strategies for minimizing the impact of systematic errors on land data assimilation
Technology Transfer Automated Retrieval System (TEKTRAN)
Data assimilation concerns itself primarily with the impact of random stochastic errors on state estimation. However, the developers of land data assimilation systems are commonly faced with systematic errors arising from both the parameterization of a land surface model and the need to pre-process ...
Optimal error regions for quantum state estimation
NASA Astrophysics Data System (ADS)
Shang, Jiangwei; Khoon Ng, Hui; Sehrawat, Arun; Li, Xikun; Englert, Berthold-Georg
2013-12-01
An estimator is a state that represents one's best guess of the actual state of the quantum system for the given data. Such estimators are points in the state space. To be statistically meaningful, they have to be endowed with error regions, the generalization of error bars beyond one dimension. As opposed to standard ad hoc constructions of error regions, we introduce the maximum-likelihood region—the region of largest likelihood among all regions of the same size—as the natural counterpart of the popular maximum-likelihood estimator. Here, the size of a region is its prior probability. A related concept is the smallest credible region—the smallest region with pre-chosen posterior probability. In both cases, the optimal error region has constant likelihood on its boundary. This surprisingly simple characterization permits concise reporting of the error regions, even in high-dimensional problems. For illustration, we identify optimal error regions for single-qubit and two-qubit states from computer-generated data that simulate incomplete tomography with few measured copies.
Systematic parameter errors in inspiraling neutron star binaries.
Favata, Marc
2014-03-14
The coalescence of two neutron stars is an important gravitational wave source for LIGO and other detectors. Numerous studies have considered the precision with which binary parameters (masses, spins, Love numbers) can be measured. Here I consider the accuracy with which these parameters can be determined in the presence of systematic errors due to waveform approximations. These approximations include truncation of the post-Newtonian (PN) series and neglect of neutron star (NS) spin, tidal deformation, or orbital eccentricity. All of these effects can yield systematic errors that exceed statistical errors for plausible parameter values. In particular, neglecting spin, eccentricity, or high-order PN terms causes a significant bias in the NS Love number. Tidal effects will not be measurable with PN inspiral waveforms if these systematic errors are not controlled. PMID:24679276
Systematic Parameter Errors in Inspiraling Neutron Star Binaries
NASA Astrophysics Data System (ADS)
Favata, Marc
2014-03-01
The coalescence of two neutron stars is an important gravitational wave source for LIGO and other detectors. Numerous studies have considered the precision with which binary parameters (masses, spins, Love numbers) can be measured. Here I consider the accuracy with which these parameters can be determined in the presence of systematic errors due to waveform approximations. These approximations include truncation of the post-Newtonian (PN) series and neglect of neutron star (NS) spin, tidal deformation, or orbital eccentricity. All of these effects can yield systematic errors that exceed statistical errors for plausible parameter values. In particular, neglecting spin, eccentricity, or high-order PN terms causes a significant bias in the NS Love number. Tidal effects will not be measurable with PN inspiral waveforms if these systematic errors are not controlled.
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…
Tolerance for error and computational estimation ability.
Hogan, Thomas P; Wyckoff, Laurie A; Krebs, Paul; Jones, William; Fitzgerald, Mark P
2004-06-01
Previous investigators have suggested that the personality variable tolerance for error is related to success in computational estimation. However, this suggestion has not been tested directly. This study examined the relationship between performance on a computational estimation test and scores on the NEO-Five Factor Inventory, a measure of the Big Five personality traits, including Openness, an index of tolerance for ambiguity. Other variables included SAT-I Verbal and Mathematics scores and self-rated mathematics ability. Participants were 65 college students. There was no significant relationship between the tolerance variable and computational estimation performance. There was a modest negative relationship between Agreeableness and estimation performance. The skepticism associated with the negative pole of the Agreeableness dimension may be important to pursue in further understanding of estimation ability. PMID:15362423
Jason-2 systematic error analysis in the GPS derived orbits
NASA Astrophysics Data System (ADS)
Melachroinos, S.; Lemoine, F. G.; Zelensky, N. P.; Rowlands, D. D.; Luthcke, S. B.; Chinn, D. S.
2011-12-01
Several results related to global or regional sea level changes still too often rely on the assumption that orbit errors coming from station coordinates adoption can be neglected in the total error budget (Ceri et al. 2010). In particular Instantaneous crust-fixed coordinates are obtained by adding to the linear ITRF model the geophysical high-frequency variations. In principle, geocenter motion should also be included in this computation, in order to reference these coordinates to the center of mass of the whole Earth. This correction is currently not applied when computing GDR orbits. Cerri et al. (2010) performed an analysis of systematic errors common to all coordinates along the North/South direction, as this type of bias, also known as Z-shift, has a clear impact on MSL estimates due to the unequal distribution of continental surface in the northern and southern hemispheres. The goal of this paper is to specifically study the main source of errors which comes from the current imprecision in the Z-axis realization of the frame. We focus here on the time variability of this Z-shift, which we can decompose in a drift and a periodic component due to the presumably omitted geocenter motion. A series of Jason-2 GPS-only orbits have been computed at NASA GSFC, using both IGS05 and IGS08. These orbits have been shown to agree radially at less than 1 cm RMS vs our SLR/DORIS std0905 and std1007 reduced-dynamic orbits and in comparison with orbits produced by other analysis centers (Melachroinos et al. 2011). Our GPS-only JASON-2 orbit accuracy is assessed using a number of tests including analysis of independent SLR and altimeter crossover residuals, orbit overlap differences, and direct comparison to orbits generated at GSFC using SLR and DORIS tracking, and to orbits generated externally at other centers. Tests based on SLR-crossover residuals provide the best performance indicator for independent validation of the NASA/GSFC GPS-only reduced dynamic orbits. Reduced
Neutrino spectrum at the far detector systematic errors
Szleper, M.; Para, A.
2001-10-01
Neutrino oscillation experiments often employ two identical detectors to minimize errors due to inadequately known neutrino beam. We examine various systematics effects related to the prediction of the neutrino spectrum in the `far' detector on the basis of the spectrum observed at the `near' detector. We propose a novel method of the derivation of the far detector spectrum. This method is less sensitive to the details of the understanding of the neutrino beam line and the hadron production spectra than the usually used `double ratio' method thus allowing to reduce the systematic errors.
Systematic error analysis for 3D nanoprofiler tracing normal vector
NASA Astrophysics Data System (ADS)
Kudo, Ryota; Tokuta, Yusuke; Nakano, Motohiro; Yamamura, Kazuya; Endo, Katsuyoshi
2015-10-01
In recent years, demand for an optical element having a high degree of freedom shape is increased. High-precision aspherical shape is required for the X-ray focusing mirror etc. For the head-mounted display etc., optical element of the free-form surface is used. For such an optical device fabrication, measurement technology is essential. We have developed a high- precision 3D nanoprofiler. By nanoprofiler, the normal vector information of the sample surface is obtained on the basis of the linearity of light. Normal vector information is differential value of the shape, it is possible to determine the shape by integrating. Repeatability of sub-nanometer has been achieved by nanoprofiler. To pursue the accuracy of shapes, systematic error is analyzed. The systematic errors are figure error of sample and assembly errors of the device. This method utilizes the information of the ideal shape of the sample, and the measurement point coordinates and normal vectors are calculated. However, measured figure is not the ideal shape by the effect of systematic errors. Therefore, the measurement point coordinate and the normal vector is calculated again by feeding back the measured figure. Correction of errors have been attempted by figure re-derivation. It was confirmed theoretically effectiveness by simulation. This approach also applies to the experiment, it was confirmed the possibility of about 4 nm PV figure correction in the employed sample.
Density Estimation Framework for Model Error Assessment
NASA Astrophysics Data System (ADS)
Sargsyan, K.; Liu, Z.; Najm, H. N.; Safta, C.; VanBloemenWaanders, B.; Michelsen, H. A.; Bambha, R.
2014-12-01
In this work we highlight the importance of model error assessment in physical model calibration studies. Conventional calibration methods often assume the model is perfect and account for data noise only. Consequently, the estimated parameters typically have biased values that implicitly compensate for model deficiencies. Moreover, improving the amount and the quality of data may not improve the parameter estimates since the model discrepancy is not accounted for. In state-of-the-art methods model discrepancy is explicitly accounted for by enhancing the physical model with a synthetic statistical additive term, which allows appropriate parameter estimates. However, these statistical additive terms do not increase the predictive capability of the model because they are tuned for particular output observables and may even violate physical constraints. We introduce a framework in which model errors are captured by allowing variability in specific model components and parameterizations for the purpose of achieving meaningful predictions that are both consistent with the data spread and appropriately disambiguate model and data errors. Here we cast model parameters as random variables, embedding the calibration problem within a density estimation framework. Further, we calibrate for the parameters of the joint input density. The likelihood function for the associated inverse problem is degenerate, therefore we use Approximate Bayesian Computation (ABC) to build prediction-constraining likelihoods and illustrate the strengths of the method on synthetic cases. We also apply the ABC-enhanced density estimation to the TransCom 3 CO2 intercomparison study (Gurney, K. R., et al., Tellus, 55B, pp. 555-579, 2003) and calibrate 15 transport models for regional carbon sources and sinks given atmospheric CO2 concentration measurements.
Ronchi, Roberta; Revol, Patrice; Katayama, Masahiro; Rossetti, Yves; Farnè, Alessandro
2011-01-01
During the procedure of prism adaptation, subjects execute pointing movements to visual targets under a lateral optical displacement: As consequence of the discrepancy between visual and proprioceptive inputs, their visuo-motor activity is characterized by pointing errors. The perception of such final errors triggers error-correction processes that eventually result into sensori-motor compensation, opposite to the prismatic displacement (i.e., after-effects). Here we tested whether the mere observation of erroneous pointing movements, similar to those executed during prism adaptation, is sufficient to produce adaptation-like after-effects. Neurotypical participants observed, from a first-person perspective, the examiner's arm making incorrect pointing movements that systematically overshot visual targets location to the right, thus simulating a rightward optical deviation. Three classical after-effect measures (proprioceptive, visual and visual-proprioceptive shift) were recorded before and after first-person's perspective observation of pointing errors. Results showed that mere visual exposure to an arm that systematically points on the right-side of a target (i.e., without error correction) produces a leftward after-effect, which mostly affects the observer's proprioceptive estimation of her body midline. In addition, being exposed to such a constant visual error induced in the observer the illusion “to feel” the seen movement. These findings indicate that it is possible to elicit sensori-motor after-effects by mere observation of movement errors. PMID:21731649
Zhang Le; Timbie, Peter; Karakci, Ata; Korotkov, Andrei; Tucker, Gregory S.; Sutter, Paul M.; Wandelt, Benjamin D.; Bunn, Emory F.
2013-06-01
We investigate the impact of instrumental systematic errors in interferometric measurements of the cosmic microwave background (CMB) temperature and polarization power spectra. We simulate interferometric CMB observations to generate mock visibilities and estimate power spectra using the statistically optimal maximum likelihood technique. We define a quadratic error measure to determine allowable levels of systematic error that does not induce power spectrum errors beyond a given tolerance. As an example, in this study we focus on differential pointing errors. The effects of other systematics can be simulated by this pipeline in a straightforward manner. We find that, in order to accurately recover the underlying B-modes for r = 0.01 at 28 < l < 384, Gaussian-distributed pointing errors must be controlled to 0. Degree-Sign 7 root mean square for an interferometer with an antenna configuration similar to QUBIC, in agreement with analytical estimates. Only the statistical uncertainty for 28 < l < 88 would be changed at {approx}10% level. With the same instrumental configuration, we find that the pointing errors would slightly bias the 2{sigma} upper limit of the tensor-to-scalar ratio r by {approx}10%. We also show that the impact of pointing errors on the TB and EB measurements is negligibly small.
Bayesian conformity assessment in presence of systematic measurement errors
NASA Astrophysics Data System (ADS)
Carobbi, Carlo; Pennecchi, Francesca
2016-04-01
Conformity assessment of the distribution of the values of a quantity is investigated by using a Bayesian approach. The effect of systematic, non-negligible measurement errors is taken into account. The analysis is general, in the sense that the probability distribution of the quantity can be of any kind, that is even different from the ubiquitous normal distribution, and the measurement model function, linking the measurand with the observable and non-observable influence quantities, can be non-linear. Further, any joint probability density function can be used to model the available knowledge about the systematic errors. It is demonstrated that the result of the Bayesian analysis here developed reduces to the standard result (obtained through a frequentistic approach) when the systematic measurement errors are negligible. A consolidated frequentistic extension of such standard result, aimed at including the effect of a systematic measurement error, is directly compared with the Bayesian result, whose superiority is demonstrated. Application of the results here obtained to the derivation of the operating characteristic curves used for sampling plans for inspection by variables is also introduced.
An Empirically Based Error-Model for Radar Rainfall Estimates
NASA Astrophysics Data System (ADS)
Ciach, G. J.
2004-05-01
Mathematical modeling of the way radar rainfall (RR) approximates the physical truth is a prospective method to quantify the RR uncertainties. In this approach one can represent RR in the form of an "observation equation," that is, as a function of the corresponding true rainfall and a random error process. The error process describes the cumulative effect of all the sources of RR uncertainties. We present the results of our work on the identification and estimation of this relationship. They are based on the Level II reflectivity data from the WSR-88D radar in Tulsa, Oklahoma, and rainfall measurements from 23 surrounding Oklahoma Mesonet raingauges. Accumulation intervals from one hour to one day were analyzed using this sample. The raingauge accumulations were used as an approximation of the true rainfall in this study. The RR error-model that we explored is factorized into a deterministic distortion, which is a function of the true rainfall, and a multiplicative random error factor that is a positively-defined random variable. The distribution of the error factor depends on the true rainfall, however, its expectation in this representation is always equal to one (all the biases are modeled by the deterministic component). With this constraint, the deterministic distortion function can be defined as the conditional mean of RR conditioned on the true rainfall. We use nonparametric regression to estimate the deterministic distortion, and the variance and quantiles of the random error factor, as functions of the true rainfall. The results show that the deterministic distortion is a nonlinear function of the true rainfall that indicates systematic overestimation of week rainfall and underestimation of strong rainfall (conditional bias). The standard deviation of the error factor is a decreasing function of the true rainfall that ranges from about 0.8 for week rainfall to about 0.3 for strong rainfall. For larger time-scales, both the deterministic distortion and the
Belashov, A V; Petrov, N V; Semenova, I V
2016-01-01
This paper explores the concept of image-plane holographic tomography applied to the measurements of laser-induced thermal gradients in an aqueous solution of a photosensitizer with respect to the reconstruction accuracy of three-dimensional variations of the refractive index. It uses the least-squares estimation algorithm to reconstruct refractive index variations in each holographic projection. Along with the bitelecentric optical system, transferring focused projection to the sensor plane, it facilitates the elimination of diffraction artifacts and noise suppression. This work estimates the influence of typical random and systematic errors in experiments and concludes that random errors such as accidental measurement errors or noise presence can be significantly suppressed by increasing the number of recorded digital holograms. On the contrary, even comparatively small systematic errors such as a displacement of the rotation axis projection in the course of a reconstruction procedure can significantly distort the results. PMID:26835625
Systematic lossy forward error protection for error-resilient digital video broadcasting
NASA Astrophysics Data System (ADS)
Rane, Shantanu D.; Aaron, Anne; Girod, Bernd
2004-01-01
We present a novel scheme for error-resilient digital video broadcasting,using the Wyner-Ziv coding paradigm. We apply the general framework of systematic lossy source-channel coding to generate a supplementary bitstream that can correct transmission errors in the decoded video waveform up to a certain residual distortion. The systematic portion consists of a conventional MPEG-coded bitstream, which is transmitted over the error-prone channel without forward error correction.The supplementary bitstream is a low rate representation of the transmitted video sequence generated using Wyner-Ziv encoding. We use the conventionally decoded error-concealed MPEG video sequence as side information to decode the Wyner-Ziv bits. The decoder combines the error-prone side information and the Wyner-Ziv description to yield an improved decoded video signal. Our results indicate that, over a large range of channel error probabilities, this scheme yields superior video quality when compared with traditional forward error correction techniques employed in digital video broadcasting.
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.
The Effect of Systematic Error in Forced Oscillation Testing
NASA Technical Reports Server (NTRS)
Williams, Brianne Y.; Landman, Drew; Flory, Isaac L., IV; Murphy, Patrick C.
2012-01-01
One of the fundamental problems in flight dynamics is the formulation of aerodynamic forces and moments acting on an aircraft in arbitrary motion. Classically, conventional stability derivatives are used for the representation of aerodynamic loads in the aircraft equations of motion. However, for modern aircraft with highly nonlinear and unsteady aerodynamic characteristics undergoing maneuvers at high angle of attack and/or angular rates the conventional stability derivative model is no longer valid. Attempts to formulate aerodynamic model equations with unsteady terms are based on several different wind tunnel techniques: for example, captive, wind tunnel single degree-of-freedom, and wind tunnel free-flying techniques. One of the most common techniques is forced oscillation testing. However, the forced oscillation testing method does not address the systematic and systematic correlation errors from the test apparatus that cause inconsistencies in the measured oscillatory stability derivatives. The primary objective of this study is to identify the possible sources and magnitude of systematic error in representative dynamic test apparatuses. Sensitivities of the longitudinal stability derivatives to systematic errors are computed, using a high fidelity simulation of a forced oscillation test rig, and assessed using both Design of Experiments and Monte Carlo methods.
GOMOS data characterization and error estimation
NASA Astrophysics Data System (ADS)
Tamminen, J.; Kyrölä, E.; Sofieva, V. F.; Laine, M.; Bertaux, J.-L.; Hauchecorne, A.; Dalaudier, F.; Fussen, D.; Vanhellemont, F.; Fanton-D'Andon, O.; Barrot, G.; Mangin, A.; Guirlet, M.; Blanot, L.; Fehr, T.; Saavedra de Miguel, L.; Fraisse, R.
2010-03-01
The Global Ozone Monitoring by Occultation of Stars (GOMOS) instrument uses stellar occultation technique for monitoring ozone and other trace gases in the stratosphere and mesosphere. The self-calibrating measurement principle of GOMOS together with a relatively simple data retrieval where only minimal use of a priori data is required, provides excellent possibilities for long term monitoring of atmospheric composition. GOMOS uses about 180 brightest stars as the light source. Depending on the individual spectral characteristics of the stars, the signal-to-noise ratio of GOMOS is changing from star to star, resulting also varying accuracy to the retrieved profiles. We present the overview of the GOMOS data characterization and error estimation, including modeling errors, for ozone, NO2, NO3 and aerosol profiles. The retrieval error (precision) of the night time measurements in the stratosphere is typically 0.5-4% for ozone, about 10-20% for NO2, 20-40% for NO3 and 2-50% for aerosols. Mesospheric O3, up to 100 km, can be measured with 2-10% precision. The main sources of the modeling error are the incompletely corrected atmospheric turbulence causing scintillation, inaccurate aerosol modeling, uncertainties in cross sections of the trace gases and in the atmospheric temperature. The sampling resolution of GOMOS varies depending on the measurement geometry. In the data inversion a Tikhonov-type regularization with pre-defined target resolution requirement is applied leading to 2-3 km resolution for ozone and 4 km resolution for other trace gases.
GOMOS data characterisation and error estimation
NASA Astrophysics Data System (ADS)
Tamminen, J.; Kyrölä, E.; Sofieva, V. F.; Laine, M.; Bertaux, J.-L.; Hauchecorne, A.; Dalaudier, F.; Fussen, D.; Vanhellemont, F.; Fanton-D'Andon, O.; Barrot, G.; Mangin, A.; Guirlet, M.; Blanot, L.; Fehr, T.; Saavedra de Miguel, L.; Fraisse, R.
2010-10-01
The Global Ozone Monitoring by Occultation of Stars (GOMOS) instrument uses stellar occultation technique for monitoring ozone, other trace gases and aerosols in the stratosphere and mesosphere. The self-calibrating measurement principle of GOMOS together with a relatively simple data retrieval where only minimal use of a priori data is required provides excellent possibilities for long-term monitoring of atmospheric composition. GOMOS uses about 180 of the brightest stars as its light source. Depending on the individual spectral characteristics of the stars, the signal-to-noise ratio of GOMOS varies from star to star, resulting also in varying accuracy of retrieved profiles. We present here an overview of the GOMOS data characterisation and error estimation, including modeling errors, for O3, NO2, NO3, and aerosol profiles. The retrieval error (precision) of night-time measurements in the stratosphere is typically 0.5-4% for ozone, about 10-20% for NO2, 20-40% for NO3 and 2-50% for aerosols. Mesospheric O3, up to 100 km, can be measured with 2-10% precision. The main sources of the modeling error are incompletely corrected scintillation, inaccurate aerosol modeling, uncertainties in cross sections of trace gases and in atmospheric temperature. The sampling resolution of GOMOS varies depending on the measurement geometry. In the data inversion a Tikhonov-type regularization with pre-defined target resolution requirement is applied leading to 2-3 km vertical resolution for ozone and 4 km resolution for other trace gases and aerosols.
Weak gravitational lensing systematic errors in the dark energy survey
NASA Astrophysics Data System (ADS)
Plazas, Andres Alejandro
Dark energy is one of the most important unsolved problems in modern Physics, and weak gravitational lensing (WL) by mass structures along the line of sight ("cosmic shear") is a promising technique to learn more about its nature. However, WL is subject to numerous systematic errors which induce biases in measured cosmological parameters and prevent the development of its full potential. In this thesis, we advance the understanding of WL systematics in the context of the Dark Energy Survey (DES). We develop a testing suite to assess the performance of the shapelet-based DES WL measurement pipeline. We determine that the measurement bias of the parameters of our Point Spread Function (PSF) model scales as (S/N )-2, implying that a PSF S/N > 75 is needed to satisfy DES requirements. PSF anisotropy suppression also satisfies the requirements for source galaxies with S/N ≳ 45. For low-noise, marginally-resolved exponential galaxies, the shear calibration errors are up to about 0.06% (for shear values ≲ 0.075). Galaxies with S/N ≳ 75 present about 1% errors, sufficient for first-year DES data. However, more work is needed to satisfy full-area DES requirements, especially in the high-noise regime. We then implement tests to validate the high accuracy of the map between pixel coordinates and sky coordinates (astrometric solution), which is crucial to detect the required number of galaxies for WL in stacked images. We also study the effect of atmospheric dispersion on cosmic shear experiments such as DES and the Large Synoptic Survey Telescope (LSST) in the four griz bands. For DES (LSST), we find systematics in the g and r (g, r, and i) bands that are larger than required. We find that a simple linear correction in galaxy color is accurate enough to reduce dispersion shear systematics to insignificant levels in the r ( i) band for DES (LSST). More complex corrections will likely reduce the systematic cosmic-shear errors below statistical errors for LSST r band
Gap filling strategies and error in estimating annual soil respiration.
Gomez-Casanovas, Nuria; Anderson-Teixeira, Kristina; Zeri, Marcelo; Bernacchi, Carl J; DeLucia, Evan H
2013-06-01
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. Although many gap filling methodologies have been employed, there is no standardized procedure for producing defensible estimates of annual Rsoil . Here, we test the reliability of nine different gap filling techniques by inserting artificial gaps into 20 automated Rsoil records and comparing gap filling Rsoil estimates of each technique to measured values. We show that although the most commonly used techniques do not, on average, produce large systematic biases, gap filling accuracy may be significantly improved through application of the most reliable methods. All methods performed best at lower gap fractions and had relatively high, systematic errors for simulated survey measurements. Overall, the most accurate technique estimated Rsoil based on the soil temperature dependence of Rsoil by assuming constant temperature sensitivity and linearly interpolating reference respiration (Rsoil at 10 °C) across gaps. The linear interpolation method was the second best-performing method. In contrast, estimating Rsoil based on a single annual Rsoil - Tsoil relationship, which is currently the most commonly used technique, was among the most poorly-performing methods. Thus, our analysis demonstrates that gap filling accuracy may be improved substantially without sacrificing computational simplicity. Improved and standardized techniques for estimation of annual Rsoil will be valuable for understanding the role of Rsoil in the global C cycle. PMID:23504959
Spatial reasoning in the treatment of systematic sensor errors
Beckerman, M.; Jones, J.P.; Mann, R.C.; Farkas, L.A.; Johnston, S.E.
1988-01-01
In processing ultrasonic and visual sensor data acquired by mobile robots systematic errors can occur. The sonar errors include distortions in size and surface orientation due to the beam resolution, and false echoes. The vision errors include, among others, ambiguities in discriminating depth discontinuities from intensity gradients generated by variations in surface brightness. In this paper we present a methodology for the removal of systematic errors using data from the sonar sensor domain to guide the processing of information in the vision domain, and vice versa. During the sonar data processing some errors are removed from 2D navigation maps through pattern analyses and consistent-labelling conditions, using spatial reasoning about the sonar beam and object characteristics. Others are removed using visual information. In the vision data processing vertical edge segments are extracted using a Canny-like algorithm, and are labelled. Object edge features are then constructed from the segments using statistical and spatial analyses. A least-squares method is used during the statistical analysis, and sonar range data are used in the spatial analysis. 7 refs., 10 figs.
SYSTEMATIC CONTINUUM ERRORS IN THE Ly{alpha} FOREST AND THE MEASURED TEMPERATURE-DENSITY RELATION
Lee, Khee-Gan
2012-07-10
Continuum fitting uncertainties are a major source of error in estimates of the temperature-density relation (usually parameterized as a power-law, T {proportional_to} {Delta}{sup {gamma}-1}) of the intergalactic medium through the flux probability distribution function (PDF) of the Ly{alpha} forest. Using a simple order-of-magnitude calculation, we show that few percent-level systematic errors in the placement of the quasar continuum due to, e.g., a uniform low-absorption Gunn-Peterson component could lead to errors in {gamma} of the order of unity. This is quantified further using a simple semi-analytic model of the Ly{alpha} forest flux PDF. We find that under(over)estimates in the continuum level can lead to a lower (higher) measured value of {gamma}. By fitting models to mock data realizations generated with current observational errors, we find that continuum errors can cause a systematic bias in the estimated temperature-density relation of ({delta}({gamma})) Almost-Equal-To -0.1, while the error is increased to {sigma}{sub {gamma}} Almost-Equal-To 0.2 compared to {sigma}{sub {gamma}} Almost-Equal-To 0.1 in the absence of continuum errors.
Mattis, Ina; Tesche, Matthias; Grein, Matthias; Freudenthaler, Volker; Müller, Detlef
2009-05-10
Signals of many types of aerosol lidars can be affected with a significant systematic error, if depolarizing scatterers are present in the atmosphere. That error is caused by a polarization-dependent receiver transmission. In this contribution we present an estimation of the magnitude of this systematic error. We show that lidar signals can be biased by more than 20%, if linearly polarized laser light is emitted, if both polarization components of the backscattered light are measured with a single detection channel, and if the receiver transmissions for these two polarization components differ by more than 50%. This signal bias increases with increasing ratio between the two transmission values (transmission ratio) or with the volume depolarization ratio of the scatterers. The resulting error of the particle backscatter coefficient increases with decreasing backscatter ratio. If the particle backscatter coefficients are to have an accuracy better than 5%, the transmission ratio has to be in the range between 0.85 and 1.15. We present a method to correct the measured signals for this bias. We demonstrate an experimental method for the determination of the transmission ratio. We use collocated measurements of a lidar system strongly affected by this signal bias and an unbiased reference system to verify the applicability of the correction scheme. The errors in the case of no correction are illustrated with example measurements of fresh Saharan dust. PMID:19424398
Systematic Errors of the Fsu Global Spectral Model
NASA Astrophysics Data System (ADS)
Surgi, Naomi
Three 20 day winter forecasts have been carried out using the Florida State University Global Spectral Model to examine the systematic errors of the model. Most GCM's and global forecast models exhibit the same kind of error patterns even though the model formulations vary somewhat between them. Some of the dominant errors are a breakdown of the trade winds in the low latitudes, an over-prediction of the subtropical jets accompanied by an upward and poleward shift of the jets, an error in the mean sea-level pressure with over-intensification of the quasi-stationary oceanic lows and continental highs and a warming of the tropical mid and upper troposphere. In this study, a number of sensitivity experiments have been performed for which orography, model physics and initialization are considered as possible causes of these errors. A parameterization of the vertical distribution of momentum due to the sub-grid scale orography has been implemented in the model to address the model deficiencies associated with orographic forcing. This scheme incorporates the effects of moisture on the wave induced stress. The parameterization of gravity wave drag is shown to substantially reduce the large-scale wind and height errors in regions of direct forcing and well downstream of the mountainous regions. Also, a parameterization of the heat and moisture transport associated with shallow convection is found to have a positive impact on the errors particularly in the tropics. This is accomplished by the increase of moisture supply from the subtropics into the deep tropics and a subsequent enhancement of the secondary circulations. A dynamic relaxation was carried out to examine the impact of the long wave errors on the shorter wave. By constraining the long wave error, improvement is shown for wavenumbers 5-7 on medium to extended range time intervals. Thus, improved predictability of the transient flow is expected by applying this initialization procedure.
From Systematic Errors to Cosmology Using Large-Scale Structure
NASA Astrophysics Data System (ADS)
Hunterer, Dragan
We propose to carry out a two-pronged program to significantly improve links between galaxy surveys and constraints on primordial cosmology and fundamental physics. We will first develop the methodology to self-calibrate the survey, that is, determine the large-angle calibration systematics internally from the survey. We will use this information to correct biases that propagate from the largest to smaller angular scales. Our approach for tackling the systematics is very complementary to existing ones, in particular in the sense that it does not assume knowledge of specific systematic maps or templates. It is timely to undertake these analyses, since none of the currently known methods addresses the multiplicative effects of large-angle calibration errors that contaminate the small-scale signal and present one of the most significant sources of error in the large-scale structure. The second part of the proposal is to precisely quantify the statistical and systematic errors in the reconstruction of the Integrated Sachs-Wolfe (ISW) contribution to the cosmic microwave background (CMB) sky map using information from galaxy surveys. Unlike the ISW contributions to CMB power, the ISW map reconstruction has not been studied in detail to date. We will create a nimble plug-and-play pipeline to ascertain how reliably a map from an arbitrary LSS survey can be used to separate the late-time and early-time contributions to CMB anisotropy at large angular scales. We will pay particular attention to partial sky coverage, incomplete redshift information, finite redshift range, and imperfect knowledge of the selection function for the galaxy survey. Our work should serve as the departure point for a variety of implications in cosmology, including the physical origin of the large-angle CMB "anomalies".
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
Estimation of discretization errors in contact pressure measurements.
Fregly, Benjamin J; Sawyer, W Gregory
2003-04-01
Contact pressure measurements in total knee replacements are often made using a discrete sensor such as the Tekscan K-Scan sensor. However, no method currently exists for predicting the magnitude of sensor discretization errors in contact force, peak pressure, average pressure, and contact area, making it difficult to evaluate the accuracy of such measurements. This study identifies a non-dimensional area variable, defined as the ratio of the number of perimeter elements to the total number of elements with pressure, which can be used to predict these errors. The variable was evaluated by simulating discrete pressure sensors subjected to Hertzian and uniform pressure distributions with two different calibration procedures. The simulations systematically varied the size of the sensor elements, the contact ellipse aspect ratio, and the ellipse's location on the sensor grid. In addition, contact pressure measurements made with a K-Scan sensor on four different total knee designs were used to evaluate the magnitude of discretization errors under practical conditions. The simulations predicted a strong power law relationship (r(2)>0.89) between worst-case discretization errors and the proposed non-dimensional area variable. In the total knee experiments, predicted discretization errors were on the order of 1-4% for contact force and peak pressure and 3-9% for average pressure and contact area. These errors are comparable to those arising from inserting a sensor into the joint space or truncating pressures with pressure sensitive film. The reported power law regression coefficients provide a simple way to estimate the accuracy of experimental measurements made with discrete pressure sensors when the contact patch is approximately elliptical. PMID:12600352
High-dimensional bolstered error estimation
Sima, Chao; Braga-Neto, Ulisses M.; Dougherty, Edward R.
2011-01-01
Motivation: In small-sample settings, bolstered error estimation has been shown to perform better than cross-validation and competitively with bootstrap with regard to various criteria. The key issue for bolstering performance is the variance setting for the bolstering kernel. Heretofore, this variance has been determined in a non-parametric manner from the data. Although bolstering based on this variance setting works well for small feature sets, results can deteriorate for high-dimensional feature spaces. Results: This article computes an optimal kernel variance depending on the classification rule, sample size, model and feature space, both the original number and the number remaining after feature selection. A key point is that the optimal variance is robust relative to the model. This allows us to develop a method for selecting a suitable variance to use in real-world applications where the model is not known, but the other factors in determining the optimal kernel are known. Availability: Companion website at http://compbio.tgen.org/paper_supp/high_dim_bolstering Contact: edward@mail.ece.tamu.edu PMID:21914630
ON THE ESTIMATION OF SYSTEMATIC UNCERTAINTIES OF STAR FORMATION HISTORIES
Dolphin, Andrew E.
2012-05-20
In most star formation history (SFH) measurements, the reported uncertainties are those due to effects whose sizes can be readily measured: Poisson noise, adopted distance and extinction, and binning choices in the solution itself. However, the largest source of error, systematics in the adopted isochrones, is usually ignored and very rarely explicitly incorporated into the uncertainties. I propose a process by which estimates of the uncertainties due to evolutionary models can be incorporated into the SFH uncertainties. This process relies on application of shifts in temperature and luminosity, the sizes of which must be calibrated for the data being analyzed. While there are inherent limitations, the ability to estimate the effect of systematic errors and include them in the overall uncertainty is significant. The effects of this are most notable in the case of shallow photometry, with which SFH measurements rely on evolved stars.
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.
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.
Analysis and Correction of Systematic Height Model Errors
NASA Astrophysics Data System (ADS)
Jacobsen, K.
2016-06-01
The geometry of digital height models (DHM) determined with optical satellite stereo combinations depends upon the image orientation, influenced by the satellite camera, the system calibration and attitude registration. As standard these days the image orientation is available in form of rational polynomial coefficients (RPC). Usually a bias correction of the RPC based on ground control points is required. In most cases the bias correction requires affine transformation, sometimes only shifts, in image or object space. For some satellites and some cases, as caused by small base length, such an image orientation does not lead to the possible accuracy of height models. As reported e.g. by Yong-hua et al. 2015 and Zhang et al. 2015, especially the Chinese stereo satellite ZiYuan-3 (ZY-3) has a limited calibration accuracy and just an attitude recording of 4 Hz which may not be satisfying. Zhang et al. 2015 tried to improve the attitude based on the color sensor bands of ZY-3, but the color images are not always available as also detailed satellite orientation information. There is a tendency of systematic deformation at a Pléiades tri-stereo combination with small base length. The small base length enlarges small systematic errors to object space. But also in some other satellite stereo combinations systematic height model errors have been detected. The largest influence is the not satisfying leveling of height models, but also low frequency height deformations can be seen. A tilt of the DHM by theory can be eliminated by ground control points (GCP), but often the GCP accuracy and distribution is not optimal, not allowing a correct leveling of the height model. In addition a model deformation at GCP locations may lead to not optimal DHM leveling. Supported by reference height models better accuracy has been reached. As reference height model the Shuttle Radar Topography Mission (SRTM) digital surface model (DSM) or the new AW3D30 DSM, based on ALOS PRISM images, are
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.
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…
Martin, D.L.
1992-01-01
Water-leaving radiances and phytoplankton pigment concentrations are calculated from Coastal Zone Color Scanner (CZCS) total radiance measurements by separating atmospheric Rayleigh and aerosol radiances from the total radiance signal measured at the satellite. Multiple scattering interactions between Rayleigh and aerosol components together with other meteorologically-moderated radiances cause systematic errors in calculated water-leaving radiances and produce errors in retrieved phytoplankton pigment concentrations. This thesis developed techniques which minimize the effects of these systematic errors in Level IIA CZCS imagery. Results of previous radiative transfer modeling by Gordon and Castano are extended to predict the pixel-specific magnitude of systematic errors caused by Rayleigh-aerosol multiple scattering interactions. CZCS orbital passes in which the ocean is viewed through a modeled, physically realistic atmosphere are simulated mathematically and radiance-retrieval errors are calculated for a range of aerosol optical depths. Pixels which exceed an error threshold in the simulated CZCS image are rejected in a corresponding actual image. Meteorological phenomena also cause artifactual errors in CZCS-derived phytoplankton pigment concentration imagery. Unless data contaminated with these effects are masked and excluded from analysis, they will be interpreted as containing valid biological information and will contribute significantly to erroneous estimates of phytoplankton temporal and spatial variability. A method is developed which minimizes these errors through a sequence of quality-control procedures including the calculation of variable cloud-threshold radiances, the computation of the extent of electronic overshoot from bright reflectors, and the imposition of a buffer zone around clouds to exclude contaminated data.
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
Estimation of Model Error Variances During Data Assimilation
NASA Technical Reports Server (NTRS)
Dee, Dick
2003-01-01
Data assimilation is all about understanding the error characteristics of the data and models that are used in the assimilation process. Reliable error estimates are needed to implement observational quality control, bias correction of observations and model fields, and intelligent data selection. Meaningful covariance specifications are obviously required for the analysis as well, since the impact of any single observation strongly depends on the assumed structure of the background errors. Operational atmospheric data assimilation systems still rely primarily on climatological background error covariances. To obtain error estimates that reflect both the character of the flow and the current state of the observing system, it is necessary to solve three problems: (1) how to account for the short-term evolution of errors in the initial conditions; (2) how to estimate the additional component of error caused by model defects; and (3) how to compute the error reduction in the analysis due to observational information. Various approaches are now available that provide approximate solutions to the first and third of these problems. However, the useful accuracy of these solutions very much depends on the size and character of the model errors and the ability to account for them. Model errors represent the real-world forcing of the error evolution in a data assimilation system. Clearly, meaningful model error estimates and/or statistics must be based on information external to the model itself. The most obvious information source is observational, and since the volume of available geophysical data is growing rapidly, there is some hope that a purely statistical approach to model error estimation can be viable. This requires that the observation errors themselves are well understood and quantifiable. We will discuss some of these challenges and present a new sequential scheme for estimating model error variances from observations in the context of an atmospheric data
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
TRAINING ERRORS AND RUNNING RELATED INJURIES: A SYSTEMATIC REVIEW
Buist, Ida; Sørensen, Henrik; Lind, Martin; Rasmussen, Sten
2012-01-01
Purpose: The purpose of this systematic review was to examine the link between training characteristics (volume, duration, frequency, and intensity) and running related injuries. Methods: A systematic search was performed in PubMed, Web of Science, Embase, and SportDiscus. Studies were included if they examined novice, recreational, or elite runners between the ages of 18 and 65. Exposure variables were training characteristics defined as volume, distance or mileage, time or duration, frequency, intensity, speed or pace, or similar terms. The outcome of interest was Running Related Injuries (RRI) in general or specific RRI in the lower extremity or lower back. Methodological quality was evaluated using quality assessment tools of 11 to 16 items. Results: After examining 4561 titles and abstracts, 63 articles were identified as potentially relevant. Finally, nine retrospective cohort studies, 13 prospective cohort studies, six case-control studies, and three randomized controlled trials were included. The mean quality score was 44.1%. Conflicting results were reported on the relationships between volume, duration, intensity, and frequency and RRI. Conclusion: It was not possible to identify which training errors were related to running related injuries. Still, well supported data on which training errors relate to or cause running related injuries is highly important for determining proper prevention strategies. If methodological limitations in measuring training variables can be resolved, more work can be conducted to define training and the interactions between different training variables, create several hypotheses, test the hypotheses in a large scale prospective study, and explore cause and effect relationships in randomized controlled trials. Level of evidence: 2a PMID:22389869
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.
Using Laser Scanners to Augment the Systematic Error Pointing Model
NASA Astrophysics Data System (ADS)
Wernicke, D. R.
2016-08-01
The antennas of the Deep Space Network (DSN) rely on precise pointing algorithms to communicate with spacecraft that are billions of miles away. Although the existing systematic error pointing model is effective at reducing blind pointing errors due to static misalignments, several of its terms have a strong dependence on seasonal and even daily thermal variation and are thus not easily modeled. Changes in the thermal state of the structure create a separation from the model and introduce a varying pointing offset. Compensating for this varying offset is possible by augmenting the pointing model with laser scanners. In this approach, laser scanners mounted to the alidade measure structural displacements while a series of transformations generate correction angles. Two sets of experiments were conducted in August 2015 using commercially available laser scanners. When compared with historical monopulse corrections under similar conditions, the computed corrections are within 3 mdeg of the mean. However, although the results show promise, several key challenges relating to the sensitivity of the optical equipment to sunlight render an implementation of this approach impractical. Other measurement devices such as inclinometers may be implementable at a significantly lower cost.
Using ridge regression in systematic pointing error corrections
NASA Technical Reports Server (NTRS)
Guiar, C. N.
1988-01-01
A pointing error model is used in the antenna calibration process. Data from spacecraft or radio star observations are used to determine the parameters in the model. However, the regression variables are not truly independent, displaying a condition known as multicollinearity. Ridge regression, a biased estimation technique, is used to combat the multicollinearity problem. Two data sets pertaining to Voyager 1 spacecraft tracking (days 105 and 106 of 1987) were analyzed using both linear least squares and ridge regression methods. The advantages and limitations of employing the technique are presented. The problem is not yet fully resolved.
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.
Systematic lossy error protection for video transmission over wireless ad hoc networks
NASA Astrophysics Data System (ADS)
Zhu, Xiaoqing; Rane, Shantanu; Girod, Bernd
2005-07-01
Wireless ad hoc networks present a challenge for error-resilient video transmission, since node mobility and multipath fading result in time-varying link qualities in terms of packet loss ratio and available bandwidth. In this paper, we propose to use a systematic lossy error protection (SLEP) scheme for video transmission over wireless ad hoc networks. The transmitted video signal has two parts-a systematic portion consisting of a video sequence transmitted without channel coding over an error-prone channel, and error protection information consisting of a bitstream generated by Wyner-Ziv encoding of the video sequence. Using an end-to-end video distortion model in conjunction with online estimates of packet loss ratio and available bandwidth, the optimal Wyner-Ziv description can be selected dynamically according to current channel conditions. The scheme can also be applied to choose one path for transmission from amongst multiple candidate routes with varying available bandwidths and packet loss ratios, so that the expected end-to-end video distortion is maximized. Experimental results of video transmission over a simulated ad hoc wireless network shows that the proposed SLEP scheme outperforms the conventional application layer FEC approach in that it provides graceful degradation of received video quality over a wider range of packet loss ratios and is less susceptible to inaccuracy in the packet loss ratio estimation.
Finite element error estimation and adaptivity based on projected stresses
Jung, J.
1990-08-01
This report investigates the behavior of a family of finite element error estimators based on projected stresses, i.e., continuous stresses that are a least squared error fit to the conventional Gauss point stresses. An error estimate based on element force equilibrium appears to be quite effective. Examples of adaptive mesh refinement for a one-dimensional problem are presented. Plans for two-dimensional adaptivity are discussed. 12 refs., 82 figs.
Error Estimation for Reduced Order Models of Dynamical Systems
Homescu, C; Petzold, L; Serban, R
2004-01-22
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 small sample statistical condition estimation and error estimation using the adjoint method. Most importantly, the proposed approach allows the assessment of regions of validity for reduced models, i.e., ranges of perturbations in the original system over which the reduced model is still appropriate. Numerical examples validate our approach: the error norm estimates approximate well the forward error while the derived bounds are within an order of magnitude.
Parameter estimation and error analysis in environmental modeling and computation
NASA Technical Reports Server (NTRS)
Kalmaz, E. E.
1986-01-01
A method for the estimation of parameters and error analysis in the development of nonlinear modeling for environmental impact assessment studies is presented. The modular computer program can interactively fit different nonlinear models to the same set of data, dynamically changing the error structure associated with observed values. Parameter estimation techniques and sequential estimation algorithms employed in parameter identification and model selection are first discussed. Then, least-square parameter estimation procedures are formulated, utilizing differential or integrated equations, and are used to define a model for association of error with experimentally observed data.
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.
Preliminary estimates of radiosonde thermistor errors
NASA Technical Reports Server (NTRS)
Schmidlin, F. J.; Luers, J. K.; Huffman, P. D.
1986-01-01
Radiosonde temperature measurements are subject to errors, not the least of which is the effect of long- and short-wave radiation. Methods of adjusting the daytime temperatures to a nighttime equivalent are used by some analysis centers. Other than providing consistent observations for analysis this procedure does not provide a true correction. The literature discusses the problem of radiosonde temperature errors but it is not apparent what effort, if any, has been taken to quantify these errors. To accomplish the latter, radiosondes containing multiple thermistors with different coatings were flown at Goddard Space Flight Center/Wallops Flight Facility. The coatings employed had different spectral characteristics and, therefore, different adsorption and emissivity properties. Discrimination of the recorded temperatures enabled day and night correction values to be determined for the US standard white-coated rod thermistor. The correction magnitudes are given and a comparison of US measured temperatures before and after correction are compared with temperatures measured with the Vaisala radiosonde. The corrections are in the proper direction, day and night, and reduce day-night temperature differences to less than 0.5 C between surface and 30 hPa. The present uncorrected temperatures used with the Viz radiosonde have day-night differences that exceed 1 C at levels below 90 hPa. Additional measurements are planned to confirm these preliminary results and determine the solar elevation angle effect on the corrections. The technique used to obtain the corrections may also be used to recover a true absolute value and might be considered a valuable contribution to the meteorological community for use as a reference instrument.
Minor Planet Observations to Identify Reference System Systematic Errors
NASA Astrophysics Data System (ADS)
Hemenway, Paul D.; Duncombe, R. L.; Castelaz, M. W.
2011-04-01
In the 1930's Brouwer proposed using minor planets to correct the Fundamental System of celestial coordinates. Since then, many projects have used or proposed to use visual, photographic, photo detector, and space based observations to that end. From 1978 to 1990, a project was undertaken at the University of Texas utilizing the long focus and attendant advantageous plate scale (c. 7.37"/mm) of the 2.1m Otto Struve reflector's Cassegrain focus. The project followed precepts given in 1979. The program had several potential advantages over previous programs including high inclination orbits to cover half the celestial sphere, and, following Kristensen, the use of crossing points to remove entirely systematic star position errors from some observations. More than 1000 plates were obtained of 34 minor planets as part of this project. In July 2010 McDonald Observatory donated the plates to the Pisgah Astronomical Research Institute (PARI) in North Carolina. PARI is in the process of renovating the Space Telescope Science Institute GAMMA II modified PDS microdensitometer to scan the plates in the archives. We plan to scan the minor planet plates, reduce the plates to the densified ICRS using the UCAC4 positions (or the best available positions at the time of the reductions), and then determine the utility of attempting to find significant systematic corrections. Here we report the current status of various aspects of the project. Support from the National Science Foundation in the last millennium is gratefully acknowledged, as is help from Judit Ries and Wayne Green in packing and transporting the plates.
A-posteriori error estimation for second order mechanical systems
NASA Astrophysics Data System (ADS)
Ruiner, Thomas; Fehr, Jörg; Haasdonk, Bernard; Eberhard, Peter
2012-06-01
One important issue for the simulation of flexible multibody systems is the reduction of the flexible bodies degrees of freedom. As far as safety questions are concerned knowledge about the error introduced by the reduction of the flexible degrees of freedom is helpful and very important. In this work, an a-posteriori error estimator for linear first order systems is extended for error estimation of mechanical second order systems. Due to the special second order structure of mechanical systems, an improvement of the a-posteriori error estimator is achieved. A major advantage of the a-posteriori error estimator is that the estimator is independent of the used reduction technique. Therefore, it can be used for moment-matching based, Gramian matrices based or modal based model reduction techniques. The capability of the proposed technique is demonstrated by the a-posteriori error estimation of a mechanical system, and a sensitivity analysis of the parameters involved in the error estimation process is conducted.
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.
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.
Systematics for checking geometric errors in CNC lathes
NASA Astrophysics Data System (ADS)
Araújo, R. P.; Rolim, T. L.
2015-10-01
Non-idealities presented in machine tools compromise directly both the geometry and the dimensions of machined parts, generating distortions in the project. Given the competitive scenario among different companies, it is necessary to have knowledge of the geometric behavior of these machines in order to be able to establish their processing capability, avoiding waste of time and materials as well as satisfying customer requirements. But despite the fact that geometric tests are important and necessary to clarify the use of the machine correctly, therefore preventing future damage, most users do not apply such tests on their machines for lack of knowledge or lack of proper motivation, basically due to two factors: long period of time and high costs of testing. This work proposes a systematics for checking straightness and perpendicularity errors in CNC lathes demanding little time and cost with high metrological reliability, to be used on factory floors of small and medium-size businesses to ensure the quality of its products and make them competitive.
Systematic Error in UAV-derived Topographic Models: The Importance of Control
NASA Astrophysics Data System (ADS)
James, M. R.; Robson, S.; d'Oleire-Oltmanns, S.
2014-12-01
UAVs equipped with consumer cameras are increasingly being used to produce high resolution digital elevation models (DEMs) for a wide variety of geoscience applications. Image processing and DEM-generation is being facilitated by parallel increases in the use of software based on 'structure from motion' algorithms. However, recent work [1] has demonstrated that image networks from UAVs, for which camera pointing directions are generally near-parallel, are susceptible to producing systematic error in the resulting topographic surfaces (a vertical 'doming'). This issue primarily reflects error in the camera lens distortion model, which is dominated by the radial K1 term. Common data processing scenarios, in which self-calibration is used to refine the camera model within the bundle adjustment, can inherently result in such systematic error via poor K1 estimates. Incorporating oblique imagery into such data sets can mitigate error by enabling more accurate calculation of camera parameters [1]. Here, using a combination of simulated image networks and real imagery collected from a fixed wing UAV, we explore the additional roles of external ground control and the precision of image measurements. We illustrate similarities and differences between a variety of structure from motion software, and underscore the importance of well distributed and suitably accurate control for projects where a demonstrated high accuracy is required. [1] James & Robson (2014) Earth Surf. Proc. Landforms, 39, 1413-1420, doi: 10.1002/esp.3609
Systematic errors in two-dimensional digital image correlation due to lens distortion
NASA Astrophysics Data System (ADS)
Pan, Bing; Yu, Liping; Wu, Dafang; Tang, Liqun
2013-02-01
Lens distortion practically presents in a real optical imaging system causing non-uniform geometric distortion in the recorded images, and gives rise to additional errors in the displacement and strain results measured by two-dimensional digital image correlation (2D-DIC). In this work, the systematic errors in the displacement and strain results measured by 2D-DIC due to lens distortion are investigated theoretically using the radial lens distortion model and experimentally through easy-to-implement rigid body, in-plane translation tests. Theoretical analysis shows that the displacement and strain errors at an interrogated image point are not only in linear proportion to the distortion coefficient of the camera lens used, but also depend on its distance relative to distortion center and its magnitude of displacement. To eliminate the systematic errors caused by lens distortion, a simple linear least-squares algorithm is proposed to estimate the distortion coefficient from the distorted displacement results of rigid body, in-plane translation tests, which can be used to correct the distorted displacement fields to obtain unbiased displacement and strain fields. Experimental results verify the correctness of the theoretical derivation and the effectiveness of the proposed lens distortion correction method.
Error Estimates for Generalized Barycentric Interpolation.
Gillette, Andrew; Rand, Alexander; Bajaj, Chandrajit
2012-10-01
We prove the optimal convergence estimate for first order interpolants used in finite element methods based on three major approaches for generalizing barycentric interpolation functions to convex planar polygonal domains. The Wachspress approach explicitly constructs rational functions, the Sibson approach uses Voronoi diagrams on the vertices of the polygon to define the functions, and the Harmonic approach defines the functions as the solution of a PDE. We show that given certain conditions on the geometry of the polygon, each of these constructions can obtain the optimal convergence estimate. In particular, we show that the well-known maximum interior angle condition required for interpolants over triangles is still required for Wachspress functions but not for Sibson functions. PMID:23338826
Error Estimates for Generalized Barycentric Interpolation
Gillette, Andrew; Rand, Alexander; Bajaj, Chandrajit
2011-01-01
We prove the optimal convergence estimate for first order interpolants used in finite element methods based on three major approaches for generalizing barycentric interpolation functions to convex planar polygonal domains. The Wachspress approach explicitly constructs rational functions, the Sibson approach uses Voronoi diagrams on the vertices of the polygon to define the functions, and the Harmonic approach defines the functions as the solution of a PDE. We show that given certain conditions on the geometry of the polygon, each of these constructions can obtain the optimal convergence estimate. In particular, we show that the well-known maximum interior angle condition required for interpolants over triangles is still required for Wachspress functions but not for Sibson functions. PMID:23338826
A study of systematic errors in the PMD CamBoard nano
NASA Astrophysics Data System (ADS)
Chow, Jacky C. K.; Lichti, Derek D.
2013-04-01
Time-of-flight-based three-dimensional cameras are the state-of-the-art imaging modality for acquiring rapid 3D position information. Unlike any other technology on the market, it can deliver 2D images co-located with distance information at every pixel location, without any shadows. Recent technological advancements have begun miniaturizing such technology to be more suitable for laptops and eventually cellphones. This paper explores the systematic errors inherent to the new PMD CamBoard nano camera. As the world's most compact 3D time-of-flight camera it has applications in a wide domain, such as gesture control and facial recognition. To model the systematic errors, a one-step point-based and plane-based bundle adjustment method is used. It simultaneously estimates all systematic errors and unknown parameters by minimizing the residuals of image measurements, distance measurements, and amplitude measurements in a least-squares sense. The presented self-calibration method only requires a standard checkerboard target on a flat plane, making it a suitable candidate for on-site calibration. In addition, because distances are only constrained to lie on a plane, the raw pixel-by-pixel distance observations can be used. This makes it possible to increase the number of distance observations in the adjustment with ease. The results from this paper indicate that amplitude dependent range errors are the dominant error source for the nano under low scattering imaging configurations. Post user self-calibration, the RMSE of the range observations reduced by almost 50%, delivering range measurements at a precision of approximately 2.5cm within a 70cm interval.
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.…
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…
Systematic vertical error in UAV-derived topographic models: Origins and solutions
NASA Astrophysics Data System (ADS)
James, Mike R.; Robson, Stuart
2014-05-01
Unmanned aerial vehicles (UAVs) equipped with consumer cameras are increasingly being used to produce high resolution digital elevation models (DEMs). However, although such DEMs may achieve centimetric detail, they can also display broad-scale systematic deformation (usually a vertical 'doming') that restricts their wider use. This effect can be particularly apparent in DEMs derived by structure-from-motion (SfM) processing, especially when control point data have not been incorporated in the bundle adjustment process. We illustrate that doming error results from a combination of inaccurate description of radial lens distortion and the use of imagery captured in near-parallel viewing directions. With such imagery, enabling camera self-calibration within the processing inherently leads to erroneous radial distortion values and associated DEM error. Using a simulation approach, we illustrate how existing understanding of systematic DEM error in stereo-pairs (from unaccounted radial distortion) up-scales in typical multiple-image blocks of UAV surveys. For image sets with dominantly parallel viewing directions, self-calibrating bundle adjustment (as normally used with images taken using consumer cameras) will not be able to derive radial lens distortion accurately, and will give associated systematic 'doming' DEM deformation. In the presence of image measurement noise (at levels characteristic of SfM software), and in the absence of control measurements, our simulations display domed deformation with amplitude of ~2 m over horizontal distances of ~100 m. We illustrate the sensitivity of this effect to variations in camera angle and flight height. Deformation will be reduced if suitable control points can be included within the bundle adjustment, but residual systematic vertical error may remain, accommodated by the estimated precision of the control measurements. Doming bias can be minimised by the inclusion of inclined images within the image set, for example
Error magnitude estimation in model-reference adaptive systems
NASA Technical Reports Server (NTRS)
Colburn, B. K.; Boland, J. S., III
1975-01-01
A second order approximation is derived from a linearized error characteristic equation for Lyapunov designed model-reference adaptive systems and is used to estimate the maximum error between the model and plant states, and the time to reach this peak following a plant perturbation. The results are applicable in the analysis of plants containing magnitude-dependent nonlinearities.
Systematic Biases in Human Heading Estimation
Cuturi, Luigi F.; MacNeilage, Paul R.
2013-01-01
Heading estimation is vital to everyday navigation and locomotion. Despite extensive behavioral and physiological research on both visual and vestibular heading estimation over more than two decades, the accuracy of heading estimation has not yet been systematically evaluated. Therefore human visual and vestibular heading estimation was assessed in the horizontal plane using a motion platform and stereo visual display. Heading angle was overestimated during forward movements and underestimated during backward movements in response to both visual and vestibular stimuli, indicating an overall multimodal bias toward lateral directions. Lateral biases are consistent with the overrepresentation of lateral preferred directions observed in neural populations that carry visual and vestibular heading information, including MSTd and otolith afferent populations. Due to this overrepresentation, population vector decoding yields patterns of bias remarkably similar to those observed behaviorally. Lateral biases are inconsistent with standard Bayesian accounts which predict that estimates should be biased toward the most common straight forward heading direction. Nevertheless, lateral biases may be functionally relevant. They effectively constitute a perceptual scale expansion around straight ahead which could allow for more precise estimation and provide a high gain feedback signal to facilitate maintenance of straight-forward heading during everyday navigation and locomotion. PMID:23457631
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.
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).
Evaluating concentration estimation errors in ELISA microarray experiments
Daly, Don Simone; White, Amanda M; Varnum, Susan M; Anderson, Kevin K; Zangar, Richard C
2005-01-01
Background Enzyme-linked immunosorbent assay (ELISA) is a standard immunoassay to estimate a protein's concentration in a sample. Deploying ELISA in a microarray format permits simultaneous estimation of the concentrations of numerous proteins in a small sample. These estimates, however, are uncertain due to processing error and biological variability. Evaluating estimation error is critical to interpreting biological significance and improving the ELISA microarray process. Estimation error evaluation must be automated to realize a reliable high-throughput ELISA microarray system. In this paper, we present a statistical method based on propagation of error to evaluate concentration estimation errors in the ELISA microarray process. Although propagation of error is central to this method and the focus of this paper, it is most 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 concentration estimation errors. Results We use an ELISA microarray investigation of breast cancer biomarkers to illustrate the evaluation of concentration estimation 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. We summarize the results with a simple, three-panel diagnostic visualization featuring a scatterplot of the standard data with logistic standard curve and 95% confidence intervals, an annotated histogram of sample measurements, and a plot of the 95% concentration coefficient of variation, or relative error, as a function of concentration. Conclusions This statistical method should be of value in the rapid evaluation and quality control of high-throughput ELISA microarray analyses
NASA Astrophysics Data System (ADS)
Gourrion, J.; Guimbard, S.; Sabia, R.; Portabella, M.; Gonzalez, V.; Turiel, A.; Ballabrera, J.; Gabarro, C.; Perez, F.; Martinez, J.
2012-04-01
boundaries such as the Sky-Earth boundary. Data acquired over the Ocean rather than over Land are prefered to characterize such errors because the variability of the emissivity sensed over the oceanic domain is an order of magnitude smaller than over land. Nevertheless, characterizing such errors over the Ocean is not a trivial task. Even if the natural variability is small, it is larger than the errors to be characterized and the characterization strategy must account for it otherwise the estimated patterns will unfortunately vary significantly with the selected dataset. The communication will present results on a systematic error characterization methodology allowing stable error pattern estimates. Particular focus will be given to the critical data selection strategy and the analysis of the X- and Y-pol patterns obtained over a wide range of SMOS subdatasets. Impact of some image reconstruction options will be evaluated. It will be shown how the methodology is also an interesting tool to diagnose specific error sources. Criticality of accurate description of Faraday rotation effects will be evidenced and latest results about the possibility to infer such information from full Stokes vector will be presented.
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.
Period Error Estimation for the Kepler Eclipsing Binary Catalog
NASA Astrophysics Data System (ADS)
Mighell, Kenneth J.; Plavchan, Peter
2013-06-01
The Kepler Eclipsing Binary Catalog (KEBC) describes 2165 eclipsing binaries identified in the 115 deg2 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 σ P ≈ - 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 ~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
Iraq War mortality estimates: A systematic review
Tapp, Christine; Burkle, Frederick M; Wilson, Kumanan; Takaro, Tim; Guyatt, Gordon H; Amad, Hani; Mills, Edward J
2008-01-01
Background In March 2003, the United States invaded Iraq. The subsequent number, rates, and causes of mortality in Iraq resulting from the war remain unclear, despite intense international attention. Understanding mortality estimates from modern warfare, where the majority of casualties are civilian, is of critical importance for public health and protection afforded under international humanitarian law. We aimed to review the studies, reports and counts on Iraqi deaths since the start of the war and assessed their methodological quality and results. Methods We performed a systematic search of 15 electronic databases from inception to January 2008. In addition, we conducted a non-structured search of 3 other databases, reviewed study reference lists and contacted subject matter experts. We included studies that provided estimates of Iraqi deaths based on primary research over a reported period of time since the invasion. We excluded studies that summarized mortality estimates and combined non-fatal injuries and also studies of specific sub-populations, e.g. under-5 mortality. We calculated crude and cause-specific mortality rates attributable to violence and average deaths per day for each study, where not already provided. Results Thirteen studies met the eligibility criteria. The studies used a wide range of methodologies, varying from sentinel-data collection to population-based surveys. Studies assessed as the highest quality, those using population-based methods, yielded the highest estimates. Average deaths per day ranged from 48 to 759. The cause-specific mortality rates attributable to violence ranged from 0.64 to 10.25 per 1,000 per year. Conclusion Our review indicates that, despite varying estimates, the mortality burden of the war and its sequelae on Iraq is large. The use of established epidemiological methods is rare. This review illustrates the pressing need to promote sound epidemiologic approaches to determining mortality estimates and to establish
Errors-in-variables modeling in optical flow estimation.
Ng, L; Solo, V
2001-01-01
Gradient-based optical flow estimation methods typically do not take into account errors in the spatial derivative estimates. The presence of these errors causes an errors-in-variables (EIV) problem. Moreover, the use of finite difference methods to calculate these derivatives ensures that the errors are strongly correlated between pixels. Total least squares (TLS) has often been used to address this EIV problem. However, its application in this context is flawed as TLS implicitly assumes that the errors between neighborhood pixels are independent. In this paper, a new optical flow estimation method (EIVM) is formulated to properly treat the EIV problem in optical flow. EIVM is based on Sprent's (1966) procedure which allows the incorporation of a general EIV model in the estimation process. In EIVM, the neighborhood size acts as a smoothing parameter. Due to the weights in the EIVM objective function, the effect of changing the neighborhood size is more complex than in other local model methods such as Lucas and Kanade (1981). These weights, which are functions of the flow estimate, can alter the effective size and orientation of the neighborhood. In this paper, we also present a data-driven method for choosing the neighborhood size based on Stein's unbiased risk estimators (SURE). PMID:18255496
NASA Astrophysics Data System (ADS)
Schumacher, Maike; Kusche, Jürgen; Döll, Petra
2016-06-01
Recently, ensemble Kalman filters (EnKF) have found increasing application for merging hydrological models with total water storage anomaly (TWSA) fields from the Gravity Recovery And Climate Experiment (GRACE) satellite mission. Previous studies have disregarded the effect of spatially correlated errors of GRACE TWSA products in their investigations. Here, for the first time, we systematically assess the impact of the GRACE error correlation structure on EnKF data assimilation into a hydrological model, i.e. on estimated compartmental and total water storages and model parameter values. Our investigations include (1) assimilating gridded GRACE-derived TWSA into the WaterGAP Global Hydrology Model and, simultaneously, calibrating its parameters; (2) introducing GRACE observations on different spatial scales; (3) modelling observation errors as either spatially white or correlated in the assimilation procedure, and (4) replacing the standard EnKF algorithm by the square root analysis scheme or, alternatively, the singular evolutive interpolated Kalman filter. Results of a synthetic experiment designed for the Mississippi River Basin indicate that the hydrological parameters are sensitive to TWSA assimilation if spatial resolution of the observation data is sufficiently high. We find a significant influence of spatial error correlation on the adjusted water states and model parameters for all implemented filter variants, in particular for subbasins with a large discrepancy between observed and initially simulated TWSA and for north-south elongated sub-basins. Considering these correlated errors, however, does not generally improve results: while some metrics indicate that it is helpful to consider the full GRACE error covariance matrix, it appears to have an adverse effect on others. We conclude that considering the characteristics of GRACE error correlation is at least as important as the selection of the spatial discretisation of TWSA observations, while the choice
NASA Astrophysics Data System (ADS)
Schumacher, Maike; Kusche, Jürgen; Döll, Petra
2016-02-01
Recently, ensemble Kalman filters (EnKF) have found increasing application for merging hydrological models with total water storage anomaly (TWSA) fields from the Gravity Recovery And Climate Experiment (GRACE) satellite mission. Previous studies have disregarded the effect of spatially correlated errors of GRACE TWSA products in their investigations. Here, for the first time, we systematically assess the impact of the GRACE error correlation structure on EnKF data assimilation into a hydrological model, i.e. on estimated compartmental and total water storages and model parameter values. Our investigations include (1) assimilating gridded GRACE-derived TWSA into the WaterGAP Global Hydrology Model and, simultaneously, calibrating its parameters; (2) introducing GRACE observations on different spatial scales; (3) modelling observation errors as either spatially white or correlated in the assimilation procedure, and (4) replacing the standard EnKF algorithm by the square root analysis scheme or, alternatively, the singular evolutive interpolated Kalman filter. Results of a synthetic experiment designed for the Mississippi River Basin indicate that the hydrological parameters are sensitive to TWSA assimilation if spatial resolution of the observation data is sufficiently high. We find a significant influence of spatial error correlation on the adjusted water states and model parameters for all implemented filter variants, in particular for subbasins with a large discrepancy between observed and initially simulated TWSA and for north-south elongated sub-basins. Considering these correlated errors, however, does not generally improve results: while some metrics indicate that it is helpful to consider the full GRACE error covariance matrix, it appears to have an adverse effect on others. We conclude that considering the characteristics of GRACE error correlation is at least as important as the selection of the spatial discretisation of TWSA observations, while the choice
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
Sensitivity analysis of DOA estimation algorithms to sensor errors
NASA Astrophysics Data System (ADS)
Li, Fu; Vaccaro, Richard J.
1992-07-01
A unified statistical performance analysis using subspace perturbation expansions is applied to subspace-based algorithms for direction-of-arrival (DOA) estimation in the presence of sensor errors. In particular, the multiple signal classification (MUSIC), min-norm, state-space realization (TAM and DDA) and estimation of signal parameters via rotational invariance techniques (ESPRIT) algorithms are analyzed. This analysis assumes that only a finite amount of data is available. An analytical expression for the mean-squared error of the DOA estimates is developed for theoretical comparison in a simple and self-contained fashion. The tractable formulas provide insight into the algorithms. Simulation results verify the analysis.
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
An analysis of the least-squares problem for the DSN systematic pointing error model
NASA Technical Reports Server (NTRS)
Alvarez, L. S.
1991-01-01
A systematic pointing error model is used to calibrate antennas in the Deep Space Network. The least squares problem is described and analyzed along with the solution methods used to determine the model's parameters. Specifically studied are the rank degeneracy problems resulting from beam pointing error measurement sets that incorporate inadequate sky coverage. A least squares parameter subset selection method is described and its applicability to the systematic error modeling process is demonstrated on Voyager 2 measurement distribution.
MONTE CARLO ERROR ESTIMATION APPLIED TO NONDESTRUCTIVE ASSAY METHODS
R. ESTEP; ET AL
2000-06-01
Monte Carlo randomization of nuclear counting data into N replicate sets is the basis of a simple and effective method for estimating error propagation through complex analysis algorithms such as those using neural networks or tomographic image reconstructions. The error distributions of properly simulated replicate data sets mimic those of actual replicate measurements and can be used to estimate the std. dev. for an assay along with other statistical quantities. We have used this technique to estimate the standard deviation in radionuclide masses determined using the tomographic gamma scanner (TGS) and combined thermal/epithermal neutron (CTEN) methods. The effectiveness of this approach is demonstrated by a comparison of our Monte Carlo error estimates with the error distributions in actual replicate measurements and simulations of measurements. We found that the std. dev. estimated this way quickly converges to an accurate value on average and has a predictable error distribution similar to N actual repeat measurements. The main drawback of the Monte Carlo method is that N additional analyses of the data are required, which may be prohibitively time consuming with slow analysis algorithms.
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.
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
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
Minimax Mean-Squared Error Location Estimation Using TOA Measurements
NASA Astrophysics Data System (ADS)
Shen, Chih-Chang; Chang, Ann-Chen
This letter deals with mobile location estimation based on a minimax mean-squared error (MSE) algorithm using time-of-arrival (TOA) measurements for mitigating the nonline-of-sight (NLOS) effects in cellular systems. Simulation results are provided for illustrating the minimax MSE estimator yields good performance than the other least squares and weighted least squares estimators under relatively low signal-to-noise ratio and moderately NLOS conditions.
NASA Astrophysics Data System (ADS)
Kanphet, J.; Suriyapee, S.; Dumrongkijudom, N.; Sanghangthum, T.; Kumkhwao, J.; Wisetrintong, M.
2016-03-01
The purpose of this study to determine the patient setup uncertainties in deep inspiration breath-hold (DIBH) radiation therapy for left breast cancer patients using real-time 3D surface tracking system. The six breast cancer patients treated by 6 MV photon beams from TrueBeam linear accelerator were selected. The patient setup errors and motion during treatment were observed and calculated for interfraction and intrafraction motions. The systematic and random errors were calculated in vertical, longitudinal and lateral directions. From 180 images tracking before and during treatment, the maximum systematic error of interfraction and intrafraction motions were 0.56 mm and 0.23 mm, the maximum random error of interfraction and intrafraction motions were 1.18 mm and 0.53 mm, respectively. The interfraction was more pronounce than the intrafraction, while the systematic error was less impact than random error. In conclusion the intrafraction motion error from patient setup uncertainty is about half of interfraction motion error, which is less impact due to the stability in organ movement from DIBH. The systematic reproducibility is also half of random error because of the high efficiency of modern linac machine that can reduce the systematic uncertainty effectively, while the random errors is uncontrollable.
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.
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.
NASA Technical Reports Server (NTRS)
Hinshaw, G.; Barnes, C.; Bennett, C. L.; Greason, M. R.; Halpern, M.; Hill, R. S.; Jarosik, N.; Kogut, A.; Limon, M.; Meyer, S. S.
2003-01-01
We describe the calibration and data processing methods used to generate full-sky maps of the cosmic microwave background (CMB) from the first year of Wilkinson Microwave Anisotropy Probe (WMAP) observations. Detailed limits on residual systematic errors are assigned based largely on analyses of the flight data supplemented, where necessary, with results from ground tests. The data are calibrated in flight using the dipole modulation of the CMB due to the observatory's motion around the Sun. This constitutes a full-beam calibration source. An iterative algorithm simultaneously fits the time-ordered data to obtain calibration parameters and pixelized sky map temperatures. The noise properties are determined by analyzing the time-ordered data with this sky signal estimate subtracted. Based on this, we apply a pre-whitening filter to the time-ordered data to remove a low level of l/f noise. We infer and correct for a small (approx. 1 %) transmission imbalance between the two sky inputs to each differential radiometer, and we subtract a small sidelobe correction from the 23 GHz (K band) map prior to further analysis. No other systematic error corrections are applied to the data. Calibration and baseline artifacts, including the response to environmental perturbations, are negligible. Systematic uncertainties are comparable to statistical uncertainties in the characterization of the beam response. Both are accounted for in the covariance matrix of the window function and are propagated to uncertainties in the final power spectrum. We characterize the combined upper limits to residual systematic uncertainties through the pixel covariance matrix.
Error estimates for Gaussian quadratures of analytic functions
NASA Astrophysics Data System (ADS)
Milovanovic, Gradimir V.; Spalevic, Miodrag M.; Pranic, Miroslav S.
2009-12-01
For analytic functions the remainder term of Gaussian quadrature formula and its Kronrod extension can be represented as a contour integral with a complex kernel. We study these kernels on elliptic contours with foci at the points ±1 and the sum of semi-axes [varrho]>1 for the Chebyshev weight functions of the first, second and third kind, and derive representation of their difference. Using this representation and following Kronrod's method of obtaining a practical error estimate in numerical integration, we derive new error estimates for Gaussian quadratures.
Application of variance components estimation to calibrate geoid error models.
Guo, Dong-Mei; Xu, Hou-Ze
2015-01-01
The method of using Global Positioning System-leveling data to obtain orthometric heights has been well studied. A simple formulation for the weighted least squares problem has been presented in an earlier work. This formulation allows one directly employing the errors-in-variables models which completely descript the covariance matrices of the observables. However, an important question that what accuracy level can be achieved has not yet to be satisfactorily solved by this traditional formulation. One of the main reasons for this is the incorrectness of the stochastic models in the adjustment, which in turn allows improving the stochastic models of measurement noises. Therefore the issue of determining the stochastic modeling of observables in the combined adjustment with heterogeneous height types will be a main focus point in this paper. Firstly, the well-known method of variance component estimation is employed to calibrate the errors of heterogeneous height data in a combined least square adjustment of ellipsoidal, orthometric and gravimetric geoid. Specifically, the iterative algorithms of minimum norm quadratic unbiased estimation are used to estimate the variance components for each of heterogeneous observations. Secondly, two different statistical models are presented to illustrate the theory. The first method directly uses the errors-in-variables as a priori covariance matrices and the second method analyzes the biases of variance components and then proposes bias-corrected variance component estimators. Several numerical test results show the capability and effectiveness of the variance components estimation procedure in combined adjustment for calibrating geoid error model. PMID:26306296
Bootstrapped DEPICT for error estimation in PET functional imaging.
Kukreja, Sunil L; Gunn, Roger N
2004-03-01
Basis pursuit denoising is a new approach for data-driven estimation of parametric images from dynamic positron emission tomography (PET) data. At present, this kinetic modeling technique does not allow for the estimation of the errors on the parameters. These estimates are useful when performing subsequent statistical analysis, such as, inference across a group of subjects or when applying partial volume correction algorithms. The difficulty with calculating the error estimates is a consequence of using an overcomplete dictionary of kinetic basis functions. In this paper, a bootstrap approach for the estimation of parameter errors from dynamic PET data is presented. This paper shows that the bootstrap can be used successfully to compute parameter errors on a region of interest or parametric image basis. Validation studies evaluate the methods performance on simulated and measured PET data ([(11)C]Diprenorphine-opiate receptor and [(11)C]Raclopride-dopamine D(2) receptor). The method is presented in the context of PET neuroreceptor binding studies, however, it has general applicability to a wide range of PET/SPET radiotracers in neurology, oncology and cardiology. PMID:15006677
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. 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
Real-Time Estimation Of Aiming Error Of Spinning Antenna
NASA Technical Reports Server (NTRS)
Dolinsky, Shlomo
1992-01-01
Spinning-spacecraft dynamics and amplitude variations in communications links studied from received-signal fluctuations. Mathematical model and associated analysis procedure provide real-time estimates of aiming error of remote rotating transmitting antenna radiating constant power in narrow, pencillike beam from spinning platform, and current amplitude of received signal. Estimates useful in analyzing and enhancing calibration of communication system, and in analyzing complicated dynamic effects in spinning platform and antenna-aiming mechanism.
Development of an integrated system for estimating human error probabilities
Auflick, J.L.; Hahn, H.A.; Morzinski, J.A.
1998-12-01
This is the final report of a three-year, Laboratory Directed Research and Development (LDRD) project at the Los Alamos National Laboratory (LANL). This project had as its main objective the development of a Human Reliability Analysis (HRA), knowledge-based expert system that would provide probabilistic estimates for potential human errors within various risk assessments, safety analysis reports, and hazard assessments. HRA identifies where human errors are most likely, estimates the error rate for individual tasks, and highlights the most beneficial areas for system improvements. This project accomplished three major tasks. First, several prominent HRA techniques and associated databases were collected and translated into an electronic format. Next, the project started a knowledge engineering phase where the expertise, i.e., the procedural rules and data, were extracted from those techniques and compiled into various modules. Finally, these modules, rules, and data were combined into a nearly complete HRA expert system.
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
Error estimates for universal back-projection-based photoacoustic tomography
NASA Astrophysics Data System (ADS)
Pandey, Prabodh K.; Naik, Naren; Munshi, Prabhat; Pradhan, Asima
2015-07-01
Photo-acoustic tomography is a hybrid imaging modality that combines the advantages of optical as well as ultrasound imaging techniques to produce images with high resolution and good contrast at high penetration depths. Choice of reconstruction algorithm as well as experimental and computational parameters plays a major role in governing the accuracy of a tomographic technique. Therefore error estimates with the variation of these parameters have extreme importance. Due to the finite support, that photo-acoustic source has, the pressure signals are not band-limited, but in practice, our detection system is. Hence the reconstructed image from ideal, noiseless band-limited forward data (for future references we will call this band-limited reconstruction) is the best approximation that we have for the unknown object. In the present study, we report the error that arises in the universal back-projection (UBP) based photo-acoustic reconstruction for planer detection geometry due to sampling and filtering of forward data (pressure signals).Computational validation of the error estimates have been carried out for synthetic phantoms. Validation with noisy forward data has also been carried out, to study the effect of noise on the error estimates derived in our work. Although here we have derived the estimates for planar detection geometry, the derivations for spherical and cylindrical geometries follow accordingly.
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.
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)
Note: Statistical errors estimation for Thomson scattering diagnostics
Maslov, M.; Beurskens, M. N. A.; Flanagan, J.; Kempenaars, M.; Collaboration: JET-EFDA Contributors
2012-09-15
A practical way of estimating statistical errors of a Thomson scattering diagnostic measuring plasma electron temperature and density is described. Analytically derived expressions are successfully tested with Monte Carlo simulations and implemented in an automatic data processing code of the JET LIDAR diagnostic.
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.
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.
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…
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.
NASA Astrophysics Data System (ADS)
Stumpe, Martin C.; Smith, J. C.; Van Cleve, J.; Jenkins, J. M.; Barclay, T. S.; Fanelli, M. N.; Girouard, F.; Kolodziejczak, J.; McCauliff, S.; Morris, R. L.; Twicken, J. D.
2012-05-01
Kepler photometric data contain significant systematic and stochastic errors as they come from the Kepler Spacecraft. The main cause for the systematic errors are changes in the photometer focus due to thermal changes in the instrument, and also residual spacecraft pointing errors. It is the main purpose of the Presearch-Data-Conditioning (PDC) module of the Kepler Science processing pipeline to remove these systematic errors from the light curves. While PDC has recently seen a dramatic performance improvement by means of a Bayesian approach to systematic error correction and improved discontinuity correction, there is still room for improvement. One problem of the current (Kepler 8.1) implementation of PDC is that injection of high frequency noise can be observed in some light curves. Although this high frequency noise does not negatively impact the general cotrending, an increased noise level can make detection of planet transits or other astrophysical signals more difficult. The origin of this noise-injection is that high frequency components of light curves sometimes get included into detrending basis vectors characterizing long term trends. Similarly, small scale features like edges can sometimes get included in basis vectors which otherwise describe low frequency trends. As a side effect to removing the trends, detrending with these basis vectors can then also mistakenly introduce these small scale features into the light curves. A solution to this problem is to perform a separation of scales, such that small scale features and large scale features are described by different basis vectors. We present our new multiscale approach that employs wavelet-based band splitting to decompose small scale from large scale features in the light curves. The PDC Bayesian detrending can then be performed on each band individually to correct small and large scale systematics independently. Funding for the Kepler Mission is provided by the NASA Science Mission Directorate.
Analysis of possible systematic errors in the Oslo method
Larsen, A. C.; Guttormsen, M.; Buerger, A.; Goergen, A.; Nyhus, H. T.; Rekstad, J.; Siem, S.; Toft, H. K.; Tveten, G. M.; Wikan, K.; Krticka, M.; Betak, E.; Schiller, A.; Voinov, A. V.
2011-03-15
In this work, we have reviewed the Oslo method, which enables the simultaneous extraction of the level density and {gamma}-ray transmission coefficient from a set of particle-{gamma} coincidence data. Possible errors and uncertainties have been investigated. Typical data sets from various mass regions as well as simulated data have been tested against the assumptions behind the data analysis.
Analysis of possible systematic errors in the Oslo method
NASA Astrophysics Data System (ADS)
Larsen, A. C.; Guttormsen, M.; Krtička, M.; Běták, E.; Bürger, A.; Görgen, A.; Nyhus, H. T.; Rekstad, J.; Schiller, A.; Siem, S.; Toft, H. K.; Tveten, G. M.; Voinov, A. V.; Wikan, K.
2011-03-01
In this work, we have reviewed the Oslo method, which enables the simultaneous extraction of the level density and γ-ray transmission coefficient from a set of particle-γ coincidence data. Possible errors and uncertainties have been investigated. Typical data sets from various mass regions as well as simulated data have been tested against the assumptions behind the data analysis.
Galli, C
2001-07-01
It is well established that the use of polychromatic radiation in spectrophotometric assays leads to excursions from the Beer-Lambert limit. This Note models the resulting systematic error as a function of assay spectral width, slope of molecular extinction coefficient, and analyte concentration. The theoretical calculations are compared with recent experimental results; a parameter is introduced which can be used to estimate the magnitude of the systematic error in both chromatographic and nonchromatographic spectrophotometric assays. It is important to realize that the polychromatic radiation employed in common laboratory equipment can yield assay errors up to approximately 4%, even at absorption levels generally considered 'safe' (i.e. absorption <1). Thus careful consideration of instrumental spectral width, analyte concentration, and slope of molecular extinction coefficient is required to ensure robust analytical methods. PMID:11377063
On causes of the origin of systematic errors in latitude determination with the Moscow PZT.
NASA Astrophysics Data System (ADS)
Volchkov, A. A.; Gutsalo, G. A.
Peculiarities of eye response during visual measurements of star positions on photographic plates are considered. It is shown that variations of the plate background density can be a source of systematic errors during latitude determinations with a PZT.
[Second victims of medical errors: a systematic review of the literature].
Panella, Massimiliano; Rinaldi, Carmela; Vanhaecht, Kris; Donnarumma, Chiara; Tozzi, Quinto; Di Stanislao, Francesco
2014-01-01
"Second victims" are health care providers who remain traumatized and suffer at the psycho-physical level after being involved in a patient adverse event. A systematic review of the literature was conducted to: a) estimate the prevalence of second victims among healthcare workers, b) describe personal and work outcomes of second victims, c) identify coping strategies used by second victims to face their problems, and d) describe current support strategies. Findings reveal that the prevalence of "second victims" of medical errors is high, ranging in four studies from 10.4% to 43.3%. Medical errors have a negative impact on healthcare providers involved, leading to physical, cognitive and behavioural symptoms including the practice of defensive medicine. Managers of health organizations need to be aware of the "second victim" phenomenon and ensure adequate support is given to healthcare providers involved. The best strategy seems to be the creation of networks of support at both the individual and organizational levels. More research is needed to evaluate the efficacy of support structures for second victims and to quantify the extent of the practice of defensive medicine following medical error. PMID:24770362
NASA Technical Reports Server (NTRS)
Davis, J. L.; Herring, T. A.; Shapiro, I. I.; Rogers, A. E. E.; Elgered, G.
1985-01-01
Analysis of very long baseline interferometry data indicates that systematic errors in prior estimates of baseline length, of order 5 cm for approximately 8000-km baselines, were due primarily to mismodeling of the electrical path length of the troposphere and mesosphere ('atmospheric delay'). Here observational evidence for the existence of such errors in the previously used models for the atmospheric delay is discussed, and a new 'mapping' function for the elevation angle dependence of this delay is developed. The delay predicted by this new mapping function differs from ray trace results by less than approximately 5 mm, at all elevations down to 5 deg elevation, and introduces errors into the estimates of baseline length of less than about 1 cm, for the multistation intercontinental experiment analyzed here.
DEB: definite error bounded tangent estimator for digital curves.
Prasad, Dilip K; Leung, Maylor K H; Quek, Chai; Brown, Michael S
2014-10-01
We propose a simple and fast method for tangent estimation of digital curves. This geometric-based method uses a small local region for tangent estimation and has a definite upper bound error for continuous as well as digital conics, i.e., circles, ellipses, parabolas, and hyperbolas. Explicit expressions of the upper bounds for continuous and digitized curves are derived, which can also be applied to nonconic curves. Our approach is benchmarked against 72 contemporary tangent estimation methods and demonstrates good performance for conic, nonconic, and noisy curves. In addition, we demonstrate a good multigrid and isotropic performance and low computational complexity of O(1) and better performance than most methods in terms of maximum and average errors in tangent computation for a large variety of digital curves. PMID:25122569
Background error covariance estimation for atmospheric CO2 data assimilation
NASA Astrophysics Data System (ADS)
Chatterjee, Abhishek; Engelen, Richard J.; Kawa, Stephan R.; Sweeney, Colm; Michalak, Anna M.
2013-09-01
any data assimilation framework, the background error covariance statistics play the critical role of filtering the observed information and determining the quality of the analysis. For atmospheric CO2 data assimilation, however, the background errors cannot be prescribed via traditional forecast or ensemble-based techniques as these fail to account for the uncertainties in the carbon emissions and uptake, or for the errors associated with the CO2 transport model. We propose an approach where the differences between two modeled CO2 concentration fields, based on different but plausible CO2 flux distributions and atmospheric transport models, are used as a proxy for the statistics of the background errors. The resulting error statistics: (1) vary regionally and seasonally to better capture the uncertainty in the background CO2 field, and (2) have a positive impact on the analysis estimates by allowing observations to adjust predictions over large areas. A state-of-the-art four-dimensional variational (4D-VAR) system developed at the European Centre for Medium-Range Weather Forecasts (ECMWF) is used to illustrate the impact of the proposed approach for characterizing background error statistics on atmospheric CO2 concentration estimates. Observations from the Greenhouse gases Observing SATellite "IBUKI" (GOSAT) are assimilated into the ECMWF 4D-VAR system along with meteorological variables, using both the new error statistics and those based on a traditional forecast-based technique. Evaluation of the four-dimensional CO2 fields against independent CO2 observations confirms that the performance of the data assimilation system improves substantially in the summer, when significant variability and uncertainty in the fluxes are present.
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.
NASA Astrophysics Data System (ADS)
Bramich, D. M.; Bachelet, E.; Alsubai, K. A.; Mislis, D.; Parley, N.
2015-05-01
Context. Understanding the source of systematic errors in photometry is essential for their calibration. Aims: We investigate how photometry performed on difference images can be influenced by errors in the photometric scale factor. Methods: We explore the equations for difference image analysis (DIA), and we derive an expression describing how errors in the difference flux, the photometric scale factor and the reference flux are propagated to the object photometry. Results: We find that the error in the photometric scale factor is important, and while a few studies have shown that it can be at a significant level, it is currently neglected by the vast majority of photometric surveys employing DIA. Conclusions: Minimising the error in the photometric scale factor, or compensating for it in a post-calibration model, is crucial for reducing the systematic errors in DIA photometry.
On systematic errors in spectral line parameters retrieved with the Voigt line profile
NASA Astrophysics Data System (ADS)
Kochanov, V. P.
2012-08-01
Systematic errors inherent in the Voigt line profile are analyzed. Molecular spectrum processing with the Voigt profile is shown to underestimate line intensities by 1-4%, with the errors in line positions being 0.0005 cm-1 and the decrease in pressure broadening coefficients varying from 5% to 55%.
NASA Astrophysics Data System (ADS)
Houweling, S.; Krol, M.; Bergamaschi, P.; Frankenberg, C.; Dlugokencky, E. J.; Morino, I.; Notholt, J.; Sherlock, V.; Wunch, D.; Beck, V.; Gerbig, C.; Chen, H.; Kort, E. A.; Röckmann, T.; Aben, I.
2013-10-01
This study investigates the use of total column CH4 (XCH4) retrievals from the SCIAMACHY satellite instrument for quantifying large scale emissions of methane. A unique data set from SCIAMACHY is available spanning almost a decade of measurements, covering a period when the global CH4 growth rate showed a marked transition from stable to increasing mixing ratios. The TM5 4DVAR inverse modelling system has been used to infer CH4 emissions from a combination of satellite and surface measurements for the period 2003-2010. In contrast to earlier inverse modelling studies, the SCIAMACHY retrievals have been corrected for systematic errors using the TCCON network of ground based Fourier transform spectrometers. The aim is to further investigate the role of bias correction of satellite data in inversions. Methods for bias correction are discussed, and the sensitivity of the optimized emissions to alternative bias correction functions is quantified. It is found that the use of SCIAMACHY retrievals in TM5 4DVAR increases the estimated inter-annual variability of large-scale fluxes by 22% compared with the use of only surface observations. The difference in global methane emissions between two year periods before and after July 2006 is estimated at 27-35 Tg yr-1. The use of SCIAMACHY retrievals causes a shift in the emissions from the extra-tropics to the tropics of 50 ± 25 Tg yr-1. The large uncertainty in this value arises from the uncertainty in the bias correction functions. Using measurements from the HIPPO and BARCA aircraft campaigns, we show that systematic errors are a main factor limiting the performance of the inversions. To further constrain tropical emissions of methane using current and future satellite missions, extended validation capabilities in the tropics are of critical importance.
NASA Astrophysics Data System (ADS)
Houweling, S.; Krol, M.; Bergamaschi, P.; Frankenberg, C.; Dlugokencky, E. J.; Morino, I.; Notholt, J.; Sherlock, V.; Wunch, D.; Beck, V.; Gerbig, C.; Chen, H.; Kort, E. A.; Röckmann, T.; Aben, I.
2014-04-01
This study investigates the use of total column CH4 (XCH4) retrievals from the SCIAMACHY satellite instrument for quantifying large-scale emissions of methane. A unique data set from SCIAMACHY is available spanning almost a decade of measurements, covering a period when the global CH4 growth rate showed a marked transition from stable to increasing mixing ratios. The TM5 4DVAR inverse modelling system has been used to infer CH4 emissions from a combination of satellite and surface measurements for the period 2003-2010. In contrast to earlier inverse modelling studies, the SCIAMACHY retrievals have been corrected for systematic errors using the TCCON network of ground-based Fourier transform spectrometers. The aim is to further investigate the role of bias correction of satellite data in inversions. Methods for bias correction are discussed, and the sensitivity of the optimized emissions to alternative bias correction functions is quantified. It is found that the use of SCIAMACHY retrievals in TM5 4DVAR increases the estimated inter-annual variability of large-scale fluxes by 22% compared with the use of only surface observations. The difference in global methane emissions between 2-year periods before and after July 2006 is estimated at 27-35 Tg yr-1. The use of SCIAMACHY retrievals causes a shift in the emissions from the extra-tropics to the tropics of 50 ± 25 Tg yr-1. The large uncertainty in this value arises from the uncertainty in the bias correction functions. Using measurements from the HIPPO and BARCA aircraft campaigns, we show that systematic errors in the SCIAMACHY measurements are a main factor limiting the performance of the inversions. To further constrain tropical emissions of methane using current and future satellite missions, extended validation capabilities in the tropics are of critical importance.
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.
Emitter location independent of systematic errors in direction finders
NASA Astrophysics Data System (ADS)
Mahapatra, P. R.
1980-11-01
A scheme is suggested for the passive location of radio emitter position by using a mobile direction finder. The vehicle carrying the direction finder is made to maneuver such that the apparent direction of arrival is held constant. The resulting trajectory of the vehicle is a logarithmic spiral. The true direction of arrival can be obtained by monitoring the parameters of the spiral trajectory without using the value of the direction finder reading. Two specific algorithms to eliminate direction finder bias are presented and their sensitivity to random errors in measurement assessed.
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.
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).
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.
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
Stress Recovery and Error Estimation for Shell Structures
NASA Technical Reports Server (NTRS)
Yazdani, A. A.; Riggs, H. R.; Tessler, A.
2000-01-01
The Penalized Discrete Least-Squares (PDLS) stress recovery (smoothing) technique developed for two dimensional linear elliptic problems is adapted here to three-dimensional shell structures. The surfaces are restricted to those which have a 2-D parametric representation, or which can be built-up of such surfaces. The proposed strategy involves mapping the finite element results to the 2-D parametric space which describes the geometry, and smoothing is carried out in the parametric space using the PDLS-based Smoothing Element Analysis (SEA). Numerical results for two well-known shell problems are presented to illustrate the performance of SEA/PDLS for these problems. The recovered stresses are used in the Zienkiewicz-Zhu a posteriori error estimator. The estimated errors are used to demonstrate the performance of SEA-recovered stresses in automated adaptive mesh refinement of shell structures. The numerical results are encouraging. Further testing involving more complex, practical structures is necessary.
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
Efficiently estimating salmon escapement uncertainty using systematically sampled data
Reynolds, Joel H.; Woody, Carol Ann; Gove, Nancy E.; Fair, Lowell F.
2007-01-01
Fish escapement is generally monitored using nonreplicated systematic sampling designs (e.g., via visual counts from towers or hydroacoustic counts). These sampling designs support a variety of methods for estimating the variance of the total escapement. Unfortunately, all the methods give biased results, with the magnitude of the bias being determined by the underlying process patterns. Fish escapement commonly exhibits positive autocorrelation and nonlinear patterns, such as diurnal and seasonal patterns. For these patterns, poor choice of variance estimator can needlessly increase the uncertainty managers have to deal with in sustaining fish populations. We illustrate the effect of sampling design and variance estimator choice on variance estimates of total escapement for anadromous salmonids from systematic samples of fish passage. Using simulated tower counts of sockeye salmon Oncorhynchus nerka escapement on the Kvichak River, Alaska, five variance estimators for nonreplicated systematic samples were compared to determine the least biased. Using the least biased variance estimator, four confidence interval estimators were compared for expected coverage and mean interval width. Finally, five systematic sampling designs were compared to determine the design giving the smallest average variance estimate for total annual escapement. For nonreplicated systematic samples of fish escapement, all variance estimators were positively biased. Compared to the other estimators, the least biased estimator reduced bias by, on average, from 12% to 98%. All confidence intervals gave effectively identical results. Replicated systematic sampling designs consistently provided the smallest average estimated variance among those compared.
Gross error detection and stage efficiency estimation in a separation process
Serth, R.W.; Srikanth, B. . Dept. of Chemical and Natural Gas Engineering); Maronga, S.J. . Dept. of Chemical and Process Engineering)
1993-10-01
Accurate process models are required for optimization and control in chemical plants and petroleum refineries. These models involve various equipment parameters, such as stage efficiencies in distillation columns, the values of which must be determined by fitting the models to process data. Since the data contain random and systematic measurement errors, some of which may be large (gross errors), they must be reconciled to obtain reliable estimates of equipment parameters. The problem thus involves parameter estimation coupled with gross error detection and data reconciliation. MacDonald and Howat (1988) studied the above problem for a single-stage flash distillation process. Their analysis was based on the definition of stage efficiency due to Hausen, which has some significant disadvantages in this context, as discussed below. In addition, they considered only data sets which contained no gross errors. The purpose of this article is to extend the above work by considering alternative definitions of state efficiency and efficiency estimation in the presence of gross errors.
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. PMID:24027379
Interpolation Error Estimates for Mean Value Coordinates over Convex Polygons
Rand, Alexander; Gillette, Andrew; Bajaj, Chandrajit
2012-01-01
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. PMID:24027379
Petyuk, Vladislav A.; Mayampurath, Anoop M.; Monroe, Matthew E.; Polpitiya, Ashoka D.; Purvine, Samuel O.; Anderson, Gordon A.; Camp, David G.; Smith, Richard D.
2010-01-01
Hybrid two-stage mass spectrometers capable of both highly accurate mass measurement and high throughput MS/MS fragmentation have become widely available in recent years, allowing for significantly better discrimination between true and false MS/MS peptide identifications by the application of a relatively narrow window for maximum allowable deviations of measured parent ion masses. To fully gain the advantage of highly accurate parent ion mass measurements, it is important to limit systematic mass measurement errors. Based on our previous studies of systematic biases in mass measurement errors, here, we have designed an algorithm and software tool that eliminates the systematic errors from the peptide ion masses in MS/MS data. We demonstrate that the elimination of the systematic mass measurement errors allows for the use of tighter criteria on the deviation of measured mass from theoretical monoisotopic peptide mass, resulting in a reduction of both false discovery and false negative rates of peptide identification. A software implementation of this algorithm called DtaRefinery reads a set of fragmentation spectra, searches for MS/MS peptide identifications using a FASTA file containing expected protein sequences, fits a regression model that can estimate systematic errors, and then corrects the parent ion mass entries by removing the estimated systematic error components. The output is a new file with fragmentation spectra with updated parent ion masses. The software is freely available. PMID:20019053
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.
Second-order systematic errors in Mueller matrix dual rotating compensator ellipsometry.
Broch, Laurent; En Naciri, Aotmane; Johann, Luc
2010-06-10
We investigate the systematic errors at the second order for a Mueller matrix ellipsometer in the dual rotating compensator configuration. Starting from a general formalism, we derive explicit second-order errors in the Mueller matrix coefficients of a given sample. We present the errors caused by the azimuthal inaccuracy of the optical components and their influences on the measurements. We demonstrate that the methods based on four-zone or two-zone averaging measurement are effective to vanish the errors due to the compensators. For the other elements, it is shown that the systematic errors at the second order can be canceled only for some coefficients of the Mueller matrix. The calibration step for the analyzer and the polarizer is developed. This important step is necessary to avoid the azimuthal inaccuracy in such elements. Numerical simulations and experimental measurements are presented and discussed. PMID:20539341
Systematic lossy error protection of video based on H.264/AVC redundant slices
NASA Astrophysics Data System (ADS)
Rane, Shantanu; Girod, Bernd
2006-01-01
We propose the use of H.264 redundant slices for Systematic Lossy Error Protection (SLEP) of a video signal transmitted over an error-prone channel. In SLEP, the video signal is transmitted to the decoder without channel coding. Additionally, a Wyner-Ziv encoded version of the video signal is transmitted in order to provide error-resilience. In the event of channel errors, the Wyner-Ziv description is decoded as a substitute for the error-prone portions of the primary video signal. Since the Wyner-Ziv description is typically coarser than the primary video signal, SLEP is a lossy error protection technique which trades-off residual quantization distortion for improved error-resilience properties, such as graceful degradation of decoder picture quality. We describe how H.264 redundant slices can be used to generate the Wyner-Ziv description, and present simulation results to demonstrate the advantages of this method over traditional methods such as FEC.
Kassabian, Nazelie; Lo Presti, Letizia; 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
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
Drug treatment of inborn errors of metabolism: a systematic review
Alfadhel, Majid; Al-Thihli, Khalid; Moubayed, Hiba; Eyaid, Wafaa; Al-Jeraisy, Majed
2013-01-01
Background The treatment of inborn errors of metabolism (IEM) has seen significant advances over the last decade. Many medicines have been developed and the survival rates of some patients with IEM have improved. Dosages of drugs used for the treatment of various IEM can be obtained from a range of sources but tend to vary among these sources. Moreover, the published dosages are not usually supported by the level of existing evidence, and they are commonly based on personal experience. Methods A literature search was conducted to identify key material published in English in relation to the dosages of medicines used for specific IEM. Textbooks, peer reviewed articles, papers and other journal items were identified. The PubMed and Embase databases were searched for material published since 1947 and 1974, respectively. The medications found and their respective dosages were graded according to their level of evidence, using the grading system of the Oxford Centre for Evidence-Based Medicine. Results 83 medicines used in various IEM were identified. The dosages of 17 medications (21%) had grade 1 level of evidence, 61 (74%) had grade 4, two medications were in level 2 and 3 respectively, and three had grade 5. Conclusions To the best of our knowledge, this is the first review to address this matter and the authors hope that it will serve as a quickly accessible reference for medications used in this important clinical field. PMID:23532493
A study for systematic errors of the GLA forecast model in tropical regions
NASA Technical Reports Server (NTRS)
Chen, Tsing-Chang; Baker, Wayman E.; Pfaendtner, James; Corrigan, Martin
1988-01-01
From the sensitivity studies performed with the Goddard Laboratory for Atmospheres (GLA) analysis/forecast system, it was revealed that the forecast errors in the tropics affect the ability to forecast midlatitude weather in some cases. Apparently, the forecast errors occurring in the tropics can propagate to midlatitudes. Therefore, the systematic error analysis of the GLA forecast system becomes a necessary step in improving the model's forecast performance. The major effort of this study is to examine the possible impact of the hydrological-cycle forecast error on dynamical fields in the GLA forecast system.
CADNA: a library for estimating round-off error propagation
NASA Astrophysics Data System (ADS)
Jézéquel, Fabienne; Chesneaux, Jean-Marie
2008-06-01
The CADNA library enables one to estimate round-off error propagation using a probabilistic approach. With CADNA the numerical quality of any simulation program can be controlled. Furthermore by detecting all the instabilities which may occur at run time, a numerical debugging of the user code can be performed. CADNA provides new numerical types on which round-off errors can be estimated. Slight modifications are required to control a code with CADNA, mainly changes in variable declarations, input and output. This paper describes the features of the CADNA library and shows how to interpret the information it provides concerning round-off error propagation in a code. Program summaryProgram title:CADNA Catalogue identifier:AEAT_v1_0 Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AEAT_v1_0.html Program obtainable from:CPC Program Library, Queen's University, Belfast, N. Ireland Licensing provisions:Standard CPC licence, http://cpc.cs.qub.ac.uk/licence/licence.html No. of lines in distributed program, including test data, etc.:53 420 No. of bytes in distributed program, including test data, etc.:566 495 Distribution format:tar.gz Programming language:Fortran Computer:PC running LINUX with an i686 or an ia64 processor, UNIX workstations including SUN, IBM Operating system:LINUX, UNIX Classification:4.14, 6.5, 20 Nature of problem:A simulation program which uses floating-point arithmetic generates round-off errors, due to the rounding performed at each assignment and at each arithmetic operation. Round-off error propagation may invalidate the result of a program. The CADNA library enables one to estimate round-off error propagation in any simulation program and to detect all numerical instabilities that may occur at run time. Solution method:The CADNA library [1] implements Discrete Stochastic Arithmetic [2-4] which is based on a probabilistic model of round-off errors. The program is run several times with a random rounding mode generating different results each
Xiong, Kun; Jiang, Jie
2015-01-01
Compared with traditional star trackers, intensified high-accuracy star trackers equipped with an image intensifier exhibit overwhelmingly superior dynamic performance. However, the multiple-fiber-optic faceplate structure in the image intensifier complicates the optoelectronic detecting system of star trackers and may cause considerable systematic centroid errors and poor attitude accuracy. All the sources of systematic centroid errors related to fiber optic faceplates (FOFPs) throughout the detection process of the optoelectronic system were analyzed. Based on the general expression of the systematic centroid error deduced in the frequency domain and the FOFP modulation transfer function, an accurate expression that described the systematic centroid error of FOFPs was obtained. Furthermore, reduction of the systematic error between the optical lens and the input FOFP of the intensifier, the one among multiple FOFPs and the one between the output FOFP of the intensifier and the imaging chip of the detecting system were discussed. Two important parametric constraints were acquired from the analysis. The correctness of the analysis on the optoelectronic detecting system was demonstrated through simulation and experiment. PMID:26016920
Xiong, Kun; Jiang, Jie
2015-01-01
Compared with traditional star trackers, intensified high-accuracy star trackers equipped with an image intensifier exhibit overwhelmingly superior dynamic performance. However, the multiple-fiber-optic faceplate structure in the image intensifier complicates the optoelectronic detecting system of star trackers and may cause considerable systematic centroid errors and poor attitude accuracy. All the sources of systematic centroid errors related to fiber optic faceplates (FOFPs) throughout the detection process of the optoelectronic system were analyzed. Based on the general expression of the systematic centroid error deduced in the frequency domain and the FOFP modulation transfer function, an accurate expression that described the systematic centroid error of FOFPs was obtained. Furthermore, reduction of the systematic error between the optical lens and the input FOFP of the intensifier, the one among multiple FOFPs and the one between the output FOFP of the intensifier and the imaging chip of the detecting system were discussed. Two important parametric constraints were acquired from the analysis. The correctness of the analysis on the optoelectronic detecting system was demonstrated through simulation and experiment. PMID:26016920
SU-E-T-613: Dosimetric Consequences of Systematic MLC Leaf Positioning Errors
Kathuria, K; Siebers, J
2014-06-01
Purpose: The purpose of this study is to determine the dosimetric consequences of systematic MLC leaf positioning errors for clinical IMRT patient plans so as to establish detection tolerances for quality assurance programs. Materials and Methods: Dosimetric consequences were simulated by extracting mlc delivery instructions from the TPS, altering the file by the specified error, reloading the delivery instructions into the TPS, recomputing dose, and extracting dose-volume metrics for one head-andneck and one prostate patient. Machine error was simulated by offsetting MLC leaves in Pinnacle in a systematic way. Three different algorithms were followed for these systematic offsets, and are as follows: a systematic sequential one-leaf offset (one leaf offset in one segment per beam), a systematic uniform one-leaf offset (same one leaf offset per segment per beam) and a systematic offset of a given number of leaves picked uniformly at random from a given number of segments (5 out of 10 total). Dose to the PTV and normal tissue was simulated. Results: A systematic 5 mm offset of 1 leaf for all delivery segments of all beams resulted in a maximum PTV D98 deviation of 1%. Results showed very low dose error in all reasonably possible machine configurations, rare or otherwise, which could be simulated. Very low error in dose to PTV and OARs was shown in all possible cases of one leaf per beam per segment being offset (<1%), or that of only one leaf per beam being offset (<.2%). The errors resulting from a high number of adjacent leaves (maximum of 5 out of 60 total leaf-pairs) being simultaneously offset in many (5) of the control points (total 10–18 in all beams) per beam, in both the PTV and the OARs analyzed, were similarly low (<2–3%). Conclusions: The above results show that patient shifts and anatomical changes are the main source of errors in dose delivered, not machine delivery. These two sources of error are “visually complementary” and uncorrelated
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.
NASA Astrophysics Data System (ADS)
Wang, Jie; Liang, Xingdong; Chen, Longyong; Ding, Chibiao
2015-01-01
Orthogonal frequency division multiplexing (OFDM) chirp waveform, which is composed of two successive identical linear frequency modulated subpulses, is a newly proposed orthogonal waveform scheme for multiinput multioutput synthetic aperture radar (SAR) systems. However, according to the waveform model, radar systematic error, which introduces phase or amplitude difference between the subpulses of the OFDM waveform, significantly degrades the orthogonality. The impact of radar systematic error on the waveform orthogonality is mainly caused by the systematic nonlinearity rather than the thermal noise or the frequency-dependent systematic error. Due to the influence of the causal filters, the first subpulse leaks into the second one. The leaked signal interacts with the second subpulse in the nonlinear components of the transmitter. This interaction renders a dramatic phase distortion in the beginning of the second subpulse. The resultant distortion, which leads to a phase difference between the subpulses, seriously damages the waveform's orthogonality. The impact of radar systematic error on the waveform orthogonality is addressed. Moreover, the impact of the systematic nonlinearity on the waveform is avoided by adding a standby between the subpulses. Theoretical analysis is validated by practical experiments based on a C-band SAR system.
SYSTEMATIC ERROR REDUCTION: NON-TILTED REFERENCE BEAM METHOD FOR LONG TRACE PROFILER.
QIAN,S.; QIAN, K.; HONG, Y.; SENG, L.; HO, T.; TAKACS, P.
2007-08-25
Systematic error in the Long Trace Profiler (LTP) has become the major error source as measurement accuracy enters the nanoradian and nanometer regime. Great efforts have been made to reduce the systematic error at a number of synchrotron radiation laboratories around the world. Generally, the LTP reference beam has to be tilted away from the optical axis in order to avoid fringe overlap between the sample and reference beams. However, a tilted reference beam will result in considerable systematic error due to optical system imperfections, which is difficult to correct. Six methods of implementing a non-tilted reference beam in the LTP are introduced: (1) application of an external precision angle device to measure and remove slide pitch error without a reference beam, (2) independent slide pitch test by use of not tilted reference beam, (3) non-tilted reference test combined with tilted sample, (4) penta-prism scanning mode without a reference beam correction, (5) non-tilted reference using a second optical head, and (6) alternate switching of data acquisition between the sample and reference beams. With a non-tilted reference method, the measurement accuracy can be improved significantly. Some measurement results are presented. Systematic error in the sample beam arm is not addressed in this paper and should be treated separately.
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.
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.
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.
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.
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…
Sources of systematic error in calibrated BOLD based mapping of baseline oxygen extraction fraction.
Blockley, Nicholas P; Griffeth, Valerie E M; Stone, Alan J; Hare, Hannah V; Bulte, Daniel P
2015-11-15
Recently a new class of calibrated blood oxygen level dependent (BOLD) functional magnetic resonance imaging (MRI) methods were introduced to quantitatively measure the baseline oxygen extraction fraction (OEF). These methods rely on two respiratory challenges and a mathematical model of the resultant changes in the BOLD functional MRI signal to estimate the OEF. However, this mathematical model does not include all of the effects that contribute to the BOLD signal, it relies on several physiological assumptions and it may be affected by intersubject physiological variability. The aim of this study was to investigate these sources of systematic error and their effect on estimating the OEF. This was achieved through simulation using a detailed model of the BOLD signal. Large ranges for intersubject variability in baseline physiological parameters such as haematocrit and cerebral blood volume were considered. Despite this the uncertainty in the relationship between the measured BOLD signals and the OEF was relatively low. Investigations of the physiological assumptions that underlie the mathematical model revealed that OEF measurements are likely to be overestimated if oxygen metabolism changes during hypercapnia or cerebral blood flow changes under hyperoxia. Hypoxic hypoxia was predicted to result in an underestimation of the OEF, whilst anaemic hypoxia was found to have only a minimal effect. PMID:26254114
NASA Astrophysics Data System (ADS)
Sun, Chuanzhi; Wang, Lei; Tan, Jiubin; Zhao, Bo; Tang, Yangchao
2016-02-01
The paper designs a roundness measurement model with multi-systematic error, which takes eccentricity, probe offset, radius of tip head of probe, and tilt error into account for roundness measurement of cylindrical components. The effects of the systematic errors and radius of components are analysed in the roundness measurement. The proposed method is built on the instrument with a high precision rotating spindle. The effectiveness of the proposed method is verified by experiment with the standard cylindrical component, which is measured on a roundness measuring machine. Compared to the traditional limacon measurement model, the accuracy of roundness measurement can be increased by about 2.2 μm using the proposed roundness measurement model for the object with a large radius of around 37 mm. The proposed method can improve the accuracy of roundness measurement and can be used for error separation, calibration, and comparison, especially for cylindrical components with a large radius.
Sun, Chuanzhi; Wang, Lei; Tan, Jiubin; Zhao, Bo; Tang, Yangchao
2016-02-01
The paper designs a roundness measurement model with multi-systematic error, which takes eccentricity, probe offset, radius of tip head of probe, and tilt error into account for roundness measurement of cylindrical components. The effects of the systematic errors and radius of components are analysed in the roundness measurement. The proposed method is built on the instrument with a high precision rotating spindle. The effectiveness of the proposed method is verified by experiment with the standard cylindrical component, which is measured on a roundness measuring machine. Compared to the traditional limacon measurement model, the accuracy of roundness measurement can be increased by about 2.2 μm using the proposed roundness measurement model for the object with a large radius of around 37 mm. The proposed method can improve the accuracy of roundness measurement and can be used for error separation, calibration, and comparison, especially for cylindrical components with a large radius. PMID:26931894
NASA Astrophysics Data System (ADS)
Lamoreaux, Steve; Wong, Douglas
2015-06-01
The basic theory of temporal mechanical fluctuation induced systematic errors in Casimir force experiments is developed and applications of this theory to several experiments is reviewed. This class of systematic error enters in a manner similar to the usual surface roughness correction, but unlike the treatment of surface roughness for which an exact result requires an electromagnetic mode analysis, time dependent fluctuations can be treated exactly, assuming the fluctuation times are much longer than the zero point and thermal fluctuation correlation times of the electromagnetic field between the plates. An experimental method for measuring absolute distance with high bandwidth is also described and measurement data presented.
Lamoreaux, Steve; Wong, Douglas
2015-06-01
The basic theory of temporal mechanical fluctuation induced systematic errors in Casimir force experiments is developed and applications of this theory to several experiments is reviewed. This class of systematic error enters in a manner similar to the usual surface roughness correction, but unlike the treatment of surface roughness for which an exact result requires an electromagnetic mode analysis, time dependent fluctuations can be treated exactly, assuming the fluctuation times are much longer than the zero point and thermal fluctuation correlation times of the electromagnetic field between the plates. An experimental method for measuring absolute distance with high bandwidth is also described and measurement data presented. PMID:25965319
NASA Astrophysics Data System (ADS)
Veit, Th.; Friedrich, J.; Offermann, E. A. J. M.
1993-12-01
The procedures used to model [J. Friedrich, Nucl. Instr. and Meth. A 293 (1990) 575] or to determine [N. Voegler et al., Nucl. Instr. and Meth. A 249 (1986) 337, H. Blok et al., ibid., vol. A 262 (1987) 291, and E.A.J.M. Offermann et al., ibid., vol. A 262 (1987) 298] the mapping properties of a magnetic spectrometer are based on a minimization of the variance of target coordinates. We show that backtracing with matrix elements, determined in this way, may contain systematic errors. As alternative, we propose to minimize the variance of the detector coordinates. This procedure avoids these systematic errors.
The Origin of Systematic Errors in the GCM Simulation of ITCZ Precipitation
NASA Technical Reports Server (NTRS)
Chao, Winston C.; Suarez, M. J.; Bacmeister, J. T.; Chen, B.; Takacs, L. L.
2006-01-01
Previous GCM studies have found that the systematic errors in the GCM simulation of the seasonal mean ITCZ intensity and location could be substantially corrected by adding suitable amount of rain re-evaporation or cumulus momentum transport. However, the reason(s) for these systematic errors and solutions has remained a puzzle. In this work the knowledge gained from previous studies of the ITCZ in an aqua-planet model with zonally uniform SST is applied to solve this puzzle. The solution is supported by further aqua-planet and full model experiments using the latest version of the Goddard Earth Observing System GCM.
Wu Yan; Shannon, Mark A.
2006-04-15
The dependence of the contact potential difference (CPD) reading on the ac driving amplitude in scanning Kelvin probe microscope (SKPM) hinders researchers from quantifying true material properties. We show theoretically and demonstrate experimentally that an ac driving amplitude dependence in the SKPM measurement can come from a systematic error, and it is common for all tip sample systems as long as there is a nonzero tracking error in the feedback control loop of the instrument. We further propose a methodology to detect and to correct the ac driving amplitude dependent systematic error in SKPM measurements. The true contact potential difference can be found by applying a linear regression to the measured CPD versus one over ac driving amplitude data. Two scenarios are studied: (a) when the surface being scanned by SKPM is not semiconducting and there is an ac driving amplitude dependent systematic error; (b) when a semiconductor surface is probed and asymmetric band bending occurs when the systematic error is present. Experiments are conducted using a commercial SKPM and CPD measurement results of two systems: platinum-iridium/gap/gold and platinum-iridium/gap/thermal oxide/silicon are discussed.
Richardson Extrapolation Based Error Estimation for Stochastic Kinetic Plasma Simulations
NASA Astrophysics Data System (ADS)
Cartwright, Keigh
2014-10-01
To have a high degree of confidence in simulations one needs code verification, validation, solution verification and uncertainty qualification. This talk will focus on numerical error estimation for stochastic kinetic plasma simulations using the Particle-In-Cell (PIC) method and how it impacts the code verification and validation. A technique Is developed to determine the full converged solution with error bounds from the stochastic output of a Particle-In-Cell code with multiple convergence parameters (e.g. ?t, ?x, and macro particle weight). The core of this method is a multi parameter regression based on a second-order error convergence model with arbitrary convergence rates. Stochastic uncertainties in the data set are propagated through the model usin gstandard bootstrapping on a redundant data sets, while a suite of nine regression models introduces uncertainties in the fitting process. These techniques are demonstrated on Flasov-Poisson Child-Langmuir diode, relaxation of an electro distribution to a Maxwellian due to collisions and undriven sheaths and pre-sheaths. Sandia National Laboratories is a multie-program laboratory managed and operated by Sandia Corporation, a wholly owned subisidiary of Lockheed Martin Corporation, for the U.S. DOE's National Nuclear Security Administration under Contract DE-AC04-94AL85000.
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.
NASA Astrophysics Data System (ADS)
Xie, J.; Zhu, J.; Yan, C.
2006-07-01
The Array for Real-time Geostrophic Oceanography (ARGO) project creates a unique opportunity to estimate the absolute velocity at mid-depths of the global oceans. However, the estimation can only be made based on float surface trajectories. The diving and resurfacing positions of the float are not available in its trajectory file. This surface drifting effect makes it difficult to estimate mid-depth current. Moreover, the vertical shear during decent or ascent between parking depth and the surface is another major error source. In this presentation, we first quantify the contributions of the two major error sources using the current estimates from Estimating the Climate and Circulation of the Ocean (ECCO) and find that the surface drifting is a primary error source. Then, a sequential surface trajectory prediction/estimation scheme based on Kalman Filter is introduced and implemented to reduce the surface drifting error in the Pacific during November 2001 to October 2004. On average, the error of the estimated velocities is greatly reduced from 2.7 to 0.2 cm s if neglecting the vertical shear. These velocities with relative error less than 25% are analyzed and compared with previous studies on mid-depth currents. The current system derived from ARGO floats in Pacific at 1000 and 2000 dB is comparable to other measured by ADCP (Reid, 1997; Firing et al., 1998). This presentation is based on two submitted manuscripts of the same authors (Xie and Zhu, 2006; Zhu et al., 2006). More detailed results can be found in the two manuscripts.
Models and error analyses in urban air quality estimation
NASA Technical Reports Server (NTRS)
Englar, T., Jr.; Diamante, J. M.; Jazwinski, A. H.
1976-01-01
Estimation theory has been applied to a wide range of aerospace problems. Application of this expertise outside the aerospace field has been extremely limited, however. This paper describes the use of covariance error analysis techniques in evaluating the accuracy of pollution estimates obtained from a variety of concentration measuring devices. It is shown how existing software developed for aerospace applications can be applied to the estimation of pollution through the processing of measurement types involving a range of spatial and temporal responses. The modeling of pollutant concentration by meandering Gaussian plumes is described in some detail. Time averaged measurements are associated with a model of the average plume, using some of the same state parameters and thus avoiding the problem of state memory. The covariance analysis has been implemented using existing batch estimation software. This usually involves problems in handling dynamic noise; however, the white dynamic noise has been replaced by a band-limited process which can be easily accommodated by the software.
NASA Astrophysics Data System (ADS)
Martin, Peter R.; Cool, Derek W.; Romagnoli, Cesare; Fenster, Aaron; Ward, Aaron D.
2015-03-01
Magnetic resonance imaging (MRI)-targeted, 3D transrectal ultrasound (TRUS)-guided "fusion" prostate biopsy aims to reduce the 21-47% false negative rate of clinical 2D TRUS-guided sextant biopsy. Although it has been reported to double the positive yield, MRI-targeted biopsy still has a substantial false negative rate. Therefore, we propose optimization of biopsy targeting to meet the clinician's desired tumor sampling probability, optimizing needle targets within each tumor and accounting for uncertainties due to guidance system errors, image registration errors, and irregular tumor shapes. As a step toward this optimization, we obtained multiparametric MRI (mpMRI) and 3D TRUS images from 49 patients. A radiologist and radiology resident contoured 81 suspicious regions, yielding 3D surfaces that were registered to 3D TRUS. We estimated the probability, P, of obtaining a tumor sample with a single biopsy, and investigated the effects of systematic errors and anisotropy on P. Our experiments indicated that a biopsy system's lateral and elevational errors have a much greater effect on sampling probabilities, relative to its axial error. We have also determined that for a system with RMS error of 3.5 mm, tumors of volume 1.9 cm3 and smaller may require more than one biopsy core to ensure 95% probability of a sample with 50% core involvement, and tumors 1.0 cm3 and smaller may require more than two cores.
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.
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.
Dede, Adam J O; Squire, Larry R; Wixted, John T
2014-01-01
For more than a decade, the high threshold dual process (HTDP) model has served as a guide for studying the functional neuroanatomy of recognition memory. The HTDP model's utility has been that it provides quantitative estimates of recollection and familiarity, two processes thought to support recognition ability. Important support for the model has been the observation that it fits experimental data well. The continuous dual process (CDP) model also fits experimental data well. However, this model does not provide quantitative estimates of recollection and familiarity, making it less immediately useful for illuminating the functional neuroanatomy of recognition memory. These two models are incompatible and cannot both be correct, and an alternative method of model comparison is needed. We tested for systematic errors in each model's ability to fit recognition memory data from four independent data sets from three different laboratories. Across participants and across data sets, the HTDP model (but not the CDP model) exhibited systematic error. In addition, the pattern of errors exhibited by the HTDP model was predicted by the CDP model. We conclude that the CDP model provides a better account of recognition memory than the HTDP model. PMID:24184486
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.
Zhu, Fangqiang; Hummer, Gerhard
2012-01-01
The weighted histogram analysis method (WHAM) has become the standard technique for the analysis of umbrella sampling simulations. In this paper, we address the challenges (1) of obtaining fast and accurate solutions of the coupled nonlinear WHAM equations, (2) of quantifying the statistical errors of the resulting free energies, (3) of diagnosing possible systematic errors, and (4) of optimal allocation of the computational resources. Traditionally, the WHAM equations are solved by a fixed-point direct iteration method, despite poor convergence and possible numerical inaccuracies in the solutions. Here we instead solve the mathematically equivalent problem of maximizing a target likelihood function, by using superlinear numerical optimization algorithms with a significantly faster convergence rate. To estimate the statistical errors in one-dimensional free energy profiles obtained from WHAM, we note that for densely spaced umbrella windows with harmonic biasing potentials, the WHAM free energy profile can be approximated by a coarse-grained free energy obtained by integrating the mean restraining forces. The statistical errors of the coarse-grained free energies can be estimated straightforwardly and then used for the WHAM results. A generalization to multidimensional WHAM is described. We also propose two simple statistical criteria to test the consistency between the histograms of adjacent umbrella windows, which help identify inadequate sampling and hysteresis in the degrees of freedom orthogonal to the reaction coordinate. Together, the estimates of the statistical errors and the diagnostics of inconsistencies in the potentials of mean force provide a basis for the efficient allocation of computational resources in free energy simulations. PMID:22109354
Error estimation for CFD aeroheating prediction under rarefied flow condition
NASA Astrophysics Data System (ADS)
Jiang, Yazhong; Gao, Zhenxun; Jiang, Chongwen; Lee, Chunhian
2014-12-01
Both direct simulation Monte Carlo (DSMC) and Computational Fluid Dynamics (CFD) methods have become widely used for aerodynamic prediction when reentry vehicles experience different flow regimes during flight. The implementation of slip boundary conditions in the traditional CFD method under Navier-Stokes-Fourier (NSF) framework can extend the validity of this approach further into transitional regime, with the benefit that much less computational cost is demanded compared to DSMC simulation. Correspondingly, an increasing error arises in aeroheating calculation as the flow becomes more rarefied. To estimate the relative error of heat flux when applying this method for a rarefied flow in transitional regime, theoretical derivation is conducted and a dimensionless parameter ɛ is proposed by approximately analyzing the ratio of the second order term to first order term in the heat flux expression in Burnett equation. DSMC simulation for hypersonic flow over a cylinder in transitional regime is performed to test the performance of parameter ɛ, compared with two other parameters, Knρ and MaṡKnρ.
Variance estimation for systematic designs in spatial surveys.
Fewster, R M
2011-12-01
In spatial surveys for estimating the density of objects in a survey region, systematic designs will generally yield lower variance than random designs. However, estimating the systematic variance is well known to be a difficult problem. Existing methods tend to overestimate the variance, so although the variance is genuinely reduced, it is over-reported, and the gain from the more efficient design is lost. The current approaches to estimating a systematic variance for spatial surveys are to approximate the systematic design by a random design, or approximate it by a stratified design. Previous work has shown that approximation by a random design can perform very poorly, while approximation by a stratified design is an improvement but can still be severely biased in some situations. We develop a new estimator based on modeling the encounter process over space. The new "striplet" estimator has negligible bias and excellent precision in a wide range of simulation scenarios, including strip-sampling, distance-sampling, and quadrat-sampling surveys, and including populations that are highly trended or have strong aggregation of objects. We apply the new estimator to survey data for the spotted hyena (Crocuta crocuta) in the Serengeti National Park, Tanzania, and find that the reported coefficient of variation for estimated density is 20% using approximation by a random design, 17% using approximation by a stratified design, and 11% using the new striplet estimator. This large reduction in reported variance is verified by simulation. PMID:21534940
Random and systematic beam modulator errors in dynamic intensity modulated radiotherapy
NASA Astrophysics Data System (ADS)
Parsai, Homayon; Cho, Paul S.; Phillips, Mark H.; Giansiracusa, Robert S.; Axen, David
2003-05-01
This paper reports on the dosimetric effects of random and systematic modulator errors in delivery of dynamic intensity modulated beams. A sliding-widow type delivery that utilizes a combination of multileaf collimators (MLCs) and backup diaphragms was examined. Gaussian functions with standard deviations ranging from 0.5 to 1.5 mm were used to simulate random positioning errors. A clinical example involving a clival meningioma was chosen with optic chiasm and brain stem as limiting critical structures in the vicinity of the tumour. Dose calculations for different modulator fluctuations were performed, and a quantitative analysis was carried out based on cumulative and differential dose volume histograms for the gross target volume and surrounding critical structures. The study indicated that random modulator errors have a strong tendency to reduce minimum target dose and homogeneity. Furthermore, it was shown that random perturbation of both MLCs and backup diaphragms in the order of σ = 1 mm can lead to 5% errors in prescribed dose. In comparison, when MLCs or backup diaphragms alone was perturbed, the system was more robust and modulator errors of at least σ = 1.5 mm were required to cause dose discrepancies greater than 5%. For systematic perturbation, even errors in the order of +/-0.5 mm were shown to result in significant dosimetric deviations.
Effects of measurement error on horizontal hydraulic gradient estimates.
Devlin, J F; McElwee, C D
2007-01-01
During the design of a natural gradient tracer experiment, it was noticed that the hydraulic gradient was too small to measure reliably on an approximately 500-m(2) site. Additional wells were installed to increase the monitored area to 26,500 m(2), and wells were instrumented with pressure transducers. The resulting monitoring system was capable of measuring heads with a precision of +/-1.3 x 10(-2) m. This measurement error was incorporated into Monte Carlo calculations, in which only hydraulic head values were varied between realizations. The standard deviation in the estimated gradient and the flow direction angle from the x-axis (east direction) were calculated. The data yielded an average hydraulic gradient of 4.5 x 10(-4)+/-25% with a flow direction of 56 degrees southeast +/-18 degrees, with the variations representing 1 standard deviation. Further Monte Carlo calculations investigated the effects of number of wells, aspect ratio of the monitored area, and the size of the monitored area on the previously mentioned uncertainties. The exercise showed that monitored areas must exceed a size determined by the magnitude of the measurement error if meaningful gradient estimates and flow directions are to be obtained. The aspect ratio of the monitored zone should be as close to 1 as possible, although departures as great as 0.5 to 2 did not degrade the quality of the data unduly. Numbers of wells beyond three to five provided little advantage. These conclusions were supported for the general case with a preliminary theoretical analysis. PMID:17257340
CTER-rapid estimation of CTF parameters with error assessment.
Penczek, Pawel A; Fang, Jia; Li, Xueming; Cheng, Yifan; Loerke, Justus; Spahn, Christian M T
2014-05-01
In structural electron microscopy, the accurate estimation of the Contrast Transfer Function (CTF) parameters, particularly defocus and astigmatism, is of utmost importance for both initial evaluation of micrograph quality and for subsequent structure determination. Due to increases in the rate of data collection on modern microscopes equipped with new generation cameras, it is also important that the CTF estimation can be done rapidly and with minimal user intervention. Finally, in order to minimize the necessity for manual screening of the micrographs by a user it is necessary to provide an assessment of the errors of fitted parameters values. In this work we introduce CTER, a CTF parameters estimation method distinguished by its computational efficiency. The efficiency of the method makes it suitable for high-throughput EM data collection, and enables the use of a statistical resampling technique, bootstrap, that yields standard deviations of estimated defocus and astigmatism amplitude and angle, thus facilitating the automation of the process of screening out inferior micrograph data. Furthermore, CTER also outputs the spatial frequency limit imposed by reciprocal space aliasing of the discrete form of the CTF and the finite window size. We demonstrate the efficiency and accuracy of CTER using a data set collected on a 300kV Tecnai Polara (FEI) using the K2 Summit DED camera in super-resolution counting mode. Using CTER we obtained a structure of the 80S ribosome whose large subunit had a resolution of 4.03Å without, and 3.85Å with, inclusion of astigmatism parameters. PMID:24562077
CTER—Rapid estimation of CTF parameters with error assessment
Penczek, Pawel A.; Fang, Jia; Li, Xueming; Cheng, Yifan; Loerke, Justus; Spahn, Christian M.T.
2014-01-01
In structural electron microscopy, the accurate estimation of the Contrast Transfer Function (CTF) parameters, particularly defocus and astigmatism, is of utmost importance for both initial evaluation of micrograph quality and for subsequent structure determination. Due to increases in the rate of data collection on modern microscopes equipped with new generation cameras, it is also important that the CTF estimation can be done rapidly and with minimal user intervention. Finally, in order to minimize the necessity for manual screening of the micrographs by a user it is necessary to provide an assessment of the errors of fitted parameters values. In this work we introduce CTER, a CTF parameters estimation method distinguished by its computational efficiency. The efficiency of the method makes it suitable for high-throughput EM data collection, and enables the use of a statistical resampling technique, bootstrap, that yields standard deviations of estimated defocus and astigmatism amplitude and angle, thus facilitating the automation of the process of screening out inferior micrograph data. Furthermore, CTER also outputs the spatial frequency limit imposed by reciprocal space aliasing of the discrete form of the CTF and the finite window size. We demonstrate the efficiency and accuracy of CTER using a data set collected on a 300 kV Tecnai Polara (FEI) using the K2 Summit DED camera in super-resolution counting mode. Using CTER we obtained a structure of the 80S ribosome whose large subunit had a resolution of 4.03 Å without, and 3.85 Å with, inclusion of astigmatism parameters. PMID:24562077
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.
NASA Astrophysics Data System (ADS)
Neelakantaswamy, P. S.; Rajaratnam, A.; Kisdnasamy, S.; Das, N. P.
1985-02-01
Systematic errors in conductimetric measurements are often encountered due to partial screening of interelectrode current paths resulting from adhesion of bubbles on the electrode surfaces of the cell. A method of assessing this error quantitatively by a simulated electrolytic tank technique is proposed here. The experimental setup simulates the bubble-curtain effect in the electrolytic tank by means of a pair of electrodes partially covered by a monolayer of small polystyrene-foam spheres representing the bubble adhesions. By varying the number of spheres stuck on the electrode surface, the fractional area covered by the bubbles is controlled; and by measuring the interelectrode impedance, the systematic error is determined as a function of the fractional area covered by the simulated bubbles. A theoretical model which depicts the interelectrode resistance and, hence, the systematic error caused by bubble adhesions is calculated by considering the random dispersal of bubbles on the electrodes. Relevant computed results are compared with the measured impedance data obtained from the electrolytic tank experiment. Results due to other models are also presented and discussed. A time-domain measurement on the simulated cell to study the capacitive effects of the bubble curtain is also explained.
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
Errors of Remapping of Radar Estimates onto Cartesian Coordinates
NASA Astrophysics Data System (ADS)
Sharif, H. O.; Ogden, F. L.
2014-12-01
Recent upgrades to operational radar rainfall products in terms of quality and resolution call for re-examination of the factors that contribute to the uncertainty of radar rainfall estimation. Remapping or gridding of radar polar observations onto Cartesian coordinates is implemented using various methods, and is often applied when radar estimates are compared against rain gauge observations, in hydrologic applications, or for merging data from different radars. However, assuming perfect radar observations, many of the widely used remapping methodologies do not conserve mass for the rainfall rate field. Research has suggested that optimal remapping should select all polar bins falling within or intersecting a Cartesian grid and assign them weights based on the proportion of each individual bin's area falling within the grid. However, to reduce computational demand practitioners use a variety of approximate remapping approaches. The most popular approximate approaches used are those based on extracting information from radar bins whose centers fall within a certain distance from the center of the Cartesian grid. This paper introduces a mass-conserving method for remapping, which we call "precise remapping", and evaluates it by comparing against two other commonly used remapping methods based on areal weighting and distance. Results show that the choice of the remapping method can lead to large errors in grid-averaged rainfall accumulations.
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.
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
Fix, MK; Volken, W; Frei, D; Terribilini, D; Dal Pra, A; Schmuecking, M; Manser, P
2014-06-15
Purpose: Treatment plan evaluations in radiotherapy are currently ignoring the dosimetric impact of setup uncertainties. The determination of the robustness for systematic errors is rather computational intensive. This work investigates interpolation schemes to quantify the robustness of treatment plans for systematic errors in terms of efficiency and accuracy. Methods: The impact of systematic errors on dose distributions for patient treatment plans is determined by using the Swiss Monte Carlo Plan (SMCP). Errors in all translational directions are considered, ranging from −3 to +3 mm in mm steps. For each systematic error a full MC dose calculation is performed leading to 343 dose calculations, used as benchmarks. The interpolation uses only a subset of the 343 calculations, namely 9, 15 or 27, and determines all dose distributions by trilinear interpolation. This procedure is applied for a prostate and a head and neck case using Volumetric Modulated Arc Therapy with 2 arcs. The relative differences of the dose volume histograms (DVHs) of the target and the organs at risks are compared. Finally, the interpolation schemes are used to compare robustness of 4- versus 2-arcs in the head and neck treatment plan. Results: Relative local differences of the DVHs increase for decreasing number of dose calculations used in the interpolation. The mean deviations are <1%, 3.5% and 6.5% for a subset of 27, 15 and 9 used dose calculations, respectively. Thereby the dose computation times are reduced by factors of 13, 25 and 43, respectively. The comparison of the 4- versus 2-arcs plan shows a decrease in robustness; however, this is outweighed by the dosimetric improvements. Conclusion: The results of this study suggest that the use of trilinear interpolation to determine the robustness of treatment plans can remarkably reduce the number of dose calculations. This work was supported by Varian Medical Systems. This work was supported by Varian Medical Systems.
NASA Astrophysics Data System (ADS)
Acquaviva, Viviana; Raichoor, Anand; Gawiser, Eric
2015-05-01
We seek to improve the accuracy of joint galaxy photometric redshift estimation and spectral energy distribution (SED) fitting. By simulating different sources of uncorrected systematic errors, we demonstrate that if the uncertainties in the photometric redshifts are estimated correctly, so are those on the other SED fitting parameters, such as stellar mass, stellar age, and dust reddening. Furthermore, we find that if the redshift uncertainties are over(under)-estimated, the uncertainties in SED parameters tend to be over(under)-estimated by similar amounts. These results hold even in the presence of severe systematics and provide, for the first time, a mechanism to validate the uncertainties on these parameters via comparison with spectroscopic redshifts. We propose a new technique (annealing) to re-calibrate the joint uncertainties in the photo-z and SED fitting parameters without compromising the performance of the SED fitting + photo-z estimation. This procedure provides a consistent estimation of the multi-dimensional probability distribution function in SED fitting + z parameter space, including all correlations. While the performance of joint SED fitting and photo-z estimation might be hindered by template incompleteness, we demonstrate that the latter is “flagged” by a large fraction of outliers in redshift, and that significant improvements can be achieved by using flexible stellar populations synthesis models and more realistic star formation histories. In all cases, we find that the median stellar age is better recovered than the time elapsed from the onset of star formation. Finally, we show that using a photometric redshift code such as EAZY to obtain redshift probability distributions that are then used as priors for SED fitting codes leads to only a modest bias in the SED fitting parameters and is thus a viable alternative to the simultaneous estimation of SED parameters and photometric redshifts.
NASA Astrophysics Data System (ADS)
Birk, Manfred; Wagner, Georg
2016-02-01
The Voigt profile commonly used in radiative transfer modeling of Earth's and planets' atmospheres for remote sensing/climate modeling produces systematic errors so far not accounted for. Saturated lines are systematically too narrow when calculated from pressure broadening parameters based on the analysis of laboratory data with the Voigt profile. This is caused by line narrowing effects leading to systematically too small fitted broadening parameters when applying the Voigt profile. These effective values are still valid to model non-saturated lines with sufficient accuracy. Saturated lines dominated by the wings of the line profile are sufficiently accurately modeled with a Voigt profile with the correct broadening parameters and are thus systematically too narrow when calculated with the effective values. The systematic error was quantified by mid infrared laboratory spectroscopy of the water ν2 fundamental. Correct Voigt profile based pressure broadening parameters for saturated lines were 3-4% larger than the effective ones in the spectroscopic database. Impacts on remote sensing and climate modeling are expected. Combination of saturated and non-saturated lines in the spectroscopic analysis will quantify line narrowing with unprecedented precision.
A Systematic Approach for Model-Based Aircraft Engine Performance Estimation
NASA Technical Reports Server (NTRS)
Simon, Donald L.; Garg, Sanjay
2010-01-01
A requirement for effective aircraft engine performance estimation is the ability to account for engine degradation, generally described in terms of unmeasurable health parameters such as efficiencies and flow capacities related to each major engine module. This paper presents a linear point design methodology for minimizing the degradation-induced error in model-based aircraft engine performance estimation applications. The technique specifically focuses on the underdetermined estimation problem, where there are more unknown health parameters than available sensor measurements. A condition for Kalman filter-based estimation is that the number of health parameters estimated cannot exceed the number of sensed measurements. In this paper, the estimated health parameter vector will be replaced by a reduced order tuner vector whose dimension is equivalent to the sensed measurement vector. The reduced order tuner vector is systematically selected to minimize the theoretical mean squared estimation error of a maximum a posteriori estimator formulation. This paper derives theoretical estimation errors at steady-state operating conditions, and presents the tuner selection routine applied to minimize these values. Results from the application of the technique to an aircraft engine simulation are presented and compared to the estimation accuracy achieved through conventional maximum a posteriori and Kalman filter estimation approaches. Maximum a posteriori estimation results demonstrate that reduced order tuning parameter vectors can be found that approximate the accuracy of estimating all health parameters directly. Kalman filter estimation results based on the same reduced order tuning parameter vectors demonstrate that significantly improved estimation accuracy can be achieved over the conventional approach of selecting a subset of health parameters to serve as the tuner vector. However, additional development is necessary to fully extend the methodology to Kalman filter
NASA Astrophysics Data System (ADS)
Sokolova, J. R.
2006-08-01
New international Pilot Project for the redetermination of the ICRF was initiated by the International VLBI Service for Geodesy and Astrometry (IVS) in January 2005. The purpose of this project is to compare the individual CRF solutions and to analyse their systematic and random errors with focus on the selection of the optimal strategy for the next ICRF realization. Eight CRF realizations provided by the IVS Analysis Centres (GA, SHAO, DGFI, GIUB-BKG, JPL, MAO NANU, GSFC, USNO) were analyzed. In present study, four analytical models were used to investigate the systematic differences between solutions: solid rotation, rotation and deformation, and expansion by orthogonal functions: Legendre-Fourier polynomials and spherical functions. It was found that expansions by orthogonal function describe the differences between individual catalogues better than the two former models. Finally, the combined CRF was generated. Using the radio source positions from this combined catalogue for estimation of EOP has shown improvement of the uncertainty of universal time and nutation.
Estimation of optical proximity effect caused by mask fabrication error
NASA Astrophysics Data System (ADS)
Kamon, Kazuya; Hanawa, Tetsuro; Moriizumi, Koichi
1997-07-01
To get wide lithography latitudes in ULSI fabrication, an optical proximity correction system is being widely used. We previously demonstrated that the optical proximity effect is highly dependent on beam interference conditions. By using an aperture with a spindle shaped opaque region and a controlling interference beam number optimized for imaging, we can obtain a high correction accuracy of less than +/- 0.01 micrometers for all kinds of pattern. To put the optical proximity correction into practical use, we must fabricate the corrected mask either by an EB or a laser writing system. But during mask writing, there is another problematic proximity effect. The optical proximity effect caused by mask fabrication error is becoming a serious problem. In this paper, we estimate the optical proximity effect caused by mask fabrication error. For EB writing, the mask feature size of 0.35 micrometers line changes dramatically in a space less than 0.8 micrometers in size; this is not tolerable. For a large pitch pattern, modified illumination reduces the DOF to 0 micrometers . Otherwise, laser writing stably fabricates a mask feature size for a 0.35 micrometers line, and the modified illumination reduces the optical proximity effect. This resist feature fluctuation is binary, so, correcting the mask pattern is easy. Although, it was wrongly thought that for larger pitch pattern, the DOF was reduced by the modified illumination, the DOF reduction actually came from the combination of the two proximity effects. Using an accurate mask produced by a laser writer, we do not observe any DOF reduction in modified illumination. Moreover, this has led to development of an optical proximity correction system with EB proximity correction.
Petyuk, Vladislav A.; Mayampurath, Anoop M.; Monroe, Matthew E.; Polpitiya, Ashoka D.; Purvine, Samuel O.; Anderson, Gordon A.; Camp, David G.; Smith, Richard D.
2009-12-16
Hybrid two-stage mass spectrometers capable of both highly accurate mass measurement and MS/MS fragmentation have become widely available in recent years and have allowed for sig-nificantly better discrimination between true and false MS/MS pep-tide identifications by applying relatively narrow windows for maxi-mum allowable deviations for parent ion mass measurements. To fully gain the advantage of highly accurate parent ion mass meas-urements, it is important to limit systematic mass measurement errors. The DtaRefinery software tool can correct systematic errors in parent ion masses by reading a set of fragmentation spectra, searching for MS/MS peptide identifications, then fitting a model that can estimate systematic errors, and removing them. This results in a new fragmentation spectrum file with updated parent ion masses.
Beckerman, M.; Oblow, E.M.
1988-04-01
A methodology has been developed for the treatment of systematic errors which arise in the processing of sparse sensor data. We present a detailed application of this methodology to the construction from wide-angle sonar sensor data of navigation maps for use in autonomous robotic navigation. In the methodology we introduce a four-valued labelling scheme and a simple logic for label combination. The four labels, conflict, occupied, empty and unknown, are used to mark the cells of the navigation maps; the logic allows for the rapid updating of these maps as new information is acquired. The systematic errors are treated by relabelling conflicting pixel assignments. Most of the new labels are obtained from analyses of the characteristic patterns of conflict which arise during the information processing. The remaining labels are determined by imposing an elementary consistent-labelling condition. 26 refs., 9 figs.
A Posteriori Error Estimation for a Nodal Method in Neutron Transport Calculations
Azmy, Y.Y.; Buscaglia, G.C.; Zamonsky, O.M.
1999-11-03
An a posteriori error analysis of the spatial approximation is developed for the one-dimensional Arbitrarily High Order Transport-Nodal method. The error estimator preserves the order of convergence of the method when the mesh size tends to zero with respect to the L{sup 2} norm. It is based on the difference between two discrete solutions that are available from the analysis. The proposed estimator is decomposed into error indicators to allow the quantification of local errors. Some test problems with isotropic scattering are solved to compare the behavior of the true error to that of the estimated error.
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…
NASA Technical Reports Server (NTRS)
Larson, T. J.; Ehernberger, L. J.
1985-01-01
The flight test technique described uses controlled survey runs to determine horizontal atmospheric pressure variations and systematic altitude errors that result from space positioning measurements. The survey data can be used not only for improved air data calibrations, but also for studies of atmospheric structure and space positioning accuracy performance. The examples presented cover a wide range of radar tracking conditions for both subsonic and supersonic flight to an altitude of 42,000 ft.
Effects of systematic errors on the mixing ratios of trace gases obtained from occulation spectra
NASA Technical Reports Server (NTRS)
Shaffer, W. A.; Shaw, J. H.; Farmer, C. B.
1983-01-01
The influence of systematic errors in the parameters of the models describing the geometry and the atmosphere on the profiles of trace gases retrieved from simulated solar occultation spectra, collected at satellite altitudes, is investigated. Because of smearing effects and other uncertainties, it may be preferable to calibrate the spectra internally by measuring absorption lines of an atmospheric gas such as CO2 whose vertical distribution is assumed rather than to relay on externally supplied information.
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.
The effect of systematic errors on the hybridization of optical critical dimension measurements
NASA Astrophysics Data System (ADS)
Henn, Mark-Alexander; Barnes, Bryan M.; Zhang, Nien Fan; Zhou, Hui; Silver, Richard M.
2015-06-01
In hybrid metrology two or more measurements of the same measurand are combined to provide a more reliable result that ideally incorporates the individual strengths of each of the measurement methods. While these multiple measurements may come from dissimilar metrology methods such as optical critical dimension microscopy (OCD) and scanning electron microscopy (SEM), we investigated the hybridization of similar OCD methods featuring a focus-resolved simulation study of systematic errors performed at orthogonal polarizations. Specifically, errors due to line edge and line width roughness (LER, LWR) and their superposition (LEWR) are known to contribute a systematic bias with inherent correlated errors. In order to investigate the sensitivity of the measurement to LEWR, we follow a modeling approach proposed by Kato et al. who studied the effect of LEWR on extreme ultraviolet (EUV) and deep ultraviolet (DUV) scatterometry. Similar to their findings, we have observed that LEWR leads to a systematic bias in the simulated data. Since the critical dimensions (CDs) are determined by fitting the respective model data to the measurement data by minimizing the difference measure or chi square function, a proper description of the systematic bias is crucial to obtaining reliable results and to successful hybridization. In scatterometry, an analytical expression for the influence of LEWR on the measured orders can be derived, and accounting for this effect leads to a modification of the model function that not only depends on the critical dimensions but also on the magnitude of the roughness. For finite arrayed structures however, such an analytical expression cannot be derived. We demonstrate how to account for the systematic bias and that, if certain conditions are met, a significant improvement of the reliability of hybrid metrology for combining both dissimilar and similar measurement tools can be achieved.
Estimation of line-based target registration error
NASA Astrophysics Data System (ADS)
Ma, Burton; Peters, Terry M.; Chen, Elvis C. S.
2016-03-01
We present a novel method for estimating target registration error (TRE) in point-to-line registration. We develop a spatial stiffness model of the registration problem and derive the stiffness matrix of the model which leads to an analytic expression for predicting the root-mean-square (RMS) TRE. Under the assumption of isotropic localization noise, we show that the stiffness matrix for line-based registration is equal to the difference of the stiffness matrices for fiducial registration and surface-based registration. The expression for TRE is validated in the context of freehand ultrasound calibration performed using a tracked line fiducial as a calibration phantom. Measurements taken during calibration of a tracked linear ultrasound probe were used in simulations to assess TRE of point-to-line registration and the results were compared to the values predicted by the analytic expression. The difference between predicted and simulated RMS TRE magnitude for targets near the centroid of the registration points was less than 5% of the simulated magnitude when using more than 6 registration points. The difference between predicted and simulated RMS TRE magnitude for targets over the entire ultrasound image was almost always less than 10% of the simulated magnitude when using more than 10 registration points. TRE magnitude was minimized near the centroid of the registration points and the isocontours of TRE were elliptic in shape.
NASA Technical Reports Server (NTRS)
Mulrooney, Dr. Mark K.; Matney, Dr. Mark J.
2007-01-01
Orbital object data acquired via optical telescopes can play a crucial role in accurately defining the space environment. Radar systems probe the characteristics of small debris by measuring the reflected electromagnetic energy from an object of the same order of size as the wavelength of the radiation. This signal is affected by electrical conductivity of the bulk of the debris object, as well as its shape and orientation. Optical measurements use reflected solar radiation with wavelengths much smaller than the size of the objects. Just as with radar, the shape and orientation of an object are important, but we only need to consider the surface electrical properties of the debris material (i.e., the surface albedo), not the bulk electromagnetic properties. As a result, these two methods are complementary in that they measure somewhat independent physical properties to estimate the same thing, debris size. Short arc optical observations such as are typical of NASA's Liquid Mirror Telescope (LMT) give enough information to estimate an Assumed Circular Orbit (ACO) and an associated range. This information, combined with the apparent magnitude, can be used to estimate an "absolute" brightness (scaled to a fixed range and phase angle). This absolute magnitude is what is used to estimate debris size. However, the shape and surface albedo effects make the size estimates subject to systematic and random errors, such that it is impossible to ascertain the size of an individual object with any certainty. However, as has been shown with radar debris measurements, that does not preclude the ability to estimate the size distribution of a number of objects statistically. After systematic errors have been eliminated (range errors, phase function assumptions, photometry) there remains a random geometric albedo distribution that relates object size to absolute magnitude. Measurements by the LMT of a subset of tracked debris objects with sizes estimated from their radar cross
Local and Global Views of Systematic Errors of Atmosphere-Ocean General Circulation Models
NASA Astrophysics Data System (ADS)
Mechoso, C. Roberto; Wang, Chunzai; Lee, Sang-Ki; Zhang, Liping; Wu, Lixin
2014-05-01
Coupled Atmosphere-Ocean General Circulation Models (CGCMs) have serious systematic errors that challenge the reliability of climate predictions. One major reason for such biases is the misrepresentations of physical processes, which can be amplified by feedbacks among climate components especially in the tropics. Much effort, therefore, is dedicated to the better representation of physical processes in coordination with intense process studies. The present paper starts with a presentation of these systematic CGCM errors with an emphasis on the sea surface temperature (SST) in simulations by 22 participants in the Coupled Model Intercomparison Project phase 5 (CMIP5). Different regions are considered for discussion of model errors, including the one around the equator, the one covered by the stratocumulus decks off Peru and Namibia, and the confluence between the Angola and Benguela currents. Hypotheses on the reasons for the errors are reviewed, with particular attention on the parameterization of low-level marine clouds, model difficulties in the simulation of the ocean heat budget under the stratocumulus decks, and location of strong SST gradients. Next the presentation turns to a global perspective of the errors and their causes. It is shown that a simulated weak Atlantic Meridional Overturning Circulation (AMOC) tends to be associated with cold biases in the entire Northern Hemisphere with an atmospheric pattern that resembles the Northern Hemisphere annular mode. The AMOC weakening is also associated with a strengthening of Antarctic bottom water formation and warm SST biases in the Southern Ocean. It is also shown that cold biases in the tropical North Atlantic and West African/Indian monsoon regions during the warm season in the Northern Hemisphere have interhemispheric links with warm SST biases in the tropical southeastern Pacific and Atlantic, respectively. The results suggest that improving the simulation of regional processes may not suffice for a more
NASA Astrophysics Data System (ADS)
Biglands, J.; Magee, D.; Boyle, R.; Larghat, A.; Plein, S.; Radjenović, A.
2011-04-01
Quantitative analysis of cardiac dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) perfusion datasets is dependent on the drawing (manually or automatically) of myocardial contours. The required accuracy of these contours for myocardial blood flow (MBF) estimation is not well understood. This study investigates the relationship between myocardial contour errors and MBF errors. Myocardial contours were manually drawn on DCE-MRI perfusion datasets of healthy volunteers imaged in systole. Systematic and random contour errors were simulated using spline curves and the resulting errors in MBF were calculated. The degree of contour error was also evaluated by two recognized segmentation metrics. We derived contour error tolerances in terms of the maximum deviation (MD) a contour could deviate radially from the 'true' contour expressed as a fraction of each volunteer's mean myocardial width (MW). Significant MBF errors were avoided by setting tolerances of MD <= 0.4 MW, when considering the whole myocardium, MD <= 0.3 MW, when considering six radial segments, and MD <= 0.2 MW for further subdivision into endo- and epicardial regions, with the exception of the anteroseptal region, which required greater accuracy. None of the considered segmentation metrics correlated with MBF error; thus, both segmentation metrics and MBF errors should be used to evaluate contouring algorithms.
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.
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).…
Systematic errors in the measurement of the permanent electric dipole moment (EDM) of the 199Hg atom
NASA Astrophysics Data System (ADS)
Chen, Yi; Graner, Brent; Lindahl, Eric; Heckel, Blayne
2016-03-01
This talk provides a discussion of the systematic errors that were encountered in the 199Hg experiment described earlier in this session. The dominant systematic error, unseen in previous 199Hg EDM experiments, arose from small motions of the Hg vapor cells due to forces exerted by the applied electric field. Methods used to understand this effect, as well as the anticipated sources of systematic errors such as leakage currents, parameter correlations, and E2 and v × E / c effects, will be presented. The total systematic error was found to be 72% as large as the statistical error of the EDM measurement. This work was supported by NSF Grant 1306743 and by DOE Grant DE-FG02-97ER41020.
NASA Astrophysics Data System (ADS)
Chen, Yi; Graner, Brent; Heckel, Blayne; Lindahl, Eric
2016-05-01
This talk provides a discussion of the systematic errors that were encountered in the 199 Hg experiment described earlier in this session. The dominant systematic error, unseen in previous 199 Hg EDM experiments, arose from small motions of the Hg vapor cells due to forces exerted by the applied electric field. Methods used to understand this effect, as well as the anticipated sources of systematic errors such as leakage currents, parameter correlations, and E2 and v × E / c effects, will be presented. The total systematic error was found to be 72% as large as the statistical error of the EDM measurement. This work was supported by NSF Grant 1306743 and by DOE Grant DE-FG02-97ER41020.
Precision calibration and systematic error reduction in the long trace profiler
Qian, Shinan; Sostero, Giovanni; Takacs, Peter Z.
2000-01-01
The long trace profiler (LTP) has become the instrument of choice for surface figure testing and slope error measurement of mirrors used for synchrotron radiation and x-ray astronomy optics. In order to achieve highly accurate measurements with the LTP, systematic errors need to be reduced by precise angle calibration and accurate focal plane position adjustment. A self-scanning method is presented to adjust the focal plane position of the detector with high precision by use of a pentaprism scanning technique. The focal plane position can be set to better than 0.25 mm for a 1250-mm-focal-length Fourier-transform lens using this technique. The use of a 0.03-arcsec-resolution theodolite combined with the sensitivity of the LTP detector system can be used to calibrate the angular linearity error very precisely. Some suggestions are introduced for reducing the system error. With these precision calibration techniques, accuracy in the measurement of figure and slope error on meter-long mirrors is now at a level of about 1 {mu}rad rms over the whole testing range of the LTP. (c) 2000 Society of Photo-Optical Instrumentation Engineers.
NASA Technical Reports Server (NTRS)
Parrott, T. L.; Smith, C. D.
1977-01-01
The effect of random and systematic errors associated with the measurement of normal incidence acoustic impedance in a zero-mean-flow environment was investigated by the transmission line method. The influence of random measurement errors in the reflection coefficients and pressure minima positions was investigated by computing fractional standard deviations of the normalized impedance. Both the standard techniques of random process theory and a simplified technique were used. Over a wavelength range of 68 to 10 cm random measurement errors in the reflection coefficients and pressure minima positions could be described adequately by normal probability distributions with standard deviations of 0.001 and 0.0098 cm, respectively. An error propagation technique based on the observed concentration of the probability density functions was found to give essentially the same results but with a computation time of about 1 percent of that required for the standard technique. The results suggest that careful experimental design reduces the effect of random measurement errors to insignificant levels for moderate ranges of test specimen impedance component magnitudes. Most of the observed random scatter can be attributed to lack of control by the mounting arrangement over mechanical boundary conditions of the test sample.
The sensitivity of patient specific IMRT QC to systematic MLC leaf bank offset errors
Rangel, Alejandra; Palte, Gesa; Dunscombe, Peter
2010-07-15
Purpose: Patient specific IMRT QC is performed routinely in many clinics as a safeguard against errors and inaccuracies which may be introduced during the complex planning, data transfer, and delivery phases of this type of treatment. The purpose of this work is to evaluate the feasibility of detecting systematic errors in MLC leaf bank position with patient specific checks. Methods: 9 head and neck (H and N) and 14 prostate IMRT beams were delivered using MLC files containing systematic offsets ({+-}1 mm in two banks, {+-}0.5 mm in two banks, and 1 mm in one bank of leaves). The beams were measured using both MAPCHECK (Sun Nuclear Corp., Melbourne, FL) and the aS1000 electronic portal imaging device (Varian Medical Systems, Palo Alto, CA). Comparisons with calculated fields, without offsets, were made using commonly adopted criteria including absolute dose (AD) difference, relative dose difference, distance to agreement (DTA), and the gamma index. Results: The criteria most sensitive to systematic leaf bank offsets were the 3% AD, 3 mm DTA for MAPCHECK and the gamma index with 2% AD and 2 mm DTA for the EPID. The criterion based on the relative dose measurements was the least sensitive to MLC offsets. More highly modulated fields, i.e., H and N, showed greater changes in the percentage of passing points due to systematic MLC inaccuracy than prostate fields. Conclusions: None of the techniques or criteria tested is sufficiently sensitive, with the population of IMRT fields, to detect a systematic MLC offset at a clinically significant level on an individual field. Patient specific QC cannot, therefore, substitute for routine QC of the MLC itself.
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.
NASA Astrophysics Data System (ADS)
Ross, A.; Czisch, M.; King, G. C.
1997-02-01
A theoretical approach to calculate the time evolution of magnetization during a CPMG pulse sequence of arbitrary parameter settings is developed and verified by experiment. The analysis reveals that off-resonance effects can cause systematic reductions in measured peak amplitudes that commonly lie in the range 5-25%, reaching 50% in unfavorable circumstances. These errors, which are finely dependent upon frequency offset and CPMG parameter settings, are subsequently transferred into erroneousT2values obtained by curve fitting, where they are reduced or amplified depending upon the magnitude of the relaxation time. Subsequent transfer to Lipari-Szabo model analysis can produce significant errors in derived motional parameters, with τeinternal correlation times being affected somewhat more thanS2order parameters. A hazard of this off-resonance phenomenon is its oscillatory nature, so that strongly affected and unaffected signals can be found at various frequencies within a CPMG spectrum. Methods for the reduction of the systematic error are discussed. Relaxation studies on biomolecules, especially at high field strengths, should take account of potential off-resonance contributions.
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
Quasi-Monte Carlo, quasi-random numbers and quasi-error estimates
NASA Astrophysics Data System (ADS)
Kleiss, Ronald
We discuss quasi-random number sequences as a basis for numerical integration with potentially better convergence properties than standard Monte Carlo. The importance of the discrepancy as both a measure of smoothness of distribution and an ingredient in the error estimate is reviewed. It is argued that the classical Koksma-Hlawka inequality is not relevant for error estimates in realistic cases, and a new class of error estimates is presented, based on a generalization of the Woźniakowski lemma.
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.
Convolution method and CTV-to-PTV margins for finite fractions and small systematic errors
NASA Astrophysics Data System (ADS)
Gordon, J. J.; Siebers, J. V.
2007-04-01
The van Herk margin formula (VHMF) relies on the accuracy of the convolution method (CM) to determine clinical target volume (CTV) to planning target volume (PTV) margins. This work (1) evaluates the accuracy of the CM and VHMF as a function of the number of fractions N and other parameters, and (2) proposes an alternative margin algorithm which ensures target coverage for a wider range of parameter values. Dose coverage was evaluated for a spherical target with uniform margin, using the same simplified dose model and CTV coverage criterion as were used in development of the VHMF. Systematic and random setup errors were assumed to be normally distributed with standard deviations Σ and σ. For clinically relevant combinations of σ, Σ and N, margins were determined by requiring that 90% of treatment course simulations have a CTV minimum dose greater than or equal to the static PTV minimum dose. Simulation results were compared with the VHMF and the alternative margin algorithm. The CM and VHMF were found to be accurate for parameter values satisfying the approximate criterion: σ[1 - γN/25] < 0.2, where γ = Σ/σ. They were found to be inaccurate for σ[1 - γN/25] > 0.2, because they failed to account for the non-negligible dose variability associated with random setup errors. These criteria are applicable when σ gap σP, where σP = 0.32 cm is the standard deviation of the normal dose penumbra. (Qualitative behaviour of the CM and VHMF will remain the same, though the criteria might vary if σP takes values other than 0.32 cm.) When σ Lt σP, dose variability due to random setup errors becomes negligible, and the CM and VHMF are valid regardless of the values of Σ and N. When σ gap σP, consistent with the above criteria, it was found that the VHMF can underestimate margins for large σ, small Σ and small N. A potential consequence of this underestimate is that the CTV minimum dose can fall below its planned value in more than the prescribed 10% of
Convolution method and CTV-to-PTV margins for finite fractions and small systematic errors.
Gordon, J J; Siebers, J V
2007-04-01
The van Herk margin formula (VHMF) relies on the accuracy of the convolution method (CM) to determine clinical target volume (CTV) to planning target volume (PTV) margins. This work (1) evaluates the accuracy of the CM and VHMF as a function of the number of fractions N and other parameters, and (2) proposes an alternative margin algorithm which ensures target coverage for a wider range of parameter values. Dose coverage was evaluated for a spherical target with uniform margin, using the same simplified dose model and CTV coverage criterion as were used in development of the VHMF. Systematic and random setup errors were assumed to be normally distributed with standard deviations Sigma and sigma. For clinically relevant combinations of sigma, Sigma and N, margins were determined by requiring that 90% of treatment course simulations have a CTV minimum dose greater than or equal to the static PTV minimum dose. Simulation results were compared with the VHMF and the alternative margin algorithm. The CM and VHMF were found to be accurate for parameter values satisfying the approximate criterion: sigma[1 - gammaN/25] < 0.2, where gamma = Sigma/sigma. They were found to be inaccurate for sigma[1 - gammaN/25] > 0.2, because they failed to account for the non-negligible dose variability associated with random setup errors. These criteria are applicable when sigma greater than or approximately egual sigma(P), where sigma(P) = 0.32 cm is the standard deviation of the normal dose penumbra. (Qualitative behaviour of the CM and VHMF will remain the same, though the criteria might vary if sigma(P) takes values other than 0.32 cm.) When sigma < sigma(P), dose variability due to random setup errors becomes negligible, and the CM and VHMF are valid regardless of the values of Sigma and N. When sigma greater than or approximately egual sigma(P), consistent with the above criteria, it was found that the VHMF can underestimate margins for large sigma, small Sigma and small N. A
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.
Castle, R.O.; Brown, B.W., Jr.; Gilmore, T.D.; Mark, R.K.; Wilson, R.C.
1983-01-01
Appraisals of the two levelings that formed the southern California field test for the accumulation of the atmospheric refraction error indicate that random error and systematic error unrelated to refraction competed with the systematic refraction error and severely complicate any analysis of the test results. If the fewer than one-third of the sections that met less than second-order, class I standards are dropped, the divergence virtually disappears between the presumably more refraction contaminated long-sight-length survey and the less contaminated short-sight-length survey. -Authors
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.
Mitigating systematic errors in angular correlation function measurements from wide field surveys
NASA Astrophysics Data System (ADS)
Morrison, C. B.; Hildebrandt, H.
2015-12-01
We present an investigation into the effects of survey systematics such as varying depth, point spread function size, and extinction on the galaxy selection and correlation in photometric, multi-epoch, wide area surveys. We take the Canada-France-Hawaii Telescope Lensing Survey (CFHTLenS) as an example. Variations in galaxy selection due to systematics are found to cause density fluctuations of up to 10 per cent for some small fraction of the area for most galaxy redshift slices and as much as 50 per cent for some extreme cases of faint high-redshift samples. This results in correlations of galaxies against survey systematics of order ˜1 per cent when averaged over the survey area. We present an empirical method for mitigating these systematic correlations from measurements of angular correlation functions using weighted random points. These weighted random catalogues are estimated from the observed galaxy overdensities by mapping these to survey parameters. We are able to model and mitigate the effect of systematic correlations allowing for non-linear dependences of density on systematics. Applied to CFHTLenS, we find that the method reduces spurious correlations in the data by a factor of 2 for most galaxy samples and as much as an order of magnitude in others. Such a treatment is particularly important for an unbiased estimation of very small correlation signals, as e.g. from weak gravitational lensing magnification bias. We impose a criterion for using a galaxy sample in a magnification measurement of the majority of the systematic correlations show improvement and are less than 10 per cent of the expected magnification signal when combined in the galaxy cross-correlation. After correction the galaxy samples in CFHTLenS satisfy this criterion for zphot < 0.9 and will be used in a future analysis of magnification.
Systematic errors in the measurement of emissivity caused by directional effects.
Kribus, Abraham; Vishnevetsky, Irna; Rotenberg, Eyal; Yakir, Dan
2003-04-01
Accurate knowledge of surface emissivity is essential for applications in remote sensing (remote temperature measurement), radiative transport, and modeling of environmental energy balances. Direct measurements of surface emissivity are difficult when there is considerable background radiation at the same wavelength as the emitted radiation. This occurs, for example, when objects at temperatures near room temperature are measured in a terrestrial environment by use ofthe infrared 8-14-microm band.This problem is usually treated by assumption of a perfectly diffuse surface or of diffuse background radiation. However, real surfaces and actual background radiation are not diffuse; therefore there will be a systematic measurement error. It is demonstrated that, in some cases, the deviations from a diffuse behavior lead to large errors in the measured emissivity. Past measurements made with simplifying assumptions should therefore be reevaluated and corrected. Recommendations are presented for improving experimental procedures in emissivity measurement. PMID:12683764
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
Optimal estimation of large structure model errors. [in Space Shuttle controller design
NASA Technical Reports Server (NTRS)
Rodriguez, G.
1979-01-01
In-flight estimation of large structure model errors is usually required as a means of detecting inevitable deficiencies in large structure controller/estimator models. The present paper deals with a least-squares formulation which seeks to minimize a quadratic functional of the model errors. The properties of these error estimates are analyzed. It is shown that an arbitrary model error can be decomposed as the sum of two components that are orthogonal in a suitably defined function space. Relations between true and estimated errors are defined. The estimates are found to be approximations that retain many of the significant dynamics of the true model errors. Current efforts are directed toward application of the analytical results to a reference large structure model.
NASA Technical Reports Server (NTRS)
Flamant, Cyrille N.; Schwemmer, Geary K.; Korb, C. Laurence; Evans, Keith D.; Palm, Stephen P.
1999-01-01
Remote airborne measurements of the vertical and horizontal structure of the atmospheric pressure field in the lower troposphere are made with an oxygen differential absorption lidar (DIAL). A detailed analysis of this measurement technique is provided which includes corrections for imprecise knowledge of the detector background level, the oxygen absorption fine parameters, and variations in the laser output energy. In addition, we analyze other possible sources of systematic errors including spectral effects related to aerosol and molecular scattering interference by rotational Raman scattering and interference by isotopic oxygen fines.
Estimating the coverage of mental health programmes: a systematic review
De Silva, Mary J; Lee, Lucy; Fuhr, Daniela C; Rathod, Sujit; Chisholm, Dan; Schellenberg, Joanna; Patel, Vikram
2014-01-01
Background The large treatment gap for people suffering from mental disorders has led to initiatives to scale up mental health services. In order to track progress, estimates of programme coverage, and changes in coverage over time, are needed. Methods Systematic review of mental health programme evaluations that assess coverage, measured either as the proportion of the target population in contact with services (contact coverage) or as the proportion of the target population who receive appropriate and effective care (effective coverage). We performed a search of electronic databases and grey literature up to March 2013 and contacted experts in the field. Methods to estimate the numerator (service utilization) and the denominator (target population) were reviewed to explore methods which could be used in programme evaluations. Results We identified 15 735 unique records of which only seven met the inclusion criteria. All studies reported contact coverage. No study explicitly measured effective coverage, but it was possible to estimate this for one study. In six studies the numerator of coverage, service utilization, was estimated using routine clinical information, whereas one study used a national community survey. The methods for estimating the denominator, the population in need of services, were more varied and included national prevalence surveys case registers, and estimates from the literature. Conclusions Very few coverage estimates are available. Coverage could be estimated at low cost by combining routine programme data with population prevalence estimates from national surveys. PMID:24760874
NASA Technical Reports Server (NTRS)
Koch, S. E.; Skillman, W. C.; Kocin, P. J.; Wetzel, P. J.; Brill, K. F.
1985-01-01
The synoptic scale performance characteristics of MASS 2.0 are determined by comparing filtered 12-24 hr model forecasts to same-case forecasts made by the National Meteorological Center's synoptic-scale Limited-area Fine Mesh model. Characteristics of the two systems are contrasted, and the analysis methodology used to determine statistical skill scores and systematic errors is described. The overall relative performance of the two models in the sample is documented, and important systematic errors uncovered are presented.
Systematic Biases in Parameter Estimation of Binary Black-Hole Mergers
NASA Technical Reports Server (NTRS)
Littenberg, Tyson B.; Baker, John G.; Buonanno, Alessandra; Kelly, Bernard J.
2012-01-01
Parameter estimation of binary-black-hole merger events in gravitational-wave data relies on matched filtering techniques, which, in turn, depend on accurate model waveforms. Here we characterize the systematic biases introduced in measuring astrophysical parameters of binary black holes by applying the currently most accurate effective-one-body templates to simulated data containing non-spinning numerical-relativity waveforms. For advanced ground-based detectors, we find that the systematic biases are well within the statistical error for realistic signal-to-noise ratios (SNR). These biases grow to be comparable to the statistical errors at high signal-to-noise ratios for ground-based instruments (SNR approximately 50) but never dominate the error budget. At the much larger signal-to-noise ratios expected for space-based detectors, these biases will become large compared to the statistical errors but are small enough (at most a few percent in the black-hole masses) that we expect they should not affect broad astrophysical conclusions that may be drawn from the data.
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
Estimation of coherent error sources from stabilizer measurements
NASA Astrophysics Data System (ADS)
Orsucci, Davide; Tiersch, Markus; Briegel, Hans J.
2016-04-01
In the context of measurement-based quantum computation a way of maintaining the coherence of a graph state is to measure its stabilizer operators. Aside from performing quantum error correction, it is possible to exploit the information gained from these measurements to characterize and then counteract a coherent source of errors; that is, to determine all the parameters of an error channel that applies a fixed—but unknown—unitary operation to the physical qubits. Such a channel is generated, e.g., by local stray fields that act on the qubits. We study the case in which each qubit of a given graph state may see a different error channel and we focus on channels given by a rotation on the Bloch sphere around either the x ̂, the y ̂, or the z ̂ axis, for which analytical results can be given in a compact form. The possibility of reconstructing the channels at all qubits depends nontrivially on the topology of the graph state. We prove via perturbation methods that the reconstruction process is robust and supplement the analytic results with numerical evidence.
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
Nonlinear and multiresolution error covariance estimation in ensemble data assimilation
NASA Astrophysics Data System (ADS)
Rainwater, Sabrina
Ensemble Kalman Filters perform data assimilation by forming a background covariance matrix from an ensemble forecast. The spread of the ensemble is intended to represent the algorithm's uncertainty about the state of the physical system that produces the data. Usually the ensemble members are evolved with the same model. The first part of my dissertation presents and tests a modified Local Ensemble Transform Kalman Filter (LETKF) that takes its background covariance from a combination of a high resolution ensemble and a low resolution ensemble. The computational time and the accuracy of this mixed-resolution LETKF are explored and compared to the standard LETKF on a high resolution ensemble, using simulated observation experiments with the Lorenz Models II and III (more complex versions of the Lorenz 96 model). The results show that, for the same computation time, mixed resolution ensemble analysis achieves higher accuracy than standard ensemble analysis. The second part of my dissertation demonstrates that it can be fruitful to rescale the ensemble spread prior to the forecast and then reverse this rescaling after the forecast. This technique, denoted “forecast spread adjustment'' provides a tunable parameter that is complementary to covariance inflation, which cumulatively increases the ensemble spread to compensate for underestimation of uncertainty. As the adjustable parameter approaches zero, the filter approaches the Extended Kalman Filter when the ensemble size is sufficiently large. The improvement provided by forecast spread adjustment depends on ensemble size, observation error, and model error. The results indicate that it is most effective for smaller ensembles, smaller observation errors, and larger model error, though the effectiveness depends significantly on the type of model error.
Suppression of Systematic Errors of Electronic Distance Meters for Measurement of Short Distances.
Braun, Jaroslav; Štroner, Martin; Urban, Rudolf; Dvoček, Filip
2015-01-01
In modern industrial geodesy, high demands are placed on the final accuracy, with expectations currently falling below 1 mm. The measurement methodology and surveying instruments used have to be adjusted to meet these stringent requirements, especially the total stations as the most often used instruments. A standard deviation of the measured distance is the accuracy parameter, commonly between 1 and 2 mm. This parameter is often discussed in conjunction with the determination of the real accuracy of measurements at very short distances (5-50 m) because it is generally known that this accuracy cannot be increased by simply repeating the measurement because a considerable part of the error is systematic. This article describes the detailed testing of electronic distance meters to determine the absolute size of their systematic errors, their stability over time, their repeatability and the real accuracy of their distance measurement. Twenty instruments (total stations) have been tested, and more than 60,000 distances in total were measured to determine the accuracy and precision parameters of the distance meters. Based on the experiments' results, calibration procedures were designed, including a special correction function for each instrument, whose usage reduces the standard deviation of the measurement of distance by at least 50%. PMID:26258777
Suppression of Systematic Errors of Electronic Distance Meters for Measurement of Short Distances
Braun, Jaroslav; Štroner, Martin; Urban, Rudolf; Dvořáček, Filip
2015-01-01
In modern industrial geodesy, high demands are placed on the final accuracy, with expectations currently falling below 1 mm. The measurement methodology and surveying instruments used have to be adjusted to meet these stringent requirements, especially the total stations as the most often used instruments. A standard deviation of the measured distance is the accuracy parameter, commonly between 1 and 2 mm. This parameter is often discussed in conjunction with the determination of the real accuracy of measurements at very short distances (5–50 m) because it is generally known that this accuracy cannot be increased by simply repeating the measurement because a considerable part of the error is systematic. This article describes the detailed testing of electronic distance meters to determine the absolute size of their systematic errors, their stability over time, their repeatability and the real accuracy of their distance measurement. Twenty instruments (total stations) have been tested, and more than 60,000 distances in total were measured to determine the accuracy and precision parameters of the distance meters. Based on the experiments’ results, calibration procedures were designed, including a special correction function for each instrument, whose usage reduces the standard deviation of the measurement of distance by at least 50%. PMID:26258777
Improving Photometry and Stellar Signal Preservation with Pixel-Level Systematic Error Correction
NASA Technical Reports Server (NTRS)
Kolodzijczak, Jeffrey J.; Smith, Jeffrey C.; Jenkins, Jon M.
2013-01-01
The Kepler Mission has demonstrated that excellent stellar photometric performance can be achieved using apertures constructed from optimally selected CCD pixels. The clever methods used to correct for systematic errors, while very successful, still have some limitations in their ability to extract long-term trends in stellar flux. They also leave poorly correlated bias sources, such as drifting moiré pattern, uncorrected. We will illustrate several approaches where applying systematic error correction algorithms to the pixel time series, rather than the co-added raw flux time series, provide significant advantages. Examples include, spatially localized determination of time varying moiré pattern biases, greater sensitivity to radiation-induced pixel sensitivity drops (SPSDs), improved precision of co-trending basis vectors (CBV), and a means of distinguishing the stellar variability from co-trending terms even when they are correlated. For the last item, the approach enables physical interpretation of appropriately scaled coefficients derived in the fit of pixel time series to the CBV as linear combinations of various spatial derivatives of the pixel response function (PRF). We demonstrate that the residuals of a fit of soderived pixel coefficients to various PRF-related components can be deterministically interpreted in terms of physically meaningful quantities, such as the component of the stellar flux time series which is correlated with the CBV, as well as, relative pixel gain, proper motion and parallax. The approach also enables us to parameterize and assess the limiting factors in the uncertainties in these quantities.
Rogers, Baxter P.; Sheffield, Julia M.; Luksik, Andrew S.; Heckers, Stephan
2012-01-01
Hemispheric asymmetry of hippocampal volume is a common finding that has biological relevance, including associations with dementia and cognitive performance. However, a recent study has reported the possibility of systematic error in measurements of hippocampal asymmetry by magnetic resonance volumetry. We manually traced the volumes of the anterior and posterior hippocampus in 40 healthy people to measure systematic error related to image orientation. We found a bias due to the side of the screen on which the hippocampus was viewed, such that hippocampal volume was larger when traced on the left side of the screen than when traced on the right (p = 0.05). However, this bias was smaller than the anatomical right > left asymmetry of the anterior hippocampus. We found right > left asymmetry of hippocampal volume regardless of image presentation (radiological versus neurological). We conclude that manual segmentation protocols can minimize the effect of image orientation in the study of hippocampal volume asymmetry, but our confirmation that such bias exists suggests strategies to avoid it in future studies. PMID:23248580
Winham, Stacey J.; Motsinger-Reif, Alison A.
2010-01-01
SUMMARY The standard in genetic association studies of complex diseases is replication and validation of positive results, with an emphasis on assessing the predictive value of associations. In response to this need, a number of analytical approaches have been developed to identify predictive models that account for complex genetic etiologies. Multifactor Dimensionality Reduction (MDR) is a commonly used, highly successful method designed to evaluate potential gene-gene interactions. MDR relies on classification error in a cross-validation framework to rank and evaluate potentially predictive models. Previous work has demonstrated the high power of MDR, but has not considered the accuracy and variance of the MDR prediction error estimate. Currently, we evaluate the bias and variance of the MDR error estimate as both a retrospective and prospective estimator and show that MDR can both underestimate and overestimate error. We argue that a prospective error estimate is necessary if MDR models are used for prediction, and propose a bootstrap resampling estimate, integrating population prevalence, to accurately estimate prospective error. We demonstrate that this bootstrap estimate is preferable for prediction to the error estimate currently produced by MDR. While demonstrated with MDR, the proposed estimation is applicable to all data-mining methods that use similar estimates. PMID:20560921
A Systematic Approach to Sensor Selection for Aircraft Engine Health Estimation
NASA Technical Reports Server (NTRS)
Simon, Donald L.; Garg, Sanjay
2009-01-01
A systematic approach for selecting an optimal suite of sensors for on-board aircraft gas turbine engine health estimation is presented. The methodology optimally chooses the engine sensor suite and the model tuning parameter vector to minimize the Kalman filter mean squared estimation error in the engine s health parameters or other unmeasured engine outputs. This technique specifically addresses the underdetermined estimation problem where there are more unknown system health parameters representing degradation than available sensor measurements. This paper presents the theoretical estimation error equations, and describes the optimization approach that is applied to select the sensors and model tuning parameters to minimize these errors. Two different model tuning parameter vector selection approaches are evaluated: the conventional approach of selecting a subset of health parameters to serve as the tuning parameters, and an alternative approach that selects tuning parameters as a linear combination of all health parameters. Results from the application of the technique to an aircraft engine simulation are presented, and compared to those from an alternative sensor selection strategy.
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…
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…
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.
Estimation of errors in partial Mueller matrix polarimeter calibration
NASA Astrophysics Data System (ADS)
Alenin, Andrey S.; Vaughn, Israel J.; Tyo, J. Scott
2016-05-01
While active polarimeters have been shown to be successful at improving discriminability of the targets of interest from their background in a wide range of applications, their use can be problematic for cases with strong bandwidth constraints. In order to limit the number of performed measurements, a number of successive studies have developed the concept of partial Mueller matrix polarimeters (pMMPs) into a competitive solution. Like all systems, pMMPs need to be calibrated in order to yield accurate results. In this treatment we provide a method by which to select a limited number of reference objects to calibrate a given pMMP design. To demonstrate the efficacy of the approach, we apply the method to a sample system and show that, depending on the kind of errors present within the system, a significantly reduced number of reference objects measurements will suffice for accurate characterization of the errors.
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.
An Examination of the Spatial Distribution of Carbon Dioxide and Systematic Errors
NASA Technical Reports Server (NTRS)
Coffey, Brennan; Gunson, Mike; Frankenberg, Christian; Osterman, Greg
2011-01-01
The industrial period and modern age is characterized by combustion of coal, oil, and natural gas for primary energy and transportation leading to rising levels of atmospheric of CO2. This increase, which is being carefully measured, has ramifications throughout the biological world. Through remote sensing, it is possible to measure how many molecules of CO2 lie in a defined column of air. However, other gases and particles are present in the atmosphere, such as aerosols and water, which make such measurements more complicated1. Understanding the detailed geometry and path length of the observation is vital to computing the concentration of CO2. Comparing these satellite readings with ground-truth data (TCCON) the systematic errors arising from these sources can be assessed. Once the error is understood, it can be scaled for in the retrieval algorithms to create a set of data, which is closer to the TCCON measurements1. Using this process, the algorithms are being developed to reduce bias, within.1% worldwide of the true value. At this stage, the accuracy is within 1%, but through correcting small errors contained in the algorithms, such as accounting for the scattering of sunlight, the desired accuracy can be achieved.
Assiri, Ghadah Asaad; Grant, Liz; Aljadhey, Hisham; Sheikh, Aziz
2016-01-01
Introduction There is a need to better understand the epidemiology of medication errors and error-related adverse events in community care contexts. Methods and analysis We will systematically search the following databases: Cumulative Index to Nursing and Allied Health Literature (CINAHL), EMBASE, Eastern Mediterranean Regional Office of the WHO (EMRO), MEDLINE, PsycINFO and Web of Science. In addition, we will search Google Scholar and contact an international panel of experts to search for unpublished and in progress work. The searches will cover the time period January 1990–December 2015 and will yield data on the incidence or prevalence of and risk factors for medication errors and error-related adverse drug events in adults living in community settings (ie, primary care, ambulatory and home). Study quality will be assessed using the Critical Appraisal Skills Program quality assessment tool for cohort and case–control studies, and cross-sectional studies will be assessed using the Joanna Briggs Institute Critical Appraisal Checklist for Descriptive Studies. Meta-analyses will be undertaken using random-effects modelling using STATA (V.14) statistical software. Ethics and dissemination This protocol will be registered with PROSPERO, an international prospective register of systematic reviews, and the systematic review will be reported in the peer-reviewed literature using Preferred Reporting Items for Systematic Reviews and Meta-Analyses. PMID:27580826
NASA Astrophysics Data System (ADS)
Pan, M.; Zhan, W.; Fisher, C. K.; Crow, W. T.; Wood, E. F.
2014-12-01
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 slightly different from the original inner product solution but easier to extend to multiple collocation cases. The Pythagorean solution is fully equivalent to the original inner product solution for the triple collocation case. The multiple collocation turns out to be an over-constrained problem and a least squared solution is presented. As the most critical assumption of uncorrelated errors will almost for sure fail in multiple collocation problems, we propose to divide the source estimates into structural categories and treat the structural and non-structural errors separately. Such error separation allows the source estimates to have their structural errors fully correlated within the same structural category, which is much more realistic than the original assumption. A new error assessment procedure is developed which performs the collocation twice, each for one type of errors, and then sums up the two types of errors. The new procedure is also fully backward compatible with the original triple collocation. Error assessment experiments are carried out for surface soil moisture data from multiple remote sensing models, land surface models, and in situ measurements.
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.
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.
Li, T.S.; et al.
2016-01-01
Meeting the science goals for many current and future ground-based optical large-area sky surveys requires that the calibrated broadband photometry is stable in time and uniform over the sky to 1% precision or better. Past surveys have achieved photometric precision of 1-2% by calibrating the survey's stellar photometry with repeated measurements of a large number of stars observed in multiple epochs. The calibration techniques employed by these surveys only consider the relative frame-by-frame photometric zeropoint offset and the focal plane position-dependent illumination corrections, which are independent of the source color. However, variations in the wavelength dependence of the atmospheric transmission and the instrumental throughput induce source color-dependent systematic errors. These systematic errors must also be considered to achieve the most precise photometric measurements. In this paper, we examine such systematic chromatic errors using photometry from the Dark Energy Survey (DES) as an example. We define a natural magnitude system for DES and calculate the systematic errors on stellar magnitudes, when the atmospheric transmission and instrumental throughput deviate from the natural system. We conclude that the systematic chromatic errors caused by the change of airmass in each exposure, the change of the precipitable water vapor and aerosol in the atmosphere over time, and the non-uniformity of instrumental throughput over the focal plane, can be up to 2% in some bandpasses. We compare the calculated systematic chromatic errors with the observed DES data. For the test sample data, we correct these errors using measurements of the atmospheric transmission and instrumental throughput. The residual after correction is less than 0.3%. We also find that the errors for non-stellar objects are redshift-dependent and can be larger than those for stars at certain redshifts.
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)
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.
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.
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.
NASA Astrophysics Data System (ADS)
Wu, Heng
2000-10-01
In this thesis, an a-posteriori error estimator is presented and employed for solving viscous incompressible flow problems. In an effort to detect local flow features, such as vortices and separation, and to resolve flow details precisely, a velocity angle error estimator e theta which is based on the spatial derivative of velocity direction fields is designed and constructed. The a-posteriori error estimator corresponds to the antisymmetric part of the deformation-rate-tensor, and it is sensitive to the second derivative of the velocity angle field. Rationality discussions reveal that the velocity angle error estimator is a curvature error estimator, and its value reflects the accuracy of streamline curves. It is also found that the velocity angle error estimator contains the nonlinear convective term of the Navier-Stokes equations, and it identifies and computes the direction difference when the convective acceleration direction and the flow velocity direction have a disparity. Through benchmarking computed variables with the analytic solution of Kovasznay flow or the finest grid of cavity flow, it is demonstrated that the velocity angle error estimator has a better performance than the strain error estimator. The benchmarking work also shows that the computed profile obtained by using etheta can achieve the best matching outcome with the true theta field, and that it is asymptotic to the true theta variation field, with a promise of fewer unknowns. Unstructured grids are adapted by employing local cell division as well as unrefinement of transition cells. Using element class and node class can efficiently construct a hierarchical data structure which provides cell and node inter-reference at each adaptive level. Employing element pointers and node pointers can dynamically maintain the connection of adjacent elements and adjacent nodes, and thus avoids time-consuming search processes. The adaptive scheme is applied to viscous incompressible flow at different
NASA Astrophysics Data System (ADS)
Lin, Q.; Neethling, S. J.; Dobson, K. J.; Courtois, L.; Lee, P. D.
2015-04-01
X-ray micro-tomography (XMT) is increasingly used for the quantitative analysis of the volumes of features within the 3D images. As with any measurement, there will be error and uncertainty associated with these measurements. In this paper a method for quantifying both the systematic and random components of this error in the measured volume is presented. The systematic error is the offset between the actual and measured volume which is consistent between different measurements and can therefore be eliminated by appropriate calibration. In XMT measurements this is often caused by an inappropriate threshold value. The random error is not associated with any systematic offset in the measured volume and could be caused, for instance, by variations in the location of the specific object relative to the voxel grid. It can be eliminated by repeated measurements. It was found that both the systematic and random components of the error are a strong function of the size of the object measured relative to the voxel size. The relative error in the volume was found to follow approximately a power law relationship with the volume of the object, but with an exponent that implied, unexpectedly, that the relative error was proportional to the radius of the object for small objects, though the exponent did imply that the relative error was approximately proportional to the surface area of the object for larger objects. In an example application involving the size of mineral grains in an ore sample, the uncertainty associated with the random error in the volume is larger than the object itself for objects smaller than about 8 voxels and is greater than 10% for any object smaller than about 260 voxels. A methodology is presented for reducing the random error by combining the results from either multiple scans of the same object or scans of multiple similar objects, with an uncertainty of less than 5% requiring 12 objects of 100 voxels or 600 objects of 4 voxels. As the systematic
The effect of errors-in-variables on variance component estimation
NASA Astrophysics Data System (ADS)
Xu, Peiliang
2016-04-01
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
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.
RANDOM AND SYSTEMATIC FIELD ERRORS IN THE SNS RING: A STUDY OF THEIR EFFECTS AND COMPENSATION
GARDNER,C.J.; LEE,Y.Y.; WENG,W.T.
1998-06-22
The Accumulator Ring for the proposed Spallation Neutron Source (SNS) [l] is to accept a 1 ms beam pulse from a 1 GeV Proton Linac at a repetition rate of 60 Hz. For each beam pulse, 10{sup 14} protons (some 1,000 turns) are to be accumulated via charge-exchange injection and then promptly extracted to an external target for the production of neutrons by spallation. At this very high intensity, stringent limits (less than two parts in 10,000 per pulse) on beam loss during accumulation must be imposed in order to keep activation of ring components at an acceptable level. To stay within the desired limit, the effects of random and systematic field errors in the ring require careful attention. This paper describes the authors studies of these effects and the magnetic corrector schemes for their compensation.
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.
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.
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 ...
NASA Astrophysics Data System (ADS)
Konings, A. G.; Gruber, A.; Mccoll, K. A.; Alemohammad, S. H.; Entekhabi, D.
2015-12-01
Validating large-scale estimates of geophysical variables by comparing them to in situ measurements neglects the fact that these in situ measurements are not generally representative of the larger area. That is, in situ measurements contain some `representativeness error'. They also have their own sensor errors. The naïve approach of characterizing the errors of a remote sensing or modeling dataset by comparison to in situ measurements thus leads to error estimates that are spuriously inflated by the representativeness and other errors in the in situ measurements. Nevertheless, this naïve approach is still very common in the literature. In this work, we introduce an alternative estimator of the large-scale dataset error that explicitly takes into account the fact that the in situ measurements have some unknown error. The performance of the two estimators is then compared in the context of soil moisture datasets under different conditions for the true soil moisture climatology and dataset biases. The new estimator is shown to lead to a more accurate characterization of the dataset errors under the most common conditions. If a third dataset is available, the principles of the triple collocation method can be used to determine the errors of both the large-scale estimates and in situ measurements. However, triple collocation requires that the errors in all datasets are uncorrelated with each other and with the truth. We show that even when the assumptions of triple collocation are violated, a triple collocation-based validation approach may still be more accurate than a naïve comparison to in situ measurements that neglects representativeness errors.
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.
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.
NASA Technical Reports Server (NTRS)
Rodriguez, G.
1979-01-01
In-flight estimation of large structure model errors in order to detect inevitable deficiencies in large structure controller/estimator models is discussed. Such an estimation process is particularly applicable in the area of shape control system design required to maintain a prescribed static structural shape and, in addition, suppress dynamic disturbances due to the vehicle vibrational modes. The paper outlines a solution to the problem of static shape estimation where the vehicle shape must be reconstructed from a set of measurements discretely located throughout the structure. The estimation process is based on the principle of least-squares that inherently contains the definition and explicit computation of model error estimates that are optimal in some sense. Consequently, a solution is provided for the problem of estimation of static model errors (e.g., external loads). A generalized formulation applicable to distributed parameters systems is first worked out and then applied to a one-dimensional beam-like structural configuration.
Mapping systematic errors in helium abundance determinations using Markov Chain Monte Carlo
Aver, Erik; Olive, Keith A.; Skillman, Evan D. E-mail: olive@umn.edu
2011-03-01
Monte Carlo techniques have been used to evaluate the statistical and systematic uncertainties in the helium abundances derived from extragalactic H II regions. The helium abundance is sensitive to several physical parameters associated with the H II region. In this work, we introduce Markov Chain Monte Carlo (MCMC) methods to efficiently explore the parameter space and determine the helium abundance, the physical parameters, and the uncertainties derived from observations of metal poor nebulae. Experiments with synthetic data show that the MCMC method is superior to previous implementations (based on flux perturbation) in that it is not affected by biases due to non-physical parameter space. The MCMC analysis allows a detailed exploration of degeneracies, and, in particular, a false minimum that occurs at large values of optical depth in the He I emission lines. We demonstrate that introducing the electron temperature derived from the [O III] emission lines as a prior, in a very conservative manner, produces negligible bias and effectively eliminates the false minima occurring at large optical depth. We perform a frequentist analysis on data from several ''high quality'' systems. Likelihood plots illustrate degeneracies, asymmetries, and limits of the determination. In agreement with previous work, we find relatively large systematic errors, limiting the precision of the primordial helium abundance for currently available spectra.
NASA Astrophysics Data System (ADS)
Koltick, David; Wang, Haoyu; Liu, Shih-Chieh; Heim, Jordan; Nistor, Jonathan
2016-03-01
Typical nuclear decay constants are measured at the accuracy level of 10-2. There are numerous reasons: tests of unconventional theories, dating of materials, and long term inventory evolution which require decay constants accuracy at a level of 10-4 to 10-5. The statistical and systematic errors associated with precision measurements of decays using the counting technique are presented. Precision requires high count rates, which introduces time dependent dead time and pile-up corrections. An approach to overcome these issues is presented by continuous recording of the detector current. Other systematic corrections include, the time dependent dead time due to background radiation, control of target motion and radiation flight path variation due to environmental conditions, and the time dependent effects caused by scattered events are presented. The incorporation of blind experimental techniques can help make measurement independent of past results. A spectrometer design and data analysis is reviewed that can accomplish these goals. The author would like to thank TechSource, Inc. and Advanced Physics Technologies, LLC. for their support in this work.
Impact of instrumental systematic errors on fine-structure constant measurements with quasar spectra
NASA Astrophysics Data System (ADS)
Whitmore, Jonathan B.; Murphy, Michael T.
2015-02-01
We present a new `supercalibration' technique for measuring systematic distortions in the wavelength scales of high-resolution spectrographs. By comparing spectra of `solar twin' stars or asteroids with a reference laboratory solar spectrum, distortions in the standard thorium-argon calibration can be tracked with ˜10 m s-1 precision over the entire optical wavelength range on scales of both echelle orders (˜50-100 Å) and entire spectrographs arms (˜1000-3000 Å). Using archival spectra from the past 20 yr, we have probed the supercalibration history of the Very Large Telescope-Ultraviolet and Visible Echelle Spectrograph (VLT-UVES) and Keck-High Resolution Echelle Spectrograph (HIRES) spectrographs. We find that systematic errors in their wavelength scales are ubiquitous and substantial, with long-range distortions varying between typically ±200 m s-1 per 1000 Å. We apply a simple model of these distortions to simulated spectra that characterize the large UVES and HIRES quasar samples which previously indicated possible evidence for cosmological variations in the fine-structure constant, α. The spurious deviations in α produced by the model closely match important aspects of the VLT-UVES quasar results at all redshifts and partially explain the HIRES results, though not self-consistently at all redshifts. That is, the apparent ubiquity, size and general characteristics of the distortions are capable of significantly weakening the evidence for variations in α from quasar absorption lines.
X-ray optics metrology limited by random noise, instrumental drifts, and systematic errors
Yashchuk, Valeriy V.; Anderson, Erik H.; Barber, Samuel K.; Cambie, Rossana; Celestre, Richard; Conley, Raymond; Goldberg, Kenneth A.; McKinney, Wayne R.; Morrison, Gregory; Takacs, Peter Z.; Voronov, Dmitriy L.; Yuan, Sheng; Padmore, Howard A.
2010-07-09
Continuous, large-scale efforts to improve and develop third- and forth-generation synchrotron radiation light sources for unprecedented high-brightness, low emittance, and coherent x-ray beams demand diffracting and reflecting x-ray optics suitable for micro- and nano-focusing, brightness preservation, and super high resolution. One of the major impediments for development of x-ray optics with the required beamline performance comes from the inadequate present level of optical and at-wavelength metrology and insufficient integration of the metrology into the fabrication process and into beamlines. Based on our experience at the ALS Optical Metrology Laboratory, we review the experimental methods and techniques that allow us to mitigate significant optical metrology problems related to random, systematic, and drift errors with super-high-quality x-ray optics. Measurement errors below 0.2 mu rad have become routine. We present recent results from the ALS of temperature stabilized nano-focusing optics and dedicated at-wavelength metrology. The international effort to develop a next generation Optical Slope Measuring System (OSMS) to address these problems is also discussed. Finally, we analyze the remaining obstacles to further improvement of beamline x-ray optics and dedicated metrology, and highlight the ways we see to overcome the problems.
Boujraf, Saïd
2014-01-01
Diffusion weighted imaging uses the signal loss associated with the random thermal motion of water molecules in the presence of magnetic field gradients to derive a number of parameters that reflect the translational mobility of the water molecules in tissues. With a suitable experimental set-up, it is possible to calculate all the elements of the local diffusion tensor (DT) and derived parameters describing the behavior of the water molecules in each voxel. One of the emerging applications of the information obtained is an interpretation of the diffusion anisotropy in terms of the architecture of the underlying tissue. These interpretations can only be made provided the experimental data which are sufficiently accurate. However, the DT results are susceptible to two systematic error sources: On one hand, the presence of signal noise can lead to artificial divergence of the diffusivities. In contrast, the use of a simplified model for the interaction of the protons with the diffusion weighting and imaging field gradients (b matrix calculation), common in the clinical setting, also leads to deviation in the derived diffusion characteristics. In this paper, we study the importance of these two sources of error on the basis of experimental data obtained on a clinical magnetic resonance imaging system for an isotropic phantom using a state of the art single-shot echo planar imaging sequence. Our results show that optimal diffusion imaging require combining a correct calculation of the b-matrix and a sufficiently large signal to noise ratio. PMID:24761372
Boujraf, Saïd
2014-04-01
Diffusion weighted imaging uses the signal loss associated with the random thermal motion of water molecules in the presence of magnetic field gradients to derive a number of parameters that reflect the translational mobility of the water molecules in tissues. With a suitable experimental set-up, it is possible to calculate all the elements of the local diffusion tensor (DT) and derived parameters describing the behavior of the water molecules in each voxel. One of the emerging applications of the information obtained is an interpretation of the diffusion anisotropy in terms of the architecture of the underlying tissue. These interpretations can only be made provided the experimental data which are sufficiently accurate. However, the DT results are susceptible to two systematic error sources: On one hand, the presence of signal noise can lead to artificial divergence of the diffusivities. In contrast, the use of a simplified model for the interaction of the protons with the diffusion weighting and imaging field gradients (b matrix calculation), common in the clinical setting, also leads to deviation in the derived diffusion characteristics. In this paper, we study the importance of these two sources of error on the basis of experimental data obtained on a clinical magnetic resonance imaging system for an isotropic phantom using a state of the art single-shot echo planar imaging sequence. Our results show that optimal diffusion imaging require combining a correct calculation of the b-matrix and a sufficiently large signal to noise ratio. PMID:24761372
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
Nelms, Benjamin E.; Chan, Maria F.; Jarry, Geneviève; Lemire, Matthieu; Lowden, John; Hampton, Carnell
2013-11-15
Purpose: This study (1) examines a variety of real-world cases where systematic errors were not detected by widely accepted methods for IMRT/VMAT dosimetric accuracy evaluation, and (2) drills-down to identify failure modes and their corresponding means for detection, diagnosis, and mitigation. The primary goal of detailing these case studies is to explore different, more sensitive methods and metrics that could be used more effectively for evaluating accuracy of dose algorithms, delivery systems, and QA devices.Methods: The authors present seven real-world case studies representing a variety of combinations of the treatment planning system (TPS), linac, delivery modality, and systematic error type. These case studies are typical to what might be used as part of an IMRT or VMAT commissioning test suite, varying in complexity. Each case study is analyzed according to TG-119 instructions for gamma passing rates and action levels for per-beam and/or composite plan dosimetric QA. Then, each case study is analyzed in-depth with advanced diagnostic methods (dose profile examination, EPID-based measurements, dose difference pattern analysis, 3D measurement-guided dose reconstruction, and dose grid inspection) and more sensitive metrics (2% local normalization/2 mm DTA and estimated DVH comparisons).Results: For these case studies, the conventional 3%/3 mm gamma passing rates exceeded 99% for IMRT per-beam analyses and ranged from 93.9% to 100% for composite plan dose analysis, well above the TG-119 action levels of 90% and 88%, respectively. However, all cases had systematic errors that were detected only by using advanced diagnostic techniques and more sensitive metrics. The systematic errors caused variable but noteworthy impact, including estimated target dose coverage loss of up to 5.5% and local dose deviations up to 31.5%. Types of errors included TPS model settings, algorithm limitations, and modeling and alignment of QA phantoms in the TPS. Most of the errors were
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.
How well can we estimate error variance of satellite precipitation data around the world?
NASA Astrophysics Data System (ADS)
Gebregiorgis, Abebe S.; Hossain, Faisal
2015-03-01
Providing error information associated with existing satellite precipitation estimates is crucial to advancing applications in hydrologic modeling. In this study, we present a method of estimating the square difference prediction of satellite precipitation (hereafter used synonymously with "error variance") using regression model for three satellite precipitation products (3B42RT, CMORPH, and PERSIANN-CCS) using easily available geophysical features and satellite precipitation rate. Building on a suite of recent studies that have developed the error variance models, the goal of this work is to explore how well the method works around the world in diverse geophysical settings. Topography, climate, and seasons are considered as the governing factors to segregate the satellite precipitation uncertainty and fit a nonlinear regression equation as a function of satellite precipitation rate. The error variance models were tested on USA, Asia, Middle East, and Mediterranean region. Rain-gauge based precipitation product was used to validate the error variance of satellite precipitation products. The regression approach yielded good performance skill with high correlation between simulated and observed error variances. The correlation ranged from 0.46 to 0.98 during the independent validation period. In most cases (~ 85% of the scenarios), the correlation was higher than 0.72. The error variance models also captured the spatial distribution of observed error variance adequately for all study regions while producing unbiased residual error. The approach is promising for regions where missed precipitation is not a common occurrence in satellite precipitation estimation. Our study attests that transferability of model estimators (which help to estimate the error variance) from one region to another is practically possible by leveraging the similarity in geophysical features. Therefore, the quantitative picture of satellite precipitation error over ungauged regions can be
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.
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...
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%. PMID:22164030
Estimating smooth distribution function in the presence of heteroscedastic measurement errors
Wang, Xiao-Feng; Fan, Zhaozhi; Wang, Bin
2009-01-01
Measurement error occurs in many biomedical fields. The challenges arise when errors are heteroscedastic since we literally have only one observation for each error distribution. This paper concerns the estimation of smooth distribution function when data are contaminated with heteroscedastic errors. We study two types of methods to recover the unknown distribution function: a Fourier-type deconvolution method and a simulation extrapolation (SIMEX) method. The asymptotics of the two estimators are explored and the asymptotic pointwise confidence bands of the SIMEX estimator are obtained. The finite sample performances of the two estimators are evaluated through a simulation study. Finally, we illustrate the methods with medical rehabilitation data from a neuro-muscular electrical stimulation experiment. PMID:20160998
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%. PMID:22164030
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)
Spatio-temporal Error on the Discharge Estimates for the SWOT Mission
NASA Astrophysics Data System (ADS)
Biancamaria, S.; Alsdorf, D. E.; Andreadis, K. M.; Clark, E.; Durand, M.; Lettenmaier, D. P.; Mognard, N. M.; Oudin, Y.; Rodriguez, E.
2008-12-01
The Surface Water and Ocean Topography (SWOT) mission measures two key quantities over rivers: water surface elevation and slope. Water surface elevation from SWOT will have a vertical accuracy, when averaged over approximately one square kilometer, on the order of centimeters. Over reaches from 1-10 km long, SWOT slope measurements will be accurate to microradians. Elevation (depth) and slope offer the potential to produce discharge as a derived quantity. Estimates of instantaneous and temporally integrated discharge from SWOT data will also contain a certain degree of error. Two primary sources of measurement error exist. The first is the temporal sub-sampling of water elevations. For example, SWOT will sample some locations twice in the 21-day repeat cycle. If these two overpasses occurred during flood stage, an estimate of monthly discharge based on these observations would be much higher than the true value. Likewise, if estimating maximum or minimum monthly discharge, in some cases, SWOT may miss those events completely. The second source of measurement error results from the instrument's capability to accurately measure the magnitude of the water surface elevation. How this error affects discharge estimates depends on errors in the model used to derive discharge from water surface elevation. We present a global distribution of estimated relative errors in mean annual discharge based on a power law relationship between stage and discharge. Additionally, relative errors in integrated and average instantaneous monthly discharge associated with temporal sub-sampling over the proposed orbital tracks are presented for several river basins.
Estimation of Error in Western Pacific Geoid Heights Derived from Gravity Data Only
NASA Astrophysics Data System (ADS)
Peters, M. F.; Brozena, J. M.
2012-12-01
The goal of the Western Pacific Geoid estimation project was to generate geoid height models for regions in the Western Pacific Ocean, and formal error estimates for those geoid heights, using all available gravity data and statistical parameters of the quality of the gravity data. Geoid heights were to be determined solely from gravity measurements, as a gravimetric geoid model and error estimates for that model would have applications in oceanography and satellite altimetry. The general method was to remove the gravity field associated with a "lower" order spherical harmonic global gravity model from the regional gravity set; to fit a covariance model to the residual gravity, and then calculate the (residual) geoid heights and error estimates by least-squares collocation fit with residual gravity, available statistical estimates of the gravity and the covariance model. The geoid heights corresponding to the lower order spherical harmonic model can be added back to the heights from the residual gravity to produce a complete geoid height model. As input we requested from NGA all unclassified available gravity data in the western Pacific between 15° to 45° N and 105° to 141°W. The total data set that was used to model and estimate errors in gravimetric geoid comprised an unclassified, open file data set (540,012 stations), a proprietary airborne survey of Taiwan (19,234 stations), and unclassified NAVO SSP survey data (95,111 stations), for official use only. Various programs were adapted to the problem including N.K. Pavlis' HSYNTH program and the covariance fit program GPFIT and least-squares collocation program GPCOL from the GRAVSOFT package (Forsberg and Schering, 2008 version) which were modified to handle larger data sets, but in some regions data were still too numerous. Formulas were derived that could be used to block-mean the data in a statistically optimal sense and still retain the error estimates required for the collocation algorithm. Running the
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.
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.
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.
Noise Estimation and Adaptive Encoding for Asymmetric Quantum Error Correcting Codes
NASA Astrophysics Data System (ADS)
Florjanczyk, Jan; Brun, Todd; Center for Quantum Information Science; Technology Team
We present a technique that improves the performance of asymmetric quantum error correcting codes in the presence of biased qubit noise channels. Our study is motivated by considering what useful information can be learned from the statistics of syndrome measurements in stabilizer quantum error correcting codes (QECC). We consider the case of a qubit dephasing channel where the dephasing axis is unknown and time-varying. We are able to estimate the dephasing angle from the statistics of the standard syndrome measurements used in stabilizer QECC's. We use this estimate to rotate the computational basis of the code in such a way that the most likely type of error is covered by the highest distance of the asymmetric code. In particular, we use the [ [ 15 , 1 , 3 ] ] shortened Reed-Muller code which can correct one phase-flip error but up to three bit-flip errors. In our simulations, we tune the computational basis to match the estimated dephasing axis which in turn leads to a decrease in the probability of a phase-flip error. With a sufficiently accurate estimate of the dephasing axis, our memory's effective error is dominated by the much lower probability of four bit-flips. Aro MURI Grant No. W911NF-11-1-0268.
Strömberg, Sten; Nistor, Mihaela; Liu, Jing
2014-11-15
Highlights: • The evaluated factors introduce significant systematic errors (10–38%) in BMP tests. • Ambient temperature (T) has the most substantial impact (∼10%) at low altitude. • Ambient pressure (p) has the most substantial impact (∼68%) at high altitude. • Continuous monitoring of T and p is not necessary for kinetic calculations. - Abstract: The Biochemical Methane Potential (BMP) test is increasingly recognised as a tool for selecting and pricing biomass material for production of biogas. However, the results for the same substrate often differ between laboratories and much work to standardise such tests is still needed. In the current study, the effects from four environmental factors (i.e. ambient temperature and pressure, water vapour content and initial gas composition of the reactor headspace) on the degradation kinetics and the determined methane potential were evaluated with a 2{sup 4} full factorial design. Four substrates, with different biodegradation profiles, were investigated and the ambient temperature was found to be the most significant contributor to errors in the methane potential. Concerning the kinetics of the process, the environmental factors’ impact on the calculated rate constants was negligible. The impact of the environmental factors on the kinetic parameters and methane potential from performing a BMP test at different geographical locations around the world was simulated by adjusting the data according to the ambient temperature and pressure of some chosen model sites. The largest effect on the methane potential was registered from tests performed at high altitudes due to a low ambient pressure. The results from this study illustrate the importance of considering the environmental factors’ influence on volumetric gas measurement in BMP tests. This is essential to achieve trustworthy and standardised results that can be used by researchers and end users from all over the world.
Interventions to reduce wrong blood in tube errors in transfusion: a systematic review.
Cottrell, Susan; Watson, Douglas; Eyre, Toby A; Brunskill, Susan J; Dorée, Carolyn; Murphy, Michael F
2013-10-01
This systematic review addresses the issue of wrong blood in tube (WBIT). The objective was to identify interventions that have been implemented and the effectiveness of these interventions to reduce WBIT incidence in red blood cell transfusion. Eligible articles were identified through a comprehensive search of The Cochrane Library, MEDLINE, EMBASE, Cinahl, BNID, and the Transfusion Evidence Library to April 2013. Initial search criteria were wide including primary intervention or observational studies, case reports, expert opinion, and guidelines. There was no restriction by study type, language, or status. Publications before 1995, reviews or reports of a secondary nature, studies of sampling errors outwith transfusion, and articles involving animals were excluded. The primary outcome was a reduction in errors. Study characteristics, outcomes measured, and methodological quality were extracted by 2 authors independently. The principal method of analysis was descriptive. A total of 12,703 references were initially identified. Preliminary secondary screening by 2 reviewers reduced articles for detailed screening to 128 articles. Eleven articles were eventually identified as eligible, resulting in 9 independent studies being included in the review. The overall finding was that all the identified interventions reduced WBIT incidence. Five studies measured the effect of a single intervention, for example, changes to blood sample labeling, weekly feedback, handwritten transfusion requests, and an electronic transfusion system. Four studies reported multiple interventions including education, second check of ID at sampling, and confirmatory sampling. It was not clear which intervention was the most effective. Sustainability of the effectiveness of interventions was also unclear. Targeted interventions, either single or multiple, can lead to a reduction in WBIT; but the sustainability of effectiveness is uncertain. Data on the pre- and postimplementation of
A function space approach to state and model error estimation for elliptic systems
NASA Technical Reports Server (NTRS)
Rodriguez, G.
1983-01-01
An approach is advanced for the concurrent estimation of the state and of the model errors of a system described by elliptic equations. The estimates are obtained by a deterministic least-squares approach that seeks to minimize a quadratic functional of the model errors, or equivalently, to find the vector of smallest norm subject to linear constraints in a suitably defined function space. The minimum norm solution can be obtained by solving either a Fredholm integral equation of the second kind for the case with continuously distributed data or a related matrix equation for the problem with discretely located measurements. Solution of either one of these equations is obtained in a batch-processing mode in which all of the data is processed simultaneously or, in certain restricted geometries, in a spatially scanning mode in which the data is processed recursively. After the methods for computation of the optimal estimates are developed, an analysis of the second-order statistics of the estimates and of the corresponding estimation error is conducted. Based on this analysis, explicit expressions for the mean-square estimation error associated with both the state and model error estimates are then developed.
Estimation of error limits for cerebral blood flow values obtained from xenon-133 clearance curves
Ryding, E.
1989-02-01
I provide the theoretical basis for an error calculus for measurements of cerebral blood flow using a freely diffusible tracer substance such as xenon-133. The use of the error calculus is exemplified by a study of the effect on the error margins in measurements of gray matter blood flow from flow level, relative weight of the gray matter compartment, and use of the earliest parts of the clearance curves. The clinical value of the error calculus is illustrated by its ability to separate different sources of measurement error. As a consequence, it is possible to optimize the method for blood flow calculation from the clearance curves, depending on the type of cerebral blood flow measurement. I show that if a true picture of the regional gray matter blood flow distribution is sought, the earliest part of the clearance curves should be used. This does, however, increase the error in the estimate of the average cerebral blood flow value.
Systematic errors in optical-flow velocimetry for turbulent flows and flames.
Fielding, J; Long, M B; Fielding, G; Komiyama, M
2001-02-20
Optical-flow (OF) velocimetry is based on extracting velocity information from two-dimensional scalar images and represents an unseeded alternative to particle-image velocimetry in turbulent flows. The performance of the technique is examined by direct comparison with simultaneous particle-image velocimetry in both an isothermal turbulent flow and a turbulent flame by use of acetone-OH laser-induced fluorescence. Two representative region-based correlation OF algorithms are applied to assess the general accuracy of the technique. Systematic discrepancies between particle-imaging velocimetry and OF velocimetry are identified with increasing distance from the center line, indicating potential limitations of the current OF techniques. Directional errors are present at all radial positions, with differences in excess of 10 degrees being typical. An experimental measurement setup is described that allows the simultaneous measurement of Mie scattering from seed particles and laser-induced fluorescence on the same CCD camera at two distinct times for validation studies. PMID:18357055
The shrinking Sun: A systematic error in local correlation tracking of solar granulation
NASA Astrophysics Data System (ADS)
Löptien, B.; Birch, A. C.; Duvall, T. L.; Gizon, L.; Schou, J.
2016-05-01
Context. Local correlation tracking of granulation (LCT) is an important method for measuring horizontal flows in the photosphere. This method exhibits a systematic error that looks like a flow converging toward disk center, which is also known as the shrinking-Sun effect. Aims: We aim to study the nature of the shrinking-Sun effect for continuum intensity data and to derive a simple model that can explain its origin. Methods: We derived LCT flow maps by running the LCT code Fourier Local Correlation Tracking (FLCT) on tracked and remapped continuum intensity maps provided by the Helioseismic and Magnetic Imager (HMI) onboard the Solar Dynamics Observatory (SDO). We also computed flow maps from synthetic continuum images generated from STAGGER code simulations of solar surface convection. We investigated the origin of the shrinking-Sun effect by generating an average granule from synthetic data from the simulations. Results: The LCT flow maps derived from the HMI data and the simulations exhibit a shrinking-Sun effect of comparable magnitude. The origin of this effect is related to the apparent asymmetry of granulation originating from radiative transfer effects when observing with a viewing angle inclined from vertical. This causes, in combination with the expansion of the granules, an apparent motion toward disk center.
NASA Astrophysics Data System (ADS)
Kollat, J. B.; Reed, P. M.
2011-12-01
This study demonstrates how many-objective long-term groundwater monitoring (LTGM) network design tradeoffs evolve across multiple management periods given systematic models errors (i.e., predictive bias), groundwater flow-and-transport forecasting uncertainties, and contaminant observation uncertainties. Our analysis utilizes the Adaptive Strategies for Sampling in Space and Time (ASSIST) framework, which is composed of three primary components: (1) bias-aware Ensemble Kalman Filtering, (2) many-objective hierarchical Bayesian optimization, and (3) interactive visual analytics for understanding spatiotemporal network design tradeoffs. A physical aquifer experiment is utilized to develop a severely challenging multi-period observation system simulation experiment (OSSE) that reflects the challenges and decisions faced in monitoring contaminated groundwater systems. The experimental aquifer OSSE shows both the influence and consequences of plume dynamics as well as alternative cost-savings strategies in shaping how LTGM many-objective tradeoffs evolve. Our findings highlight the need to move beyond least cost purely statistical monitoring frameworks to consider many-objective evaluations of LTGM tradeoffs. The ASSIST framework provides a highly flexible approach for measuring the value of observables that simultaneously improves how the data are used to inform decisions.
Accounting for systematic errors in bioluminescence imaging to improve quantitative accuracy
NASA Astrophysics Data System (ADS)
Taylor, Shelley L.; Perry, Tracey A.; Styles, Iain B.; Cobbold, Mark; Dehghani, Hamid
2015-07-01
Bioluminescence imaging (BLI) is a widely used pre-clinical imaging technique, but there are a number of limitations to its quantitative accuracy. This work uses an animal model to demonstrate some significant limitations of BLI and presents processing methods and algorithms which overcome these limitations, increasing the quantitative accuracy of the technique. The position of the imaging subject and source depth are both shown to affect the measured luminescence intensity. Free Space Modelling is used to eliminate the systematic error due to the camera/subject geometry, removing the dependence of luminescence intensity on animal position. Bioluminescence tomography (BLT) is then used to provide additional information about the depth and intensity of the source. A substantial limitation in the number of sources identified using BLI is also presented. It is shown that when a given source is at a significant depth, it can appear as multiple sources when imaged using BLI, while the use of BLT recovers the true number of sources present.
Analysis of systematic errors of the ASM/RXTE monitor and GT-48 γ-ray telescope
NASA Astrophysics Data System (ADS)
Fidelis, V. V.
2011-06-01
The observational data concerning variations of light curves of supernovae remnants—the Crab Nebula, Cassiopeia A, Tycho Brahe, and pulsar Vela—over 14 days scale that may be attributed to systematic errors of the ASM/RXTE monitor are presented. The experimental systematic errors of the GT-48 γ-ray telescope in the mono mode of operation were also determined. For this the observational data of TeV J2032 + 4130 (Cyg γ-2, according to the Crimean version) were used and the stationary nature of its γ-ray emission was confirmed by long-term observations performed with HEGRA and MAGIC. The results of research allow us to draw the following conclusions: (1) light curves of supernovae remnants averaged for long observing periods have false statistically significant flux variations, (2) the level of systematic errors is proportional to the registered flux and decreases with increasing temporal scale of averaging, (3) the light curves of sources may be modulated by the year period, and (4) the systematic errors of the GT-48 γ-ray telescope, in the amount caused by observations in the mono mode and data processing with the stereo-algorithm come to 0.12 min-1.
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.
A function space approach to state and model error estimation for elliptic systems
NASA Technical Reports Server (NTRS)
Rodriguez, G.
1983-01-01
An approach is advanced for the concurrent estimation of the state and of the model errors of a system described by elliptic equations. The estimates are obtained by a deterministic least-squares approach that seeks to minimize a quadratic functional of the model errors, or equivalently, to find the vector of smallest norm subject to linear constraints in a suitably defined function space. The minimum norm solution can be obtained by solving either a Fredholm integral equation of the second kind for the case with continuously distributed data or a related matrix equation for the problem with discretely located measurements. Solution of either one of these equations is obtained in a batch-processing mode in which all of the data is processed simultaneously or, in certain restricted geometries, in a spatially scanning mode in which the data is processed recursively. After the methods for computation of the optimal esimates are developed, an analysis of the second-order statistics of the estimates and of the corresponding estimation error is conducted. Based on this analysis, explicit expressions for the mean-square estimation error associated with both the state and model error estimates are then developed. While this paper focuses on theoretical developments, applications arising in the area of large structure static shape determination are contained in a closely related paper (Rodriguez and Scheid, 1982).
Adjustment of wind-drift effect for real-time systematic error correction in radar rainfall data
NASA Astrophysics Data System (ADS)
Dai, Qiang; Han, Dawei; Zhuo, Lu; Huang, Jing; Islam, Tanvir; Zhang, Shuliang
An effective bias correction procedure using gauge measurement is a significant step for radar data processing to reduce the systematic error in hydrological applications. In these bias correction methods, the spatial matching of precipitation patterns between radar and gauge networks is an important premise. However, the wind-drift effect on radar measurement induces an inconsistent spatial relationship between radar and gauge measurements as the raindrops observed by radar do not fall vertically to the ground. Consequently, a rain gauge does not correspond to the radar pixel based on the projected location of the radar beam. In this study, we introduce an adjustment method to incorporate the wind-drift effect into a bias correlation scheme. We first simulate the trajectory of raindrops in the air using downscaled three-dimensional wind data from the weather research and forecasting model (WRF) and calculate the final location of raindrops on the ground. The displacement of rainfall is then estimated and a radar-gauge spatial relationship is reconstructed. Based on this, the local real-time biases of the bin-average radar data were estimated for 12 selected events. Then, the reference mean local gauge rainfall, mean local bias, and adjusted radar rainfall calculated with and without consideration of the wind-drift effect are compared for different events and locations. There are considerable differences for three estimators, indicating that wind drift has a considerable impact on the real-time radar bias correction. Based on these facts, we suggest bias correction schemes based on the spatial correlation between radar and gauge measurements should consider the adjustment of the wind-drift effect and the proposed adjustment method is a promising solution to achieve this.
Estimating Conditional Standard Errors of Measurement for Tests Composed of Testlets.
ERIC Educational Resources Information Center
Lee, Guemin
The primary purpose of this study was to investigate the appropriateness and implication of incorporating a testlet definition into the estimation of the conditional standard error of measurement (SEM) for tests composed of testlets. The five conditional SEM estimation methods used in this study were classified into two categories: item-based and…
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…
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…
NASA Astrophysics Data System (ADS)
Xue, Haile; Shen, Xueshun; Chou, Jifan
2015-11-01
An online systematic error correction is presented and examined as a technique to improve the accuracy of real-time numerical weather prediction, based on the dataset of model errors (MEs) in past intervals. Given the analyses, the ME in each interval (6 h) between two analyses can be iteratively obtained by introducing an unknown tendency term into the prediction equation, shown in Part I of this two-paper series. In this part, after analyzing the 5-year (2001-2005) GRAPES-GFS (Global Forecast System of the Global and Regional Assimilation and Prediction System) error patterns and evolution, a systematic model error correction is given based on the least-squares approach by firstly using the past MEs. To test the correction, we applied the approach in GRAPES-GFS for July 2009 and January 2010. The datasets associated with the initial condition and SST used in this study were based on NCEP (National Centers for Environmental Prediction) FNL (final) data. The results indicated that the Northern Hemispheric systematically underestimated equator-to-pole geopotential gradient and westerly wind of GRAPES-GFS were largely enhanced, and the biases of temperature and wind in the tropics were strongly reduced. Therefore, the correction results in a more skillful forecast with lower mean bias and root-mean-square error and higher anomaly correlation coefficient.
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)
Miller, B.; O'Shaughnessy, R.; Littenberg, T. B.; Farr, B.
2015-08-01
Reliable low-latency gravitational wave parameter estimation is essential to target limited electromagnetic follow-up facilities toward astrophysically interesting and electromagnetically relevant sources of gravitational waves. In this study, we examine the trade-off between speed and accuracy. Specifically, we estimate the astrophysical relevance of systematic errors in the posterior parameter distributions derived using a fast-but-approximate waveform model, SpinTaylorF2 (stf2), in parameter estimation with lalinference_mcmc. Though efficient, the stf2 approximation to compact binary inspiral employs approximate kinematics (e.g., a single spin) and an approximate waveform (e.g., frequency domain versus time domain). More broadly, using a large astrophysically motivated population of generic compact binary merger signals, we report on the effectualness and limitations of this single-spin approximation as a method to infer parameters of generic compact binary sources. For most low-mass compact binary sources, we find that the stf2 approximation estimates compact binary parameters with biases comparable to systematic uncertainties in the waveform. We illustrate by example the effect these systematic errors have on posterior probabilities most relevant to low-latency electromagnetic follow-up: whether the secondary has a mass consistent with a neutron star (NS); whether the masses, spins, and orbit are consistent with that neutron star's tidal disruption; and whether the binary's angular momentum axis is oriented along the line of sight.
Facial motion parameter estimation and error criteria in model-based image coding
NASA Astrophysics Data System (ADS)
Liu, Yunhai; Yu, Lu; Yao, Qingdong
2000-04-01
Model-based image coding has been given extensive attention due to its high subject image quality and low bit-rates. But the estimation of object motion parameter is still a difficult problem, and there is not a proper error criteria for the quality assessment that are consistent with visual properties. This paper presents an algorithm of the facial motion parameter estimation based on feature point correspondence and gives the motion parameter error criteria. The facial motion model comprises of three parts. The first part is the global 3-D rigid motion of the head, the second part is non-rigid translation motion in jaw area, and the third part consists of local non-rigid expression motion in eyes and mouth areas. The feature points are automatically selected by a function of edges, brightness and end-node outside the blocks of eyes and mouth. The numbers of feature point are adjusted adaptively. The jaw translation motion is tracked by the changes of the feature point position of jaw. The areas of non-rigid expression motion can be rebuilt by using block-pasting method. The estimation approach of motion parameter error based on the quality of reconstructed image is suggested, and area error function and the error function of contour transition-turn rate are used to be quality criteria. The criteria reflect the image geometric distortion caused by the error of estimated motion parameters properly.
Casas, Francisco J.; Ortiz, David; Villa, Enrique; Cano, Juan L.; Cagigas, Jaime; Pérez, Ana R.; Aja, Beatriz; Terán, J. Vicente; de la Fuente, Luisa; Artal, Eduardo; Hoyland, Roger; Génova-Santos, Ricardo
2015-01-01
This paper presents preliminary polarization measurements and systematic-error characterization of the Thirty Gigahertz Instrument receiver developed for the QUIJOTE experiment. The instrument has been designed to measure the polarization of Cosmic Microwave Background radiation from the sky, obtaining the Q, U, and I Stokes parameters of the incoming signal simultaneously. Two kinds of linearly polarized input signals have been used as excitations in the polarimeter measurement tests in the laboratory; these show consistent results in terms of the Stokes parameters obtained. A measurement-based systematic-error characterization technique has been used in order to determine the possible sources of instrumental errors and to assist in the polarimeter calibration process. PMID:26251906
Casas, Francisco J; Ortiz, David; Villa, Enrique; Cano, Juan L; Cagigas, Jaime; Pérez, Ana R; Aja, Beatriz; Terán, J Vicente; de la Fuente, Luisa; Artal, Eduardo; Hoyland, Roger; Génova-Santos, Ricardo
2015-01-01
This paper presents preliminary polarization measurements and systematic-error characterization of the Thirty Gigahertz Instrument receiver developed for the QUIJOTE experiment. The instrument has been designed to measure the polarization of Cosmic Microwave Background radiation from the sky, obtaining the Q, U, and I Stokes parameters of the incoming signal simultaneously. Two kinds of linearly polarized input signals have been used as excitations in the polarimeter measurement tests in the laboratory; these show consistent results in terms of the Stokes parameters obtained. A measurement-based systematic-error characterization technique has been used in order to determine the possible sources of instrumental errors and to assist in the polarimeter calibration process. PMID:26251906
NASA Astrophysics Data System (ADS)
Li, T. S.; DePoy, D. L.; Marshall, J. L.; Tucker, D.; Kessler, R.; Annis, J.; Bernstein, G. M.; Boada, S.; Burke, D. L.; Finley, D. A.; James, D. J.; Kent, S.; Lin, H.; Marriner, J.; Mondrik, N.; Nagasawa, D.; Rykoff, E. S.; Scolnic, D.; Walker, A. R.; Wester, W.; Abbott, T. M. C.; Allam, S.; Benoit-Lévy, A.; Bertin, E.; Brooks, D.; Capozzi, D.; Carnero Rosell, A.; Carrasco Kind, M.; Carretero, J.; Crocce, M.; Cunha, C. E.; D’Andrea, C. B.; da Costa, L. N.; Desai, S.; Diehl, H. T.; Doel, P.; Flaugher, B.; Fosalba, P.; Frieman, J.; Gaztanaga, E.; Goldstein, D. A.; Gruen, D.; Gruendl, R. A.; Gutierrez, G.; Honscheid, K.; Kuehn, K.; Kuropatkin, N.; Maia, M. A. G.; Melchior, P.; Miller, C. J.; Miquel, R.; Mohr, J. J.; Neilsen, E.; Nichol, R. C.; Nord, B.; Ogando, R.; Plazas, A. A.; Romer, A. K.; Roodman, A.; Sako, M.; Sanchez, E.; Scarpine, V.; Schubnell, M.; Sevilla-Noarbe, I.; Smith, R. C.; Soares-Santos, M.; Sobreira, F.; Suchyta, E.; Tarle, G.; Thomas, D.; Vikram, V.; The DES Collaboration
2016-06-01
Meeting the science goals for many current and future ground-based optical large-area sky surveys requires that the calibrated broadband photometry is both stable in time and uniform over the sky to 1% precision or better. Past and current surveys have achieved photometric precision of 1%–2% by calibrating the survey’s stellar photometry with repeated measurements of a large number of stars observed in multiple epochs. The calibration techniques employed by these surveys only consider the relative frame-by-frame photometric zeropoint offset and the focal plane position-dependent illumination corrections, which are independent of the source color. However, variations in the wavelength dependence of the atmospheric transmission and the instrumental throughput induce source color-dependent systematic errors. These systematic errors must also be considered to achieve the most precise photometric measurements. In this paper, we examine such systematic chromatic errors (SCEs) using photometry from the Dark Energy Survey (DES) as an example. We first define a natural magnitude system for DES and calculate the systematic errors on stellar magnitudes when the atmospheric transmission and instrumental throughput deviate from the natural system. We conclude that the SCEs caused by the change of airmass in each exposure, the change of the precipitable water vapor and aerosol in the atmosphere over time, and the non-uniformity of instrumental throughput over the focal plane can be up to 2% in some bandpasses. We then compare the calculated SCEs with the observed DES data. For the test sample data, we correct these errors using measurements of the atmospheric transmission and instrumental throughput from auxiliary calibration systems. The residual after correction is less than 0.3%. Moreover, we calculate such SCEs for Type Ia supernovae and elliptical galaxies and find that the chromatic errors for non-stellar objects are redshift-dependent and can be larger than those for
Doolan, P; Dias, M; Collins Fekete, C; Seco, J
2014-06-01
Purpose: The procedure for proton treatment planning involves the conversion of the patient's X-ray CT from Hounsfield units into relative stopping powers (RSP), using a stoichiometric calibration curve (Schneider 1996). In clinical practice a 3.5% margin is added to account for the range uncertainty introduced by this process and other errors. RSPs for real tissues are calculated using composition data and the Bethe-Bloch formula (ICRU 1993). The purpose of this work is to investigate the impact that systematic errors in the stoichiometric calibration have on the proton range. Methods: Seven tissue inserts of the Gammex 467 phantom were imaged using our CT scanner. Their known chemical compositions (Watanabe 1999) were then used to calculate the theoretical RSPs, using the same formula as would be used for human tissues in the stoichiometric procedure. The actual RSPs of these inserts were measured using a Bragg peak shift measurement in the proton beam at our institution. Results: The theoretical calculation of the RSP was lower than the measured RSP values, by a mean/max error of - 1.5/-3.6%. For all seven inserts the theoretical approach underestimated the RSP, with errors variable across the range of Hounsfield units. Systematic errors for lung (average of two inserts), adipose and cortical bone were - 3.0/-2.1/-0.5%, respectively. Conclusion: There is a systematic underestimation caused by the theoretical calculation of RSP; a crucial step in the stoichiometric calibration procedure. As such, we propose that proton calibration curves should be based on measured RSPs. Investigations will be made to see if the same systematic errors exist for biological tissues. The impact of these differences on the range of proton beams, for phantoms and patient scenarios, will be investigated. This project was funded equally by the Engineering and Physical Sciences Research Council (UK) and Ion Beam Applications (Louvain-La-Neuve, Belgium)
NASA Technical Reports Server (NTRS)
Martin, D. L.; Perry, M. J.
1994-01-01
Water-leaving radiances and phytoplankton pigment concentrations are calculated from coastal zone color scanner (CZCS) radiance measurements by removing atmospheric Rayleigh and aerosol radiances from the total radiance signal measured at the satellite. The single greatest source of error in CZCS atmospheric correction algorithms in the assumption that these Rayleigh and aerosol radiances are separable. Multiple-scattering interactions between Rayleigh and aerosol components cause systematic errors in calculated aerosol radiances, and the magnitude of these errors is dependent on aerosol type and optical depth and on satellite viewing geometry. A technique was developed which extends the results of previous radiative transfer modeling by Gordon and Castano to predict the magnitude of these systematic errors for simulated CZCS orbital passes in which the ocean is viewed through a modeled, physically realistic atmosphere. The simulated image mathematically duplicates the exact satellite, Sun, and pixel locations of an actual CZCS image. Errors in the aerosol radiance at 443 nm are calculated for a range of aerosol optical depths. When pixels in the simulated image exceed an error threshhold, the corresponding pixels in the actual CZCS image are flagged and excluded from further analysis or from use in image compositing or compilation of pigment concentration databases. Studies based on time series analyses or compositing of CZCS imagery which do not address Rayleigh-aerosol multiple scattering should be interpreted cautiously, since the fundamental assumption used in their atmospheric correction algorithm is flawed.
Motion-induced phase error estimation and correction in 3D diffusion tensor imaging.
Van, Anh T; Hernando, Diego; Sutton, Bradley P
2011-11-01
A multishot data acquisition strategy is one way to mitigate B0 distortion and T2∗ blurring for high-resolution diffusion-weighted magnetic resonance imaging experiments. However, different object motions that take place during different shots cause phase inconsistencies in the data, leading to significant image artifacts. This work proposes a maximum likelihood estimation and k-space correction of motion-induced phase errors in 3D multishot diffusion tensor imaging. The proposed error estimation is robust, unbiased, and approaches the Cramer-Rao lower bound. For rigid body motion, the proposed correction effectively removes motion-induced phase errors regardless of the k-space trajectory used and gives comparable performance to the more computationally expensive 3D iterative nonlinear phase error correction method. The method has been extended to handle multichannel data collected using phased-array coils. Simulation and in vivo data are shown to demonstrate the performance of the method. PMID:21652284
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
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. PMID:25670023
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. PMID:27164045
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.
NASA Technical Reports Server (NTRS)
Kirstettier, Pierre-Emmanual; Honh, Y.; Gourley, J. J.; Chen, S.; Flamig, Z.; Zhang, J.; Howard, K.; Schwaller, M.; Petersen, W.; Amitai, E.
2011-01-01
Characterization of the error associated to satellite rainfall estimates is a necessary component of deterministic and probabilistic frameworks involving space-born passive and active microwave measurement") for applications ranging from water budget studies to forecasting natural hazards related to extreme rainfall events. We focus here on the error structure of NASA's Tropical Rainfall Measurement Mission (TRMM) Precipitation Radar (PR) quantitative precipitation estimation (QPE) at ground. The problem is addressed by comparison of PR QPEs with reference values derived from ground-based measurements using NOAA/NSSL ground radar-based National Mosaic and QPE system (NMQ/Q2). A preliminary investigation of this subject has been carried out at the PR estimation scale (instantaneous and 5 km) using a three-month data sample in the southern part of US. The primary contribution of this study is the presentation of the detailed steps required to derive trustworthy reference rainfall dataset from Q2 at the PR pixel resolution. It relics on a bias correction and a radar quality index, both of which provide a basis to filter out the less trustworthy Q2 values. Several aspects of PR errors arc revealed and quantified including sensitivity to the processing steps with the reference rainfall, comparisons of rainfall detectability and rainfall rate distributions, spatial representativeness of error, and separation of systematic biases and random errors. The methodology and framework developed herein applies more generally to rainfall rate estimates from other sensors onboard low-earth orbiting satellites such as microwave imagers and dual-wavelength radars such as with the Global Precipitation Measurement (GPM) mission.
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
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.
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
NASA Astrophysics Data System (ADS)
Locatelli, R.; Bousquet, P.; Chevallier, F.; Fortems-Cheney, A.; Szopa, S.; Saunois, M.; Agusti-Panareda, A.; Bergmann, D.; Bian, H.; Cameron-Smith, P.; Chipperfield, M. P.; Gloor, E.; Houweling, S.; Kawa, S. R.; Krol, M.; Patra, P. K.; Prinn, R. G.; Rigby, M.; Saito, R.; Wilson, C.
2013-10-01
A modelling experiment has been conceived to assess the impact of transport model errors on methane emissions estimated in an atmospheric inversion system. Synthetic methane observations, obtained from 10 different model outputs from the international TransCom-CH4 model inter-comparison exercise, are combined with a prior scenario of methane emissions and sinks, and integrated into the three-component PYVAR-LMDZ-SACS (PYthon VARiational-Laboratoire de Météorologie Dynamique model with Zooming capability-Simplified Atmospheric Chemistry System) inversion system to produce 10 different methane emission estimates at the global scale for the year 2005. The same methane sinks, emissions and initial conditions have been applied to produce the 10 synthetic observation datasets. The same inversion set-up (statistical errors, prior emissions, inverse procedure) is then applied to derive flux estimates by inverse modelling. Consequently, only differences in the modelling of atmospheric transport may cause differences in the estimated fluxes. In our framework, we show that transport model errors lead to a discrepancy of 27 Tg yr-1 at the global scale, representing 5% of total methane emissions. At continental and annual scales, transport model errors are proportionally larger than at the global scale, with errors ranging from 36 Tg yr-1 in North America to 7 Tg yr-1 in Boreal Eurasia (from 23 to 48%, respectively). At the model grid-scale, the spread of inverse estimates can reach 150% of the prior flux. Therefore, transport model errors contribute significantly to overall uncertainties in emission estimates by inverse modelling, especially when small spatial scales are examined. Sensitivity tests have been carried out to estimate the impact of the measurement network and the advantage of higher horizontal resolution in transport models. The large differences found between methane flux estimates inferred in these different configurations highly question the consistency of
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.
NASA Astrophysics Data System (ADS)
Shafer, Daniel L.; Huterer, Dragan
2015-03-01
We develop a general method to `self-calibrate' observations of galaxy clustering with respect to systematics associated with photometric calibration errors. We first point out the danger posed by the multiplicative effect of calibration errors, where large-angle error propagates to small scales and may be significant even if the large-scale information is cleaned or not used in the cosmological analysis. We then propose a method to measure the arbitrary large-scale calibration errors and use these measurements to correct the small-scale (high-multipole) power which is most useful for constraining the majority of cosmological parameters. We demonstrate the effectiveness of our approach on synthetic examples and briefly discuss how it may be applied to real data.
Estimation of bias errors in measured airplane responses using maximum likelihood method
NASA Technical Reports Server (NTRS)
Klein, Vladiaslav; Morgan, Dan R.
1987-01-01
A maximum likelihood method is used for estimation of unknown bias errors in measured airplane responses. The mathematical model of an airplane is represented by six-degrees-of-freedom kinematic equations. In these equations the input variables are replaced by their measured values which are assumed to be without random errors. The resulting algorithm is verified with a simulation and flight test data. The maximum likelihood estimates from in-flight measured data are compared with those obtained by using a nonlinear-fixed-interval-smoother and an extended Kalmar filter.
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.
Error Analysis for Estimation of Trace Vapor Concentration Pathlength in Stack Plumes
Gallagher, Neal B.; Wise, Barry M.; Sheen, David M.
2003-06-01
Near infrared hpyerspectral imaging is finding utility in remote sensing applications such as detection and quantification of chemical vapor effluents in stack plumes. Optimizing the sensing system or quantification algorithms is difficult since reference images are rarely well characterized. The present work uses a radiance model for a down looking scene and a detailed noise model for a dispersive and Fourier transform spectrometer to generate well-characterized synthetic data. These data were used in conjunction with a classical least squares based estimation procedure in an error analysis to provide estimates of different sources of concentration-pathlength quantification error in the remote sensing problem.
NASA Astrophysics Data System (ADS)
Flynn, Lawrence E.; Labow, Gordon J.; Beach, Robert A.; Rawlins, Michael A.; Flittner, David E.
1996-10-01
Inexpensive devices to measure solar UV irradiance are available to monitor atmospheric ozone, for example, total ozone portable spectroradiometers (TOPS instruments). A procedure to convert these measurements into ozone estimates is examined. For well-characterized filters with 7-nm FWHM bandpasses, the method provides ozone values (from 304- and 310-nm channels) with less than 0.4 error attributable to inversion of the theoretical model. Analysis of sensitivity to model assumptions and parameters yields estimates of 3 bias in total ozone results with dependence on total ozone and path length. Unmodeled effects of atmospheric constituents and instrument components can result in additional 2 errors.
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.
Assumption-free estimation of the genetic contribution to refractive error across childhood
St Pourcain, Beate; McMahon, George; Timpson, Nicholas J.; Evans, David M.; Williams, Cathy
2015-01-01
Purpose Studies in relatives have generally yielded high heritability estimates for refractive error: twins 75–90%, families 15–70%. However, because related individuals often share a common environment, these estimates are inflated (via misallocation of unique/common environment variance). We calculated a lower-bound heritability estimate for refractive error free from such bias. Methods Between the ages 7 and 15 years, participants in the Avon Longitudinal Study of Parents and Children (ALSPAC) underwent non-cycloplegic autorefraction at regular research clinics. At each age, an estimate of the variance in refractive error explained by single nucleotide polymorphism (SNP) genetic variants was calculated using genome-wide complex trait analysis (GCTA) using high-density genome-wide SNP genotype information (minimum N at each age=3,404). Results The variance in refractive error explained by the SNPs (“SNP heritability”) was stable over childhood: Across age 7–15 years, SNP heritability averaged 0.28 (SE=0.08, p<0.001). The genetic correlation for refractive error between visits varied from 0.77 to 1.00 (all p<0.001) demonstrating that a common set of SNPs was responsible for the genetic contribution to refractive error across this period of childhood. Simulations suggested lack of cycloplegia during autorefraction led to a small underestimation of SNP heritability (adjusted SNP heritability=0.35; SE=0.09). To put these results in context, the variance in refractive error explained (or predicted) by the time participants spent outdoors was <0.005 and by the time spent reading was <0.01, based on a parental questionnaire completed when the child was aged 8–9 years old. Conclusions Genetic variation captured by common SNPs explained approximately 35% of the variation in refractive error between unrelated subjects. This value sets an upper limit for predicting refractive error using existing SNP genotyping arrays, although higher-density genotyping in
NASA Astrophysics Data System (ADS)
Cha, Dong-Hyun; Lee, Dong-Kyou
2009-07-01
In this study, the systematic errors in regional climate simulation of 28-year summer monsoon over East Asia and the western North Pacific (WNP) and the impact of the spectral nudging technique (SNT) on the reduction of the systematic errors are investigated. The experiment in which the SNT is not applied (the CLT run) has large systematic errors in seasonal mean climatology such as overestimated precipitation, weakened subtropical high, and enhanced low-level southwesterly over the subtropical WNP, while in the experiment using the SNT (the SP run) considerably smaller systematic errors are resulted. In the CTL run, the systematic error of simulated precipitation over the ocean increases significantly after mid-June, since the CTL run cannot reproduce the principal intraseasonal variation of summer monsoon precipitation. The SP run can appropriately capture the spatial distribution as well as temporal variation of the principal empirical orthogonal function mode, and therefore, the systematic error over the ocean does not increase after mid-June. The systematic error of simulated precipitation over the subtropical WNP in the CTL run results from the unreasonable positive feedback between precipitation and surface latent heat flux induced by the warm sea surface temperature anomaly. Since the SNT plays a role in decreasing the positive feedback by improving monsoon circulations, the SP run can considerably reduce the systematic errors of simulated precipitation as well as atmospheric fields over the subtropical WNP region.
NASA Technical Reports Server (NTRS)
Harwit, M.
1977-01-01
Sources of noise and error correcting procedures characteristic of Hadamard transform optical systems were investigated. Reduction of spectral noise due to noise spikes in the data, the effect of random errors, the relative performance of Fourier and Hadamard transform spectrometers operated under identical detector-noise-limited conditions, and systematic means for dealing with mask defects are among the topics discussed. The distortion in Hadamard transform optical instruments caused by moving Masks, incorrect mask alignment, missing measurements, and diffraction is analyzed and techniques for reducing or eliminating this distortion are described.
Standard errors for EM estimates in generalized linear models with random effects.
Friedl, H; Kauermann, G
2000-09-01
A procedure is derived for computing standard errors of EM estimates in generalized linear models with random effects. Quadrature formulas are used to approximate the integrals in the EM algorithm, where two different approaches are pursued, i.e., Gauss-Hermite quadrature in the case of Gaussian random effects and nonparametric maximum likelihood estimation for an unspecified random effect distribution. An approximation of the expected Fisher information matrix is derived from an expansion of the EM estimating equations. This allows for inferential arguments based on EM estimates, as demonstrated by an example and simulations. PMID:10985213
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
NASA Astrophysics Data System (ADS)
Bao, WeiZhu; Cai, YongYong; Jia, XiaoWei; Yin, Jia
2016-08-01
We present several numerical methods and establish their error estimates for the discretization of the nonlinear Dirac equation in the nonrelativistic limit regime, involving a small dimensionless parameter $0<\\varepsilon\\ll 1$ which is inversely proportional to the speed of light. In this limit regime, the solution is highly oscillatory in time, i.e. there are propagating waves with wavelength $O(\\varepsilon^2)$ and $O(1)$ in time and space, respectively. We begin with the conservative Crank-Nicolson finite difference (CNFD) method and establish rigorously its error estimate which depends explicitly on the mesh size $h$ and time step $\\tau$ as well as the small parameter $0<\\varepsilon\\le 1$. Based on the error bound, in order to obtain `correct' numerical solutions in the nonrelativistic limit regime, i.e. $0<\\varepsilon\\ll 1$, the CNFD method requests the $\\varepsilon$-scalability: $\\tau=O(\\varepsilon^3)$ and $h=O(\\sqrt{\\varepsilon})$. Then we propose and analyze two numerical methods for the discretization of the nonlinear Dirac equation by using the Fourier spectral discretization for spatial derivatives combined with the exponential wave integrator and time-splitting technique for temporal derivatives, respectively. Rigorous error bounds for the two numerical methods show that their $\\varepsilon$-scalability is improved to $\\tau=O(\\varepsilon^2)$ and $h=O(1)$ when $0<\\varepsilon\\ll 1$ compared with the CNFD method. Extensive numerical results are reported to confirm our error estimates.
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
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.
Estimation of bias errors in angle-of-arrival measurements using platform motion
NASA Astrophysics Data System (ADS)
Grindlay, A.
1981-08-01
An algorithm has been developed to estimate the bias errors in angle-of-arrival measurements made by electromagnetic detection devices on-board a pitching and rolling platform. The algorithm assumes that continuous exact measurements of the platform's roll and pitch conditions are available. When the roll and pitch conditions are used to transform deck-plane angular measurements of a nearly fixed target's position to a stabilized coordinate system, the resulting stabilized coordinates (azimuth and elevation) should not vary with changes in the roll and pitch conditions. If changes do occur they are a result of bias errors in the measurement system and the algorithm which has been developed uses these changes to estimate the sense and magnitude of angular bias errors.
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.
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
Nuclear power plant fault-diagnosis using neural networks with error estimation
Kim, K.; Bartlett, E.B.
1994-12-31
The assurance of the diagnosis obtained from a nuclear power plant (NPP) fault-diagnostic advisor based on artificial neural networks (ANNs) is essential for the practical implementation of the advisor to fault detection and identification. The objectives of this study are to develop an error estimation technique (EET) for diagnosis validation and apply it to the NPP fault-diagnostic advisor. Diagnosis validation is realized by estimating error bounds on the advisor`s diagnoses. The 22 transients obtained from the Duane Arnold Energy Center (DAEC) training simulator are used for this research. The results show that the NPP fault-diagnostic advisor are effective at producing proper diagnoses on which errors are assessed for validation and verification purposes.
Reckmann, Margaret H; Westbrook, Johanna I; Koh, Yvonne; Lo, Connie; Day, Richard O
2009-01-01
Previous reviews have examined evidence of the impact of CPOE on medication errors, but have used highly variable definitions of "error". We attempted to answer a very focused question, namely, what evidence exists that CPOE systems reduce prescribing errors among hospital inpatients? We identified 13 papers (reporting 12 studies) published between 1998 and 2007. Nine demonstrated a significant reduction in prescribing error rates for all or some drug types. Few studies examined changes in error severity, but minor errors were most often reported as decreasing. Several studies reported increases in the rate of duplicate orders and failures to discontinue drugs, often attributed to inappropriate selection from a dropdown menu or to an inability to view all active medication orders concurrently. The evidence-base reporting the effectiveness of CPOE to reduce prescribing errors is not compelling and is limited by modest study sample sizes and designs. Future studies should include larger samples including multiple sites, controlled study designs, and standardized error and severity reporting. The role of decision support in minimizing severe prescribing error rates also requires investigation. PMID:19567798
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.
Alles, B.; D'Elia, M.; Di Giacomo, A.; Pica, C.
2006-11-01
A Ginsparg-Wilson based calibration of the topological charge is used to calculate the renormalization constants which appear in the field-theoretical determination of the topological susceptibility on the lattice. A systematic comparison is made with calculations based on cooling. The two methods agree within present statistical errors (3%-4%). We also discuss the independence of the multiplicative renormalization constant Z from the background topological charge used to determine it.
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.
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 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…
Superconvergence of the derivative patch recovery technique and a posteriorii error estimation
Zhang, Z.; Zhu, J.Z.
1995-12-31
The derivative patch recovery technique developed by Zienkiewicz and Zhu for the finite element method is analyzed. It is shown that, for one dimensional problems and two dimensional problems using tensor product elements, the patch recovery technique yields superconvergence recovery for the derivatives. Consequently, the error estimator based on the recovered derivative is asymptotically exact.
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…
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…
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…
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.
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.
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…
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.
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…
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…
Accounting for uncertainty in systematic bias in exposure estimates used in relative risk regression
Gilbert, E.S.
1995-12-01
In many epidemiologic studies addressing exposure-response relationships, sources of error that lead to systematic bias in exposure measurements are known to be present, but there is uncertainty in the magnitude and nature of the bias. Two approaches that allow this uncertainty to be reflected in confidence limits and other statistical inferences were developed, and are applicable to both cohort and case-control studies. The first approach is based on a numerical approximation to the likelihood ratio statistic, and the second uses computer simulations based on the score statistic. These approaches were applied to data from a cohort study of workers at the Hanford site (1944-86) exposed occupationally to external radiation; to combined data on workers exposed at Hanford, Oak Ridge National Laboratory, and Rocky Flats Weapons plant; and to artificial data sets created to examine the effects of varying sample size and the magnitude of the risk estimate. For the worker data, sampling uncertainty dominated and accounting for uncertainty in systematic bias did not greatly modify confidence limits. However, with increased sample size, accounting for these uncertainties became more important, and is recommended when there is interest in comparing or combining results from different studies.
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
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
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
NASA Technical Reports Server (NTRS)
Chhikara, R. S.; Feiveson, A. H. (Principal Investigator)
1979-01-01
Aggregation formulas are given for production estimation of a crop type for a zone, a region, and a country, and methods for estimating yield prediction errors for the three areas are described. A procedure is included for obtaining a combined yield prediction and its mean-squared error estimate for a mixed wheat pseudozone.
Aydin, Denis; Feychting, Maria; Schüz, Joachim; Andersen, Tina Veje; Poulsen, Aslak Harbo; Prochazka, Michaela; Klaeboe, Lars; Kuehni, Claudia E; Tynes, Tore; Röösli, Martin
2011-07-01
Whether the use of mobile phones is a risk factor for brain tumors in adolescents is currently being studied. Case--control studies investigating this possible relationship are prone to recall error and selection bias. We assessed the potential impact of random and systematic recall error and selection bias on odds ratios (ORs) by performing simulations based on real data from an ongoing case--control study of mobile phones and brain tumor risk in children and adolescents (CEFALO study). Simulations were conducted for two mobile phone exposure categories: regular and heavy use. Our choice of levels of recall error was guided by a validation study that compared objective network operator data with the self-reported amount of mobile phone use in CEFALO. In our validation study, cases overestimated their number of calls by 9% on average and controls by 34%. Cases also overestimated their duration of calls by 52% on average and controls by 163%. The participation rates in CEFALO were 83% for cases and 71% for controls. In a variety of scenarios, the combined impact of recall error and selection bias on the estimated ORs was complex. These simulations are useful for the interpretation of previous case-control studies on brain tumor and mobile phone use in adults as well as for the interpretation of future studies on adolescents. PMID:21294138
Estimation of local error by a neural model in an inverse scattering problem
NASA Astrophysics Data System (ADS)
Robert, S.; Mure-Rauvaud, A.; Thiria, S.; Badran, F.
2005-07-01
Characterization of optical gratings by resolution of inverse scattering problem has become a widely used tool. Indeed, it is known as a non-destructive, rapid and non-invasive method in opposition with microscopic characterizations. Use of a neural model is generally implemented and has shown better results by comparison with other regression methods. The neural network learns the relationship between the optical signature and the corresponding profile shape. The performance of such a non-linear regression method is usually estimated by the root mean square error calculated on a data set not involved in the training process. However, this error estimation is not very significant and tends to flatten the error in the different areas of variable space. We introduce, in this paper, the calculation of local error for each geometrical parameter representing the profile shape. For this purpose a second neural network is implemented to learn the variance of results obtained by the first one. A comparison with the root mean square error confirms a gain of local precision. Finally, the method is applied in the optical characterization of a semi-conductor grating with a 1 μ m period.
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.
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
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.
Allowance for random dose estimation errors in atomic bomb survivor studies: a revision.
Pierce, Donald A; Vaeth, Michael; Cologne, John B
2008-07-01
Allowing for imprecision of radiation dose estimates for A-bomb survivors followed up by the Radiation Effects Research Foundation can be improved through recent statistical methodology. Since the entire RERF dosimetry system has recently been revised, it is timely to reconsider this. We have found that the dosimetry revision itself does not warrant changes in these methods but that the new methodology does. In addition to assumptions regarding the form and magnitude of dose estimation errors, previous and current methods involve the apparent distribution of true doses in the cohort. New formulas give results conveniently and explicitly in terms of these inputs. Further, it is now possible to use assumptions about two components of the dose errors, referred to in the statistical literature as "classical" and "Berkson-type". There are indirect statistical indications, involving non-cancer biological effects, that errors may be somewhat larger than assumed before, in line with recommendations made here. Inevitably, methods must rely on uncertain assumptions about the magnitude of dose errors, and it is comforting to find that, within the range of plausibility, eventual cancer risk estimates are not very sensitive to these. PMID:18582151
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. PMID:26007726
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).
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.
NASA Technical Reports Server (NTRS)
Gabrielsen, R. E.; Karel, S.
1975-01-01
An algorithm for solving the nonlinear stationary Navier-Stokes problem is developed. Explicit error estimates are given. This mathematical technique is potentially adaptable to the separation problem.
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
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.
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.
Estimation of chromatic errors from broadband images for high contrast imaging: sensitivity analysis
NASA Astrophysics Data System (ADS)
Sirbu, Dan; Belikov, Ruslan
2016-01-01
Many concepts have been proposed to enable direct imaging of planets around nearby stars, and which would enable spectroscopic observations of their atmospheric observations and the potential discovery of biomarkers. The main technical challenge associated with direct imaging of exoplanets is to effectively control both the diffraction and scattered light from the star so that the dim planetary companion can be seen. Usage of an internal coronagraph with an adaptive optical system for wavefront correction is one of the most mature methods and is being developed as an instrument addition to the WFIRST-AFTA space mission. In addition, such instruments as GPI and SPHERE are already being used on the ground and are yielding spectra of giant planets. For the deformable mirror (DM) to recover a dark hole region with sufficiently high contrast in the image plane, mid-spatial frequency wavefront errors must be estimated. To date, most broadband lab demonstrations use narrowband filters to obtain an estimate of the the chromaticity of the wavefront error and this can result in usage of a large percentage of the total integration time. Previously, we have proposed a method to estimate the chromaticity of wavefront errors using only broadband images; we have demonstrated that under idealized conditions wavefront errors can be estimated from images composed of discrete wavelengths. This is achieved by using DM probes with sufficient spatially-localized chromatic diversity. Here we report on the results of a study of the performance of this method with respect to realistic broadband images including noise. Additionally, we study optimal probe patterns that enable reduction of the number of probes used and compare the integration time with narrowband and IFS estimation methods.
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
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.
Doherty, Carole; Stavropoulou, Charitini
2012-07-01
This systematic review identifies the factors that both support and deter patients from being willing and able to participate actively in reducing clinical errors. Specifically, we add to our understanding of the safety culture in healthcare by engaging with the call for more focus on the relational and subjective factors which enable patients' participation (Iedema, Jorm, & Lum, 2009; Ovretveit, 2009). A systematic search of six databases, ten journals and seven healthcare organisations' web sites resulted in the identification of 2714 studies of which 68 were included in the review. These studies investigated initiatives involving patients in safety or studies of patients' perspectives of being actively involved in the safety of their care. The factors explored varied considerably depending on the scope, setting and context of the study. Using thematic analysis we synthesized the data to build an explanation of why, when and how patients are likely to engage actively in helping to reduce clinical errors. The findings show that the main factors for engaging patients in their own safety can be summarised in four categories: illness; individual cognitive characteristics; the clinician-patient relationship; and organisational factors. We conclude that illness and patients' perceptions of their role and status as subordinate to that of clinicians are the most important barriers to their involvement in error reduction. In sum, patients' fear of being labelled "difficult" and a consequent desire for clinicians' approbation may cause them to assume a passive role as a means of actively protecting their personal safety. PMID:22541799
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)
Evin, Guillaume; Thyer, Mark; Kavetski, Dmitri; McInerney, David; Kuczera, George
2014-03-01
The paper appraises two approaches for the treatment of heteroscedasticity and autocorrelation in residual errors of hydrological models. Both approaches use weighted least squares (WLS), with heteroscedasticity modeled as a linear function of predicted flows and autocorrelation represented using an AR(1) process. In the first approach, heteroscedasticity and autocorrelation parameters are inferred jointly with hydrological model parameters. The second approach is a two-stage "postprocessor" scheme, where Stage 1 infers the hydrological parameters ignoring autocorrelation and Stage 2 conditionally infers the heteroscedasticity and autocorrelation parameters. These approaches are compared to a WLS scheme that ignores autocorrelation. Empirical analysis is carried out using daily data from 12 US catchments from the MOPEX set using two conceptual rainfall-runoff models, GR4J, and HBV. Under synthetic conditions, the postprocessor and joint approaches provide similar predictive performance, though the postprocessor approach tends to underestimate parameter uncertainty. However, the MOPEX results indicate that the joint approach can be nonrobust. In particular, when applied to GR4J, it often produces poor predictions due to strong multiway interactions between a hydrological water balance parameter and the error model parameters. The postprocessor approach is more robust precisely because it ignores these interactions. Practical benefits of accounting for error autocorrelation are demonstrated by analyzing streamflow predictions aggregated to a monthly scale (where ignoring daily-scale error autocorrelation leads to significantly underestimated predictive uncertainty), and by analyzing one-day-ahead predictions (where accounting for the error autocorrelation produces clearly higher precision and better tracking of observed data). Including autocorrelation into the residual error model also significantly affects calibrated parameter values and uncertainty estimates. The
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.
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.
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
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).
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.
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.
Analysis of systematic errors in lateral shearing interferometry for EUV optical testing
Miyakawa, Ryan; Naulleau, Patrick; Goldberg, Kenneth A.
2009-02-24
Lateral shearing interferometry (LSI) provides a simple means for characterizing the aberrations in optical systems at EUV wavelengths. In LSI, the test wavefront is incident on a low-frequency grating which causes the resulting diffracted orders to interfere on the CCD. Due to its simple experimental setup and high photon efficiency, LSI is an attractive alternative to point diffraction interferometry and other methods that require spatially filtering the wavefront through small pinholes which notoriously suffer from low contrast fringes and improper alignment. In order to demonstrate that LSI can be accurate and robust enough to meet industry standards, analytic models are presented to study the effects of unwanted grating and detector tilt on the system aberrations, and a method for identifying and correcting for these errors in alignment is proposed. The models are subsequently verified by numerical simulation. Finally, an analysis is performed of how errors in the identification and correction of grating and detector misalignment propagate to errors in fringe analysis.
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
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.
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. PMID:26698389
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).
NASA Astrophysics Data System (ADS)
Li, Y.; Ryu, D.; Western, A. W.; Wang, Q.; Robertson, D.; Crow, W. T.
2013-12-01
Timely and reliable streamflow forecasting with acceptable accuracy is fundamental for flood response and risk management. However, streamflow forecasting models are subject to uncertainties from inputs, state variables, model parameters and structures. This has led to an ongoing development of methods for uncertainty quantification (e.g. generalized likelihood and Bayesian approaches) and methods for uncertainty reduction (e.g. sequential and variational data assimilation approaches). These two classes of methods are distinct yet related, e.g., the validity of data assimilation is essentially determined by the reliability of error specification. Error specification has been one of the most challenging areas in hydrologic data assimilation and there is a major opportunity for implementing uncertainty quantification approaches to inform both model and observation uncertainties. In this study, ensemble data assimilation methods are combined with the maximum a posteriori (MAP) error estimation approach to construct an integrated error estimation and data assimilation scheme for operational streamflow forecasting. We contrast the performance of two different data assimilation schemes: a lag-aware ensemble Kalman smoother (EnKS) and the conventional ensemble Kalman filter (EnKF). The schemes are implemented for a catchment upstream of Myrtleford in the Ovens river basin, Australia to assimilate real-time discharge observations into a conceptual catchment model, modèle du Génie Rural à 4 paramètres Horaire (GR4H). The performance of the integrated system is evaluated in both a synthetic forecasting scenario with observed precipitation and an operational forecasting scenario with Numerical Weather Prediction (NWP) forecast rainfall. The results show that the error parameters estimated by the MAP approach generates a reliable spread of streamflow prediction. Continuous state updating reduces uncertainty in initial states and thereby improves the forecasting accuracy
Calibration and systematic error analysis for the COBE(1) DMR 4year sky maps
Kogut, A.; Banday, A.J.; Bennett, C.L.; Gorski, K.M.; Hinshaw,G.; Jackson, P.D.; Keegstra, P.; Lineweaver, C.; Smoot, G.F.; Tenorio,L.; Wright, E.L.
1996-01-04
The Differential Microwave Radiometers (DMR) instrument aboard the Cosmic Background Explorer (COBE) has mapped the full microwave sky to mean sensitivity 26 mu K per 7 degrees held of view. The absolute calibration is determined to 0.7 percent with drifts smaller than 0.2 percent per year. We have analyzed both the raw differential data and the pixelized sky maps for evidence of contaminating sources such as solar system foregrounds, instrumental susceptibilities, and artifacts from data recovery and processing. Most systematic effects couple only weakly to the sky maps. The largest uncertainties in the maps result from the instrument susceptibility to Earth's magnetic field, microwave emission from Earth, and upper limits to potential effects at the spacecraft spin period. Systematic effects in the maps are small compared to either the noise or the celestial signal: the 95 percent confidence upper limit for the pixel-pixel rms from all identified systematics is less than 6 mu K in the worst channel. A power spectrum analysis of the (A-B)/2 difference maps shows no evidence for additional undetected systematic effects.
Systematic Errors in Stereo PIV When Imaging through a Glass Window
NASA Technical Reports Server (NTRS)
Green, Richard; McAlister, Kenneth W.
2004-01-01
This document assesses the magnitude of velocity measurement errors that may arise when performing stereo particle image velocimetry (PIV) with cameras viewing through thick, refractive window and where the calibration is performed in one plane only. The effect of the window is to introduce a refractive error that increases with window thickness and the camera angle of incidence. The calibration should be performed while viewing through the test section window, otherwise a potentially significant error may be introduced that affects each velocity component differently. However, even when the calibration is performed correctly, another error may arise during the stereo reconstruction if the perspective angle determined for each camera does not account for the displacement of the light rays as they refract through the thick window. Care should be exercised when applying in a single-plane calibration since certain implicit assumptions may in fact require conditions that are extremely difficult to meet in a practical laboratory environment. It is suggested that the effort expended to ensure this accuracy may be better expended in performing a more lengthy volumetric calibration procedure, which does not rely upon the assumptions implicit in the single plane method and avoids the need for the perspective angle to be calculated.
Welter-Schultes, Francisco; Görlich, Angela; Lutze, Alexandra
2016-01-01
This study is aimed to shed light on the reliability of Sherborn's Index Animalium in terms of modern usage. The AnimalBase project spent several years' worth of teamwork dedicated to extracting new names from original sources in the period ranging from 1757 to the mid-1790s. This allowed us to closely analyse Sherborn's work and verify the completeness and correctness of his record. We found the reliability of Sherborn's resource generally very high, but in some special situations the reliability was reduced due to systematic errors or incompleteness in source material. Index Animalium is commonly used by taxonomists today who rely strongly on Sherborn's record; our study is directed most pointedly at those users. We recommend paying special attention to the situations where we found that Sherborn's data should be read with caution. In addition to some categories of systematic errors and mistakes that were Sherborn's own responsibility, readers should also take into account that nomenclatural rules have been changed or refined in the past 100 years, and that Sherborn's resource could eventually present outdated information. One of our main conclusions is that error rates in nomenclatoral compilations tend to be lower if one single and highly experienced person such as Sherborn carries out the work, than if a team is trying to do the task. Based on our experience with extracting names from original sources we came to the conclusion that error rates in such a manual work on names in a list are difficult to reduce below 2-4%. We suggest this is a natural limit and a point of diminishing returns for projects of this nature. PMID:26877658
Welter-Schultes, Francisco; Görlich, Angela; Lutze, Alexandra
2016-01-01
Abstract This study is aimed to shed light on the reliability of Sherborn’s Index Animalium in terms of modern usage. The AnimalBase project spent several years’ worth of teamwork dedicated to extracting new names from original sources in the period ranging from 1757 to the mid-1790s. This allowed us to closely analyse Sherborn’s work and verify the completeness and correctness of his record. We found the reliability of Sherborn’s resource generally very high, but in some special situations the reliability was reduced due to systematic errors or incompleteness in source material. Index Animalium is commonly used by taxonomists today who rely strongly on Sherborn’s record; our study is directed most pointedly at those users. We recommend paying special attention to the situations where we found that Sherborn’s data should be read with caution. In addition to some categories of systematic errors and mistakes that were Sherborn’s own responsibility, readers should also take into account that nomenclatural rules have been changed or refined in the past 100 years, and that Sherborn’s resource could eventually present outdated information. One of our main conclusions is that error rates in nomenclatoral compilations tend to be lower if one single and highly experienced person such as Sherborn carries out the work, than if a team is trying to do the task. Based on our experience with extracting names from original sources we came to the conclusion that error rates in such a manual work on names in a list are difficult to reduce below 2–4%. We suggest this is a natural limit and a point of diminishing returns for projects of this nature. PMID:26877658
Yu, Jaehyung; Wagner, Lucas K.; Ertekin, Elif
2015-12-14
The fixed node diffusion Monte Carlo (DMC) method has attracted interest in recent years as a way to calculate properties of solid materials with high accuracy. However, the framework for the calculation of properties such as total energies, atomization energies, and excited state energies is not yet fully established. Several outstanding questions remain as to the effect of pseudopotentials, the magnitude of the fixed node error, and the size of supercell finite size effects. Here, we consider in detail the semiconductors ZnSe and ZnO and carry out systematic studies to assess the magnitude of the energy differences arising from controlled and uncontrolled approximations in DMC. The former include time step errors and supercell finite size effects for ground and optically excited states, and the latter include pseudopotentials, the pseudopotential localization approximation, and the fixed node approximation. We find that for these compounds, the errors can be controlled to good precision using modern computational resources and that quantum Monte Carlo calculations using Dirac-Fock pseudopotentials can offer good estimates of both cohesive energy and the gap of these systems. We do however observe differences in calculated optical gaps that arise when different pseudopotentials are used.
NASA Astrophysics Data System (ADS)
Yu, Jaehyung; Wagner, Lucas K.; Ertekin, Elif
2015-12-01
The fixed node diffusion Monte Carlo (DMC) method has attracted interest in recent years as a way to calculate properties of solid materials with high accuracy. However, the framework for the calculation of properties such as total energies, atomization energies, and excited state energies is not yet fully established. Several outstanding questions remain as to the effect of pseudopotentials, the magnitude of the fixed node error, and the size of supercell finite size effects. Here, we consider in detail the semiconductors ZnSe and ZnO and carry out systematic studies to assess the magnitude of the energy differences arising from controlled and uncontrolled approximations in DMC. The former include time step errors and supercell finite size effects for ground and optically excited states, and the latter include pseudopotentials, the pseudopotential localization approximation, and the fixed node approximation. We find that for these compounds, the errors can be controlled to good precision using modern computational resources and that quantum Monte Carlo calculations using Dirac-Fock pseudopotentials can offer good estimates of both cohesive energy and the gap of these systems. We do however observe differences in calculated optical gaps that arise when different pseudopotentials are used.
Yu, Jaehyung; Wagner, Lucas K; Ertekin, Elif
2015-12-14
The fixed node diffusion Monte Carlo (DMC) method has attracted interest in recent years as a way to calculate properties of solid materials with high accuracy. However, the framework for the calculation of properties such as total energies, atomization energies, and excited state energies is not yet fully established. Several outstanding questions remain as to the effect of pseudopotentials, the magnitude of the fixed node error, and the size of supercell finite size effects. Here, we consider in detail the semiconductors ZnSe and ZnO and carry out systematic studies to assess the magnitude of the energy differences arising from controlled and uncontrolled approximations in DMC. The former include time step errors and supercell finite size effects for ground and optically excited states, and the latter include pseudopotentials, the pseudopotential localization approximation, and the fixed node approximation. We find that for these compounds, the errors can be controlled to good precision using modern computational resources and that quantum Monte Carlo calculations using Dirac-Fock pseudopotentials can offer good estimates of both cohesive energy and the gap of these systems. We do however observe differences in calculated optical gaps that arise when different pseudopotentials are used. PMID:26671396
NASA Astrophysics Data System (ADS)
Chang, Fi-John; Chen, Pin-An; Liu, Chen-Wuing; Liao, Vivian Hsiu-Chuan; Liao, Chung-Min
2013-08-01
Arsenic (As) is an odorless semi-metal that occurs naturally in rock and soil, and As contamination in groundwater resources has become a serious threat to human health. Thus, assessing the spatial and temporal variability of As concentration is highly desirable, particularly in heavily As-contaminated areas. However, various difficulties may be encountered in the regional estimation of As concentration such as cost-intensive field monitoring, scarcity of field data, identification of important factors affecting As, over-fitting or poor estimation accuracy. This study develops a novel systematical dynamic-neural modeling (SDM) for effectively estimating regional As-contaminated water quality by using easily-measured water quality variables. To tackle the difficulties commonly encountered in regional estimation, the SDM comprises of a neural network and four statistical techniques: the Nonlinear Autoregressive with eXogenous input (NARX) network, Gamma test, cross-validation, Bayesian regularization method and indicator kriging (IK). For practical application, this study investigated a heavily As-contaminated area in Taiwan. The backpropagation neural network (BPNN) is adopted for comparison purpose. The results demonstrate that the NARX network (Root mean square error (RMSE): 95.11 μg l-1 for training; 106.13 μg l-1 for validation) outperforms the BPNN (RMSE: 121.54 μg l-1 for training; 143.37 μg l-1 for validation). The constructed SDM can provide reliable estimation (R2 > 0.89) of As concentration at ungauged sites based merely on three easily-measured water quality variables (Alk, Ca2+ and pH). In addition, risk maps under the threshold of the WHO drinking water standard (10 μg l-1) are derived by the IK to visually display the spatial and temporal variation of the As concentration in the whole study area at different time spans. The proposed SDM can be practically applied with satisfaction to the regional estimation in study areas of interest and the
Heath, G.
2012-06-01
This powerpoint presentation to be presented at the World Renewable Energy Forum on May 14, 2012, in Denver, CO, discusses systematic review and harmonization of life cycle GHG emission estimates for electricity generation technologies.
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
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.
NASA Astrophysics Data System (ADS)
Vannière, Benoît; Guilyardi, Eric; Toniazzo, Thomas; Madec, Gurvan; Woolnough, Steve
2014-10-01
Understanding the sources of systematic errors in climate models is challenging because of coupled feedbacks and errors compensation. The developing seamless approach proposes that the identification and the correction of short term climate model errors have the potential to improve the modeled climate on longer time scales. In previous studies, initialised atmospheric simulations of a few days have been used to compare fast physics processes (convection, cloud processes) among models. The present study explores how initialised seasonal to decadal hindcasts (re-forecasts) relate transient week-to-month errors of the ocean and atmospheric components to the coupled model long-term pervasive SST errors. A protocol is designed to attribute the SST biases to the source processes. It includes five steps: (1) identify and describe biases in a coupled stabilized simulation, (2) determine the time scale of the advent of the bias and its propagation, (3) find the geographical origin of the bias, (4) evaluate the degree of coupling in the development of the bias, (5) find the field responsible for the bias. This strategy has been implemented with a set of experiments based on the initial adjustment of initialised simulations and exploring various degrees of coupling. In particular, hindcasts give the time scale of biases advent, regionally restored experiments show the geographical origin and ocean-only simulations isolate the field responsible for the bias and evaluate the degree of coupling in the bias development. This strategy is applied to four prominent SST biases of the IPSLCM5A-LR coupled model in the tropical Pacific, that are largely shared by other coupled models, including the Southeast Pacific warm bias and the equatorial cold tongue bias. Using the proposed protocol, we demonstrate that the East Pacific warm bias appears in a few months and is caused by a lack of upwelling due to too weak meridional coastal winds off Peru. The cold equatorial bias, which
p-adaption for compressible flow problems using a goal-based error estimator
NASA Astrophysics Data System (ADS)
Ekelschot, Dirk; Moxey, David; Peiro, Joaquim; Sherwin, Spencer
2014-11-01
We present an approach of applying p-adaption to compressible flow problems using a dual-weighted error estimator. This technique has been implemented in the high-order h/p spectral element library Nektar + + . The compressible solver uses a high-order discontinuous Galerkin (DG) discretization. This approach is generally considered to be expensive and that is why the introduced p-adaption technique aims for lowering the computational cost while preserving the high-order accuracy and the exponential convergence properties. The numerical fluxes between the elements are discontinuous which allows one to use a different polynomial order in each element. After identifying and localizing the sources of error, the order of approximation of the solution within the element is improved. The solution to the adjoint equations for the compressible Euler equations is used to weigh the local residual of the primal solution. This provides both the error in the target quantity, which is typically the lift or drag coefficient, and an indication on how sensitive the local solution is to the target quantity. The dual-weighted error within each element serves then as a local refinement indicator that drives the p-adaptive algorithm. The performance of this p-adaptive method is demonstrated using a test case of subsonic flow past a 3D wing geometry.
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.
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
2016-06-01
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.
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.
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.
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.
Thomas, Nicholas; Taylor, Peter; Padayachee, Soundrie
2002-02-01
Two potential errors in velocity estimation, Doppler angle misalignment and intrinsic spectral broadening (ISB), were determined and used to correct recorded blood velocities obtained from 20 patients (38 bifurcations). The recorded and corrected velocities were used to grade stenoses of greater than 70% using two duplex classification schemes. The first scheme used a peak systolic velocity (PSV) of > 250 cm/s in the internal carotid artery (ICA), and the second a PSV ratio of > 3.4 (ICA PSV/common carotid artery PSV). The "gold standard" was digital subtraction angiography (DSA). The maximum error in velocity estimation due to Doppler angle misalignment was 33 cm/s, but this did not alter sensitivity of stenosis detection. ISB correction caused a reduction in PSV that decreased the sensitivity of the PSV scheme from 65% to 45%. The PSV ratio classification was not affected by ISB errors. Centres using a PSV criterion for grading stenosis should use a fixed Doppler angle and should establish velocity thresholds in-house. PMID:11937281
Optimum data weighting and error calibration for estimation of gravitational parameters
NASA Technical Reports Server (NTRS)
Lerch, Francis 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-T1 (GEM-T1) 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. The method employs subset solutions of the data associated with the complete solution 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. The technique is general and may be applied to orbit parameters, station coordinates, or other parameters than the gravity model.
Model for the fast estimation of basis set superposition error in biomolecular systems
Faver, John C.; Zheng, Zheng; Merz, Kenneth M.
2011-01-01
Basis set superposition error (BSSE) is a significant contributor to errors in quantum-based energy functions, especially for large chemical systems with many molecular contacts such as folded proteins and protein-ligand complexes. While the counterpoise method has become a standard procedure for correcting intermolecular BSSE, most current approaches to correcting intramolecular BSSE are simply fragment-based analogues of the counterpoise method which require many (two times the number of fragments) additional quantum calculations in their application. We propose that magnitudes of both forms of BSSE can be quickly estimated by dividing a system into interacting fragments, estimating each fragment's contribution to the overall BSSE with a simple statistical model, and then propagating these errors throughout the entire system. Such a method requires no additional quantum calculations, but rather only an analysis of the system's interacting fragments. The method is described herein and is applied to a protein-ligand system, a small helical protein, and a set of native and decoy protein folds. PMID:22010701
NASA Technical Reports Server (NTRS)
Bryant, W. H.; Hodge, W. F.
1974-01-01
An error analysis program based on an output error estimation method was used to evaluate the effects of sensor and instrumentation errors on the estimation of aircraft stability and control derivatives. A Monte Carlo analysis was performed using simulated flight data for a high performance military aircraft, a large commercial transport, and a small general aviation aircraft for typical cruise flight conditions. The effects of varying the input sequence and combinations of the sensor and instrumentation errors were investigated. The results indicate that both the parameter accuracy and the corresponding measurement trajectory fit error can be significantly affected. Of the error sources considered, instrumentation lags and control measurement errors were found to be most significant.
DTI Quality Control Assessment via Error Estimation From Monte Carlo Simulations
Farzinfar, Mahshid; Li, Yin; Verde, Audrey R.; Oguz, Ipek; Gerig, Guido; Styner, Martin A.
2013-01-01
Diffusion Tensor Imaging (DTI) is currently the state of the art method for characterizing microscopic tissue structure in the 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 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. PMID:23833547
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.
NASA Astrophysics Data System (ADS)
Kalton, G.
1983-05-01
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 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.
Turner, David S
2004-04-10
An underlying assumption of satellite data assimilation systems is that the radiative transfer model used to simulate observed satellite radiances has no errors. For practical reasons a fast-forward radiative transfer model is used instead of a highly accurate line-by-line model. The fast model usually replaces the spectral integration of spectral quantities with their monochromatic equivalents, and the errors due to these approximations are assumed to be negligible. The reflected downward flux term contains many approximations of this nature, which are shown to introduce systematic errors. In addition, many fast-forward radiative transfer models simulate the downward flux as the downward radiance along a path defined by the secant of the mean emergent angle, the diffusivity factor. The diffusivity factor is commonly set to 1.66 or to the secant of the satellite zenith angle. Neither case takes into account that the diffusivity factor varies with optical depth, which introduces further errors. I review the two most commonly used methods for simulating reflected downward flux by fast-forward radiative transfer models and point out their inadequacies and limitations. An alternate method of simulating the reflected downward flux is proposed. This method transforms the surface-to-satellite transmittance profile to a transmittance profile suitable for simulating the reflected downward flux by raising the former transmittance to the power of kappa, where kappa itself is a function of channel, surface pressure, and satellite zenith angle. It is demonstrated that this method reduces the fast-forward model error for low to moderate reflectivities. PMID:15098841
Skinner, Thomas R; Scott, Ian A; Martin, Jennifer H
2016-01-01
Background Misdiagnosis, either over- or underdiagnosis, exposes older patients to increased risk of inappropriate or omitted investigations and treatments, psychological distress, and financial burden. Objective To evaluate the frequency and nature of diagnostic errors in 16 conditions prevalent in older patients by undertaking a systematic literature review. Data sources and study selection Cohort studies, cross-sectional studies, or systematic reviews of such studies published in Medline between September 1993 and May 2014 were searched using key search terms of “diagnostic error”, “misdiagnosis”, “accuracy”, “validity”, or “diagnosis” and terms relating to each disease. Data synthesis A total of 938 articles were retrieved. Diagnostic error rates of >10% for both over- and underdiagnosis were seen in chronic obstructive pulmonary disease, dementia, Parkinson’s disease, heart failure, stroke/transient ischemic attack, and acute myocardial infarction. Diabetes was overdiagnosed in <5% of cases. Conclusion Over- and underdiagnosis are common in older patients. Explanations for over-diagnosis include subjective diagnostic criteria and the use of criteria not validated in older patients. Underdiagnosis was associated with long preclinical phases of disease or lack of sensitive diagnostic criteria. Factors that predispose to misdiagnosis in older patients must be emphasized in education and clinical guidelines. PMID:27284262
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.
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
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.
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 Astrophysics Data System (ADS)
Leetmaa, Mikael; Skorodumova, Natalia V.
2015-06-01
We present a method to calculate mean square displacements (MSD) with error estimates from kinetic Monte Carlo (KMC) simulations of diffusion processes with non-equidistant time-steps. An analytical solution for estimating the errors is presented for the special case of one moving particle at fixed rate constant. The method is generalized to an efficient computational algorithm that can handle any number of moving particles or different rates in the simulated system. We show with examples that the proposed method gives the correct statistical error when the MSD curve describes pure Brownian motion and can otherwise be used as an upper bound for the true error.
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.
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
Viana, Michele; Tassorelli, Cristina; Allena, Marta; Nappi, Giuseppe; Sjaastad, Ottar; Antonaci, Fabio
2013-01-01
Trigeminal autonomic cephalalgias (TACs) and hemicrania continua (HC) are relatively rare but clinically rather well-defined primary headaches. Despite the existence of clear-cut diagnostic criteria (The International Classification of Headache Disorders, 2nd edition - ICHD-II) and several therapeutic guidelines, errors in workup and treatment of these conditions are frequent in clinical practice. We set out to review all available published data on mismanagement of TACs and HC patients in order to understand and avoid its causes. The search strategy identified 22 published studies. The most frequent errors described in the management of patients with TACs and HC are: referral to wrong type of specialist, diagnostic delay, misdiagnosis, and the use of treatments without overt indication. Migraine with and without aura, trigeminal neuralgia, sinus infection, dental pain and temporomandibular dysfunction are the disorders most frequently overdiagnosed. Even when the clinical picture is clear-cut, TACs and HC are frequently not recognized and/or mistaken for other disorders, not only by general physicians, dentists and ENT surgeons, but also by neurologists and headache specialists. This seems to be due to limited knowledge of the specific characteristics and variants of these disorders, and it results in the unnecessary prescription of ineffective and sometimes invasive treatments which may have negative consequences for patients. Greater knowledge of and education about these disorders, among both primary care physicians and headache specialists, might contribute to improving the quality of life of TACs and HC patients. PMID:23565739
The Curious Anomaly of Skewed Judgment Distributions and Systematic Error in the Wisdom of Crowds
Nash, Ulrik W.
2014-01-01
Judgment distributions are often skewed and we know little about why. This paper explains the phenomenon of skewed judgment distributions by introducing the augmented quincunx (AQ) model of sequential and probabilistic cue categorization by neurons of judges. In the process of developing inferences about true values, when neurons categorize cues better than chance, and when the particular true value is extreme compared to what is typical and anchored upon, then populations of judges form skewed judgment distributions with high probability. Moreover, the collective error made by these people can be inferred from how skewed their judgment distributions are, and in what direction they tilt. This implies not just that judgment distributions are shaped by cues, but that judgment distributions are cues themselves for the wisdom of crowds. The AQ model also predicts that judgment variance correlates positively with collective error, thereby challenging what is commonly believed about how diversity and collective intelligence relate. Data from 3053 judgment surveys about US macroeconomic variables obtained from the Federal Reserve Bank of Philadelphia and the Wall Street Journal provide strong support, and implications are discussed with reference to three central ideas on collective intelligence, these being Galton's conjecture on the distribution of judgments, Muth's rational expectations hypothesis, and Page's diversity prediction theorem. PMID:25406078
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:
Systematization of problems on ball estimates of a convex compactum
NASA Astrophysics Data System (ADS)
Dudov, S. I.
2015-09-01
We consider a class of finite-dimensional problems on the estimation of a convex compactum by a ball of an arbitrary norm in the form of extremal problems whose goal function is expressed via the function of the distance to the farthest point of the compactum and the function of the distance to the nearest point of the compactum or its complement. Special attention is devoted to the problem of estimating (approximating) a convex compactum by a ball of fixed radius in the Hausdorff metric. It is proved that this problem plays the role of the canonical problem: solutions of any problem in the class under consideration can be expressed via solutions of this problem for certain values of the radius. Based on studying and using the properties of solutions of this canonical problem, we obtain ranges of values of the radius in which the canonical problem expresses solutions of the problems on inscribed and circumscribed balls, the problem of uniform estimate by a ball in the Hausdorff metric, the problem of asphericity of a convex body, the problems of spherical shells of the least thickness and of the least volume for the boundary of a convex body. This makes it possible to arrange the problems in increasing order of the corresponding values of the radius. Bibliography: 34 titles.
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.
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
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