Fringe order correction for the absolute phase recovered by two selected spatial frequency fringe projections in fringe projection profilometry.

PubMed

Ding, Yi; Peng, Kai; Yu, Miao; Lu, Lei; Zhao, Kun

2017-08-01

The performance of the two selected spatial frequency phase unwrapping methods is limited by a phase error bound beyond which errors will occur in the fringe order leading to a significant error in the recovered absolute phase map. In this paper, we propose a method to detect and correct the wrong fringe orders. Two constraints are introduced during the fringe order determination of two selected spatial frequency phase unwrapping methods. A strategy to detect and correct the wrong fringe orders is also described. Compared with the existing methods, we do not need to estimate the threshold associated with absolute phase values to determine the fringe order error, thus making it more reliable and avoiding the procedure of search in detecting and correcting successive fringe order errors. The effectiveness of the proposed method is validated by the experimental results.

Correction of motion measurement errors beyond the range resolution of a synthetic aperture radar

DOEpatents

Doerry, Armin W [Albuquerque, NM; Heard, Freddie E [Albuquerque, NM; Cordaro, J Thomas [Albuquerque, NM

2008-06-24

Motion measurement errors that extend beyond the range resolution of a synthetic aperture radar (SAR) can be corrected by effectively decreasing the range resolution of the SAR in order to permit measurement of the error. Range profiles can be compared across the slow-time dimension of the input data in order to estimate the error. Once the error has been determined, appropriate frequency and phase correction can be applied to the uncompressed input data, after which range and azimuth compression can be performed to produce a desired SAR image.

AMT-200S Motor Glider Parameter and Performance Estimation

NASA Technical Reports Server (NTRS)

Taylor, Brian R.

2011-01-01

Parameter and performance estimation of an instrumented motor glider was conducted at the National Aeronautics and Space Administration Dryden Flight Research Center in order to provide the necessary information to create a simulation of the aircraft. An output-error technique was employed to generate estimates from doublet maneuvers, and performance estimates were compared with results from a well-known flight-test evaluation of the aircraft in order to provide a complete set of data. Aircraft specifications are given along with information concerning instrumentation, flight-test maneuvers flown, and the output-error technique. Discussion of Cramer-Rao bounds based on both white noise and colored noise assumptions is given. Results include aerodynamic parameter and performance estimates for a range of angles of attack.

Identification of dynamic systems, theory and formulation

NASA Technical Reports Server (NTRS)

Maine, R. E.; Iliff, K. W.

1985-01-01

The problem of estimating parameters of dynamic systems is addressed in order to present the theoretical basis of system identification and parameter estimation in a manner that is complete and rigorous, yet understandable with minimal prerequisites. Maximum likelihood and related estimators are highlighted. The approach used requires familiarity with calculus, linear algebra, and probability, but does not require knowledge of stochastic processes or functional analysis. The treatment emphasizes unification of the various areas in estimation in dynamic systems is treated as a direct outgrowth of the static system theory. Topics covered include basic concepts and definitions; numerical optimization methods; probability; statistical estimators; estimation in static systems; stochastic processes; state estimation in dynamic systems; output error, filter error, and equation error methods of parameter estimation in dynamic systems, and the accuracy of the estimates.

On the Calculation of Uncertainty Statistics with Error Bounds for CFD Calculations Containing Random Parameters and Fields

NASA Technical Reports Server (NTRS)

Barth, Timothy J.

2016-01-01

This chapter discusses the ongoing development of combined uncertainty and error bound estimates for computational fluid dynamics (CFD) calculations subject to imposed random parameters and random fields. An objective of this work is the construction of computable error bound formulas for output uncertainty statistics that guide CFD practitioners in systematically determining how accurately CFD realizations should be approximated and how accurately uncertainty statistics should be approximated for output quantities of interest. Formal error bounds formulas for moment statistics that properly account for the presence of numerical errors in CFD calculations and numerical quadrature errors in the calculation of moment statistics have been previously presented in [8]. In this past work, hierarchical node-nested dense and sparse tensor product quadratures are used to calculate moment statistics integrals. In the present work, a framework has been developed that exploits the hierarchical structure of these quadratures in order to simplify the calculation of an estimate of the quadrature error needed in error bound formulas. When signed estimates of realization error are available, this signed error may also be used to estimate output quantity of interest probability densities as a means to assess the impact of realization error on these density estimates. Numerical results are presented for CFD problems with uncertainty to demonstrate the capabilities of this framework.

Measurement System Characterization in the Presence of Measurement Errors

NASA Technical Reports Server (NTRS)

Commo, Sean A.

2012-01-01

In the calibration of a measurement system, data are collected in order to estimate a mathematical model between one or more factors of interest and a response. Ordinary least squares is a method employed to estimate the regression coefficients in the model. The method assumes that the factors are known without error; yet, it is implicitly known that the factors contain some uncertainty. In the literature, this uncertainty is known as measurement error. The measurement error affects both the estimates of the model coefficients and the prediction, or residual, errors. There are some methods, such as orthogonal least squares, that are employed in situations where measurement errors exist, but these methods do not directly incorporate the magnitude of the measurement errors. This research proposes a new method, known as modified least squares, that combines the principles of least squares with knowledge about the measurement errors. This knowledge is expressed in terms of the variance ratio - the ratio of response error variance to measurement error variance.

Unscented predictive variable structure filter for satellite attitude estimation with model errors when using low precision sensors

NASA Astrophysics Data System (ADS)

Cao, Lu; Li, Hengnian

2016-10-01

For the satellite attitude estimation problem, the serious model errors always exist and hider the estimation performance of the Attitude Determination and Control System (ACDS), especially for a small satellite with low precision sensors. To deal with this problem, a new algorithm for the attitude estimation, referred to as the unscented predictive variable structure filter (UPVSF) is presented. This strategy is proposed based on the variable structure control concept and unscented transform (UT) sampling method. It can be implemented in real time with an ability to estimate the model errors on-line, in order to improve the state estimation precision. In addition, the model errors in this filter are not restricted only to the Gaussian noises; therefore, it has the advantages to deal with the various kinds of model errors or noises. It is anticipated that the UT sampling strategy can further enhance the robustness and accuracy of the novel UPVSF. Numerical simulations show that the proposed UPVSF is more effective and robustness in dealing with the model errors and low precision sensors compared with the traditional unscented Kalman filter (UKF).

Estimation of clear-sky insolation using satellite and ground meteorological data

NASA Technical Reports Server (NTRS)

Staylor, W. F.; Darnell, W. L.; Gupta, S. K.

1983-01-01

Ground based pyranometer measurements were combined with meteorological data from the Tiros N satellite in order to estimate clear-sky insolations at five U.S. sites for five weeks during the spring of 1979. The estimates were used to develop a semi-empirical model of clear-sky insolation for the interpretation of input data from the Tiros Operational Vertical Sounder (TOVS). Using only satellite data, the estimated standard errors in the model were about 2 percent. The introduction of ground based data reduced errors to around 1 percent. It is shown that although the errors in the model were reduced by only 1 percent, TOVS data products are still adequate for estimating clear-sky insolation.

Decreasing range resolution of a SAR image to permit correction of motion measurement errors beyond the SAR range resolution

DOEpatents

Doerry, Armin W.; Heard, Freddie E.; Cordaro, J. Thomas

2010-07-20

Motion measurement errors that extend beyond the range resolution of a synthetic aperture radar (SAR) can be corrected by effectively decreasing the range resolution of the SAR in order to permit measurement of the error. Range profiles can be compared across the slow-time dimension of the input data in order to estimate the error. Once the error has been determined, appropriate frequency and phase correction can be applied to the uncompressed input data, after which range and azimuth compression can be performed to produce a desired SAR image.

Comparing range data across the slow-time dimension to correct motion measurement errors beyond the range resolution of a synthetic aperture radar

DOEpatents

Doerry, Armin W.; Heard, Freddie E.; Cordaro, J. Thomas

2010-08-17

Motion measurement errors that extend beyond the range resolution of a synthetic aperture radar (SAR) can be corrected by effectively decreasing the range resolution of the SAR in order to permit measurement of the error. Range profiles can be compared across the slow-time dimension of the input data in order to estimate the error. Once the error has been determined, appropriate frequency and phase correction can be applied to the uncompressed input data, after which range and azimuth compression can be performed to produce a desired SAR image.

GRAVSAT/GEOPAUSE covariance analysis including geopotential aliasing

NASA Technical Reports Server (NTRS)

Koch, D. W.

1975-01-01

A conventional covariance analysis for the GRAVSAT/GEOPAUSE mission is described in which the uncertainties of approximately 200 parameters, including the geopotential coefficients to degree and order 12, are estimated over three different tracking intervals. The estimated orbital uncertainties for both GRAVSAT and GEOPAUSE reach levels more accurate than presently available. The adjusted measurement bias errors approach the mission goal. Survey errors in the low centimeter range are achieved after ten days of tracking. The ability of the mission to obtain accuracies of geopotential terms to (12, 12) one to two orders of magnitude superior to present accuracy levels is clearly shown. A unique feature of this report is that the aliasing structure of this (12, 12) field is examined. It is shown that uncertainties for unadjusted terms to (12, 12) still exert a degrading effect upon the adjusted error of an arbitrarily selected term of lower degree and order. Finally, the distribution of the aliasing from the unestimated uncertainty of a particular high degree and order geopotential term upon the errors of all remaining adjusted terms is listed in detail.

An optimization-based framework for anisotropic simplex mesh adaptation

NASA Astrophysics Data System (ADS)

Yano, Masayuki; Darmofal, David L.

2012-09-01

We present a general framework for anisotropic h-adaptation of simplex meshes. Given a discretization and any element-wise, localizable error estimate, our adaptive method iterates toward a mesh that minimizes error for a given degrees of freedom. Utilizing mesh-metric duality, we consider a continuous optimization problem of the Riemannian metric tensor field that provides an anisotropic description of element sizes. First, our method performs a series of local solves to survey the behavior of the local error function. This information is then synthesized using an affine-invariant tensor manipulation framework to reconstruct an approximate gradient of the error function with respect to the metric tensor field. Finally, we perform gradient descent in the metric space to drive the mesh toward optimality. The method is first demonstrated to produce optimal anisotropic meshes minimizing the L2 projection error for a pair of canonical problems containing a singularity and a singular perturbation. The effectiveness of the framework is then demonstrated in the context of output-based adaptation for the advection-diffusion equation using a high-order discontinuous Galerkin discretization and the dual-weighted residual (DWR) error estimate. The method presented provides a unified framework for optimizing both the element size and anisotropy distribution using an a posteriori error estimate and enables efficient adaptation of anisotropic simplex meshes for high-order discretizations.

Enhanced Pedestrian Navigation Based on Course Angle Error Estimation Using Cascaded Kalman Filters

PubMed Central

Park, Chan Gook

2018-01-01

An enhanced pedestrian dead reckoning (PDR) based navigation algorithm, which uses two cascaded Kalman filters (TCKF) for the estimation of course angle and navigation errors, is proposed. The proposed algorithm uses a foot-mounted inertial measurement unit (IMU), waist-mounted magnetic sensors, and a zero velocity update (ZUPT) based inertial navigation technique with TCKF. The first stage filter estimates the course angle error of a human, which is closely related to the heading error of the IMU. In order to obtain the course measurements, the filter uses magnetic sensors and a position-trace based course angle. For preventing magnetic disturbance from contaminating the estimation, the magnetic sensors are attached to the waistband. Because the course angle error is mainly due to the heading error of the IMU, and the characteristic error of the heading angle is highly dependent on that of the course angle, the estimated course angle error is used as a measurement for estimating the heading error in the second stage filter. At the second stage, an inertial navigation system-extended Kalman filter-ZUPT (INS-EKF-ZUPT) method is adopted. As the heading error is estimated directly by using course-angle error measurements, the estimation accuracy for the heading and yaw gyro bias can be enhanced, compared with the ZUPT-only case, which eventually enhances the position accuracy more efficiently. The performance enhancements are verified via experiments, and the way-point position error for the proposed method is compared with those for the ZUPT-only case and with other cases that use ZUPT and various types of magnetic heading measurements. The results show that the position errors are reduced by a maximum of 90% compared with the conventional ZUPT based PDR algorithms. PMID:29690539

The formulation and estimation of a spatial skew-normal generalized ordered-response model.

DOT National Transportation Integrated Search

2016-06-01

This paper proposes a new spatial generalized ordered response model with skew-normal kernel error terms and an : associated estimation method. It contributes to the spatial analysis field by allowing a flexible and parametric skew-normal : distribut...

Fast maximum likelihood estimation using continuous-time neural point process models.

PubMed

Lepage, Kyle Q; MacDonald, Christopher J

2015-06-01

A recent report estimates that the number of simultaneously recorded neurons is growing exponentially. A commonly employed statistical paradigm using discrete-time point process models of neural activity involves the computation of a maximum-likelihood estimate. The time to computate this estimate, per neuron, is proportional to the number of bins in a finely spaced discretization of time. By using continuous-time models of neural activity and the optimally efficient Gaussian quadrature, memory requirements and computation times are dramatically decreased in the commonly encountered situation where the number of parameters p is much less than the number of time-bins n. In this regime, with q equal to the quadrature order, memory requirements are decreased from O(np) to O(qp), and the number of floating-point operations are decreased from O(np(2)) to O(qp(2)). Accuracy of the proposed estimates is assessed based upon physiological consideration, error bounds, and mathematical results describing the relation between numerical integration error and numerical error affecting both parameter estimates and the observed Fisher information. A check is provided which is used to adapt the order of numerical integration. The procedure is verified in simulation and for hippocampal recordings. It is found that in 95 % of hippocampal recordings a q of 60 yields numerical error negligible with respect to parameter estimate standard error. Statistical inference using the proposed methodology is a fast and convenient alternative to statistical inference performed using a discrete-time point process model of neural activity. It enables the employment of the statistical methodology available with discrete-time inference, but is faster, uses less memory, and avoids any error due to discretization.

Influence of conservative corrections on parameter estimation for extreme-mass-ratio inspirals

NASA Astrophysics Data System (ADS)

Huerta, E. A.; Gair, Jonathan R.

2009-04-01

We present an improved numerical kludge waveform model for circular, equatorial extreme-mass-ratio inspirals (EMRIs). The model is based on true Kerr geodesics, augmented by radiative self-force corrections derived from perturbative calculations, and in this paper for the first time we include conservative self-force corrections that we derive by comparison to post-Newtonian results. We present results of a Monte Carlo simulation of parameter estimation errors computed using the Fisher matrix and also assess the theoretical errors that would arise from omitting the conservative correction terms we include here. We present results for three different types of system, namely, the inspirals of black holes, neutron stars, or white dwarfs into a supermassive black hole (SMBH). The analysis shows that for a typical source (a 10M⊙ compact object captured by a 106M⊙ SMBH at a signal to noise ratio of 30) we expect to determine the two masses to within a fractional error of ˜10-4, measure the spin parameter q to ˜10-4.5, and determine the location of the source on the sky and the spin orientation to within 10-3 steradians. We show that, for this kludge model, omitting the conservative corrections leads to a small error over much of the parameter space, i.e., the ratio R of the theoretical model error to the Fisher matrix error is R<1 for all ten parameters in the model. For the few systems with larger errors typically R<3 and hence the conservative corrections can be marginally ignored. In addition, we use our model and first-order self-force results for Schwarzschild black holes to estimate the error that arises from omitting the second-order radiative piece of the self-force. This indicates that it may not be necessary to go beyond first order to recover accurate parameter estimates.

Tissue resistivity estimation in the presence of positional and geometrical uncertainties.

PubMed

Baysal, U; Eyüboğlu, B M

2000-08-01

Geometrical uncertainties (organ boundary variation and electrode position uncertainties) are the biggest sources of error in estimating electrical resistivity of tissues from body surface measurements. In this study, in order to decrease estimation errors, the statistically constrained minimum mean squared error estimation algorithm (MiMSEE) is constrained with a priori knowledge of the geometrical uncertainties in addition to the constraints based on geometry, resistivity range, linearization and instrumentation errors. The MiMSEE calculates an optimum inverse matrix, which maps the surface measurements to the unknown resistivity distribution. The required data are obtained from four-electrode impedance measurements, similar to injected-current electrical impedance tomography (EIT). In this study, the surface measurements are simulated by using a numerical thorax model. The data are perturbed with additive instrumentation noise. Simulated surface measurements are then used to estimate the tissue resistivities by using the proposed algorithm. The results are compared with the results of conventional least squares error estimator (LSEE). Depending on the region, the MiMSEE yields an estimation error between 0.42% and 31.3% compared with 7.12% to 2010% for the LSEE. It is shown that the MiMSEE is quite robust even in the case of geometrical uncertainties.

Rapid estimation of frequency response functions by close-range photogrammetry

NASA Technical Reports Server (NTRS)

Tripp, J. S.

1985-01-01

The accuracy of a rapid method which estimates the frequency response function from stereoscopic dynamic data is computed. It is shown that reversal of the order of the operations of coordinate transformation and Fourier transformation, which provides a significant increase in computational speed, introduces error. A portion of the error, proportional to the perturbation components normal to the camera focal planes, cannot be eliminated. The remaining error may be eliminated by proper scaling of frequency data prior to coordinate transformation. Methods are developed for least squares estimation of the full 3x3 frequency response matrix for a three dimensional structure.

On High-Order Radiation Boundary Conditions

NASA Technical Reports Server (NTRS)

Hagstrom, Thomas

1995-01-01

In this paper we develop the theory of high-order radiation boundary conditions for wave propagation problems. In particular, we study the convergence of sequences of time-local approximate conditions to the exact boundary condition, and subsequently estimate the error in the solutions obtained using these approximations. We show that for finite times the Pade approximants proposed by Engquist and Majda lead to exponential convergence if the solution is smooth, but that good long-time error estimates cannot hold for spatially local conditions. Applications in fluid dynamics are also discussed.

Error associated with a reduced order linear model of a spur gear pair

NASA Technical Reports Server (NTRS)

Kahraman, A.; Singh, R.

1991-01-01

The paper proposes a reduced-order analytical model of a spur gear pair which consists of two identical spur gears, two identical flexible shafts, and four identical rolling element bearings of a given radial stiffness. The error associated with the undamped eigensolution is estimated by a comparison with a benchmark finite element model.

Estimation of Separation Buffers for Wind-Prediction Error in an Airborne Separation Assistance System

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.

Estimation of geopotential differences over intercontinental locations using satellite and terrestrial measurements. Ph.D. Thesis

NASA Technical Reports Server (NTRS)

Pavlis, Nikolaos K.

1991-01-01

An error analysis study was conducted in order to assess the current accuracies and the future anticipated improvements in the estimation of geopotential differences over intercontinental locations. An observation/estimation scheme was proposed and studied, whereby gravity disturbance measurements on the Earth's surface, in caps surrounding the estimation points, are combined with corresponding data in caps directly over these points at the altitude of a low orbiting satellite, for the estimation of the geopotential difference between the terrestrial stations. The mathematical modeling required to relate the primary observables to the parameters to be estimated, was studied for the terrestrial data and the data at altitude. Emphasis was placed on the examination of systematic effects and on the corresponding reductions that need to be applied to the measurements to avoid systematic errors. The error estimation for the geopotential differences was performed using both truncation theory and least squares collocation with ring averages, in case observations on the Earth's surface only are used. The error analysis indicated that with the currently available global geopotential model OSU89B and with gravity disturbance data in 2 deg caps surrounding the estimation points, the error of the geopotential difference arising from errors in the reference model and the cap data is about 23 kgal cm, for 30 deg station separation.

First-order approximation error analysis of Risley-prism-based beam directing system.

PubMed

Zhao, Yanyan; Yuan, Yan

2014-12-01

To improve the performance of a Risley-prism system for optical detection and measuring applications, it is necessary to be able to determine the direction of the outgoing beam with high accuracy. In previous works, error sources and their impact on the performance of the Risley-prism system have been analyzed, but their numerical approximation accuracy was not high. Besides, pointing error analysis of the Risley-prism system has provided results for the case when the component errors, prism orientation errors, and assembly errors are certain. In this work, the prototype of a Risley-prism system was designed. The first-order approximations of the error analysis were derived and compared with the exact results. The directing errors of a Risley-prism system associated with wedge-angle errors, prism mounting errors, and bearing assembly errors were analyzed based on the exact formula and the first-order approximation. The comparisons indicated that our first-order approximation is accurate. In addition, the combined errors produced by the wedge-angle errors and mounting errors of the two prisms together were derived and in both cases were proved to be the sum of errors caused by the first and the second prism separately. Based on these results, the system error of our prototype was estimated. The derived formulas can be implemented to evaluate beam directing errors of any Risley-prism beam directing system with a similar configuration.

Medication errors in chemotherapy preparation and administration: a survey conducted among oncology nurses in Turkey.

PubMed

Ulas, Arife; Silay, Kamile; Akinci, Sema; Dede, Didem Sener; Akinci, Muhammed Bulent; Sendur, Mehmet Ali Nahit; Cubukcu, Erdem; Coskun, Hasan Senol; Degirmenci, Mustafa; Utkan, Gungor; Ozdemir, Nuriye; Isikdogan, Abdurrahman; Buyukcelik, Abdullah; Inanc, Mevlude; Bilici, Ahmet; Odabasi, Hatice; Cihan, Sener; Avci, Nilufer; Yalcin, Bulent

2015-01-01

Medication errors in oncology may cause severe clinical problems due to low therapeutic indices and high toxicity of chemotherapeutic agents. We aimed to investigate unintentional medication errors and underlying factors during chemotherapy preparation and administration based on a systematic survey conducted to reflect oncology nurses experience. This study was conducted in 18 adult chemotherapy units with volunteer participation of 206 nurses. A survey developed by primary investigators and medication errors (MAEs) defined preventable errors during prescription of medication, ordering, preparation or administration. The survey consisted of 4 parts: demographic features of nurses; workload of chemotherapy units; errors and their estimated monthly number during chemotherapy preparation and administration; and evaluation of the possible factors responsible from ME. The survey was conducted by face to face interview and data analyses were performed with descriptive statistics. Chi-square or Fisher exact tests were used for a comparative analysis of categorical data. Some 83.4% of the 210 nurses reported one or more than one error during chemotherapy preparation and administration. Prescribing or ordering wrong doses by physicians (65.7%) and noncompliance with administration sequences during chemotherapy administration (50.5%) were the most common errors. The most common estimated average monthly error was not following the administration sequence of the chemotherapeutic agents (4.1 times/month, range 1-20). The most important underlying reasons for medication errors were heavy workload (49.7%) and insufficient number of staff (36.5%). Our findings suggest that the probability of medication error is very high during chemotherapy preparation and administration, the most common involving prescribing and ordering errors. Further studies must address the strategies to minimize medication error in chemotherapy receiving patients, determine sufficient protective measures and establishing multistep control mechanisms.

Optimum nonparametric estimation of population density based on ordered distances

USGS Publications Warehouse

Patil, S.A.; Kovner, J.L.; Burnham, Kenneth P.

1982-01-01

The asymptotic mean and error mean square are determined for the nonparametric estimator of plant density by distance sampling proposed by Patil, Burnham and Kovner (1979, Biometrics 35, 597-604. On the basis of these formulae, a bias-reduced version of this estimator is given, and its specific form is determined which gives minimum mean square error under varying assumptions about the true probability density function of the sampled data. Extension is given to line-transect sampling.

The impact of 3D volume of interest definition on accuracy and precision of activity estimation in quantitative SPECT and planar processing methods

NASA Astrophysics Data System (ADS)

He, Bin; Frey, Eric C.

2010-06-01

Accurate and precise estimation of organ activities is essential for treatment planning in targeted radionuclide therapy. We have previously evaluated the impact of processing methodology, statistical noise and variability in activity distribution and anatomy on the accuracy and precision of organ activity estimates obtained with quantitative SPECT (QSPECT) and planar (QPlanar) processing. Another important factor impacting the accuracy and precision of organ activity estimates is accuracy of and variability in the definition of organ regions of interest (ROI) or volumes of interest (VOI). The goal of this work was thus to systematically study the effects of VOI definition on the reliability of activity estimates. To this end, we performed Monte Carlo simulation studies using randomly perturbed and shifted VOIs to assess the impact on organ activity estimates. The 3D NCAT phantom was used with activities that modeled clinically observed 111In ibritumomab tiuxetan distributions. In order to study the errors resulting from misdefinitions due to manual segmentation errors, VOIs of the liver and left kidney were first manually defined. Each control point was then randomly perturbed to one of the nearest or next-nearest voxels in three ways: with no, inward or outward directional bias, resulting in random perturbation, erosion or dilation, respectively, of the VOIs. In order to study the errors resulting from the misregistration of VOIs, as would happen, e.g. in the case where the VOIs were defined using a misregistered anatomical image, the reconstructed SPECT images or projections were shifted by amounts ranging from -1 to 1 voxels in increments of with 0.1 voxels in both the transaxial and axial directions. The activity estimates from the shifted reconstructions or projections were compared to those from the originals, and average errors were computed for the QSPECT and QPlanar methods, respectively. For misregistration, errors in organ activity estimations were linear in the shift for both the QSPECT and QPlanar methods. QPlanar was less sensitive to object definition perturbations than QSPECT, especially for dilation and erosion cases. Up to 1 voxel misregistration or misdefinition resulted in up to 8% error in organ activity estimates, with the largest errors for small or low uptake organs. Both types of VOI definition errors produced larger errors in activity estimates for a small and low uptake organs (i.e. -7.5% to 5.3% for the left kidney) than for a large and high uptake organ (i.e. -2.9% to 2.1% for the liver). We observed that misregistration generally had larger effects than misdefinition, with errors ranging from -7.2% to 8.4%. The different imaging methods evaluated responded differently to the errors from misregistration and misdefinition. We found that QSPECT was more sensitive to misdefinition errors, but less sensitive to misregistration errors, as compared to the QPlanar method. Thus, sensitivity to VOI definition errors should be an important criterion in evaluating quantitative imaging methods.

Two-dimensional radial laser scanning for circular marker detection and external mobile robot tracking.

PubMed

Teixidó, Mercè; Pallejà, Tomàs; Font, Davinia; Tresanchez, Marcel; Moreno, Javier; Palacín, Jordi

2012-11-28

This paper presents the use of an external fixed two-dimensional laser scanner to detect cylindrical targets attached to moving devices, such as a mobile robot. This proposal is based on the detection of circular markers in the raw data provided by the laser scanner by applying an algorithm for outlier avoidance and a least-squares circular fitting. Some experiments have been developed to empirically validate the proposal with different cylindrical targets in order to estimate the location and tracking errors achieved, which are generally less than 20 mm in the area covered by the laser sensor. As a result of the validation experiments, several error maps have been obtained in order to give an estimate of the uncertainty of any location computed. This proposal has been validated with a medium-sized mobile robot with an attached cylindrical target (diameter 200 mm). The trajectory of the mobile robot was estimated with an average location error of less than 15 mm, and the real location error in each individual circular fitting was similar to the error estimated with the obtained error maps. The radial area covered in this validation experiment was up to 10 m, a value that depends on the radius of the cylindrical target and the radial density of the distance range points provided by the laser scanner but this area can be increased by combining the information of additional external laser scanners.

A Monte-Carlo Bayesian framework for urban rainfall error modelling

NASA Astrophysics Data System (ADS)

Ochoa Rodriguez, Susana; Wang, Li-Pen; Willems, Patrick; Onof, Christian

2016-04-01

Rainfall estimates of the highest possible accuracy and resolution are required for urban hydrological applications, given the small size and fast response which characterise urban catchments. While significant progress has been made in recent years towards meeting rainfall input requirements for urban hydrology -including increasing use of high spatial resolution radar rainfall estimates in combination with point rain gauge records- rainfall estimates will never be perfect and the true rainfall field is, by definition, unknown [1]. Quantifying the residual errors in rainfall estimates is crucial in order to understand their reliability, as well as the impact that their uncertainty may have in subsequent runoff estimates. The quantification of errors in rainfall estimates has been an active topic of research for decades. However, existing rainfall error models have several shortcomings, including the fact that they are limited to describing errors associated to a single data source (i.e. errors associated to rain gauge measurements or radar QPEs alone) and to a single representative error source (e.g. radar-rain gauge differences, spatial temporal resolution). Moreover, rainfall error models have been mostly developed for and tested at large scales. Studies at urban scales are mostly limited to analyses of propagation of errors in rain gauge records-only through urban drainage models and to tests of model sensitivity to uncertainty arising from unmeasured rainfall variability. Only few radar rainfall error models -originally developed for large scales- have been tested at urban scales [2] and have been shown to fail to well capture small-scale storm dynamics, including storm peaks, which are of utmost important for urban runoff simulations. In this work a Monte-Carlo Bayesian framework for rainfall error modelling at urban scales is introduced, which explicitly accounts for relevant errors (arising from insufficient accuracy and/or resolution) in multiple data sources (in this case radar and rain gauge estimates typically available at present), while at the same time enabling dynamic combination of these data sources (thus not only quantifying uncertainty, but also reducing it). This model generates an ensemble of merged rainfall estimates, which can then be used as input to urban drainage models in order to examine how uncertainties in rainfall estimates propagate to urban runoff estimates. The proposed model is tested using as case study a detailed rainfall and flow dataset, and a carefully verified urban drainage model of a small (~9 km2) pilot catchment in North-East London. The model has shown to well characterise residual errors in rainfall data at urban scales (which remain after the merging), leading to improved runoff estimates. In fact, the majority of measured flow peaks are bounded within the uncertainty area produced by the runoff ensembles generated with the ensemble rainfall inputs. REFERENCES: [1] Ciach, G. J. & Krajewski, W. F. (1999). On the estimation of radar rainfall error variance. Advances in Water Resources, 22 (6), 585-595. [2] Rico-Ramirez, M. A., Liguori, S. & Schellart, A. N. A. (2015). Quantifying radar-rainfall uncertainties in urban drainage flow modelling. Journal of Hydrology, 528, 17-28.

Heading Estimation for Pedestrian Dead Reckoning Based on Robust Adaptive Kalman Filtering.

PubMed

Wu, Dongjin; Xia, Linyuan; Geng, Jijun

2018-06-19

Pedestrian dead reckoning (PDR) using smart phone-embedded micro-electro-mechanical system (MEMS) sensors plays a key role in ubiquitous localization indoors and outdoors. However, as a relative localization method, it suffers from the problem of error accumulation which prevents it from long term independent running. Heading estimation error is one of the main location error sources, and therefore, in order to improve the location tracking performance of the PDR method in complex environments, an approach based on robust adaptive Kalman filtering (RAKF) for estimating accurate headings is proposed. In our approach, outputs from gyroscope, accelerometer, and magnetometer sensors are fused using the solution of Kalman filtering (KF) that the heading measurements derived from accelerations and magnetic field data are used to correct the states integrated from angular rates. In order to identify and control measurement outliers, a maximum likelihood-type estimator (M-estimator)-based model is used. Moreover, an adaptive factor is applied to resist the negative effects of state model disturbances. Extensive experiments under static and dynamic conditions were conducted in indoor environments. The experimental results demonstrate the proposed approach provides more accurate heading estimates and supports more robust and dynamic adaptive location tracking, compared with methods based on conventional KF.

Pre- and postprocessing techniques for determining goodness of computational meshes

NASA Technical Reports Server (NTRS)

Oden, J. Tinsley; Westermann, T.; Bass, J. M.

1993-01-01

Research in error estimation, mesh conditioning, and solution enhancement for finite element, finite difference, and finite volume methods has been incorporated into AUDITOR, a modern, user-friendly code, which operates on 2D and 3D unstructured neutral files to improve the accuracy and reliability of computational results. Residual error estimation capabilities provide local and global estimates of solution error in the energy norm. Higher order results for derived quantities may be extracted from initial solutions. Within the X-MOTIF graphical user interface, extensive visualization capabilities support critical evaluation of results in linear elasticity, steady state heat transfer, and both compressible and incompressible fluid dynamics.

Application of Parallel Adjoint-Based Error Estimation and Anisotropic Grid Adaptation for Three-Dimensional Aerospace Configurations

NASA Technical Reports Server (NTRS)

Lee-Rausch, E. M.; Park, M. A.; Jones, W. T.; Hammond, D. P.; Nielsen, E. J.

2005-01-01

This paper demonstrates the extension of error estimation and adaptation methods to parallel computations enabling larger, more realistic aerospace applications and the quantification of discretization errors for complex 3-D solutions. Results were shown for an inviscid sonic-boom prediction about a double-cone configuration and a wing/body segmented leading edge (SLE) configuration where the output function of the adjoint was pressure integrated over a part of the cylinder in the near field. After multiple cycles of error estimation and surface/field adaptation, a significant improvement in the inviscid solution for the sonic boom signature of the double cone was observed. Although the double-cone adaptation was initiated from a very coarse mesh, the near-field pressure signature from the final adapted mesh compared very well with the wind-tunnel data which illustrates that the adjoint-based error estimation and adaptation process requires no a priori refinement of the mesh. Similarly, the near-field pressure signature for the SLE wing/body sonic boom configuration showed a significant improvement from the initial coarse mesh to the final adapted mesh in comparison with the wind tunnel results. Error estimation and field adaptation results were also presented for the viscous transonic drag prediction of the DLR-F6 wing/body configuration, and results were compared to a series of globally refined meshes. Two of these globally refined meshes were used as a starting point for the error estimation and field-adaptation process where the output function for the adjoint was the total drag. The field-adapted results showed an improvement in the prediction of the drag in comparison with the finest globally refined mesh and a reduction in the estimate of the remaining drag error. The adjoint-based adaptation parameter showed a need for increased resolution in the surface of the wing/body as well as a need for wake resolution downstream of the fuselage and wing trailing edge in order to achieve the requested drag tolerance. Although further adaptation was required to meet the requested tolerance, no further cycles were computed in order to avoid large discrepancies between the surface mesh spacing and the refined field spacing.

Stochastic stability of sigma-point Unscented Predictive Filter.

PubMed

Cao, Lu; Tang, Yu; Chen, Xiaoqian; Zhao, Yong

2015-07-01

In this paper, the Unscented Predictive Filter (UPF) is derived based on unscented transformation for nonlinear estimation, which breaks the confine of conventional sigma-point filters by employing Kalman filter as subject investigated merely. In order to facilitate the new method, the algorithm flow of UPF is given firstly. Then, the theoretical analyses demonstrate that the estimate accuracy of the model error and system for the UPF is higher than that of the conventional PF. Moreover, the authors analyze the stochastic boundedness and the error behavior of Unscented Predictive Filter (UPF) for general nonlinear systems in a stochastic framework. In particular, the theoretical results present that the estimation error remains bounded and the covariance keeps stable if the system׳s initial estimation error, disturbing noise terms as well as the model error are small enough, which is the core part of the UPF theory. All of the results have been demonstrated by numerical simulations for a nonlinear example system. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

Statistical models for estimating daily streamflow in Michigan

USGS Publications Warehouse

Holtschlag, D.J.; Salehi, Habib

1992-01-01

Statistical models for estimating daily streamflow were analyzed for 25 pairs of streamflow-gaging stations in Michigan. Stations were paired by randomly choosing a station operated in 1989 at which 10 or more years of continuous flow data had been collected and at which flow is virtually unregulated; a nearby station was chosen where flow characteristics are similar. Streamflow data from the 25 randomly selected stations were used as the response variables; streamflow data at the nearby stations were used to generate a set of explanatory variables. Ordinary-least squares regression (OLSR) equations, autoregressive integrated moving-average (ARIMA) equations, and transfer function-noise (TFN) equations were developed to estimate the log transform of flow for the 25 randomly selected stations. The precision of each type of equation was evaluated on the basis of the standard deviation of the estimation errors. OLSR equations produce one set of estimation errors; ARIMA and TFN models each produce l sets of estimation errors corresponding to the forecast lead. The lead-l forecast is the estimate of flow l days ahead of the most recent streamflow used as a response variable in the estimation. In this analysis, the standard deviation of lead l ARIMA and TFN forecast errors were generally lower than the standard deviation of OLSR errors for l < 2 days and l < 9 days, respectively. Composite estimates were computed as a weighted average of forecasts based on TFN equations and backcasts (forecasts of the reverse-ordered series) based on ARIMA equations. The standard deviation of composite errors varied throughout the length of the estimation interval and generally was at maximum near the center of the interval. For comparison with OLSR errors, the mean standard deviation of composite errors were computed for intervals of length 1 to 40 days. The mean standard deviation of length-l composite errors were generally less than the standard deviation of the OLSR errors for l < 32 days. In addition, the composite estimates ensure a gradual transition between periods of estimated and measured flows. Model performance among stations of differing model error magnitudes were compared by computing ratios of the mean standard deviation of the length l composite errors to the standard deviation of OLSR errors. The mean error ratio for the set of 25 selected stations was less than 1 for intervals l < 32 days. Considering the frequency characteristics of the length of intervals of estimated record in Michigan, the effective mean error ratio for intervals < 30 days was 0.52. Thus, for intervals of estimation of 1 month or less, the error of the composite estimate is substantially lower than error of the OLSR estimate.

A 1400-MHz survey of 1478 Abell clusters of galaxies

NASA Technical Reports Server (NTRS)

Owen, F. N.; White, R. A.; Hilldrup, K. C.; Hanisch, R. J.

1982-01-01

Observations of 1478 Abell clusters of galaxies with the NRAO 91-m telescope at 1400 MHz are reported. The measured beam shape was deconvolved from the measured source Gaussian fits in order to estimate the source size and position angle. All detected sources within 0.5 corrected Abell cluster radii are listed, including the cluster number, richness class, distance class, magnitude of the tenth brightest galaxy, redshift estimate, corrected cluster radius in arcmin, right ascension and error, declination and error, total flux density and error, and angular structure for each source.

Radial orbit error reduction and sea surface topography determination using satellite altimetry

NASA Technical Reports Server (NTRS)

Engelis, Theodossios

1987-01-01

A method is presented in satellite altimetry that attempts to simultaneously determine the geoid and sea surface topography with minimum wavelengths of about 500 km and to reduce the radial orbit error caused by geopotential errors. The modeling of the radial orbit error is made using the linearized Lagrangian perturbation theory. Secular and second order effects are also included. After a rather extensive validation of the linearized equations, alternative expressions of the radial orbit error are derived. Numerical estimates for the radial orbit error and geoid undulation error are computed using the differences of two geopotential models as potential coefficient errors, for a SEASAT orbit. To provide statistical estimates of the radial distances and the geoid, a covariance propagation is made based on the full geopotential covariance. Accuracy estimates for the SEASAT orbits are given which agree quite well with already published results. Observation equations are develped using sea surface heights and crossover discrepancies as observables. A minimum variance solution with prior information provides estimates of parameters representing the sea surface topography and corrections to the gravity field that is used for the orbit generation. The simulation results show that the method can be used to effectively reduce the radial orbit error and recover the sea surface topography.

Error analysis and new dual-cosine window for estimating the sensor frequency response function from the step response data

NASA Astrophysics Data System (ADS)

Yang, Shuang-Long; Liang, Li-Ping; Liu, Hou-De; Xu, Ke-Jun

2018-03-01

Aiming at reducing the estimation error of the sensor frequency response function (FRF) estimated by the commonly used window-based spectral estimation method, the error models of interpolation and transient errors are derived in the form of non-parameter models. Accordingly, window effects on the errors are analyzed and reveal that the commonly used hanning window leads to smaller interpolation error which can also be significantly eliminated by the cubic spline interpolation method when estimating the FRF from the step response data, and window with smaller front-end value can restrain more transient error. Thus, a new dual-cosine window with its non-zero discrete Fourier transform bins at -3, -1, 0, 1, and 3 is constructed for FRF estimation. Compared with the hanning window, the new dual-cosine window has the equivalent interpolation error suppression capability and better transient error suppression capability when estimating the FRF from the step response; specifically, it reduces the asymptotic property of the transient error from O(N-2) of the hanning window method to O(N-4) while only increases the uncertainty slightly (about 0.4 dB). Then, one direction of a wind tunnel strain gauge balance which is a high order, small damping, and non-minimum phase system is employed as the example for verifying the new dual-cosine window-based spectral estimation method. The model simulation result shows that the new dual-cosine window method is better than the hanning window method for FRF estimation, and compared with the Gans method and LPM method, it has the advantages of simple computation, less time consumption, and short data requirement; the actual data calculation result of the balance FRF is consistent to the simulation result. Thus, the new dual-cosine window is effective and practical for FRF estimation.

Geodesy by radio interferometry - Effects of atmospheric modeling errors on estimates of baseline length

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.

Errors in the estimation of approximate entropy and other recurrence-plot-derived indices due to the finite resolution of RR time series.

PubMed

García-González, Miguel A; Fernández-Chimeno, Mireya; Ramos-Castro, Juan

2009-02-01

An analysis of the errors due to the finite resolution of RR time series in the estimation of the approximate entropy (ApEn) is described. The quantification errors in the discrete RR time series produce considerable errors in the ApEn estimation (bias and variance) when the signal variability or the sampling frequency is low. Similar errors can be found in indices related to the quantification of recurrence plots. An easy way to calculate a figure of merit [the signal to resolution of the neighborhood ratio (SRN)] is proposed in order to predict when the bias in the indices could be high. When SRN is close to an integer value n, the bias is higher than when near n - 1/2 or n + 1/2. Moreover, if SRN is close to an integer value, the lower this value, the greater the bias is.

Estimation of the Cesium-137 Source Term from the Fukushima Daiichi Power Plant Using Air Concentration and Deposition Data

NASA Astrophysics Data System (ADS)

Winiarek, Victor; Bocquet, Marc; Duhanyan, Nora; Roustan, Yelva; Saunier, Olivier; Mathieu, Anne

2013-04-01

A major difficulty when inverting the source term of an atmospheric tracer dispersion problem is the estimation of the prior errors: those of the atmospheric transport model, those ascribed to the representativeness of the measurements, the instrumental errors, and those attached to the prior knowledge on the variables one seeks to retrieve. In the case of an accidental release of pollutant, and specially in a situation of sparse observability, the reconstructed source is sensitive to these assumptions. This sensitivity makes the quality of the retrieval dependent on the methods used to model and estimate the prior errors of the inverse modeling scheme. In Winiarek et al. (2012), we proposed to use an estimation method for the errors' amplitude based on the maximum likelihood principle. Under semi-Gaussian assumptions, it takes into account, without approximation, the positivity assumption on the source. We applied the method to the estimation of the Fukushima Daiichi cesium-137 and iodine-131 source terms using activity concentrations in the air. The results were compared to an L-curve estimation technique, and to Desroziers's scheme. Additionally to the estimations of released activities, we provided related uncertainties (12 PBq with a std. of 15 - 20 % for cesium-137 and 190 - 380 PBq with a std. of 5 - 10 % for iodine-131). We also enlightened that, because of the low number of available observations (few hundreds) and even if orders of magnitude were consistent, the reconstructed activities significantly depended on the method used to estimate the prior errors. In order to use more data, we propose to extend the methods to the use of several data types, such as activity concentrations in the air and fallout measurements. The idea is to simultaneously estimate the prior errors related to each dataset, in order to fully exploit the information content of each one. Using the activity concentration measurements, but also daily fallout data from prefectures and cumulated deposition data over a region lying approximately 150 km around the nuclear power plant, we can use a few thousands of data in our inverse modeling algorithm to reconstruct the Cesium-137 source term. To improve the parameterization of removal processes, rainfall fields have also been corrected using outputs from the mesoscale meteorological model WRF and ground station rainfall data. As expected, the different methods yield closer results as the number of data increases. Reference : Winiarek, V., M. Bocquet, O. Saunier, A. Mathieu (2012), Estimation of errors in the inverse modeling of accidental release of atmospheric pollutant : Application to the reconstruction of the cesium-137 and iodine-131 source terms from the Fukushima Daiichi power plant, J. Geophys. Res., 117, D05122, doi:10.1029/2011JD016932.

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 uniformly distributed model error and time-dependent model error statistics.

On the Error State Selection for Stationary SINS Alignment and Calibration Kalman Filters—Part II: Observability/Estimability Analysis

PubMed Central

Silva, Felipe O.; Hemerly, Elder M.; Leite Filho, Waldemar C.

2017-01-01

This paper presents the second part of a study aiming at the error state selection in Kalman filters applied to the stationary self-alignment and calibration (SSAC) problem of strapdown inertial navigation systems (SINS). The observability properties of the system are systematically investigated, and the number of unobservable modes is established. Through the analytical manipulation of the full SINS error model, the unobservable modes of the system are determined, and the SSAC error states (except the velocity errors) are proven to be individually unobservable. The estimability of the system is determined through the examination of the major diagonal terms of the covariance matrix and their eigenvalues/eigenvectors. Filter order reduction based on observability analysis is shown to be inadequate, and several misconceptions regarding SSAC observability and estimability deficiencies are removed. As the main contributions of this paper, we demonstrate that, except for the position errors, all error states can be minimally estimated in the SSAC problem and, hence, should not be removed from the filter. Corroborating the conclusions of the first part of this study, a 12-state Kalman filter is found to be the optimal error state selection for SSAC purposes. Results from simulated and experimental tests support the outlined conclusions. PMID:28241494

Computer simulation results of attitude estimation of earth orbiting satellites

NASA Technical Reports Server (NTRS)

Kou, S. R.

1976-01-01

Computer simulation results of attitude estimation of Earth-orbiting satellites (including Space Telescope) subjected to environmental disturbances and noises are presented. Decomposed linear recursive filter and Kalman filter were used as estimation tools. Six programs were developed for this simulation, and all were written in the basic language and were run on HP 9830A and HP 9866A computers. Simulation results show that a decomposed linear recursive filter is accurate in estimation and fast in response time. Furthermore, for higher order systems, this filter has computational advantages (i.e., less integration errors and roundoff errors) over a Kalman filter.

Blind third-order dispersion estimation based on fractional Fourier transformation for coherent optical communication

NASA Astrophysics Data System (ADS)

Yang, Lin; Guo, Peng; Yang, Aiying; Qiao, Yaojun

2018-02-01

In this paper, we propose a blind third-order dispersion estimation method based on fractional Fourier transformation (FrFT) in optical fiber communication system. By measuring the chromatic dispersion (CD) at different wavelengths, this method can estimation dispersion slope and further calculate the third-order dispersion. The simulation results demonstrate that the estimation error is less than 2 % in 28GBaud dual polarization quadrature phase-shift keying (DP-QPSK) and 28GBaud dual polarization 16 quadrature amplitude modulation (DP-16QAM) system. Through simulations, the proposed third-order dispersion estimation method is shown to be robust against nonlinear and amplified spontaneous emission (ASE) noise. In addition, to reduce the computational complexity, searching step with coarse and fine granularity is chosen to search optimal order of FrFT. The third-order dispersion estimation method based on FrFT can be used to monitor the third-order dispersion in optical fiber system.

Nonlinear observation of internal states of fuel cell cathode utilizing a high-order sliding-mode algorithm

NASA Astrophysics Data System (ADS)

Xu, Liangfei; Hu, Junming; Cheng, Siliang; Fang, Chuan; Li, Jianqiu; Ouyang, Minggao; Lehnert, Werner

2017-07-01

A scheme for designing a second-order sliding-mode (SOSM) observer that estimates critical internal states on the cathode side of a polymer electrolyte membrane (PEM) fuel cell system is presented. A nonlinear, isothermal dynamic model for the cathode side and a membrane electrolyte assembly are first described. A nonlinear observer topology based on an SOSM algorithm is then introduced, and equations for the SOSM observer deduced. Online calculation of the inverse matrix produces numerical errors, so a modified matrix is introduced to eliminate the negative effects of these on the observer. The simulation results indicate that the SOSM observer performs well for the gas partial pressures and air stoichiometry. The estimation results follow the simulated values in the model with relative errors within ± 2% at stable status. Large errors occur during the fast dynamic processes (<1 s). Moreover, the nonlinear observer shows good robustness against variations in the initial values of the internal states, but less robustness against variations in system parameters. The partial pressures are more sensitive than the air stoichiometry to system parameters. Finally, the order of effects of parameter uncertainties on the estimation results is outlined and analyzed.

An evaluation of inferential procedures for adaptive clinical trial designs with pre-specified rules for modifying the sample size.

PubMed

Levin, Gregory P; Emerson, Sarah C; Emerson, Scott S

2014-09-01

Many papers have introduced adaptive clinical trial methods that allow modifications to the sample size based on interim estimates of treatment effect. There has been extensive commentary on type I error control and efficiency considerations, but little research on estimation after an adaptive hypothesis test. We evaluate the reliability and precision of different inferential procedures in the presence of an adaptive design with pre-specified rules for modifying the sampling plan. We extend group sequential orderings of the outcome space based on the stage at stopping, likelihood ratio statistic, and sample mean to the adaptive setting in order to compute median-unbiased point estimates, exact confidence intervals, and P-values uniformly distributed under the null hypothesis. The likelihood ratio ordering is found to average shorter confidence intervals and produce higher probabilities of P-values below important thresholds than alternative approaches. The bias adjusted mean demonstrates the lowest mean squared error among candidate point estimates. A conditional error-based approach in the literature has the benefit of being the only method that accommodates unplanned adaptations. We compare the performance of this and other methods in order to quantify the cost of failing to plan ahead in settings where adaptations could realistically be pre-specified at the design stage. We find the cost to be meaningful for all designs and treatment effects considered, and to be substantial for designs frequently proposed in the literature. © 2014, The International Biometric Society.

Analyzing a stochastic time series obeying a second-order differential equation.

PubMed

Lehle, B; Peinke, J

2015-06-01

The stochastic properties of a Langevin-type Markov process can be extracted from a given time series by a Markov analysis. Also processes that obey a stochastically forced second-order differential equation can be analyzed this way by employing a particular embedding approach: To obtain a Markovian process in 2N dimensions from a non-Markovian signal in N dimensions, the system is described in a phase space that is extended by the temporal derivative of the signal. For a discrete time series, however, this derivative can only be calculated by a differencing scheme, which introduces an error. If the effects of this error are not accounted for, this leads to systematic errors in the estimation of the drift and diffusion functions of the process. In this paper we will analyze these errors and we will propose an approach that correctly accounts for them. This approach allows an accurate parameter estimation and, additionally, is able to cope with weak measurement noise, which may be superimposed to a given time series.

Performability modeling based on real data: A case study

NASA Technical Reports Server (NTRS)

Hsueh, M. C.; Iyer, R. K.; Trivedi, K. S.

1988-01-01

Described is a measurement-based performability model based on error and resource usage data collected on a multiprocessor system. A method for identifying the model structure is introduced and the resulting model is validated against real data. Model development from the collection of raw data to the estimation of the expected reward is described. Both normal and error behavior of the system are characterized. The measured data show that the holding times in key operational and error states are not simple exponentials and that a semi-Markov process is necessary to model system behavior. A reward function, based on the service rate and the error rate in each state, is then defined in order to estimate the performability of the system and to depict the cost of apparent types of errors.

Performability modeling based on real data: A casestudy

NASA Technical Reports Server (NTRS)

Hsueh, M. C.; Iyer, R. K.; Trivedi, K. S.

1987-01-01

Described is a measurement-based performability model based on error and resource usage data collected on a multiprocessor system. A method for identifying the model structure is introduced and the resulting model is validated against real data. Model development from the collection of raw data to the estimation of the expected reward is described. Both normal and error behavior of the system are characterized. The measured data show that the holding times in key operational and error states are not simple exponentials and that a semi-Markov process is necessary to model the system behavior. A reward function, based on the service rate and the error rate in each state, is then defined in order to estimate the performability of the system and to depict the cost of different types of errors.

Error Estimation for the Linearized Auto-Localization Algorithm

PubMed Central

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

Attitude Error Representations for Kalman Filtering

NASA Technical Reports Server (NTRS)

Markley, F. Landis; Bauer, Frank H. (Technical Monitor)

2002-01-01

The quaternion has the lowest dimensionality possible for a globally nonsingular attitude representation. The quaternion must obey a unit norm constraint, though, which has led to the development of an extended Kalman filter using a quaternion for the global attitude estimate and a three-component representation for attitude errors. We consider various attitude error representations for this Multiplicative Extended Kalman Filter and its second-order extension.

A new phase correction method in NMR imaging based on autocorrelation and histogram analysis.

PubMed

Ahn, C B; Cho, Z H

1987-01-01

A new statistical approach to phase correction in NMR imaging is proposed. The proposed scheme consists of first-and zero-order phase corrections each by the inverse multiplication of estimated phase error. The first-order error is estimated by the phase of autocorrelation calculated from the complex valued phase distorted image while the zero-order correction factor is extracted from the histogram of phase distribution of the first-order corrected image. Since all the correction procedures are performed on the spatial domain after completion of data acquisition, no prior adjustments or additional measurements are required. The algorithm can be applicable to most of the phase-involved NMR imaging techniques including inversion recovery imaging, quadrature modulated imaging, spectroscopic imaging, and flow imaging, etc. Some experimental results with inversion recovery imaging as well as quadrature spectroscopic imaging are shown to demonstrate the usefulness of the algorithm.

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.

Tree STEM and Canopy Biomass Estimates from Terrestrial Laser Scanning Data

NASA Astrophysics Data System (ADS)

Olofsson, K.; Holmgren, J.

2017-10-01

In this study an automatic method for estimating both the tree stem and the tree canopy biomass is presented. The point cloud tree extraction techniques operate on TLS data and models the biomass using the estimated stem and canopy volume as independent variables. The regression model fit error is of the order of less than 5 kg, which gives a relative model error of about 5 % for the stem estimate and 10-15 % for the spruce and pine canopy biomass estimates. The canopy biomass estimate was improved by separating the models by tree species which indicates that the method is allometry dependent and that the regression models need to be recomputed for different areas with different climate and different vegetation.

The Hurst Phenomenon in Error Estimates Related to Atmospheric Turbulence

NASA Astrophysics Data System (ADS)

Dias, Nelson Luís; Crivellaro, Bianca Luhm; Chamecki, Marcelo

2018-05-01

The Hurst phenomenon is a well-known feature of long-range persistence first observed in hydrological and geophysical time series by E. Hurst in the 1950s. It has also been found in several cases in turbulence time series measured in the wind tunnel, the atmosphere, and in rivers. Here, we conduct a systematic investigation of the value of the Hurst coefficient H in atmospheric surface-layer data, and its impact on the estimation of random errors. We show that usually H > 0.5 , which implies the non-existence (in the statistical sense) of the integral time scale. Since the integral time scale is present in the Lumley-Panofsky equation for the estimation of random errors, this has important practical consequences. We estimated H in two principal ways: (1) with an extension of the recently proposed filtering method to estimate the random error (H_p ), and (2) with the classical rescaled range introduced by Hurst (H_R ). Other estimators were tried but were found less able to capture the statistical behaviour of the large scales of turbulence. Using data from three micrometeorological campaigns we found that both first- and second-order turbulence statistics display the Hurst phenomenon. Usually, H_R is larger than H_p for the same dataset, raising the question that one, or even both, of these estimators, may be biased. For the relative error, we found that the errors estimated with the approach adopted by us, that we call the relaxed filtering method, and that takes into account the occurrence of the Hurst phenomenon, are larger than both the filtering method and the classical Lumley-Panofsky estimates. Finally, we found that there is no apparent relationship between H and the Obukhov stability parameter. The relative errors, however, do show stability dependence, particularly in the case of the error of the kinematic momentum flux in unstable conditions, and that of the kinematic sensible heat flux in stable conditions.

Influence of eye micromotions on spatially resolved refractometry

NASA Astrophysics Data System (ADS)

Chyzh, Igor H.; Sokurenko, Vyacheslav M.; Osipova, Irina Y.

2001-01-01

The influence eye micromotions on the accuracy of estimation of Zernike coefficients form eye transverse aberration measurements was investigated. By computer modeling, the following found eye aberrations have been examined: defocusing, primary astigmatism, spherical aberration of the 3rd and the 5th orders, as well as their combinations. It was determined that the standard deviation of estimated Zernike coefficients is proportional to the standard deviation of angular eye movements. Eye micromotions cause the estimation errors of Zernike coefficients of present aberrations and produce the appearance of Zernike coefficients of aberrations, absent in the eye. When solely defocusing is present, the biggest errors, cased by eye micromotions, are obtained for aberrations like coma and astigmatism. In comparison with other aberrations, spherical aberration of the 3rd and the 5th orders evokes the greatest increase of the standard deviation of other Zernike coefficients.

Estimation of the daily global solar radiation based on the Gaussian process regression methodology in the Saharan climate

NASA Astrophysics Data System (ADS)

Guermoui, Mawloud; Gairaa, Kacem; Rabehi, Abdelaziz; Djafer, Djelloul; Benkaciali, Said

2018-06-01

Accurate estimation of solar radiation is the major concern in renewable energy applications. Over the past few years, a lot of machine learning paradigms have been proposed in order to improve the estimation performances, mostly based on artificial neural networks, fuzzy logic, support vector machine and adaptive neuro-fuzzy inference system. The aim of this work is the prediction of the daily global solar radiation, received on a horizontal surface through the Gaussian process regression (GPR) methodology. A case study of Ghardaïa region (Algeria) has been used in order to validate the above methodology. In fact, several combinations have been tested; it was found that, GPR-model based on sunshine duration, minimum air temperature and relative humidity gives the best results in term of mean absolute bias error (MBE), root mean square error (RMSE), relative mean square error (rRMSE), and correlation coefficient ( r) . The obtained values of these indicators are 0.67 MJ/m2, 1.15 MJ/m2, 5.2%, and 98.42%, respectively.

Spin Contamination Error in Optimized Geometry of Singlet Carbene (1A1) by Broken-Symmetry Method

NASA Astrophysics Data System (ADS)

Kitagawa, Yasutaka; Saito, Toru; Nakanishi, Yasuyuki; Kataoka, Yusuke; Matsui, Toru; Kawakami, Takashi; Okumura, Mitsutaka; Yamaguchi, Kizashi

2009-10-01

Spin contamination errors of a broken-symmetry (BS) method in optimized structural parameters of the singlet methylene (1A1) molecule are quantitatively estimated for the Hartree-Fock (HF) method, post-HF methods (CID, CCD, MP2, MP3, MP4(SDQ)), and a hybrid DFT (B3LYP) method. For the purpose, the optimized geometry by the BS method is compared with that of an approximate spin projection (AP) method. The difference between the BS and the AP methods is about 10-20° in the HCH angle. In order to examine the basis set dependency of the spin contamination error, calculated results by STO-3G, 6-31G*, and 6-311++G** are compared. The error depends on the basis sets, but the tendencies of each method are classified into two types. Calculated energy splitting values between the triplet and the singlet states (ST gap) indicate that the contamination of the stable triplet state makes the BS singlet solution stable and the ST gap becomes small. The energy order of the spin contamination error in the ST gap is estimated to be 10-1 eV.

LANDSAT-4/5 image data quality analysis

NASA Technical Reports Server (NTRS)

Malaret, E.; Bartolucci, L. A.; Lozano, D. F.; Anuta, P. E.; Mcgillem, C. D.

1984-01-01

A LANDSAT Thematic Mapper (TM) quality evaluation study was conducted to identify geometric and radiometric sensor errors in the post-launch environment. The study began with the launch of LANDSAT-4. Several error conditions were found, including band-to-band misregistration and detector-to detector radiometric calibration errors. Similar analysis was made for the LANDSAT-5 Thematic Mapper and compared with results for LANDSAT-4. Remaining band-to-band misregistration was found to be within tolerances and detector-to-detector calibration errors were not severe. More coherent noise signals were observed in TM-5 than in TM-4, although the amplitude was generally less. The scan direction differences observed in TM-4 were still evident in TM-5. The largest effect was in Band 4 where nearly a one digital count difference was observed. Resolution estimation was carried out using roads in TM-5 for the primary focal plane bands rather than field edges as in TM-4. Estimates using roads gave better resolution. Thermal IR band calibration studies were conducted and new nonlinear calibration procedures were defined for TM-5. The overall conclusion is that there are no first order errors in TM-5 and any remaining problems are second or third order.

Calibrating First-Order Strong Lensing Mass Estimates in Clusters of Galaxies

NASA Astrophysics Data System (ADS)

Reed, Brendan; Remolian, Juan; Sharon, Keren; Li, Nan; SPT Clusters Cooperation

2018-01-01

We investigate methods to reduce the statistical and systematic errors inherent to using the Einstein Radius as a first-order mass estimate in strong lensing galaxy clusters. By finding an empirical universal calibration function, we aim to enable a first-order mass estimate of large cluster data sets in a fraction of the time and effort of full-scale strong lensing mass modeling. We use 74 simulated cluster data from the Argonne National Laboratory in a lens redshift slice of [0.159, 0.667] with various source redshifts in the range of [1.23, 2.69]. From the simulated density maps, we calculate the exact mass enclosed within the Einstein Radius. We find that the mass inferred from the Einstein Radius alone produces an error width of ~39% with respect to the true mass. We explore an array of polynomial and exponential correction functions with dependence on cluster redshift and projected radii of the lensed images, aiming to reduce the statistical and systematic uncertainty. We find that the error on the the mass inferred from the Einstein Radius can be reduced significantly by using a universal correction function. Our study has implications for current and future large galaxy cluster surveys aiming to measure cluster mass, and the mass-concentration relation.

SEAHT: A computer program for the use of intersecting arcs of altimeter data for sea surface height refinement

NASA Technical Reports Server (NTRS)

Allen, C. P.; Martin, C. F.

1977-01-01

The SEAHT program is designed to process multiple passes of altimeter data with intersecting ground tracks, with the estimation of corrections for orbital errors to each pass such that the data has the best overall agreement at the crossover points. Orbit error for each pass is modeled as a polynomial in time, with optional orders of 0, 1, or 2. One or more passes may be constrained in the adjustment process, thus allowing passes with the best orbits to provide the overall level and orientation of the estimated sea surface heights. Intersections which disagree by more than an input edit level are not used in the error parameter estimation. In the program implementation, passes are grouped into South-North passes and North-South passes, with the North-South passes partitioned out for the estimation of orbit error parameters. Computer core utilization is thus dependent on the number of parameters estimated for the set of South-North arcs, but is independent on the number of North-South passes. Estimated corrections for each pass are applied to the data at its input data rate and an output tape is written which contains the corrected data.

True covariance simulation of the EUVE update filter

NASA Technical Reports Server (NTRS)

Bar-Itzhack, Itzhack Y.; Harman, R. R.

1989-01-01

A covariance analysis of the performance and sensitivity of the attitude determination Extended Kalman Filter (EKF) used by the On Board Computer (OBC) of the Extreme Ultra Violet Explorer (EUVE) spacecraft is presented. The linearized dynamics and measurement equations of the error states are derived which constitute the truth model describing the real behavior of the systems involved. The design model used by the OBC EKF is then obtained by reducing the order of the truth model. The covariance matrix of the EKF which uses the reduced order model is not the correct covariance of the EKF estimation error. A true covariance analysis has to be carried out in order to evaluate the correct accuracy of the OBC generated estimates. The results of such analysis are presented which indicate both the performance and the sensitivity of the OBC EKF.

Robust Tracking of Small Displacements with a Bayesian Estimator

PubMed Central

Dumont, Douglas M.; Byram, Brett C.

2016-01-01

Radiation-force-based elasticity imaging describes a group of techniques that use acoustic radiation force (ARF) to displace tissue in order to obtain qualitative or quantitative measurements of tissue properties. Because ARF-induced displacements are on the order of micrometers, tracking these displacements in vivo can be challenging. Previously, it has been shown that Bayesian-based estimation can overcome some of the limitations of a traditional displacement estimator like normalized cross-correlation (NCC). In this work, we describe a Bayesian framework that combines a generalized Gaussian-Markov random field (GGMRF) prior with an automated method for selecting the prior’s width. We then evaluate its performance in the context of tracking the micrometer-order displacements encountered in an ARF-based method like acoustic radiation force impulse (ARFI) imaging. The results show that bias, variance, and mean-square error performance vary with prior shape and width, and that an almost one order-of-magnitude reduction in mean-square error can be achieved by the estimator at the automatically-selected prior width. Lesion simulations show that the proposed estimator has a higher contrast-to-noise ratio but lower contrast than NCC, median-filtered NCC, and the previous Bayesian estimator, with a non-Gaussian prior shape having better lesion-edge resolution than a Gaussian prior. In vivo results from a cardiac, radiofrequency ablation ARFI imaging dataset show quantitative improvements in lesion contrast-to-noise ratio over NCC as well as the previous Bayesian estimator. PMID:26529761

An Empirical Study of Re-sampling Techniques as a Method for Improving Error Estimates in Split-plot Designs

DTIC Science & Technology

2010-03-01

sufficient replications often lead to models that lack precision in error estimation and thus imprecision in corresponding conclusions. This work develops...v Preface This work is dedicated to all who gave and continue to give in order for me to achieve some semblance of success. Benjamin M. Lee vi...develop, examine and test methodologies for an- alyzing test results from split-plot designs. In particular, this work determines the applicability

A discontinuous Poisson-Boltzmann equation with interfacial jump: homogenisation and residual error estimate.

PubMed

Fellner, Klemens; Kovtunenko, Victor A

2016-01-01

A nonlinear Poisson-Boltzmann equation with inhomogeneous Robin type boundary conditions at the interface between two materials is investigated. The model describes the electrostatic potential generated by a vector of ion concentrations in a periodic multiphase medium with dilute solid particles. The key issue stems from interfacial jumps, which necessitate discontinuous solutions to the problem. Based on variational techniques, we derive the homogenisation of the discontinuous problem and establish a rigorous residual error estimate up to the first-order correction.

Experimental Verification of Sparse Aperture Mask for Low Order Wavefront Sensing

NASA Astrophysics Data System (ADS)

Subedi, Hari; Kasdin, N. Jeremy

2017-01-01

To directly image exoplanets, future space-based missions are equipped with coronagraphs which manipulate the diffraction of starlight and create regions of high contrast called dark holes. Theoretically, coronagraphs can be designed to achieve the high level of contrast required to image exoplanets, which are billions of times dimmer than their host stars, however the aberrations caused by optical imperfections and thermal fluctuations cause the degradation of contrast in the dark holes. Focal plane wavefront control (FPWC) algorithms using deformable mirrors (DMs) are used to mitigate the quasi-static aberrations caused by optical imperfections. Although the FPWC methods correct the quasi-static aberrations, they are blind to dynamic errors caused by telescope jitter and thermal fluctuations. At Princeton's High Contrast Imaging Lab we have developed a new technique that integrates a sparse aperture mask with the coronagraph to estimate these low-order dynamic wavefront errors. This poster shows the effectiveness of a SAM Low-Order Wavefront Sensor in estimating and correcting these errors via simulation and experiment and compares the results to other methods, such as the Zernike Wavefront Sensor planned for WFIRST.

Evaluation of the predicted error of the soil moisture retrieval from C-band SAR by comparison against modelled soil moisture estimates over Australia

PubMed Central

Doubková, Marcela; Van Dijk, Albert I.J.M.; Sabel, Daniel; Wagner, Wolfgang; Blöschl, Günter

2012-01-01

The Sentinel-1 will carry onboard a C-band radar instrument that will map the European continent once every four days and the global land surface at least once every twelve days with finest 5 × 20 m spatial resolution. The high temporal sampling rate and operational configuration make Sentinel-1 of interest for operational soil moisture monitoring. Currently, updated soil moisture data are made available at 1 km spatial resolution as a demonstration service using Global Mode (GM) measurements from the Advanced Synthetic Aperture Radar (ASAR) onboard ENVISAT. The service demonstrates the potential of the C-band observations to monitor variations in soil moisture. Importantly, a retrieval error estimate is also available; these are needed to assimilate observations into models. The retrieval error is estimated by propagating sensor errors through the retrieval model. In this work, the existing ASAR GM retrieval error product is evaluated using independent top soil moisture estimates produced by the grid-based landscape hydrological model (AWRA-L) developed within the Australian Water Resources Assessment system (AWRA). The ASAR GM retrieval error estimate, an assumed prior AWRA-L error estimate and the variance in the respective datasets were used to spatially predict the root mean square error (RMSE) and the Pearson's correlation coefficient R between the two datasets. These were compared with the RMSE calculated directly from the two datasets. The predicted and computed RMSE showed a very high level of agreement in spatial patterns as well as good quantitative agreement; the RMSE was predicted within accuracy of 4% of saturated soil moisture over 89% of the Australian land mass. Predicted and calculated R maps corresponded within accuracy of 10% over 61% of the continent. The strong correspondence between the predicted and calculated RMSE and R builds confidence in the retrieval error model and derived ASAR GM error estimates. The ASAR GM and Sentinel-1 have the same basic physical measurement characteristics, and therefore very similar retrieval error estimation method can be applied. Because of the expected improvements in radiometric resolution of the Sentinel-1 backscatter measurements, soil moisture estimation errors can be expected to be an order of magnitude less than those for ASAR GM. This opens the possibility for operationally available medium resolution soil moisture estimates with very well-specified errors that can be assimilated into hydrological or crop yield models, with potentially large benefits for land-atmosphere fluxes, crop growth, and water balance monitoring and modelling. PMID:23483015

Robust estimation of thermodynamic parameters (ΔH, ΔS and ΔCp) for prediction of retention time in gas chromatography - Part II (Application).

PubMed

Claumann, Carlos Alberto; Wüst Zibetti, André; Bolzan, Ariovaldo; Machado, Ricardo A F; Pinto, Leonel Teixeira

2015-12-18

For this work, an analysis of parameter estimation for the retention factor in GC model was performed, considering two different criteria: sum of square error, and maximum error in absolute value; relevant statistics are described for each case. The main contribution of this work is the implementation of an initialization scheme (specialized) for the estimated parameters, which features fast convergence (low computational time) and is based on knowledge of the surface of the error criterion. In an application to a series of alkanes, specialized initialization resulted in significant reduction to the number of evaluations of the objective function (reducing computational time) in the parameter estimation. The obtained reduction happened between one and two orders of magnitude, compared with the simple random initialization. Copyright © 2015 Elsevier B.V. All rights reserved.

Neural network uncertainty assessment using Bayesian statistics: a remote sensing application

NASA Technical Reports Server (NTRS)

Aires, F.; Prigent, C.; Rossow, W. B.

2004-01-01

Neural network (NN) techniques have proved successful for many regression problems, in particular for remote sensing; however, uncertainty estimates are rarely provided. In this article, a Bayesian technique to evaluate uncertainties of the NN parameters (i.e., synaptic weights) is first presented. In contrast to more traditional approaches based on point estimation of the NN weights, we assess uncertainties on such estimates to monitor the robustness of the NN model. These theoretical developments are illustrated by applying them to the problem of retrieving surface skin temperature, microwave surface emissivities, and integrated water vapor content from a combined analysis of satellite microwave and infrared observations over land. The weight uncertainty estimates are then used to compute analytically the uncertainties in the network outputs (i.e., error bars and correlation structure of these errors). Such quantities are very important for evaluating any application of an NN model. The uncertainties on the NN Jacobians are then considered in the third part of this article. Used for regression fitting, NN models can be used effectively to represent highly nonlinear, multivariate functions. In this situation, most emphasis is put on estimating the output errors, but almost no attention has been given to errors associated with the internal structure of the regression model. The complex structure of dependency inside the NN is the essence of the model, and assessing its quality, coherency, and physical character makes all the difference between a blackbox model with small output errors and a reliable, robust, and physically coherent model. Such dependency structures are described to the first order by the NN Jacobians: they indicate the sensitivity of one output with respect to the inputs of the model for given input data. We use a Monte Carlo integration procedure to estimate the robustness of the NN Jacobians. A regularization strategy based on principal component analysis is proposed to suppress the multicollinearities in order to make these Jacobians robust and physically meaningful.

Efficient estimation of Pareto model: Some modified percentile estimators.

PubMed

Bhatti, Sajjad Haider; Hussain, Shahzad; Ahmad, Tanvir; Aslam, Muhammad; Aftab, Muhammad; Raza, Muhammad Ali

2018-01-01

The article proposes three modified percentile estimators for parameter estimation of the Pareto distribution. These modifications are based on median, geometric mean and expectation of empirical cumulative distribution function of first-order statistic. The proposed modified estimators are compared with traditional percentile estimators through a Monte Carlo simulation for different parameter combinations with varying sample sizes. Performance of different estimators is assessed in terms of total mean square error and total relative deviation. It is determined that modified percentile estimator based on expectation of empirical cumulative distribution function of first-order statistic provides efficient and precise parameter estimates compared to other estimators considered. The simulation results were further confirmed using two real life examples where maximum likelihood and moment estimators were also considered.

Gradient nonlinearity calibration and correction for a compact, asymmetric magnetic resonance imaging gradient system.

PubMed

Tao, S; Trzasko, J D; Gunter, J L; Weavers, P T; Shu, Y; Huston, J; Lee, S K; Tan, E T; Bernstein, M A

2017-01-21

Due to engineering limitations, the spatial encoding gradient fields in conventional magnetic resonance imaging cannot be perfectly linear and always contain higher-order, nonlinear components. If ignored during image reconstruction, gradient nonlinearity (GNL) manifests as image geometric distortion. Given an estimate of the GNL field, this distortion can be corrected to a degree proportional to the accuracy of the field estimate. The GNL of a gradient system is typically characterized using a spherical harmonic polynomial model with model coefficients obtained from electromagnetic simulation. Conventional whole-body gradient systems are symmetric in design; typically, only odd-order terms up to the 5th-order are required for GNL modeling. Recently, a high-performance, asymmetric gradient system was developed, which exhibits more complex GNL that requires higher-order terms including both odd- and even-orders for accurate modeling. This work characterizes the GNL of this system using an iterative calibration method and a fiducial phantom used in ADNI (Alzheimer's Disease Neuroimaging Initiative). The phantom was scanned at different locations inside the 26 cm diameter-spherical-volume of this gradient, and the positions of fiducials in the phantom were estimated. An iterative calibration procedure was utilized to identify the model coefficients that minimize the mean-squared-error between the true fiducial positions and the positions estimated from images corrected using these coefficients. To examine the effect of higher-order and even-order terms, this calibration was performed using spherical harmonic polynomial of different orders up to the 10th-order including even- and odd-order terms, or odd-order only. The results showed that the model coefficients of this gradient can be successfully estimated. The residual root-mean-squared-error after correction using up to the 10th-order coefficients was reduced to 0.36 mm, yielding spatial accuracy comparable to conventional whole-body gradients. The even-order terms were necessary for accurate GNL modeling. In addition, the calibrated coefficients improved image geometric accuracy compared with the simulation-based coefficients.

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 entire error is placed in the am data while in the other it is placed in the pm data. Global temperature trend is increased or decreased by about 0.03 K/decade depending upon this placement. Taking into account all random errors and systematic errors our analysis of MSU observations leads us to conclude that a conservative estimate of the global warming is 0. 11 (+-) 0.04 K/decade during 1980 to 1998.

An Astronomical Test of CCD Photometric Precision

NASA Technical Reports Server (NTRS)

Koch, David; Dunham, Edward; Borucki, William; Jenkins, Jon; DeVingenzi, D. (Technical Monitor)

1998-01-01

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.

Development of a Precise Polarization Modulator for UV Spectropolarimetry

NASA Astrophysics Data System (ADS)

Ishikawa, S.; Shimizu, T.; Kano, R.; Bando, T.; Ishikawa, R.; Giono, G.; Tsuneta, S.; Nakayama, S.; Tajima, T.

2015-10-01

We developed a polarization modulation unit (PMU) to rotate a waveplate continuously in order to observe solar magnetic fields by spectropolarimetry. The non-uniformity of the PMU rotation may cause errors in the measurement of the degree of linear polarization (scale error) and its angle (crosstalk between Stokes-Q and -U), although it does not cause an artificial linear polarization signal (spurious polarization). We rotated a waveplate with the PMU to obtain a polarization modulation curve and estimated the scale error and crosstalk caused by the rotation non-uniformity. The estimated scale error and crosstalk were {<} 0.01 % for both. This PMU will be used as a waveplate motor for the Chromospheric Lyman-Alpha SpectroPolarimeter (CLASP) rocket experiment. We confirm that the PMU performs and functions sufficiently well for CLASP.

An algorithm for management of deep brain stimulation battery replacements: devising a web-based battery estimator and clinical symptom approach.

PubMed

Montuno, Michael A; Kohner, Andrew B; Foote, Kelly D; Okun, Michael S

2013-01-01

Deep brain stimulation (DBS) is an effective technique that has been utilized to treat advanced and medication-refractory movement and psychiatric disorders. In order to avoid implanted pulse generator (IPG) failure and consequent adverse symptoms, a better understanding of IPG battery longevity and management is necessary. Existing methods for battery estimation lack the specificity required for clinical incorporation. Technical challenges prevent higher accuracy longevity estimations, and a better approach to managing end of DBS battery life is needed. The literature was reviewed and DBS battery estimators were constructed by the authors and made available on the web at http://mdc.mbi.ufl.edu/surgery/dbs-battery-estimator. A clinical algorithm for management of DBS battery life was constructed. The algorithm takes into account battery estimations and clinical symptoms. Existing methods of DBS battery life estimation utilize an interpolation of averaged current drains to calculate how long a battery will last. Unfortunately, this technique can only provide general approximations. There are inherent errors in this technique, and these errors compound with each iteration of the battery estimation. Some of these errors cannot be accounted for in the estimation process, and some of the errors stem from device variation, battery voltage dependence, battery usage, battery chemistry, impedance fluctuations, interpolation error, usage patterns, and self-discharge. We present web-based battery estimators along with an algorithm for clinical management. We discuss the perils of using a battery estimator without taking into account the clinical picture. Future work will be needed to provide more reliable management of implanted device batteries; however, implementation of a clinical algorithm that accounts for both estimated battery life and for patient symptoms should improve the care of DBS patients. © 2012 International Neuromodulation Society.

Hartman Testing of X-Ray Telescopes

NASA Technical Reports Server (NTRS)

Saha, Timo T.; Biskasch, Michael; Zhang, William W.

2013-01-01

Hartmann testing of x-ray telescopes is a simple test method to retrieve and analyze alignment errors and low-order circumferential errors of x-ray telescopes and their components. A narrow slit is scanned along the circumference of the telescope in front of the mirror and the centroids of the images are calculated. From the centroid data, alignment errors, radius variation errors, and cone-angle variation errors can be calculated. Mean cone angle, mean radial height (average radius), and the focal length of the telescope can also be estimated if the centroid data is measured at multiple focal plane locations. In this paper we present the basic equations that are used in the analysis process. These equations can be applied to full circumference or segmented x-ray telescopes. We use the Optical Surface Analysis Code (OSAC) to model a segmented x-ray telescope and show that the derived equations and accompanying analysis retrieves the alignment errors and low order circumferential errors accurately.

Impact of electronic chemotherapy order forms on prescribing errors at an urban medical center: results from an interrupted time-series analysis.

PubMed

Elsaid, K; Truong, T; Monckeberg, M; McCarthy, H; Butera, J; Collins, C

2013-12-01

To evaluate the impact of electronic standardized chemotherapy templates on incidence and types of prescribing errors. A quasi-experimental interrupted time series with segmented regression. A 700-bed multidisciplinary tertiary care hospital with an ambulatory cancer center. A multidisciplinary team including oncology physicians, nurses, pharmacists and information technologists. Standardized, regimen-specific, chemotherapy prescribing forms were developed and implemented over a 32-month period. Trend of monthly prevented prescribing errors per 1000 chemotherapy doses during the pre-implementation phase (30 months), immediate change in the error rate from pre-implementation to implementation and trend of errors during the implementation phase. Errors were analyzed according to their types: errors in communication or transcription, errors in dosing calculation and errors in regimen frequency or treatment duration. Relative risk (RR) of errors in the post-implementation phase (28 months) compared with the pre-implementation phase was computed with 95% confidence interval (CI). Baseline monthly error rate was stable with 16.7 prevented errors per 1000 chemotherapy doses. A 30% reduction in prescribing errors was observed with initiating the intervention. With implementation, a negative change in the slope of prescribing errors was observed (coefficient = -0.338; 95% CI: -0.612 to -0.064). The estimated RR of transcription errors was 0.74; 95% CI (0.59-0.92). The estimated RR of dosing calculation errors was 0.06; 95% CI (0.03-0.10). The estimated RR of chemotherapy frequency/duration errors was 0.51; 95% CI (0.42-0.62). Implementing standardized chemotherapy-prescribing templates significantly reduced all types of prescribing errors and improved chemotherapy safety.

Probablilistic evaluation of earthquake detection and location capability for Illinois, Indiana, Kentucky, Ohio, and West Virginia

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mauk, F.J.; Christensen, D.H.

1980-09-01

Probabilistic estimations of earthquake detection and location capabilities for the states of Illinois, Indiana, Kentucky, Ohio and West Virginia are presented in this document. The algorithm used in these epicentrality and minimum-magnitude estimations is a version of the program NETWORTH by Wirth, Blandford, and Husted (DARPA Order No. 2551, 1978) which was modified for local array evaluation at the University of Michigan Seismological Observatory. Estimations of earthquake detection capability for the years 1970 and 1980 are presented in four regional minimum m/sub b/ magnitude contour maps. Regional 90% confidence error ellipsoids are included for m/sub b/ magnitude events from 2.0more » through 5.0 at 0.5 m/sub b/ unit increments. The close agreement between these predicted epicentral 90% confidence estimates and the calculated error ellipses associated with actual earthquakes within the studied region suggest that these error determinations can be used to estimate the reliability of epicenter location. 8 refs., 14 figs., 2 tabs.« less

Testing boundary conditions for the conjunction fallacy: effects of response mode, conceptual focus, and problem type.

PubMed

Wedell, Douglas H; Moro, Rodrigo

2008-04-01

Two experiments used within-subject designs to examine how conjunction errors depend on the use of (1) choice versus estimation tasks, (2) probability versus frequency language, and (3) conjunctions of two likely events versus conjunctions of likely and unlikely events. All problems included a three-option format verified to minimize misinterpretation of the base event. In both experiments, conjunction errors were reduced when likely events were conjoined. Conjunction errors were also reduced for estimations compared with choices, with this reduction greater for likely conjuncts, an interaction effect. Shifting conceptual focus from probabilities to frequencies did not affect conjunction error rates. Analyses of numerical estimates for a subset of the problems provided support for the use of three general models by participants for generating estimates. Strikingly, the order in which the two tasks were carried out did not affect the pattern of results, supporting the idea that the mode of responding strongly determines the mode of thinking about conjunctions and hence the occurrence of the conjunction fallacy. These findings were evaluated in terms of implications for rationality of human judgment and reasoning.

Low-dimensional Representation of Error Covariance

NASA Technical Reports Server (NTRS)

Tippett, Michael K.; Cohn, Stephen E.; Todling, Ricardo; Marchesin, Dan

2000-01-01

Ensemble and reduced-rank approaches to prediction and assimilation rely on low-dimensional approximations of the estimation error covariances. Here stability properties of the forecast/analysis cycle for linear, time-independent systems are used to identify factors that cause the steady-state analysis error covariance to admit a low-dimensional representation. A useful measure of forecast/analysis cycle stability is the bound matrix, a function of the dynamics, observation operator and assimilation method. Upper and lower estimates for the steady-state analysis error covariance matrix eigenvalues are derived from the bound matrix. The estimates generalize to time-dependent systems. If much of the steady-state analysis error variance is due to a few dominant modes, the leading eigenvectors of the bound matrix approximate those of the steady-state analysis error covariance matrix. The analytical results are illustrated in two numerical examples where the Kalman filter is carried to steady state. The first example uses the dynamics of a generalized advection equation exhibiting nonmodal transient growth. Failure to observe growing modes leads to increased steady-state analysis error variances. Leading eigenvectors of the steady-state analysis error covariance matrix are well approximated by leading eigenvectors of the bound matrix. The second example uses the dynamics of a damped baroclinic wave model. The leading eigenvectors of a lowest-order approximation of the bound matrix are shown to approximate well the leading eigenvectors of the steady-state analysis error covariance matrix.

Polarizabilities and hyperpolarizabilities for the atoms Al, Si, P, S, Cl, and Ar: Coupled cluster calculations.

PubMed

Lupinetti, Concetta; Thakkar, Ajit J

2005-01-22

Accurate static dipole polarizabilities and hyperpolarizabilities are calculated for the ground states of the Al, Si, P, S, Cl, and Ar atoms. The finite-field computations use energies obtained with various ab initio methods including Moller-Plesset perturbation theory and the coupled cluster approach. Excellent agreement with experiment is found for argon. The experimental alpha for Al is likely to be in error. Only limited comparisons are possible for the other atoms because hyperpolarizabilities have not been reported previously for most of these atoms. Our recommended values of the mean dipole polarizability (in the order Al-Ar) are alpha/e(2)a(0) (2)E(h) (-1)=57.74, 37.17, 24.93, 19.37, 14.57, and 11.085 with an error estimate of +/-0.5%. The recommended values of the mean second dipole hyperpolarizability (in the order Al-Ar) are gamma/e(4)a(0) (4)E(h) (-3)=2.02 x 10(5), 4.31 x 10(4), 1.14 x 10(4), 6.51 x 10(3), 2.73 x 10(3), and 1.18 x 10(3) with an error estimate of +/-2%. Our recommended polarizability anisotropy values are Deltaalpha/e(2)a(0) (2)E(h) (-1)=-25.60, 8.41, -3.63, and 1.71 for Al, Si, S, and Cl respectively, with an error estimate of +/-1%. The recommended hyperpolarizability anisotropies are Deltagamma/e(4)a(0) (4)E(h) (-3)=-3.88 x 10(5), 4.16 x 10(4), -7.00 x 10(3), and 1.65 x 10(3) for Al, Si, S, and Cl, respectively, with an error estimate of +/-4%. (c) 2005 American Institute of Physics.

A technique for evaluating the influence of spatial sampling on the determination of global mean total columnar ozone

NASA Technical Reports Server (NTRS)

Tolson, R. H.

1981-01-01

A technique is described for providing a means of evaluating the influence of spatial sampling on the determination of global mean total columnar ozone. A finite number of coefficients in the expansion are determined, and the truncated part of the expansion is shown to contribute an error to the estimate, which depends strongly on the spatial sampling and is relatively insensitive to data noise. First and second order statistics are derived for each term in a spherical harmonic expansion which represents the ozone field, and the statistics are used to estimate systematic and random errors in the estimates of total ozone.

A simulation for gravity fine structure recovery from low-low GRAVSAT SST data

NASA Technical Reports Server (NTRS)

Estes, R. H.; Lancaster, E. R.

1976-01-01

Covariance error analysis techniques were applied to investigate estimation strategies for the low-low SST mission for accurate local recovery of gravitational fine structure, considering the aliasing effects of unsolved for parameters. A 5 degree by 5 degree surface density block representation of the high order geopotential was utilized with the drag-free low-low GRAVSAT configuration in a circular polar orbit at 250 km altitude. Recovery of local sets of density blocks from long data arcs was found not to be feasible due to strong aliasing effects. The error analysis for the recovery of local sets of density blocks using independent short data arcs demonstrated that the estimation strategy of simultaneously estimating a local set of blocks covered by data and two "buffer layers" of blocks not covered by data greatly reduced aliasing errors.

Estimation of genetic parameters for milk yield in Murrah buffaloes by Bayesian inference.

PubMed

Breda, F C; Albuquerque, L G; Euclydes, R F; Bignardi, A B; Baldi, F; Torres, R A; Barbosa, L; Tonhati, H

2010-02-01

Random regression models were used to estimate genetic parameters for test-day milk yield in Murrah buffaloes using Bayesian inference. Data comprised 17,935 test-day milk records from 1,433 buffaloes. Twelve models were tested using different combinations of third-, fourth-, fifth-, sixth-, and seventh-order orthogonal polynomials of weeks of lactation for additive genetic and permanent environmental effects. All models included the fixed effects of contemporary group, number of daily milkings and age of cow at calving as covariate (linear and quadratic effect). In addition, residual variances were considered to be heterogeneous with 6 classes of variance. Models were selected based on the residual mean square error, weighted average of residual variance estimates, and estimates of variance components, heritabilities, correlations, eigenvalues, and eigenfunctions. Results indicated that changes in the order of fit for additive genetic and permanent environmental random effects influenced the estimation of genetic parameters. Heritability estimates ranged from 0.19 to 0.31. Genetic correlation estimates were close to unity between adjacent test-day records, but decreased gradually as the interval between test-days increased. Results from mean squared error and weighted averages of residual variance estimates suggested that a model considering sixth- and seventh-order Legendre polynomials for additive and permanent environmental effects, respectively, and 6 classes for residual variances, provided the best fit. Nevertheless, this model presented the largest degree of complexity. A more parsimonious model, with fourth- and sixth-order polynomials, respectively, for these same effects, yielded very similar genetic parameter estimates. Therefore, this last model is recommended for routine applications. Copyright 2010 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

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.

Cross Time-Frequency Analysis for Combining Information of Several Sources: Application to Estimation of Spontaneous Respiratory Rate from Photoplethysmography

PubMed Central

Peláez-Coca, M. D.; Orini, M.; Lázaro, J.; Bailón, R.; Gil, E.

2013-01-01

A methodology that combines information from several nonstationary biological signals is presented. This methodology is based on time-frequency coherence, that quantifies the similarity of two signals in the time-frequency domain. A cross time-frequency analysis method, based on quadratic time-frequency distribution, has been used for combining information of several nonstationary biomedical signals. In order to evaluate this methodology, the respiratory rate from the photoplethysmographic (PPG) signal is estimated. The respiration provokes simultaneous changes in the pulse interval, amplitude, and width of the PPG signal. This suggests that the combination of information from these sources will improve the accuracy of the estimation of the respiratory rate. Another target of this paper is to implement an algorithm which provides a robust estimation. Therefore, respiratory rate was estimated only in those intervals where the features extracted from the PPG signals are linearly coupled. In 38 spontaneous breathing subjects, among which 7 were characterized by a respiratory rate lower than 0.15 Hz, this methodology provided accurate estimates, with the median error {0.00; 0.98} mHz ({0.00; 0.31}%) and the interquartile range error {4.88; 6.59} mHz ({1.60; 1.92}%). The estimation error of the presented methodology was largely lower than the estimation error obtained without combining different PPG features related to respiration. PMID:24363777

Estimation of genetic connectedness diagnostics based on prediction errors without the prediction error variance-covariance matrix.

PubMed

Holmes, John B; Dodds, Ken G; Lee, Michael A

2017-03-02

An important issue in genetic evaluation is the comparability of random effects (breeding values), particularly between pairs of animals in different contemporary groups. This is usually referred to as genetic connectedness. While various measures of connectedness have been proposed in the literature, there is general agreement that the most appropriate measure is some function of the prediction error variance-covariance matrix. However, obtaining the prediction error variance-covariance matrix is computationally demanding for large-scale genetic evaluations. Many alternative statistics have been proposed that avoid the computational cost of obtaining the prediction error variance-covariance matrix, such as counts of genetic links between contemporary groups, gene flow matrices, and functions of the variance-covariance matrix of estimated contemporary group fixed effects. In this paper, we show that a correction to the variance-covariance matrix of estimated contemporary group fixed effects will produce the exact prediction error variance-covariance matrix averaged by contemporary group for univariate models in the presence of single or multiple fixed effects and one random effect. We demonstrate the correction for a series of models and show that approximations to the prediction error matrix based solely on the variance-covariance matrix of estimated contemporary group fixed effects are inappropriate in certain circumstances. Our method allows for the calculation of a connectedness measure based on the prediction error variance-covariance matrix by calculating only the variance-covariance matrix of estimated fixed effects. Since the number of fixed effects in genetic evaluation is usually orders of magnitudes smaller than the number of random effect levels, the computational requirements for our method should be reduced.

Limited-memory adaptive snapshot selection for proper orthogonal decomposition

DOE Office of Scientific and Technical Information (OSTI.GOV)

Oxberry, Geoffrey M.; Kostova-Vassilevska, Tanya; Arrighi, Bill

2015-04-02

Reduced order models are useful for accelerating simulations in many-query contexts, such as optimization, uncertainty quantification, and sensitivity analysis. However, offline training of reduced order models can have prohibitively expensive memory and floating-point operation costs in high-performance computing applications, where memory per core is limited. To overcome this limitation for proper orthogonal decomposition, we propose a novel adaptive selection method for snapshots in time that limits offline training costs by selecting snapshots according an error control mechanism similar to that found in adaptive time-stepping ordinary differential equation solvers. The error estimator used in this work is related to theory boundingmore » the approximation error in time of proper orthogonal decomposition-based reduced order models, and memory usage is minimized by computing the singular value decomposition using a single-pass incremental algorithm. Results for a viscous Burgers’ test problem demonstrate convergence in the limit as the algorithm error tolerances go to zero; in this limit, the full order model is recovered to within discretization error. The resulting method can be used on supercomputers to generate proper orthogonal decomposition-based reduced order models, or as a subroutine within hyperreduction algorithms that require taking snapshots in time, or within greedy algorithms for sampling parameter space.« less

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.

Evaluation Of Statistical Models For Forecast Errors From The HBV-Model

NASA Astrophysics Data System (ADS)

Engeland, K.; Kolberg, S.; Renard, B.; Stensland, I.

2009-04-01

Three statistical models for the forecast errors for inflow to the Langvatn reservoir in Northern Norway have been constructed and tested according to how well the distribution and median values of the forecasts errors fit to the observations. For the first model observed and forecasted inflows were transformed by the Box-Cox transformation before a first order autoregressive model was constructed for the forecast errors. The parameters were conditioned on climatic conditions. In the second model the Normal Quantile Transformation (NQT) was applied on observed and forecasted inflows before a similar first order autoregressive model was constructed for the forecast errors. For the last model positive and negative errors were modeled separately. The errors were first NQT-transformed before a model where the mean values were conditioned on climate, forecasted inflow and yesterday's error. To test the three models we applied three criterions: We wanted a) the median values to be close to the observed values; b) the forecast intervals to be narrow; c) the distribution to be correct. The results showed that it is difficult to obtain a correct model for the forecast errors, and that the main challenge is to account for the auto-correlation in the errors. Model 1 and 2 gave similar results, and the main drawback is that the distributions are not correct. The 95% forecast intervals were well identified, but smaller forecast intervals were over-estimated, and larger intervals were under-estimated. Model 3 gave a distribution that fits better, but the median values do not fit well since the auto-correlation is not properly accounted for. If the 95% forecast interval is of interest, Model 2 is recommended. If the whole distribution is of interest, Model 3 is recommended.

Kernel Wiener filter and its application to pattern recognition.

PubMed

Yoshino, Hirokazu; Dong, Chen; Washizawa, Yoshikazu; Yamashita, Yukihiko

2010-11-01

The Wiener filter (WF) is widely used for inverse problems. From an observed signal, it provides the best estimated signal with respect to the squared error averaged over the original and the observed signals among linear operators. The kernel WF (KWF), extended directly from WF, has a problem that an additive noise has to be handled by samples. Since the computational complexity of kernel methods depends on the number of samples, a huge computational cost is necessary for the case. By using the first-order approximation of kernel functions, we realize KWF that can handle such a noise not by samples but as a random variable. We also propose the error estimation method for kernel filters by using the approximations. In order to show the advantages of the proposed methods, we conducted the experiments to denoise images and estimate errors. We also apply KWF to classification since KWF can provide an approximated result of the maximum a posteriori classifier that provides the best recognition accuracy. The noise term in the criterion can be used for the classification in the presence of noise or a new regularization to suppress changes in the input space, whereas the ordinary regularization for the kernel method suppresses changes in the feature space. In order to show the advantages of the proposed methods, we conducted experiments of binary and multiclass classifications and classification in the presence of noise.

Deep-space navigation with differenced data types. Part 3: An expanded information content and sensitivity analysis

NASA Technical Reports Server (NTRS)

Estefan, J. A.; Thurman, S. W.

1992-01-01

An approximate six-parameter analytic model for Earth-based differential range measurements is presented and is used to derive a representative analytic approximation for differenced Doppler measurements. The analytical models are tasked to investigate the ability of these data types to estimate spacecraft geocentric angular motion, Deep Space Network station oscillator (clock/frequency) offsets, and signal-path calibration errors over a period of a few days, in the presence of systematic station location and transmission media calibration errors. Quantitative results indicate that a few differenced Doppler plus ranging passes yield angular position estimates with a precision on the order of 0.1 to 0.4 micro-rad, and angular rate precision on the order of 10 to 25 x 10(exp -12) rad/sec, assuming no a priori information on the coordinate parameters. Sensitivity analyses suggest that troposphere zenith delay calibration error is the dominant systematic error source in most of the tracking scenarios investigated; as expected, the differenced Doppler data were found to be much more sensitive to troposphere calibration errors than differenced range. By comparison, results computed using wideband and narrowband (delta) VLBI under similar circumstances yielded angular precisions of 0.07 to 0.4 micro-rad, and angular rate precisions of 0.5 to 1.0 x 10(exp -12) rad/sec.

Deep-space navigation with differenced data types. Part 3: An expanded information content and sensitivity analysis

NASA Technical Reports Server (NTRS)

Estefan, J. A.; Thurman, S. W.

1992-01-01

An approximate six-parameter analytic model for Earth-based differenced range measurements is presented and is used to derive a representative analytic approximation for differenced Doppler measurements. The analytical models are tasked to investigate the ability of these data types to estimate spacecraft geocentric angular motion, Deep Space Network station oscillator (clock/frequency) offsets, and signal-path calibration errors over a period of a few days, in the presence of systematic station location and transmission media calibration errors. Quantitative results indicate that a few differenced Doppler plus ranging passes yield angular position estimates with a precision on the order of 0.1 to 0.4 microrad, and angular rate precision on the order of 10 to 25(10)(exp -12) rad/sec, assuming no a priori information on the coordinate parameters. Sensitivity analyses suggest that troposphere zenith delay calibration error is the dominant systematic error source in most of the tracking scenarios investigated; as expected, the differenced Doppler data were found to be much more sensitive to troposphere calibration errors than differenced range. By comparison, results computed using wide band and narrow band (delta)VLBI under similar circumstances yielded angular precisions of 0.07 to 0.4 /microrad, and angular rate precisions of 0.5 to 1.0(10)(exp -12) rad/sec.

Evaluation of statistical models for forecast errors from the HBV model

NASA Astrophysics Data System (ADS)

Engeland, Kolbjørn; Renard, Benjamin; Steinsland, Ingelin; Kolberg, Sjur

2010-04-01

SummaryThree statistical models for the forecast errors for inflow into the Langvatn reservoir in Northern Norway have been constructed and tested according to the agreement between (i) the forecast distribution and the observations and (ii) median values of the forecast distribution and the observations. For the first model observed and forecasted inflows were transformed by the Box-Cox transformation before a first order auto-regressive model was constructed for the forecast errors. The parameters were conditioned on weather classes. In the second model the Normal Quantile Transformation (NQT) was applied on observed and forecasted inflows before a similar first order auto-regressive model was constructed for the forecast errors. For the third model positive and negative errors were modeled separately. The errors were first NQT-transformed before conditioning the mean error values on climate, forecasted inflow and yesterday's error. To test the three models we applied three criterions: we wanted (a) the forecast distribution to be reliable; (b) the forecast intervals to be narrow; (c) the median values of the forecast distribution to be close to the observed values. Models 1 and 2 gave almost identical results. The median values improved the forecast with Nash-Sutcliffe R eff increasing from 0.77 for the original forecast to 0.87 for the corrected forecasts. Models 1 and 2 over-estimated the forecast intervals but gave the narrowest intervals. Their main drawback was that the distributions are less reliable than Model 3. For Model 3 the median values did not fit well since the auto-correlation was not accounted for. Since Model 3 did not benefit from the potential variance reduction that lies in bias estimation and removal it gave on average wider forecasts intervals than the two other models. At the same time Model 3 on average slightly under-estimated the forecast intervals, probably explained by the use of average measures to evaluate the fit.

Analytical Plug-In Method for Kernel Density Estimator Applied to Genetic Neutrality Study

NASA Astrophysics Data System (ADS)

Troudi, Molka; Alimi, Adel M.; Saoudi, Samir

2008-12-01

The plug-in method enables optimization of the bandwidth of the kernel density estimator in order to estimate probability density functions (pdfs). Here, a faster procedure than that of the common plug-in method is proposed. The mean integrated square error (MISE) depends directly upon [InlineEquation not available: see fulltext.] which is linked to the second-order derivative of the pdf. As we intend to introduce an analytical approximation of [InlineEquation not available: see fulltext.], the pdf is estimated only once, at the end of iterations. These two kinds of algorithm are tested on different random variables having distributions known for their difficult estimation. Finally, they are applied to genetic data in order to provide a better characterisation in the mean of neutrality of Tunisian Berber populations.

A statistical methodology for estimating transport parameters: Theory and applications to one-dimensional advectivec-dispersive systems

USGS Publications Warehouse

Wagner, Brian J.; Gorelick, Steven M.

1986-01-01

A simulation nonlinear multiple-regression methodology for estimating parameters that characterize the transport of contaminants is developed and demonstrated. Finite difference contaminant transport simulation is combined with a nonlinear weighted least squares multiple-regression procedure. The technique provides optimal parameter estimates and gives statistics for assessing the reliability of these estimates under certain general assumptions about the distributions of the random measurement errors. Monte Carlo analysis is used to estimate parameter reliability for a hypothetical homogeneous soil column for which concentration data contain large random measurement errors. The value of data collected spatially versus data collected temporally was investigated for estimation of velocity, dispersion coefficient, effective porosity, first-order decay rate, and zero-order production. The use of spatial data gave estimates that were 2–3 times more reliable than estimates based on temporal data for all parameters except velocity. Comparison of estimated linear and nonlinear confidence intervals based upon Monte Carlo analysis showed that the linear approximation is poor for dispersion coefficient and zero-order production coefficient when data are collected over time. In addition, examples demonstrate transport parameter estimation for two real one-dimensional systems. First, the longitudinal dispersivity and effective porosity of an unsaturated soil are estimated using laboratory column data. We compare the reliability of estimates based upon data from individual laboratory experiments versus estimates based upon pooled data from several experiments. Second, the simulation nonlinear regression procedure is extended to include an additional governing equation that describes delayed storage during contaminant transport. The model is applied to analyze the trends, variability, and interrelationship of parameters in a mourtain stream in northern California.

The use of fractional orders in the determination of birefringence of highly dispersive materials by the channelled spectrum method

NASA Astrophysics Data System (ADS)

Nagarajan, K.; Shashidharan Nair, C. K.

2007-07-01

The channelled spectrum employing polarized light interference is a very convenient method for the study of dispersion of birefringence. However, while using this method, the absolute order of the polarized light interference fringes cannot be determined easily. Approximate methods are therefore used to estimate the order. One of the approximations is that the dispersion of birefringence across neighbouring integer order fringes is negligible. In this paper, we show how this approximation can cause errors. A modification is reported whereby the error in the determination of absolute fringe order can be reduced using fractional orders instead of integer orders. The theoretical background for this method supported with computer simulation is presented. An experimental arrangement implementing these modifications is described. This method uses a Constant Deviation Spectrometer (CDS) and a Soleil Babinet Compensator (SBC).

Angular sensitivities of scintillator slab configurations for location of gamma ray bursts

NASA Technical Reports Server (NTRS)

Gregory, J. C.

1976-01-01

Thin flat scintillator slabs are a useful means of measuring the angular location of gamma ray fluxes of astronomical interest. A statistical estimate of position error was made of two scintillator systems suitable for gamma ray burst location from a balloon or satellite platform. A single rotating scintillator with associated flux monitor is compared with a pair of stationary orthogonal scintillators. Position error for a strong burst is of the order of a few arcmin if systematic errors are ignored.

A Robust Nonlinear Observer for Real-Time Attitude Estimation Using Low-Cost MEMS Inertial Sensors

PubMed Central

Guerrero-Castellanos, José Fermi; Madrigal-Sastre, Heberto; Durand, Sylvain; Torres, Lizeth; Muñoz-Hernández, German Ardul

2013-01-01

This paper deals with the attitude estimation of a rigid body equipped with angular velocity sensors and reference vector sensors. A quaternion-based nonlinear observer is proposed in order to fuse all information sources and to obtain an accurate estimation of the attitude. It is shown that the observer error dynamics can be separated into two passive subsystems connected in “feedback”. Then, this property is used to show that the error dynamics is input-to-state stable when the measurement disturbance is seen as an input and the error as the state. These results allow one to affirm that the observer is “robustly stable”. The proposed observer is evaluated in real-time with the design and implementation of an Attitude and Heading Reference System (AHRS) based on low-cost MEMS (Micro-Electro-Mechanical Systems) Inertial Measure Unit (IMU) and magnetic sensors and a 16-bit microcontroller. The resulting estimates are compared with a high precision motion system to demonstrate its performance. PMID:24201316

Reducing Bias and Error in the Correlation Coefficient Due to Nonnormality.

PubMed

Bishara, Anthony J; Hittner, James B

2015-10-01

It is more common for educational and psychological data to be nonnormal than to be approximately normal. This tendency may lead to bias and error in point estimates of the Pearson correlation coefficient. In a series of Monte Carlo simulations, the Pearson correlation was examined under conditions of normal and nonnormal data, and it was compared with its major alternatives, including the Spearman rank-order correlation, the bootstrap estimate, the Box-Cox transformation family, and a general normalizing transformation (i.e., rankit), as well as to various bias adjustments. Nonnormality caused the correlation coefficient to be inflated by up to +.14, particularly when the nonnormality involved heavy-tailed distributions. Traditional bias adjustments worsened this problem, further inflating the estimate. The Spearman and rankit correlations eliminated this inflation and provided conservative estimates. Rankit also minimized random error for most sample sizes, except for the smallest samples ( n = 10), where bootstrapping was more effective. Overall, results justify the use of carefully chosen alternatives to the Pearson correlation when normality is violated.

Estimating a child's age from an image using whole body proportions.

PubMed

Lucas, Teghan; Henneberg, Maciej

2017-09-01

The use and distribution of child pornography is an increasing problem. Forensic anthropologists are often asked to estimate a child's age from a photograph. Previous studies have attempted to estimate the age of children from photographs using ratios of the face. Here, we propose to include body measurement ratios into age estimates. A total of 1603 boys and 1833 girls aged 5-16 years were measured over a 10-year period. They are 'Cape Coloured' children from South Africa. Their age was regressed on ratios derived from anthropometric measurements of the head as well as the body. Multiple regression equations including four ratios for each sex (head height to shoulder and hip width, knee width, leg length and trunk length) have a standard error of 1.6-1.7 years. The error is of the same order as variation of differences between biological and chronological ages of the children. Thus, the error cannot be minimised any further as it is a direct reflection of a naturally occurring phenomenon.

Reducing Bias and Error in the Correlation Coefficient Due to Nonnormality

PubMed Central

Hittner, James B.

2014-01-01

It is more common for educational and psychological data to be nonnormal than to be approximately normal. This tendency may lead to bias and error in point estimates of the Pearson correlation coefficient. In a series of Monte Carlo simulations, the Pearson correlation was examined under conditions of normal and nonnormal data, and it was compared with its major alternatives, including the Spearman rank-order correlation, the bootstrap estimate, the Box–Cox transformation family, and a general normalizing transformation (i.e., rankit), as well as to various bias adjustments. Nonnormality caused the correlation coefficient to be inflated by up to +.14, particularly when the nonnormality involved heavy-tailed distributions. Traditional bias adjustments worsened this problem, further inflating the estimate. The Spearman and rankit correlations eliminated this inflation and provided conservative estimates. Rankit also minimized random error for most sample sizes, except for the smallest samples (n = 10), where bootstrapping was more effective. Overall, results justify the use of carefully chosen alternatives to the Pearson correlation when normality is violated. PMID:29795841

Use of the Magnetic Field for Improving Gyroscopes’ Biases Estimation

PubMed Central

Munoz Diaz, Estefania; de Ponte Müller, Fabian; García Domínguez, Juan Jesús

2017-01-01

An accurate orientation is crucial to a satisfactory position in pedestrian navigation. The orientation estimation, however, is greatly affected by errors like the biases of gyroscopes. In order to minimize the error in the orientation, the biases of gyroscopes must be estimated and subtracted. In the state of the art it has been proposed, but not proved, that the estimation of the biases can be accomplished using magnetic field measurements. The objective of this work is to evaluate the effectiveness of using magnetic field measurements to estimate the biases of medium-cost micro-electromechanical sensors (MEMS) gyroscopes. We carry out the evaluation with experiments that cover both, quasi-error-free turn rate and magnetic measurements and medium-cost MEMS turn rate and magnetic measurements. The impact of different homogeneous magnetic field distributions and magnetically perturbed environments is analyzed. Additionally, the effect of the successful biases subtraction on the orientation and the estimated trajectory is detailed. Our results show that the use of magnetic field measurements is beneficial to the correct biases estimation. Further, we show that different magnetic field distributions affect differently the biases estimation process. Moreover, the biases are likewise correctly estimated under perturbed magnetic fields. However, for indoor and urban scenarios the biases estimation process is very slow. PMID:28398232

Determination of suitable drying curve model for bread moisture loss during baking

NASA Astrophysics Data System (ADS)

Soleimani Pour-Damanab, A. R.; Jafary, A.; Rafiee, S.

2013-03-01

This study presents mathematical modelling of bread moisture loss or drying during baking in a conventional bread baking process. In order to estimate and select the appropriate moisture loss curve equation, 11 different models, semi-theoretical and empirical, were applied to the experimental data and compared according to their correlation coefficients, chi-squared test and root mean square error which were predicted by nonlinear regression analysis. Consequently, of all the drying models, a Page model was selected as the best one, according to the correlation coefficients, chi-squared test, and root mean square error values and its simplicity. Mean absolute estimation error of the proposed model by linear regression analysis for natural and forced convection modes was 2.43, 4.74%, respectively.

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.

Estimation of pulse rate from ambulatory PPG using ensemble empirical mode decomposition and adaptive thresholding.

PubMed

Pittara, Melpo; Theocharides, Theocharis; Orphanidou, Christina

2017-07-01

A new method for deriving pulse rate from PPG obtained from ambulatory patients is presented. The method employs Ensemble Empirical Mode Decomposition to identify the pulsatile component from noise-corrupted PPG, and then uses a set of physiologically-relevant rules followed by adaptive thresholding, in order to estimate the pulse rate in the presence of noise. The method was optimized and validated using 63 hours of data obtained from ambulatory hospital patients. The F1 score obtained with respect to expertly annotated data was 0.857 and the mean absolute errors of estimated pulse rates with respect to heart rates obtained from ECG collected in parallel were 1.72 bpm for "good" quality PPG and 4.49 bpm for "bad" quality PPG. Both errors are within the clinically acceptable margin-of-error for pulse rate/heart rate measurements, showing the promise of the proposed approach for inclusion in next generation wearable sensors.

GPS/DR Error Estimation for Autonomous Vehicle Localization.

PubMed

Lee, Byung-Hyun; Song, Jong-Hwa; Im, Jun-Hyuck; Im, Sung-Hyuck; Heo, Moon-Beom; Jee, Gyu-In

2015-08-21

Autonomous vehicles require highly reliable navigation capabilities. For example, a lane-following method cannot be applied in an intersection without lanes, and since typical lane detection is performed using a straight-line model, errors can occur when the lateral distance is estimated in curved sections due to a model mismatch. Therefore, this paper proposes a localization method that uses GPS/DR error estimation based on a lane detection method with curved lane models, stop line detection, and curve matching in order to improve the performance during waypoint following procedures. The advantage of using the proposed method is that position information can be provided for autonomous driving through intersections, in sections with sharp curves, and in curved sections following a straight section. The proposed method was applied in autonomous vehicles at an experimental site to evaluate its performance, and the results indicate that the positioning achieved accuracy at the sub-meter level.

GPS/DR Error Estimation for Autonomous Vehicle Localization

PubMed Central

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

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 error ec on the global temperature trend. In one path the entire error ec is placed in the am data while in the other it is placed in the pm data. Global temperature trend is increased or decreased by approximately 0.03 K/decade depending upon this placement. Taking into account all random errors and systematic errors our analysis of MSU observations leads us to conclude that a conservative estimate of the global warming is 0. 11 (+/-) 0.04 K/decade during 1980 to 1998.

Analysis of the impact of error detection on computer performance

NASA Technical Reports Server (NTRS)

Shin, K. C.; Lee, Y. H.

1983-01-01

Conventionally, reliability analyses either assume that a fault/error is detected immediately following its occurrence, or neglect damages caused by latent errors. Though unrealistic, this assumption was imposed in order to avoid the difficulty of determining the respective probabilities that a fault induces an error and the error is then detected in a random amount of time after its occurrence. As a remedy for this problem a model is proposed to analyze the impact of error detection on computer performance under moderate assumptions. Error latency, the time interval between occurrence and the moment of detection, is used to measure the effectiveness of a detection mechanism. This model is used to: (1) predict the probability of producing an unreliable result, and (2) estimate the loss of computation due to fault and/or error.

Inverse modeling for seawater intrusion in coastal aquifers: Insights about parameter sensitivities, variances, correlations and estimation procedures derived from the Henry problem

USGS Publications Warehouse

Sanz, E.; Voss, C.I.

2006-01-01

Inverse modeling studies employing data collected from the classic Henry seawater intrusion problem give insight into several important aspects of inverse modeling of seawater intrusion problems and effective measurement strategies for estimation of parameters for seawater intrusion. Despite the simplicity of the Henry problem, it embodies the behavior of a typical seawater intrusion situation in a single aquifer. Data collected from the numerical problem solution are employed without added noise in order to focus on the aspects of inverse modeling strategies dictated by the physics of variable-density flow and solute transport during seawater intrusion. Covariances of model parameters that can be estimated are strongly dependent on the physics. The insights gained from this type of analysis may be directly applied to field problems in the presence of data errors, using standard inverse modeling approaches to deal with uncertainty in data. Covariance analysis of the Henry problem indicates that in order to generally reduce variance of parameter estimates, the ideal places to measure pressure are as far away from the coast as possible, at any depth, and the ideal places to measure concentration are near the bottom of the aquifer between the center of the transition zone and its inland fringe. These observations are located in and near high-sensitivity regions of system parameters, which may be identified in a sensitivity analysis with respect to several parameters. However, both the form of error distribution in the observations and the observation weights impact the spatial sensitivity distributions, and different choices for error distributions or weights can result in significantly different regions of high sensitivity. Thus, in order to design effective sampling networks, the error form and weights must be carefully considered. For the Henry problem, permeability and freshwater inflow can be estimated with low estimation variance from only pressure or only concentration observations. Permeability, freshwater inflow, solute molecular diffusivity, and porosity can be estimated with roughly equivalent confidence using observations of only the logarithm of concentration. Furthermore, covariance analysis allows a logical reduction of the number of estimated parameters for ill-posed inverse seawater intrusion problems. Ill-posed problems may exhibit poor estimation convergence, have a non-unique solution, have multiple minima, or require excessive computational effort, and the condition often occurs when estimating too many or co-dependent parameters. For the Henry problem, such analysis allows selection of the two parameters that control system physics from among all possible system parameters. ?? 2005 Elsevier Ltd. All rights reserved.

An Error-Reduction Algorithm to Improve Lidar Turbulence Estimates for Wind Energy

DOE Office of Scientific and Technical Information (OSTI.GOV)

Newman, Jennifer F.; Clifton, Andrew

2016-08-01

Currently, cup anemometers on meteorological (met) towers are used to measure wind speeds and turbulence intensity to make decisions about wind turbine class and site suitability. However, as modern turbine hub heights increase and wind energy expands to complex and remote sites, it becomes more difficult and costly to install met towers at potential sites. As a result, remote sensing devices (e.g., lidars) are now commonly used by wind farm managers and researchers to estimate the flow field at heights spanned by a turbine. While lidars can accurately estimate mean wind speeds and wind directions, there is still a largemore » amount of uncertainty surrounding the measurement of turbulence with lidars. This uncertainty in lidar turbulence measurements is one of the key roadblocks that must be overcome in order to replace met towers with lidars for wind energy applications. In this talk, a model for reducing errors in lidar turbulence estimates is presented. Techniques for reducing errors from instrument noise, volume averaging, and variance contamination are combined in the model to produce a corrected value of the turbulence intensity (TI), a commonly used parameter in wind energy. In the next step of the model, machine learning techniques are used to further decrease the error in lidar TI estimates.« less

A continuous optimization approach for inferring parameters in mathematical models of regulatory networks.

PubMed

Deng, Zhimin; Tian, Tianhai

2014-07-29

The advances of systems biology have raised a large number of sophisticated mathematical models for describing the dynamic property of complex biological systems. One of the major steps in developing mathematical models is to estimate unknown parameters of the model based on experimentally measured quantities. However, experimental conditions limit the amount of data that is available for mathematical modelling. The number of unknown parameters in mathematical models may be larger than the number of observation data. The imbalance between the number of experimental data and number of unknown parameters makes reverse-engineering problems particularly challenging. To address the issue of inadequate experimental data, we propose a continuous optimization approach for making reliable inference of model parameters. This approach first uses a spline interpolation to generate continuous functions of system dynamics as well as the first and second order derivatives of continuous functions. The expanded dataset is the basis to infer unknown model parameters using various continuous optimization criteria, including the error of simulation only, error of both simulation and the first derivative, or error of simulation as well as the first and second derivatives. We use three case studies to demonstrate the accuracy and reliability of the proposed new approach. Compared with the corresponding discrete criteria using experimental data at the measurement time points only, numerical results of the ERK kinase activation module show that the continuous absolute-error criteria using both function and high order derivatives generate estimates with better accuracy. This result is also supported by the second and third case studies for the G1/S transition network and the MAP kinase pathway, respectively. This suggests that the continuous absolute-error criteria lead to more accurate estimates than the corresponding discrete criteria. We also study the robustness property of these three models to examine the reliability of estimates. Simulation results show that the models with estimated parameters using continuous fitness functions have better robustness properties than those using the corresponding discrete fitness functions. The inference studies and robustness analysis suggest that the proposed continuous optimization criteria are effective and robust for estimating unknown parameters in mathematical models.

A hybrid frame concealment algorithm for H.264/AVC.

PubMed

Yan, Bo; Gharavi, Hamid

2010-01-01

In packet-based video transmissions, packets loss due to channel errors may result in the loss of the whole video frame. Recently, many error concealment algorithms have been proposed in order to combat channel errors; however, most of the existing algorithms can only deal with the loss of macroblocks and are not able to conceal the whole missing frame. In order to resolve this problem, in this paper, we have proposed a new hybrid motion vector extrapolation (HMVE) algorithm to recover the whole missing frame, and it is able to provide more accurate estimation for the motion vectors of the missing frame than other conventional methods. Simulation results show that it is highly effective and significantly outperforms other existing frame recovery methods.

Parameter estimation for terrain modeling from gradient data. [navigation system for Martian rover

NASA Technical Reports Server (NTRS)

Dangelo, K. R.

1974-01-01

A method is developed for modeling terrain surfaces for use on an unmanned Martian roving vehicle. The modeling procedure employs a two-step process which uses gradient as well as height data in order to improve the accuracy of the model's gradient. Least square approximation is used in order to stochastically determine the parameters which describe the modeled surface. A complete error analysis of the modeling procedure is included which determines the effect of instrumental measurement errors on the model's accuracy. Computer simulation is used as a means of testing the entire modeling process which includes the acquisition of data points, the two-step modeling process and the error analysis. Finally, to illustrate the procedure, a numerical example is included.

Global Warming Estimation from MSU

NASA Technical Reports Server (NTRS)

Prabhakara, C.; Iacovazzi, Robert, Jr.

1999-01-01

In this study, we have developed time series of global temperature from 1980-97 based on the Microwave Sounding Unit (MSU) Ch 2 (53.74 GHz) observations taken from polar-orbiting NOAA operational satellites. In order to create these time series, systematic errors (approx. 0.1 K) in the Ch 2 data arising from inter-satellite differences are removed objectively. On the other hand, smaller systematic errors (approx. 0.03 K) in the data due to orbital drift of each satellite cannot be removed objectively. Such errors are expected to remain in the time series and leave an uncertainty in the inferred global temperature trend. With the help of a statistical method, the error in the MSU inferred global temperature trend resulting from orbital drifts and residual inter-satellite differences of all satellites is estimated to be 0.06 K decade. Incorporating this error, our analysis shows that the global temperature increased at a rate of 0.13 +/- 0.06 K decade during 1980-97.

Parameter Estimation for GRACE-FO Geometric Ranging Errors

NASA Astrophysics Data System (ADS)

Wegener, H.; Mueller, V.; Darbeheshti, N.; Naeimi, M.; Heinzel, G.

2017-12-01

Onboard GRACE-FO, the novel Laser Ranging Instrument (LRI) serves as a technology demonstrator, but it is a fully functional instrument to provide an additional high-precision measurement of the primary mission observable: the biased range between the two spacecraft. Its (expectedly) two largest error sources are laser frequency noise and tilt-to-length (TTL) coupling. While not much can be done about laser frequency noise, the mechanics of the TTL error are widely understood. They depend, however, on unknown parameters. In order to improve the quality of the ranging data, it is hence essential to accurately estimate these parameters and remove the resulting TTL error from the data.Means to do so will be discussed. In particular, the possibility of using calibration maneuvers, the utility of the attitude information provided by the LRI via Differential Wavefront Sensing (DWS), and the benefit from combining ranging data from LRI with ranging data from the established microwave ranging, will be mentioned.

Estimating the domain of applicability for machine learning QSAR models: a study on aqueous solubility of drug discovery molecules.

PubMed

Schroeter, Timon Sebastian; Schwaighofer, Anton; Mika, Sebastian; Ter Laak, Antonius; Suelzle, Detlev; Ganzer, Ursula; Heinrich, Nikolaus; Müller, Klaus-Robert

2007-12-01

We investigate the use of different Machine Learning methods to construct models for aqueous solubility. Models are based on about 4000 compounds, including an in-house set of 632 drug discovery molecules of Bayer Schering Pharma. For each method, we also consider an appropriate method to obtain error bars, in order to estimate the domain of applicability (DOA) for each model. Here, we investigate error bars from a Bayesian model (Gaussian Process (GP)), an ensemble based approach (Random Forest), and approaches based on the Mahalanobis distance to training data (for Support Vector Machine and Ridge Regression models). We evaluate all approaches in terms of their prediction accuracy (in cross-validation, and on an external validation set of 536 molecules) and in how far the individual error bars can faithfully represent the actual prediction error.

Estimating the domain of applicability for machine learning QSAR models: a study on aqueous solubility of drug discovery molecules.

PubMed

Schroeter, Timon Sebastian; Schwaighofer, Anton; Mika, Sebastian; Ter Laak, Antonius; Suelzle, Detlev; Ganzer, Ursula; Heinrich, Nikolaus; Müller, Klaus-Robert

2007-09-01

We investigate the use of different Machine Learning methods to construct models for aqueous solubility. Models are based on about 4000 compounds, including an in-house set of 632 drug discovery molecules of Bayer Schering Pharma. For each method, we also consider an appropriate method to obtain error bars, in order to estimate the domain of applicability (DOA) for each model. Here, we investigate error bars from a Bayesian model (Gaussian Process (GP)), an ensemble based approach (Random Forest), and approaches based on the Mahalanobis distance to training data (for Support Vector Machine and Ridge Regression models). We evaluate all approaches in terms of their prediction accuracy (in cross-validation, and on an external validation set of 536 molecules) and in how far the individual error bars can faithfully represent the actual prediction error.

Estimating the domain of applicability for machine learning QSAR models: a study on aqueous solubility of drug discovery molecules

NASA Astrophysics Data System (ADS)

Schroeter, Timon Sebastian; Schwaighofer, Anton; Mika, Sebastian; Ter Laak, Antonius; Suelzle, Detlev; Ganzer, Ursula; Heinrich, Nikolaus; Müller, Klaus-Robert

2007-12-01

We investigate the use of different Machine Learning methods to construct models for aqueous solubility. Models are based on about 4000 compounds, including an in-house set of 632 drug discovery molecules of Bayer Schering Pharma. For each method, we also consider an appropriate method to obtain error bars, in order to estimate the domain of applicability (DOA) for each model. Here, we investigate error bars from a Bayesian model (Gaussian Process (GP)), an ensemble based approach (Random Forest), and approaches based on the Mahalanobis distance to training data (for Support Vector Machine and Ridge Regression models). We evaluate all approaches in terms of their prediction accuracy (in cross-validation, and on an external validation set of 536 molecules) and in how far the individual error bars can faithfully represent the actual prediction error.

Estimating the domain of applicability for machine learning QSAR models: a study on aqueous solubility of drug discovery molecules

NASA Astrophysics Data System (ADS)

Schroeter, Timon Sebastian; Schwaighofer, Anton; Mika, Sebastian; Ter Laak, Antonius; Suelzle, Detlev; Ganzer, Ursula; Heinrich, Nikolaus; Müller, Klaus-Robert

2007-09-01

We investigate the use of different Machine Learning methods to construct models for aqueous solubility. Models are based on about 4000 compounds, including an in-house set of 632 drug discovery molecules of Bayer Schering Pharma. For each method, we also consider an appropriate method to obtain error bars, in order to estimate the domain of applicability (DOA) for each model. Here, we investigate error bars from a Bayesian model (Gaussian Process (GP)), an ensemble based approach (Random Forest), and approaches based on the Mahalanobis distance to training data (for Support Vector Machine and Ridge Regression models). We evaluate all approaches in terms of their prediction accuracy (in cross-validation, and on an external validation set of 536 molecules) and in how far the individual error bars can faithfully represent the actual prediction error.

Gradient nonlinearity calibration and correction for a compact, asymmetric magnetic resonance imaging gradient system

PubMed Central

Tao, S; Trzasko, J D; Gunter, J L; Weavers, P T; Shu, Y; Huston, J; Lee, S K; Tan, E T; Bernstein, M A

2017-01-01

Due to engineering limitations, the spatial encoding gradient fields in conventional magnetic resonance imaging cannot be perfectly linear and always contain higher-order, nonlinear components. If ignored during image reconstruction, gradient nonlinearity (GNL) manifests as image geometric distortion. Given an estimate of the GNL field, this distortion can be corrected to a degree proportional to the accuracy of the field estimate. The GNL of a gradient system is typically characterized using a spherical harmonic polynomial model with model coefficients obtained from electromagnetic simulation. Conventional whole-body gradient systems are symmetric in design; typically, only odd-order terms up to the 5th-order are required for GNL modeling. Recently, a high-performance, asymmetric gradient system was developed, which exhibits more complex GNL that requires higher-order terms including both odd- and even-orders for accurate modeling. This work characterizes the GNL of this system using an iterative calibration method and a fiducial phantom used in ADNI (Alzheimer’s Disease Neuroimaging Initiative). The phantom was scanned at different locations inside the 26-cm diameter-spherical-volume of this gradient, and the positions of fiducials in the phantom were estimated. An iterative calibration procedure was utilized to identify the model coefficients that minimize the mean-squared-error between the true fiducial positions and the positions estimated from images corrected using these coefficients. To examine the effect of higher-order and even-order terms, this calibration was performed using spherical harmonic polynomial of different orders up to the 10th-order including even- and odd-order terms, or odd-order only. The results showed that the model coefficients of this gradient can be successfully estimated. The residual root-mean-squared-error after correction using up to the 10th-order coefficients was reduced to 0.36 mm, yielding spatial accuracy comparable to conventional whole-body gradients. The even-order terms were necessary for accurate GNL modeling. In addition, the calibrated coefficients improved image geometric accuracy compared with the simulation-based coefficients. PMID:28033119

High-Order Model and Dynamic Filtering for Frame Rate Up-Conversion.

PubMed

Bao, Wenbo; Zhang, Xiaoyun; Chen, Li; Ding, Lianghui; Gao, Zhiyong

2018-08-01

This paper proposes a novel frame rate up-conversion method through high-order model and dynamic filtering (HOMDF) for video pixels. Unlike the constant brightness and linear motion assumptions in traditional methods, the intensity and position of the video pixels are both modeled with high-order polynomials in terms of time. Then, the key problem of our method is to estimate the polynomial coefficients that represent the pixel's intensity variation, velocity, and acceleration. We propose to solve it with two energy objectives: one minimizes the auto-regressive prediction error of intensity variation by its past samples, and the other minimizes video frame's reconstruction error along the motion trajectory. To efficiently address the optimization problem for these coefficients, we propose the dynamic filtering solution inspired by video's temporal coherence. The optimal estimation of these coefficients is reformulated into a dynamic fusion of the prior estimate from pixel's temporal predecessor and the maximum likelihood estimate from current new observation. Finally, frame rate up-conversion is implemented using motion-compensated interpolation by pixel-wise intensity variation and motion trajectory. Benefited from the advanced model and dynamic filtering, the interpolated frame has much better visual quality. Extensive experiments on the natural and synthesized videos demonstrate the superiority of HOMDF over the state-of-the-art methods in both subjective and objective comparisons.

A New Adaptive H-Infinity Filtering Algorithm for the GPS/INS Integrated Navigation

PubMed Central

Jiang, Chen; Zhang, Shu-Bi; Zhang, Qiu-Zhao

2016-01-01

The Kalman filter is an optimal estimator with numerous applications in technology, especially in systems with Gaussian distributed noise. Moreover, the adaptive Kalman filtering algorithms, based on the Kalman filter, can control the influence of dynamic model errors. In contrast to the adaptive Kalman filtering algorithms, the H-infinity filter is able to address the interference of the stochastic model by minimization of the worst-case estimation error. In this paper, a novel adaptive H-infinity filtering algorithm, which integrates the adaptive Kalman filter and the H-infinity filter in order to perform a comprehensive filtering algorithm, is presented. In the proposed algorithm, a robust estimation method is employed to control the influence of outliers. In order to verify the proposed algorithm, experiments with real data of the Global Positioning System (GPS) and Inertial Navigation System (INS) integrated navigation, were conducted. The experimental results have shown that the proposed algorithm has multiple advantages compared to the other filtering algorithms. PMID:27999361

A New Adaptive H-Infinity Filtering Algorithm for the GPS/INS Integrated Navigation.

PubMed

Jiang, Chen; Zhang, Shu-Bi; Zhang, Qiu-Zhao

2016-12-19

The Kalman filter is an optimal estimator with numerous applications in technology, especially in systems with Gaussian distributed noise. Moreover, the adaptive Kalman filtering algorithms, based on the Kalman filter, can control the influence of dynamic model errors. In contrast to the adaptive Kalman filtering algorithms, the H-infinity filter is able to address the interference of the stochastic model by minimization of the worst-case estimation error. In this paper, a novel adaptive H-infinity filtering algorithm, which integrates the adaptive Kalman filter and the H-infinity filter in order to perform a comprehensive filtering algorithm, is presented. In the proposed algorithm, a robust estimation method is employed to control the influence of outliers. In order to verify the proposed algorithm, experiments with real data of the Global Positioning System (GPS) and Inertial Navigation System (INS) integrated navigation, were conducted. The experimental results have shown that the proposed algorithm has multiple advantages compared to the other filtering algorithms.

Clutch pressure estimation for a power-split hybrid transmission using nonlinear robust observer

NASA Astrophysics Data System (ADS)

Zhou, Bin; Zhang, Jianwu; Gao, Ji; Yu, Haisheng; Liu, Dong

2018-06-01

For a power-split hybrid transmission, using the brake clutch to realize the transition from electric drive mode to hybrid drive mode is an available strategy. Since the pressure information of the brake clutch is essential for the mode transition control, this research designs a nonlinear robust reduced-order observer to estimate the brake clutch pressure. Model uncertainties or disturbances are considered as additional inputs, thus the observer is designed in order that the error dynamics is input-to-state stable. The nonlinear characteristics of the system are expressed as the lookup tables in the observer. Moreover, the gain matrix of the observer is solved by two optimization procedures under the constraints of the linear matrix inequalities. The proposed observer is validated by offline simulation and online test, the results have shown that the observer achieves significant performance during the mode transition, as the estimation error is within a reasonable range, more importantly, it is asymptotically stable.

An error covariance model for sea surface topography and velocity derived from TOPEX/POSEIDON altimetry

NASA Technical Reports Server (NTRS)

Tsaoussi, Lucia S.; Koblinsky, Chester J.

1994-01-01

In order to facilitate the use of satellite-derived sea surface topography and velocity oceanographic models, methodology is presented for deriving the total error covariance and its geographic distribution from TOPEX/POSEIDON measurements. The model is formulated using a parametric model fit to the altimeter range observations. The topography and velocity modeled with spherical harmonic expansions whose coefficients are found through optimal adjustment to the altimeter range residuals using Bayesian statistics. All other parameters, including the orbit, geoid, surface models, and range corrections are provided as unadjusted parameters. The maximum likelihood estimates and errors are derived from the probability density function of the altimeter range residuals conditioned with a priori information. Estimates of model errors for the unadjusted parameters are obtained from the TOPEX/POSEIDON postlaunch verification results and the error covariances for the orbit and the geoid, except for the ocean tides. The error in the ocean tides is modeled, first, as the difference between two global tide models and, second, as the correction to the present tide model, the correction derived from the TOPEX/POSEIDON data. A formal error covariance propagation scheme is used to derive the total error. Our global total error estimate for the TOPEX/POSEIDON topography relative to the geoid for one 10-day period is found tio be 11 cm RMS. When the error in the geoid is removed, thereby providing an estimate of the time dependent error, the uncertainty in the topography is 3.5 cm root mean square (RMS). This level of accuracy is consistent with direct comparisons of TOPEX/POSEIDON altimeter heights with tide gauge measurements at 28 stations. In addition, the error correlation length scales are derived globally in both east-west and north-south directions, which should prove useful for data assimilation. The largest error correlation length scales are found in the tropics. Errors in the velocity field are smallest in midlatitude regions. For both variables the largest errors caused by uncertainty in the geoid. More accurate representations of the geoid await a dedicated geopotential satellite mission. Substantial improvements in the accuracy of ocean tide models are expected in the very near future from research with TOPEX/POSEIDON data.

New methodology to reconstruct in 2-D the cuspal enamel of modern human lower molars.

PubMed

Modesto-Mata, Mario; García-Campos, Cecilia; Martín-Francés, Laura; Martínez de Pinillos, Marina; García-González, Rebeca; Quintino, Yuliet; Canals, Antoni; Lozano, Marina; Dean, M Christopher; Martinón-Torres, María; Bermúdez de Castro, José María

2017-08-01

In the last years different methodologies have been developed to reconstruct worn teeth. In this article, we propose a new 2-D methodology to reconstruct the worn enamel of lower molars. Our main goals are to reconstruct molars with a high level of accuracy when measuring relevant histological variables and to validate the methodology calculating the errors associated with the measurements. This methodology is based on polynomial regression equations, and has been validated using two different dental variables: cuspal enamel thickness and crown height of the protoconid. In order to perform the validation process, simulated worn modern human molars were employed. The associated errors of the measurements were also estimated applying methodologies previously proposed by other authors. The mean percentage error estimated in reconstructed molars for these two variables in comparison with their own real values is -2.17% for the cuspal enamel thickness of the protoconid and -3.18% for the crown height of the protoconid. This error significantly improves the results of other methodologies, both in the interobserver error and in the accuracy of the measurements. The new methodology based on polynomial regressions can be confidently applied to the reconstruction of cuspal enamel of lower molars, as it improves the accuracy of the measurements and reduces the interobserver error. The present study shows that it is important to validate all methodologies in order to know the associated errors. This new methodology can be easily exportable to other modern human populations, the human fossil record and forensic sciences. © 2017 Wiley Periodicals, Inc.

An affordable cuff-less blood pressure estimation solution.

PubMed

Jain, Monika; Kumar, Niranjan; Deb, Sujay

2016-08-01

This paper presents a cuff-less hypertension pre-screening device that non-invasively monitors the Blood Pressure (BP) and Heart Rate (HR) continuously. The proposed device simultaneously records two clinically significant and highly correlated biomedical signals, viz., Electrocardiogram (ECG) and Photoplethysmogram (PPG). The device provides a common data acquisition platform that can interface with PC/laptop, Smart phone/tablet and Raspberry-pi etc. The hardware stores and processes the recorded ECG and PPG in order to extract the real-time BP and HR using kernel regression approach. The BP and HR estimation error is measured in terms of normalized mean square error, Error Standard Deviation (ESD) and Mean Absolute Error (MAE), with respect to a clinically proven digital BP monitor (OMRON HBP1300). The computed error falls under the maximum standard allowable error mentioned by Association for the Advancement of Medical Instrumentation; MAE <; 5 mmHg and ESD <; 8mmHg. The results are validated using two-tailed dependent sample t-test also. The proposed device is a portable low-cost home and clinic bases solution for continuous health monitoring.

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.

A New Linearized Crank-Nicolson Mixed Element Scheme for the Extended Fisher-Kolmogorov Equation

PubMed Central

Wang, Jinfeng; Li, Hong; He, Siriguleng; Gao, Wei

2013-01-01

We present a new mixed finite element method for solving the extended Fisher-Kolmogorov (EFK) equation. We first decompose the EFK equation as the two second-order equations, then deal with a second-order equation employing finite element method, and handle the other second-order equation using a new mixed finite element method. In the new mixed finite element method, the gradient ∇u belongs to the weaker (L 2(Ω))2 space taking the place of the classical H(div; Ω) space. We prove some a priori bounds for the solution for semidiscrete scheme and derive a fully discrete mixed scheme based on a linearized Crank-Nicolson method. At the same time, we get the optimal a priori error estimates in L 2 and H 1-norm for both the scalar unknown u and the diffusion term w = −Δu and a priori error estimates in (L 2)2-norm for its gradient χ = ∇u for both semi-discrete and fully discrete schemes. PMID:23864831

A new linearized Crank-Nicolson mixed element scheme for the extended Fisher-Kolmogorov equation.

PubMed

Wang, Jinfeng; Li, Hong; He, Siriguleng; Gao, Wei; Liu, Yang

2013-01-01

We present a new mixed finite element method for solving the extended Fisher-Kolmogorov (EFK) equation. We first decompose the EFK equation as the two second-order equations, then deal with a second-order equation employing finite element method, and handle the other second-order equation using a new mixed finite element method. In the new mixed finite element method, the gradient ∇u belongs to the weaker (L²(Ω))² space taking the place of the classical H(div; Ω) space. We prove some a priori bounds for the solution for semidiscrete scheme and derive a fully discrete mixed scheme based on a linearized Crank-Nicolson method. At the same time, we get the optimal a priori error estimates in L² and H¹-norm for both the scalar unknown u and the diffusion term w = -Δu and a priori error estimates in (L²)²-norm for its gradient χ = ∇u for both semi-discrete and fully discrete schemes.

Errors in the estimation method for the rejection of vibrations in adaptive optics systems

NASA Astrophysics Data System (ADS)

Kania, Dariusz

2017-06-01

In recent years the problem of the mechanical vibrations impact in adaptive optics (AO) systems has been renewed. These signals are damped sinusoidal signals and have deleterious effect on the system. One of software solutions to reject the vibrations is an adaptive method called AVC (Adaptive Vibration Cancellation) where the procedure has three steps: estimation of perturbation parameters, estimation of the frequency response of the plant, update the reference signal to reject/minimalize the vibration. In the first step a very important problem is the estimation method. A very accurate and fast (below 10 ms) estimation method of these three parameters has been presented in several publications in recent years. The method is based on using the spectrum interpolation and MSD time windows and it can be used to estimate multifrequency signals. In this paper the estimation method is used in the AVC method to increase the system performance. There are several parameters that affect the accuracy of obtained results, e.g. CiR - number of signal periods in a measurement window, N - number of samples in the FFT procedure, H - time window order, SNR, b - number of ADC bits, γ - damping ratio of the tested signal. Systematic errors increase when N, CiR, H decrease and when γ increases. The value for systematic error is approximately 10^-10 Hz/Hz for N = 2048 and CiR = 0.1. This paper presents equations that can used to estimate maximum systematic errors for given values of H, CiR and N before the start of the estimation process.

Impact of transport and modelling errors on the estimation of methane sources and sinks by inverse modelling

NASA Astrophysics Data System (ADS)

Locatelli, Robin; Bousquet, Philippe; Chevallier, Frédéric

2013-04-01

Since the nineties, inverse modelling by assimilating atmospheric measurements into a chemical transport model (CTM) has been used to derive sources and sinks of atmospheric trace gases. More recently, the high global warming potential of methane (CH4) and unexplained variations of its atmospheric mixing ratio caught the attention of several research groups. Indeed, the diversity and the variability of methane sources induce high uncertainty on the present and the future evolution of CH4 budget. With the increase of available measurement data to constrain inversions (satellite data, high frequency surface and tall tower observations, FTIR spectrometry,...), the main limiting factor is about to become the representation of atmospheric transport in CTMs. Indeed, errors in transport modelling directly converts into flux changes when assuming perfect transport in atmospheric inversions. Hence, we propose an inter-model comparison in order to quantify the impact of transport and modelling errors on the CH4 fluxes estimated into a variational inversion framework. Several inversion experiments are conducted using the same set-up (prior emissions, measurement and prior errors, OH field, initial conditions) of the variational system PYVAR, developed at LSCE (Laboratoire des Sciences du Climat et de l'Environnement, France). Nine different models (ACTM, IFS, IMPACT, IMPACT1x1, MOZART, PCTM, TM5, TM51x1 and TOMCAT) used in TRANSCOM-CH4 experiment (Patra el al, 2011) provide synthetic measurements data at up to 280 surface sites to constrain the inversions performed using the PYVAR system. Only the CTM (and the meteorological drivers which drive them) used to create the pseudo-observations vary among inversions. Consequently, the comparisons of the nine inverted methane fluxes obtained for 2005 give a good order of magnitude of the impact of transport and modelling errors on the estimated fluxes with current and future networks. It is shown that transport and modelling errors lead to a discrepancy of 27 TgCH4 per year at global scale, representing 5% of the total methane emissions for 2005. At continental scale, transport and modelling errors have bigger impacts in proportion to the area of the regions, ranging from 36 TgCH4 in North America to 7 TgCH4 in Boreal Eurasian, with a percentage range from 23% to 48%. Thus, contribution of transport and modelling errors to the mismatch between measurements and simulated methane concentrations is large considering the present questions on the methane budget. Moreover, diagnostics of statistics errors included in our inversions have been computed. It shows that errors contained in measurement errors covariance matrix are under-estimated in current inversions, suggesting to include more properly transport and modelling errors in future inversions.

Error Distribution Evaluation of the Third Vanishing Point Based on Random Statistical Simulation

NASA Astrophysics Data System (ADS)

Li, C.

2012-07-01

POS, integrated by GPS / INS (Inertial Navigation Systems), has allowed rapid and accurate determination of position and attitude of remote sensing equipment for MMS (Mobile Mapping Systems). However, not only does INS have system error, but also it is very expensive. Therefore, in this paper error distributions of vanishing points are studied and tested in order to substitute INS for MMS in some special land-based scene, such as ground façade where usually only two vanishing points can be detected. Thus, the traditional calibration approach based on three orthogonal vanishing points is being challenged. In this article, firstly, the line clusters, which parallel to each others in object space and correspond to the vanishing points, are detected based on RANSAC (Random Sample Consensus) and parallelism geometric constraint. Secondly, condition adjustment with parameters is utilized to estimate nonlinear error equations of two vanishing points (VX, VY). How to set initial weights for the adjustment solution of single image vanishing points is presented. Solving vanishing points and estimating their error distributions base on iteration method with variable weights, co-factor matrix and error ellipse theory. Thirdly, under the condition of known error ellipses of two vanishing points (VX, VY) and on the basis of the triangle geometric relationship of three vanishing points, the error distribution of the third vanishing point (VZ) is calculated and evaluated by random statistical simulation with ignoring camera distortion. Moreover, Monte Carlo methods utilized for random statistical estimation are presented. Finally, experimental results of vanishing points coordinate and their error distributions are shown and analyzed.

Product code optimization for determinate state LDPC decoding in robust image transmission.

PubMed

Thomos, Nikolaos; Boulgouris, Nikolaos V; Strintzis, Michael G

2006-08-01

We propose a novel scheme for error-resilient image transmission. The proposed scheme employs a product coder consisting of low-density parity check (LDPC) codes and Reed-Solomon codes in order to deal effectively with bit errors. The efficiency of the proposed scheme is based on the exploitation of determinate symbols in Tanner graph decoding of LDPC codes and a novel product code optimization technique based on error estimation. Experimental evaluation demonstrates the superiority of the proposed system in comparison to recent state-of-the-art techniques for image transmission.

Improvements to photometry. Part 1: Better estimation of derivatives in extinction and transformation equations

NASA Technical Reports Server (NTRS)

Young, Andrew T.

1988-01-01

Atmospheric extinction in wideband photometry is examined both analytically and through numerical simulations. If the derivatives that appear in the Stromgren-King theory are estimated carefully, it appears that wideband measurements can be transformed to outside the atmosphere with errors no greater than a millimagnitude. A numerical analysis approach is used to estimate derivatives of both the stellar and atmospheric extinction spectra, avoiding previous assumptions that the extinction follows a power law. However, it is essential to satify the requirements of the sampling theorem to keep aliasing errors small. Typically, this means that band separations cannot exceed half of the full width at half-peak response. Further work is needed to examine higher order effects, which may well be significant.

Estimating Relative Positions of Outer-Space Structures

NASA Technical Reports Server (NTRS)

Balian, Harry; Breckenridge, William; Brugarolas, Paul

2009-01-01

A computer program estimates the relative position and orientation of two structures from measurements, made by use of electronic cameras and laser range finders on one structure, of distances and angular positions of fiducial objects on the other structure. The program was written specifically for use in determining errors in the alignment of large structures deployed in outer space from a space shuttle. The program is based partly on equations for transformations among the various coordinate systems involved in the measurements and on equations that account for errors in the transformation operators. It computes a least-squares estimate of the relative position and orientation. Sequential least-squares estimates, acquired at a measurement rate of 4 Hz, are averaged by passing them through a fourth-order Butterworth filter. The program is executed in a computer aboard the space shuttle, and its position and orientation estimates are displayed to astronauts on a graphical user interface.

Trans-dimensional inversion of microtremor array dispersion data with hierarchical autoregressive error models

NASA Astrophysics Data System (ADS)

Dettmer, Jan; Molnar, Sheri; Steininger, Gavin; Dosso, Stan E.; Cassidy, John F.

2012-02-01

This paper applies a general trans-dimensional Bayesian inference methodology and hierarchical autoregressive data-error models to the inversion of microtremor array dispersion data for shear wave velocity (vs) structure. This approach accounts for the limited knowledge of the optimal earth model parametrization (e.g. the number of layers in the vs profile) and of the data-error statistics in the resulting vs parameter uncertainty estimates. The assumed earth model parametrization influences estimates of parameter values and uncertainties due to different parametrizations leading to different ranges of data predictions. The support of the data for a particular model is often non-unique and several parametrizations may be supported. A trans-dimensional formulation accounts for this non-uniqueness by including a model-indexing parameter as an unknown so that groups of models (identified by the indexing parameter) are considered in the results. The earth model is parametrized in terms of a partition model with interfaces given over a depth-range of interest. In this work, the number of interfaces (layers) in the partition model represents the trans-dimensional model indexing. In addition, serial data-error correlations are addressed by augmenting the geophysical forward model with a hierarchical autoregressive error model that can account for a wide range of error processes with a small number of parameters. Hence, the limited knowledge about the true statistical distribution of data errors is also accounted for in the earth model parameter estimates, resulting in more realistic uncertainties and parameter values. Hierarchical autoregressive error models do not rely on point estimates of the model vector to estimate data-error statistics, and have no requirement for computing the inverse or determinant of a data-error covariance matrix. This approach is particularly useful for trans-dimensional inverse problems, as point estimates may not be representative of the state space that spans multiple subspaces of different dimensionalities. The order of the autoregressive process required to fit the data is determined here by posterior residual-sample examination and statistical tests. Inference for earth model parameters is carried out on the trans-dimensional posterior probability distribution by considering ensembles of parameter vectors. In particular, vs uncertainty estimates are obtained by marginalizing the trans-dimensional posterior distribution in terms of vs-profile marginal distributions. The methodology is applied to microtremor array dispersion data collected at two sites with significantly different geology in British Columbia, Canada. At both sites, results show excellent agreement with estimates from invasive measurements.

Spectral iterative method and convergence analysis for solving nonlinear fractional differential equation

NASA Astrophysics Data System (ADS)

Yarmohammadi, M.; Javadi, S.; Babolian, E.

2018-04-01

In this study a new spectral iterative method (SIM) based on fractional interpolation is presented for solving nonlinear fractional differential equations (FDEs) involving Caputo derivative. This method is equipped with a pre-algorithm to find the singularity index of solution of the problem. This pre-algorithm gives us a real parameter as the index of the fractional interpolation basis, for which the SIM achieves the highest order of convergence. In comparison with some recent results about the error estimates for fractional approximations, a more accurate convergence rate has been attained. We have also proposed the order of convergence for fractional interpolation error under the L2-norm. Finally, general error analysis of SIM has been considered. The numerical results clearly demonstrate the capability of the proposed method.

Desert soil clay content estimation using reflectance spectroscopy preprocessed by fractional derivative

PubMed Central

Tiyip, Tashpolat; Ding, Jianli; Zhang, Dong; Liu, Wei; Wang, Fei; Tashpolat, Nigara

2017-01-01

Effective pretreatment of spectral reflectance is vital to model accuracy in soil parameter estimation. However, the classic integer derivative has some disadvantages, including spectral information loss and the introduction of high-frequency noise. In this paper, the fractional order derivative algorithm was applied to the pretreatment and partial least squares regression (PLSR) was used to assess the clay content of desert soils. Overall, 103 soil samples were collected from the Ebinur Lake basin in the Xinjiang Uighur Autonomous Region of China, and used as data sets for calibration and validation. Following laboratory measurements of spectral reflectance and clay content, the raw spectral reflectance and absorbance data were treated using the fractional derivative order from the 0.0 to the 2.0 order (order interval: 0.2). The ratio of performance to deviation (RPD), determinant coefficients of calibration (Rc2), root mean square errors of calibration (RMSEC), determinant coefficients of prediction (Rp2), and root mean square errors of prediction (RMSEP) were applied to assess the performance of predicting models. The results showed that models built on the fractional derivative order performed better than when using the classic integer derivative. Comparison of the predictive effects of 22 models for estimating clay content, calibrated by PLSR, showed that those models based on the fractional derivative 1.8 order of spectral reflectance (Rc2 = 0.907, RMSEC = 0.425%, Rp2 = 0.916, RMSEP = 0.364%, and RPD = 2.484 ≥ 2.000) and absorbance (Rc2 = 0.888, RMSEC = 0.446%, Rp2 = 0.918, RMSEP = 0.383% and RPD = 2.511 ≥ 2.000) were most effective. Furthermore, they performed well in quantitative estimations of the clay content of soils in the study area. PMID:28934274

Utilization of electrical impedance imaging for estimation of in-vivo tissue resistivities

NASA Astrophysics Data System (ADS)

Eyuboglu, B. Murat; Pilkington, Theo C.

1993-08-01

In order to determine in vivo resistivity of tissues in the thorax, the possibility of combining electrical impedance imaging (EII) techniques with (1) anatomical data extracted from high resolution images, (2) a prior knowledge of tissue resistivities, and (3) a priori noise information was assessed in this study. A Least Square Error Estimator (LSEE) and a statistically constrained Minimum Mean Square Error Estimator (MiMSEE) were implemented to estimate regional electrical resistivities from potential measurements made on the body surface. A two dimensional boundary element model of the human thorax, which consists of four different conductivity regions (the skeletal muscle, the heart, the right lung, and the left lung) was adopted to simulate the measured EII torso potentials. The calculated potentials were then perturbed by simulated instrumentation noise. The signal information used to form the statistical constraint for the MiMSEE was obtained from a prior knowledge of the physiological range of tissue resistivities. The noise constraint was determined from a priori knowledge of errors due to linearization of the forward problem and to the instrumentation noise.

An iteratively reweighted least-squares approach to adaptive robust adjustment of parameters in linear regression models with autoregressive and t-distributed deviations

NASA Astrophysics Data System (ADS)

Kargoll, Boris; Omidalizarandi, Mohammad; Loth, Ina; Paffenholz, Jens-André; Alkhatib, Hamza

2018-03-01

In this paper, we investigate a linear regression time series model of possibly outlier-afflicted observations and autocorrelated random deviations. This colored noise is represented by a covariance-stationary autoregressive (AR) process, in which the independent error components follow a scaled (Student's) t-distribution. This error model allows for the stochastic modeling of multiple outliers and for an adaptive robust maximum likelihood (ML) estimation of the unknown regression and AR coefficients, the scale parameter, and the degree of freedom of the t-distribution. This approach is meant to be an extension of known estimators, which tend to focus only on the regression model, or on the AR error model, or on normally distributed errors. For the purpose of ML estimation, we derive an expectation conditional maximization either algorithm, which leads to an easy-to-implement version of iteratively reweighted least squares. The estimation performance of the algorithm is evaluated via Monte Carlo simulations for a Fourier as well as a spline model in connection with AR colored noise models of different orders and with three different sampling distributions generating the white noise components. We apply the algorithm to a vibration dataset recorded by a high-accuracy, single-axis accelerometer, focusing on the evaluation of the estimated AR colored noise model.

Measurement errors when estimating the vertical jump height with flight time using photocell devices: the example of Optojump.

PubMed

Attia, A; Dhahbi, W; Chaouachi, A; Padulo, J; Wong, D P; Chamari, K

2017-03-01

Common methods to estimate vertical jump height (VJH) are based on the measurements of flight time (FT) or vertical reaction force. This study aimed to assess the measurement errors when estimating the VJH with flight time using photocell devices in comparison with the gold standard jump height measured by a force plate (FP). The second purpose was to determine the intrinsic reliability of the Optojump photoelectric cells in estimating VJH. For this aim, 20 subjects (age: 22.50±1.24 years) performed maximal vertical jumps in three modalities in randomized order: the squat jump (SJ), counter-movement jump (CMJ), and CMJ with arm swing (CMJarm). Each trial was simultaneously recorded by the FP and Optojump devices. High intra-class correlation coefficients (ICCs) for validity (0.98-0.99) and low limits of agreement (less than 1.4 cm) were found; even a systematic difference in jump height was consistently observed between FT and double integration of force methods (-31% to -27%; p<0.001) and a large effect size (Cohen's d >1.2). Intra-session reliability of Optojump was excellent, with ICCs ranging from 0.98 to 0.99, low coefficients of variation (3.98%), and low standard errors of measurement (0.8 cm). It was concluded that there was a high correlation between the two methods to estimate the vertical jump height, but the FT method cannot replace the gold standard, due to the large systematic bias. According to our results, the equations of each of the three jump modalities were presented in order to obtain a better estimation of the jump height.

Measurement errors when estimating the vertical jump height with flight time using photocell devices: the example of Optojump

PubMed Central

Attia, A; Chaouachi, A; Padulo, J; Wong, DP; Chamari, K

2016-01-01

Common methods to estimate vertical jump height (VJH) are based on the measurements of flight time (FT) or vertical reaction force. This study aimed to assess the measurement errors when estimating the VJH with flight time using photocell devices in comparison with the gold standard jump height measured by a force plate (FP). The second purpose was to determine the intrinsic reliability of the Optojump photoelectric cells in estimating VJH. For this aim, 20 subjects (age: 22.50±1.24 years) performed maximal vertical jumps in three modalities in randomized order: the squat jump (SJ), counter-movement jump (CMJ), and CMJ with arm swing (CMJarm). Each trial was simultaneously recorded by the FP and Optojump devices. High intra-class correlation coefficients (ICCs) for validity (0.98-0.99) and low limits of agreement (less than 1.4 cm) were found; even a systematic difference in jump height was consistently observed between FT and double integration of force methods (-31% to -27%; p<0.001) and a large effect size (Cohen’s d>1.2). Intra-session reliability of Optojump was excellent, with ICCs ranging from 0.98 to 0.99, low coefficients of variation (3.98%), and low standard errors of measurement (0.8 cm). It was concluded that there was a high correlation between the two methods to estimate the vertical jump height, but the FT method cannot replace the gold standard, due to the large systematic bias. According to our results, the equations of each of the three jump modalities were presented in order to obtain a better estimation of the jump height. PMID:28416900

Phase error statistics of a phase-locked loop synchronized direct detection optical PPM communication system

NASA Technical Reports Server (NTRS)

Natarajan, Suresh; Gardner, C. S.

1987-01-01

Receiver timing synchronization of an optical Pulse-Position Modulation (PPM) communication system can be achieved using a phased-locked loop (PLL), provided the photodetector output is suitably processed. The magnitude of the PLL phase error is a good indicator of the timing error at the receiver decoder. The statistics of the phase error are investigated while varying several key system parameters such as PPM order, signal and background strengths, and PPL bandwidth. A practical optical communication system utilizing a laser diode transmitter and an avalanche photodiode in the receiver is described, and the sampled phase error data are presented. A linear regression analysis is applied to the data to obtain estimates of the relational constants involving the phase error variance and incident signal power.

A fiber Bragg grating sensor system for estimating the large deflection of a lightweight flexible beam

NASA Astrophysics Data System (ADS)

Peng, Te; Yang, Yangyang; Ma, Lina; Yang, Huayong

2016-10-01

A sensor system based on fiber Bragg grating (FBG) is presented which is to estimate the deflection of a lightweight flexible beam, including the tip position and the tip rotation angle. In this paper, the classical problem of the deflection of a lightweight flexible beam of linear elastic material is analysed. We present the differential equation governing the behavior of a physical system and show that this equation although straightforward in appearance, is in fact rather difficult to solve due to the presence of a non-linear term. We used epoxy glue to attach the FBG sensors to specific locations upper and lower surface of the beam in order to measure local strain measurements. A quasi-distributed FBG static strain sensor network is designed and established. The estimation results from FBG sensors are also compared to reference displacements from the ANSYS simulation results and the experimental results obtained in the laboratory in the static case. The errors of the estimation by FBG sensors are analysed for further error-correction and option-design. When the load weight is 20g, the precision is the highest, the position errors ex and ex are 0.19%, 0.14% respectively, the rotation error eθ, is 1.23%.

A First Order Wavefront Estimation Algorithm for P1640 Calibrator

NASA Technical Reports Server (NTRS)

Zhaia, C.; Vasisht, G.; Shao, M.; Lockhart, T.; Cady, E.; Oppenheimer, B.; Burruss, R.; Roberts, J.; Beichman, C.; Brenner, D.;

High accuracy navigation information estimation for inertial system using the multi-model EKF fusing adams explicit formula applied to underwater gliders.

PubMed

Huang, Haoqian; Chen, Xiyuan; Zhang, Bo; Wang, Jian

2017-01-01

The underwater navigation system, mainly consisting of MEMS inertial sensors, is a key technology for the wide application of underwater gliders and plays an important role in achieving high accuracy navigation and positioning for a long time of period. However, the navigation errors will accumulate over time because of the inherent errors of inertial sensors, especially for MEMS grade IMU (Inertial Measurement Unit) generally used in gliders. The dead reckoning module is added to compensate the errors. In the complicated underwater environment, the performance of MEMS sensors is degraded sharply and the errors will become much larger. It is difficult to establish the accurate and fixed error model for the inertial sensor. Therefore, it is very hard to improve the accuracy of navigation information calculated by sensors. In order to solve the problem mentioned, the more suitable filter which integrates the multi-model method with an EKF approach can be designed according to different error models to give the optimal estimation for the state. The key parameters of error models can be used to determine the corresponding filter. The Adams explicit formula which has an advantage of high precision prediction is simultaneously fused into the above filter to achieve the much more improvement in attitudes estimation accuracy. The proposed algorithm has been proved through theory analyses and has been tested by both vehicle experiments and lake trials. Results show that the proposed method has better accuracy and effectiveness in terms of attitudes estimation compared with other methods mentioned in the paper for inertial navigation applied to underwater gliders. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

System IDentification Programs for AirCraft (SIDPAC)

NASA Technical Reports Server (NTRS)

Morelli, Eugene A.

2002-01-01

A collection of computer programs for aircraft system identification is described and demonstrated. The programs, collectively called System IDentification Programs for AirCraft, or SIDPAC, were developed in MATLAB as m-file functions. SIDPAC has been used successfully at NASA Langley Research Center with data from many different flight test programs and wind tunnel experiments. SIDPAC includes routines for experiment design, data conditioning, data compatibility analysis, model structure determination, equation-error and output-error parameter estimation in both the time and frequency domains, real-time and recursive parameter estimation, low order equivalent system identification, estimated parameter error calculation, linear and nonlinear simulation, plotting, and 3-D visualization. An overview of SIDPAC capabilities is provided, along with a demonstration of the use of SIDPAC with real flight test data from the NASA Glenn Twin Otter aircraft. The SIDPAC software is available without charge to U.S. citizens by request to the author, contingent on the requestor completing a NASA software usage agreement.

Spacecraft attitude determination using a second-order nonlinear filter

NASA Technical Reports Server (NTRS)

Vathsal, S.

1987-01-01

The stringent attitude determination accuracy and faster slew maneuver requirements demanded by present-day spacecraft control systems motivate the development of recursive nonlinear filters for attitude estimation. This paper presents the second-order filter development for the estimation of attitude quaternion using three-axis gyro and star tracker measurement data. Performance comparisons have been made by computer simulation of system models and filter mechanization. It is shown that the second-order filter consistently performs better than the extended Kalman filter when the performance index of the root sum square estimation error of the quaternion vector is compared. The second-order filter identifies the gyro drift rates faster than the extended Kalman filter. The uniqueness of this algorithm is the online generation of the time-varying process and measurement noise covariance matrices, derived as a function or the process and measurement nonlinearity, respectively.

Automated River Reach Definition Strategies: Applications for the Surface Water and Ocean Topography Mission

NASA Astrophysics Data System (ADS)

Frasson, Renato Prata de Moraes; Wei, Rui; Durand, Michael; Minear, J. Toby; Domeneghetti, Alessio; Schumann, Guy; Williams, Brent A.; Rodriguez, Ernesto; Picamilh, Christophe; Lion, Christine; Pavelsky, Tamlin; Garambois, Pierre-André

2017-10-01

The upcoming Surface Water and Ocean Topography (SWOT) mission will measure water surface heights and widths for rivers wider than 100 m. At its native resolution, SWOT height errors are expected to be on the order of meters, which prevent the calculation of water surface slopes and the use of slope-dependent discharge equations. To mitigate height and width errors, the high-resolution measurements will be grouped into reaches (˜5 to 15 km), where slope and discharge are estimated. We describe three automated river segmentation strategies for defining optimum reaches for discharge estimation: (1) arbitrary lengths, (2) identification of hydraulic controls, and (3) sinuosity. We test our methodologies on 9 and 14 simulated SWOT overpasses over the Sacramento and the Po Rivers, respectively, which we compare against hydraulic models of each river. Our results show that generally, height, width, and slope errors decrease with increasing reach length. However, the hydraulic controls and the sinuosity methods led to better slopes and often height errors that were either smaller or comparable to those of arbitrary reaches of compatible sizes. Estimated discharge errors caused by the propagation of height, width, and slope errors through the discharge equation were often smaller for sinuosity (on average 8.5% for the Sacramento and 6.9% for the Po) and hydraulic control (Sacramento: 7.3% and Po: 5.9%) reaches than for arbitrary reaches of comparable lengths (Sacramento: 8.6% and Po: 7.8%). This analysis suggests that reach definition methods that preserve the hydraulic properties of the river network may lead to better discharge estimates.

Landmark-Based Drift Compensation Algorithm for Inertial Pedestrian Navigation

PubMed Central

Munoz Diaz, Estefania; Caamano, Maria; Fuentes Sánchez, Francisco Javier

2017-01-01

The navigation of pedestrians based on inertial sensors, i.e., accelerometers and gyroscopes, has experienced a great growth over the last years. However, the noise of medium- and low-cost sensors causes a high error in the orientation estimation, particularly in the yaw angle. This error, called drift, is due to the bias of the z-axis gyroscope and other slow changing errors, such as temperature variations. We propose a seamless landmark-based drift compensation algorithm that only uses inertial measurements. The proposed algorithm adds a great value to the state of the art, because the vast majority of the drift elimination algorithms apply corrections to the estimated position, but not to the yaw angle estimation. Instead, the presented algorithm computes the drift value and uses it to prevent yaw errors and therefore position errors. In order to achieve this goal, a detector of landmarks, i.e., corners and stairs, and an association algorithm have been developed. The results of the experiments show that it is possible to reliably detect corners and stairs using only inertial measurements eliminating the need that the user takes any action, e.g., pressing a button. Associations between re-visited landmarks are successfully made taking into account the uncertainty of the position. After that, the drift is computed out of all associations and used during a post-processing stage to obtain a low-drifted yaw angle estimation, that leads to successfully drift compensated trajectories. The proposed algorithm has been tested with quasi-error-free turn rate measurements introducing known biases and with medium-cost gyroscopes in 3D indoor and outdoor scenarios. PMID:28671622

An adaptive discontinuous Galerkin solver for aerodynamic flows

NASA Astrophysics Data System (ADS)

Burgess, Nicholas K.

This work considers the accuracy, efficiency, and robustness of an unstructured high-order accurate discontinuous Galerkin (DG) solver for computational fluid dynamics (CFD). Recently, there has been a drive to reduce the discretization error of CFD simulations using high-order methods on unstructured grids. However, high-order methods are often criticized for lacking robustness and having high computational cost. The goal of this work is to investigate methods that enhance the robustness of high-order discontinuous Galerkin (DG) methods on unstructured meshes, while maintaining low computational cost and high accuracy of the numerical solutions. This work investigates robustness enhancement of high-order methods by examining effective non-linear solvers, shock capturing methods, turbulence model discretizations and adaptive refinement techniques. The goal is to develop an all encompassing solver that can simulate a large range of physical phenomena, where all aspects of the solver work together to achieve a robust, efficient and accurate solution strategy. The components and framework for a robust high-order accurate solver that is capable of solving viscous, Reynolds Averaged Navier-Stokes (RANS) and shocked flows is presented. In particular, this work discusses robust discretizations of the turbulence model equation used to close the RANS equations, as well as stable shock capturing strategies that are applicable across a wide range of discretization orders and applicable to very strong shock waves. Furthermore, refinement techniques are considered as both efficiency and robustness enhancement strategies. Additionally, efficient non-linear solvers based on multigrid and Krylov subspace methods are presented. The accuracy, efficiency, and robustness of the solver is demonstrated using a variety of challenging aerodynamic test problems, which include turbulent high-lift and viscous hypersonic flows. Adaptive mesh refinement was found to play a critical role in obtaining a robust and efficient high-order accurate flow solver. A goal-oriented error estimation technique has been developed to estimate the discretization error of simulation outputs. For high-order discretizations, it is shown that functional output error super-convergence can be obtained, provided the discretization satisfies a property known as dual consistency. The dual consistency of the DG methods developed in this work is shown via mathematical analysis and numerical experimentation. Goal-oriented error estimation is also used to drive an hp-adaptive mesh refinement strategy, where a combination of mesh or h-refinement, and order or p-enrichment, is employed based on the smoothness of the solution. The results demonstrate that the combination of goal-oriented error estimation and hp-adaptation yield superior accuracy, as well as enhanced robustness and efficiency for a variety of aerodynamic flows including flows with strong shock waves. This work demonstrates that DG discretizations can be the basis of an accurate, efficient, and robust CFD solver. Furthermore, enhancing the robustness of DG methods does not adversely impact the accuracy or efficiency of the solver for challenging and complex flow problems. In particular, when considering the computation of shocked flows, this work demonstrates that the available shock capturing techniques are sufficiently accurate and robust, particularly when used in conjunction with adaptive mesh refinement . This work also demonstrates that robust solutions of the Reynolds Averaged Navier-Stokes (RANS) and turbulence model equations can be obtained for complex and challenging aerodynamic flows. In this context, the most robust strategy was determined to be a low-order turbulence model discretization coupled to a high-order discretization of the RANS equations. Although RANS solutions using high-order accurate discretizations of the turbulence model were obtained, the behavior of current-day RANS turbulence models discretized to high-order was found to be problematic, leading to solver robustness issues. This suggests that future work is warranted in the area of turbulence model formulation for use with high-order discretizations. Alternately, the use of Large-Eddy Simulation (LES) subgrid scale models with high-order DG methods offers the potential to leverage the high accuracy of these methods for very high fidelity turbulent simulations. This thesis has developed the algorithmic improvements that will lay the foundation for the development of a three-dimensional high-order flow solution strategy that can be used as the basis for future LES simulations.

Estimation of errors in the inverse modeling of accidental release of atmospheric pollutant: Application to the reconstruction of the cesium-137 and iodine-131 source terms from the Fukushima Daiichi power plant

NASA Astrophysics Data System (ADS)

Winiarek, Victor; Bocquet, Marc; Saunier, Olivier; Mathieu, Anne

2012-03-01

A major difficulty when inverting the source term of an atmospheric tracer dispersion problem is the estimation of the prior errors: those of the atmospheric transport model, those ascribed to the representativity of the measurements, those that are instrumental, and those attached to the prior knowledge on the variables one seeks to retrieve. In the case of an accidental release of pollutant, the reconstructed source is sensitive to these assumptions. This sensitivity makes the quality of the retrieval dependent on the methods used to model and estimate the prior errors of the inverse modeling scheme. We propose to use an estimation method for the errors' amplitude based on the maximum likelihood principle. Under semi-Gaussian assumptions, it takes into account, without approximation, the positivity assumption on the source. We apply the method to the estimation of the Fukushima Daiichi source term using activity concentrations in the air. The results are compared to an L-curve estimation technique and to Desroziers's scheme. The total reconstructed activities significantly depend on the chosen method. Because of the poor observability of the Fukushima Daiichi emissions, these methods provide lower bounds for cesium-137 and iodine-131 reconstructed activities. These lower bound estimates, 1.2 × 1016 Bq for cesium-137, with an estimated standard deviation range of 15%-20%, and 1.9 - 3.8 × 1017 Bq for iodine-131, with an estimated standard deviation range of 5%-10%, are of the same order of magnitude as those provided by the Japanese Nuclear and Industrial Safety Agency and about 5 to 10 times less than the Chernobyl atmospheric releases.

Robust double gain unscented Kalman filter for small satellite attitude estimation

NASA Astrophysics Data System (ADS)

Cao, Lu; Yang, Weiwei; Li, Hengnian; Zhang, Zhidong; Shi, Jianjun

2017-08-01

Limited by the low precision of small satellite sensors, the estimation theories with high performance remains the most popular research topic for the attitude estimation. The Kalman filter (KF) and its extensions have been widely applied in the satellite attitude estimation and achieved plenty of achievements. However, most of the existing methods just take use of the current time-step's priori measurement residuals to complete the measurement update and state estimation, which always ignores the extraction and utilization of the previous time-step's posteriori measurement residuals. In addition, the uncertainty model errors always exist in the attitude dynamic system, which also put forward the higher performance requirements for the classical KF in attitude estimation problem. Therefore, the novel robust double gain unscented Kalman filter (RDG-UKF) is presented in this paper to satisfy the above requirements for the small satellite attitude estimation with the low precision sensors. It is assumed that the system state estimation errors can be exhibited in the measurement residual; therefore, the new method is to derive the second Kalman gain Kk2 for making full use of the previous time-step's measurement residual to improve the utilization efficiency of the measurement data. Moreover, the sequence orthogonal principle and unscented transform (UT) strategy are introduced to robust and enhance the performance of the novel Kalman Filter in order to reduce the influence of existing uncertainty model errors. Numerical simulations show that the proposed RDG-UKF is more effective and robustness in dealing with the model errors and low precision sensors for the attitude estimation of small satellite by comparing with the classical unscented Kalman Filter (UKF).

Three-dimensional holoscopic image coding scheme using high-efficiency video coding with kernel-based minimum mean-square-error estimation

NASA Astrophysics Data System (ADS)

Liu, Deyang; An, Ping; Ma, Ran; Yang, Chao; Shen, Liquan; Li, Kai

2016-07-01

Three-dimensional (3-D) holoscopic imaging, also known as integral imaging, light field imaging, or plenoptic imaging, can provide natural and fatigue-free 3-D visualization. However, a large amount of data is required to represent the 3-D holoscopic content. Therefore, efficient coding schemes for this particular type of image are needed. A 3-D holoscopic image coding scheme with kernel-based minimum mean square error (MMSE) estimation is proposed. In the proposed scheme, the coding block is predicted by an MMSE estimator under statistical modeling. In order to obtain the signal statistical behavior, kernel density estimation (KDE) is utilized to estimate the probability density function of the statistical modeling. As bandwidth estimation (BE) is a key issue in the KDE problem, we also propose a BE method based on kernel trick. The experimental results demonstrate that the proposed scheme can achieve a better rate-distortion performance and a better visual rendering quality.

Reverse Transcription Errors and RNA-DNA Differences at Short Tandem Repeats.

PubMed

Fungtammasan, Arkarachai; Tomaszkiewicz, Marta; Campos-Sánchez, Rebeca; Eckert, Kristin A; DeGiorgio, Michael; Makova, Kateryna D

2016-10-01

Transcript variation has important implications for organismal function in health and disease. Most transcriptome studies focus on assessing variation in gene expression levels and isoform representation. Variation at the level of transcript sequence is caused by RNA editing and transcription errors, and leads to nongenetically encoded transcript variants, or RNA-DNA differences (RDDs). Such variation has been understudied, in part because its detection is obscured by reverse transcription (RT) and sequencing errors. It has only been evaluated for intertranscript base substitution differences. Here, we investigated transcript sequence variation for short tandem repeats (STRs). We developed the first maximum-likelihood estimator (MLE) to infer RT error and RDD rates, taking next generation sequencing error rates into account. Using the MLE, we empirically evaluated RT error and RDD rates for STRs in a large-scale DNA and RNA replicated sequencing experiment conducted in a primate species. The RT error rates increased exponentially with STR length and were biased toward expansions. The RDD rates were approximately 1 order of magnitude lower than the RT error rates. The RT error rates estimated with the MLE from a primate data set were concordant with those estimated with an independent method, barcoded RNA sequencing, from a Caenorhabditis elegans data set. Our results have important implications for medical genomics, as STR allelic variation is associated with >40 diseases. STR nonallelic transcript variation can also contribute to disease phenotype. The MLE and empirical rates presented here can be used to evaluate the probability of disease-associated transcripts arising due to RDD. © The Author 2016. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution.

Image processing methods to compensate for IFOV errors in microgrid imaging polarimeters

NASA Astrophysics Data System (ADS)

Ratliff, Bradley M.; Boger, James K.; Fetrow, Matthew P.; Tyo, J. Scott; Black, Wiley T.

2006-05-01

Long-wave infrared imaging Stokes vector polarimeters are used in many remote sensing applications. Imaging polarimeters require that several measurements be made under optically different conditions in order to estimate the polarization signature at a given scene point. This multiple-measurement requirement introduces error in the signature estimates, and the errors differ depending upon the type of measurement scheme used. Here, we investigate a LWIR linear microgrid polarimeter. This type of instrument consists of a mosaic of micropolarizers at different orientations that are masked directly onto a focal plane array sensor. In this scheme, each polarization measurement is acquired spatially and hence each is made at a different point in the scene. This is a significant source of error, as it violates the requirement that each polarization measurement have the same instantaneous field-of-view (IFOV). In this paper, we first study the amount of error introduced by the IFOV handicap in microgrid instruments. We then proceed to investigate means for mitigating the effects of these errors to improve the quality of polarimetric imagery. In particular, we examine different interpolation schemes and gauge their performance. These studies are completed through the use of both real instrumental and modeled data.

Five-equation and robust three-equation methods for solution verification of large eddy simulation

NASA Astrophysics Data System (ADS)

Dutta, Rabijit; Xing, Tao

2018-02-01

This study evaluates the recently developed general framework for solution verification methods for large eddy simulation (LES) using implicitly filtered LES of periodic channel flows at friction Reynolds number of 395 on eight systematically refined grids. The seven-equation method shows that the coupling error based on Hypothesis I is much smaller as compared with the numerical and modeling errors and therefore can be neglected. The authors recommend five-equation method based on Hypothesis II, which shows a monotonic convergence behavior of the predicted numerical benchmark ( S C ), and provides realistic error estimates without the need of fixing the orders of accuracy for either numerical or modeling errors. Based on the results from seven-equation and five-equation methods, less expensive three and four-equation methods for practical LES applications were derived. It was found that the new three-equation method is robust as it can be applied to any convergence types and reasonably predict the error trends. It was also observed that the numerical and modeling errors usually have opposite signs, which suggests error cancellation play an essential role in LES. When Reynolds averaged Navier-Stokes (RANS) based error estimation method is applied, it shows significant error in the prediction of S C on coarse meshes. However, it predicts reasonable S C when the grids resolve at least 80% of the total turbulent kinetic energy.

A minimalist approach to bias estimation for passive sensor measurements with targets of opportunity

NASA Astrophysics Data System (ADS)

Belfadel, Djedjiga; Osborne, Richard W.; Bar-Shalom, Yaakov

2013-09-01

In order to carry out data fusion, registration error correction is crucial in multisensor systems. This requires estimation of the sensor measurement biases. It is important to correct for these bias errors so that the multiple sensor measurements and/or tracks can be referenced as accurately as possible to a common tracking coordinate system. This paper provides a solution for bias estimation for the minimum number of passive sensors (two), when only targets of opportunity are available. The sensor measurements are assumed time-coincident (synchronous) and perfectly associated. Since these sensors provide only line of sight (LOS) measurements, the formation of a single composite Cartesian measurement obtained from fusing the LOS measurements from different sensors is needed to avoid the need for nonlinear filtering. We evaluate the Cramer-Rao Lower Bound (CRLB) on the covariance of the bias estimate, i.e., the quantification of the available information about the biases. Statistical tests on the results of simulations show that this method is statistically efficient, even for small sample sizes (as few as two sensors and six points on the trajectory of a single target of opportunity). We also show that the RMS position error is significantly improved with bias estimation compared with the target position estimation using the original biased measurements.

Nonlinear estimation theory applied to orbit determination

NASA Technical Reports Server (NTRS)

Choe, C. Y.

1972-01-01

The development of an approximate nonlinear filter using the Martingale theory and appropriate smoothing properties is considered. Both the first order and the second order moments were estimated. The filter developed can be classified as a modified Gaussian second order filter. Its performance was evaluated in a simulated study of the problem of estimating the state of an interplanetary space vehicle during both a simulated Jupiter flyby and a simulated Jupiter orbiter mission. In addition to the modified Gaussian second order filter, the modified truncated second order filter was also evaluated in the simulated study. Results obtained with each of these filters were compared with numerical results obtained with the extended Kalman filter and the performance of each filter is determined by comparison with the actual estimation errors. The simulations were designed to determine the effects of the second order terms in the dynamic state relations, the observation state relations, and the Kalman gain compensation term. It is shown that the Kalman gain-compensated filter which includes only the Kalman gain compensation term is superior to all of the other filters.

An Alternate Method for Estimating Dynamic Height from XBT Profiles Using Empirical Vertical Modes

NASA Technical Reports Server (NTRS)

Lagerloef, Gary S. E.

1994-01-01

A technique is presented that applies modal decomposition to estimate dynamic height (0-450 db) from Expendable BathyThermograph (XBT) temperature profiles. Salinity-Temperature-Depth (STD) data are used to establish empirical relationships between vertically integrated temperature profiles and empirical dynamic height modes. These are then applied to XBT data to estimate dynamic height. A standard error of 0.028 dynamic meters is obtained for the waters of the Gulf of Alaska- an ocean region subject to substantial freshwater buoyancy forcing and with a T-S relationship that has considerable scatter. The residual error is a substantial improvement relative to the conventional T-S correlation technique when applied to this region. Systematic errors between estimated and true dynamic height were evaluated. The 20-year-long time series at Ocean Station P (50 deg N, 145 deg W) indicated weak variations in the error interannually, but not seasonally. There were no evident systematic alongshore variations in the error in the ocean boundary current regime near the perimeter of the Alaska gyre. The results prove satisfactory for the purpose of this work, which is to generate dynamic height from XBT data for coanalysis with satellite altimeter data, given that the altimeter height precision is likewise on the order of 2-3 cm. While the technique has not been applied to other ocean regions where the T-S relation has less scatter, it is suggested that it could provide some improvement over previously applied methods, as well.

Identification of Low Order Equivalent System Models From Flight Test Data

NASA Technical Reports Server (NTRS)

Morelli, Eugene A.

2000-01-01

Identification of low order equivalent system dynamic models from flight test data was studied. Inputs were pilot control deflections, and outputs were aircraft responses, so the models characterized the total aircraft response including bare airframe and flight control system. Theoretical investigations were conducted and related to results found in the literature. Low order equivalent system modeling techniques using output error and equation error parameter estimation in the frequency domain were developed and validated on simulation data. It was found that some common difficulties encountered in identifying closed loop low order equivalent system models from flight test data could be overcome using the developed techniques. Implications for data requirements and experiment design were discussed. The developed methods were demonstrated using realistic simulation cases, then applied to closed loop flight test data from the NASA F-18 High Alpha Research Vehicle.

Rotorcraft system identification techniques for handling qualities and stability and control evaluation

NASA Technical Reports Server (NTRS)

Hall, W. E., Jr.; Gupta, N. K.; Hansen, R. S.

1978-01-01

An integrated approach to rotorcraft system identification is described. This approach consists of sequential application of (1) data filtering to estimate states of the system and sensor errors, (2) model structure estimation to isolate significant model effects, and (3) parameter identification to quantify the coefficient of the model. An input design algorithm is described which can be used to design control inputs which maximize parameter estimation accuracy. Details of each aspect of the rotorcraft identification approach are given. Examples of both simulated and actual flight data processing are given to illustrate each phase of processing. The procedure is shown to provide means of calibrating sensor errors in flight data, quantifying high order state variable models from the flight data, and consequently computing related stability and control design models.

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.

Building unbiased estimators from non-gaussian likelihoods with application to shear estimation

DOE PAGES

Madhavacheril, Mathew S.; McDonald, Patrick; Sehgal, Neelima; ...

2015-01-15

We develop a general framework for generating estimators of a given quantity which are unbiased to a given order in the difference between the true value of the underlying quantity and the fiducial position in theory space around which we expand the likelihood. We apply this formalism to rederive the optimal quadratic estimator and show how the replacement of the second derivative matrix with the Fisher matrix is a generic way of creating an unbiased estimator (assuming choice of the fiducial model is independent of data). Next we apply the approach to estimation of shear lensing, closely following the workmore » of Bernstein and Armstrong (2014). Our first order estimator reduces to their estimator in the limit of zero shear, but it also naturally allows for the case of non-constant shear and the easy calculation of correlation functions or power spectra using standard methods. Both our first-order estimator and Bernstein and Armstrong’s estimator exhibit a bias which is quadratic in true shear. Our third-order estimator is, at least in the realm of the toy problem of Bernstein and Armstrong, unbiased to 0.1% in relative shear errors Δg/g for shears up to |g| = 0.2.« less

Building unbiased estimators from non-Gaussian likelihoods with application to shear estimation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Madhavacheril, Mathew S.; Sehgal, Neelima; McDonald, Patrick

2015-01-01

We develop a general framework for generating estimators of a given quantity which are unbiased to a given order in the difference between the true value of the underlying quantity and the fiducial position in theory space around which we expand the likelihood. We apply this formalism to rederive the optimal quadratic estimator and show how the replacement of the second derivative matrix with the Fisher matrix is a generic way of creating an unbiased estimator (assuming choice of the fiducial model is independent of data). Next we apply the approach to estimation of shear lensing, closely following the workmore » of Bernstein and Armstrong (2014). Our first order estimator reduces to their estimator in the limit of zero shear, but it also naturally allows for the case of non-constant shear and the easy calculation of correlation functions or power spectra using standard methods. Both our first-order estimator and Bernstein and Armstrong's estimator exhibit a bias which is quadratic in true shear. Our third-order estimator is, at least in the realm of the toy problem of Bernstein and Armstrong, unbiased to 0.1% in relative shear errors Δg/g for shears up to |g|=0.2.« less

Stochastic process approximation for recursive estimation with guaranteed bound on the error covariance

NASA Technical Reports Server (NTRS)

Menga, G.

1975-01-01

An approach, is proposed for the design of approximate, fixed order, discrete time realizations of stochastic processes from the output covariance over a finite time interval, was proposed. No restrictive assumptions are imposed on the process; it can be nonstationary and lead to a high dimension realization. Classes of fixed order models are defined, having the joint covariance matrix of the combined vector of the outputs in the interval of definition greater or equal than the process covariance; (the difference matrix is nonnegative definite). The design is achieved by minimizing, in one of those classes, a measure of the approximation between the model and the process evaluated by the trace of the difference of the respective covariance matrices. Models belonging to these classes have the notable property that, under the same measurement system and estimator structure, the output estimation error covariance matrix computed on the model is an upper bound of the corresponding covariance on the real process. An application of the approach is illustrated by the modeling of random meteorological wind profiles from the statistical analysis of historical data.

Estimating and comparing microbial diversity in the presence of sequencing errors

PubMed Central

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 approach aims to compare diversity estimates for equally-large or equally-complete samples; it is based on the seamless rarefaction and extrapolation sampling curves of Hill numbers, specifically for q = 0, 1 and 2. (2) An asymptotic approach refers to the comparison of the estimated asymptotic diversity profiles. That is, this approach compares the estimated profiles for complete samples or samples whose size tends to be sufficiently large. It is based on statistical estimation of the true Hill number of any order q ≥ 0. In the two approaches, replacing the spurious singleton count by our estimated count, we can greatly remove the positive biases associated with diversity estimates due to spurious singletons and also make fair comparisons across microbial communities, as illustrated in our simulation results and in applying our method to analyze sequencing data from viral metagenomes. PMID:26855872

Accelerating Convergence in Molecular Dynamics Simulations of Solutes in Lipid Membranes by Conducting a Random Walk along the Bilayer Normal.

PubMed

Neale, Chris; Madill, Chris; Rauscher, Sarah; Pomès, Régis

2013-08-13

All molecular dynamics simulations are susceptible to sampling errors, which degrade the accuracy and precision of observed values. The statistical convergence of simulations containing atomistic lipid bilayers is limited by the slow relaxation of the lipid phase, which can exceed hundreds of nanoseconds. These long conformational autocorrelation times are exacerbated in the presence of charged solutes, which can induce significant distortions of the bilayer structure. Such long relaxation times represent hidden barriers that induce systematic sampling errors in simulations of solute insertion. To identify optimal methods for enhancing sampling efficiency, we quantitatively evaluate convergence rates using generalized ensemble sampling algorithms in calculations of the potential of mean force for the insertion of the ionic side chain analog of arginine in a lipid bilayer. Umbrella sampling (US) is used to restrain solute insertion depth along the bilayer normal, the order parameter commonly used in simulations of molecular solutes in lipid bilayers. When US simulations are modified to conduct random walks along the bilayer normal using a Hamiltonian exchange algorithm, systematic sampling errors are eliminated more rapidly and the rate of statistical convergence of the standard free energy of binding of the solute to the lipid bilayer is increased 3-fold. We compute the ratio of the replica flux transmitted across a defined region of the order parameter to the replica flux that entered that region in Hamiltonian exchange simulations. We show that this quantity, the transmission factor, identifies sampling barriers in degrees of freedom orthogonal to the order parameter. The transmission factor is used to estimate the depth-dependent conformational autocorrelation times of the simulation system, some of which exceed the simulation time, and thereby identify solute insertion depths that are prone to systematic sampling errors and estimate the lower bound of the amount of sampling that is required to resolve these sampling errors. Finally, we extend our simulations and verify that the conformational autocorrelation times estimated by the transmission factor accurately predict correlation times that exceed the simulation time scale-something that, to our knowledge, has never before been achieved.

Accurate characterisation of hole size and location by projected fringe profilometry

NASA Astrophysics Data System (ADS)

Wu, Yuxiang; Dantanarayana, Harshana G.; Yue, Huimin; Huntley, Jonathan M.

2018-06-01

The ability to accurately estimate the location and geometry of holes is often required in the field of quality control and automated assembly. Projected fringe profilometry is a potentially attractive technique on account of being non-contacting, of lower cost, and orders of magnitude faster than the traditional coordinate measuring machine. However, we demonstrate in this paper that fringe projection is susceptible to significant (hundreds of µm) measurement artefacts in the neighbourhood of hole edges, which give rise to errors of a similar magnitude in the estimated hole geometry. A mechanism for the phenomenon is identified based on the finite size of the imaging system’s point spread function and the resulting bias produced near to sample discontinuities in geometry and reflectivity. A mathematical model is proposed, from which a post-processing compensation algorithm is developed to suppress such errors around the holes. The algorithm includes a robust and accurate sub-pixel edge detection method based on a Fourier descriptor of the hole contour. The proposed algorithm was found to reduce significantly the measurement artefacts near the hole edges. As a result, the errors in estimated hole radius were reduced by up to one order of magnitude, to a few tens of µm for hole radii in the range 2–15 mm, compared to those from the uncompensated measurements.

Simultaneous estimation of cross-validation errors in least squares collocation applied for statistical testing and evaluation of the noise variance components

NASA Astrophysics Data System (ADS)

Behnabian, Behzad; Mashhadi Hossainali, Masoud; Malekzadeh, Ahad

2018-02-01

The cross-validation technique is a popular method to assess and improve the quality of prediction by least squares collocation (LSC). We present a formula for direct estimation of the vector of cross-validation errors (CVEs) in LSC which is much faster than element-wise CVE computation. We show that a quadratic form of CVEs follows Chi-squared distribution. Furthermore, a posteriori noise variance factor is derived by the quadratic form of CVEs. In order to detect blunders in the observations, estimated standardized CVE is proposed as the test statistic which can be applied when noise variances are known or unknown. We use LSC together with the methods proposed in this research for interpolation of crustal subsidence in the northern coast of the Gulf of Mexico. The results show that after detection and removing outliers, the root mean square (RMS) of CVEs and estimated noise standard deviation are reduced about 51 and 59%, respectively. In addition, RMS of LSC prediction error at data points and RMS of estimated noise of observations are decreased by 39 and 67%, respectively. However, RMS of LSC prediction error on a regular grid of interpolation points covering the area is only reduced about 4% which is a consequence of sparse distribution of data points for this case study. The influence of gross errors on LSC prediction results is also investigated by lower cutoff CVEs. It is indicated that after elimination of outliers, RMS of this type of errors is also reduced by 19.5% for a 5 km radius of vicinity. We propose a method using standardized CVEs for classification of dataset into three groups with presumed different noise variances. The noise variance components for each of the groups are estimated using restricted maximum-likelihood method via Fisher scoring technique. Finally, LSC assessment measures were computed for the estimated heterogeneous noise variance model and compared with those of the homogeneous model. The advantage of the proposed method is the reduction in estimated noise levels for those groups with the fewer number of noisy data points.

An Expansion Formula with Higher-Order Derivatives for Fractional Operators of Variable Order

PubMed Central

Almeida, Ricardo

2013-01-01

We obtain approximation formulas for fractional integrals and derivatives of Riemann-Liouville and Marchaud types with a variable fractional order. The approximations involve integer-order derivatives only. An estimation for the error is given. The efficiency of the approximation method is illustrated with examples. As applications, we show how the obtained results are useful to solve differential equations, and problems of the calculus of variations that depend on fractional derivatives of Marchaud type. PMID:24319382

Adaptive reduction of constitutive model-form error using a posteriori error estimation techniques

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bishop, Joseph E.; Brown, Judith Alice

In engineering practice, models are typically kept as simple as possible for ease of setup and use, computational efficiency, maintenance, and overall reduced complexity to achieve robustness. In solid mechanics, a simple and efficient constitutive model may be favored over one that is more predictive, but is difficult to parameterize, is computationally expensive, or is simply not available within a simulation tool. In order to quantify the modeling error due to the choice of a relatively simple and less predictive constitutive model, we adopt the use of a posteriori model-form error-estimation techniques. Based on local error indicators in the energymore » norm, an algorithm is developed for reducing the modeling error by spatially adapting the material parameters in the simpler constitutive model. The resulting material parameters are not material properties per se, but depend on the given boundary-value problem. As a first step to the more general nonlinear case, we focus here on linear elasticity in which the “complex” constitutive model is general anisotropic elasticity and the chosen simpler model is isotropic elasticity. As a result, the algorithm for adaptive error reduction is demonstrated using two examples: (1) A transversely-isotropic plate with hole subjected to tension, and (2) a transversely-isotropic tube with two side holes subjected to torsion.« less

Adaptive reduction of constitutive model-form error using a posteriori error estimation techniques

DOE PAGES

Bishop, Joseph E.; Brown, Judith Alice

2018-06-15

In engineering practice, models are typically kept as simple as possible for ease of setup and use, computational efficiency, maintenance, and overall reduced complexity to achieve robustness. In solid mechanics, a simple and efficient constitutive model may be favored over one that is more predictive, but is difficult to parameterize, is computationally expensive, or is simply not available within a simulation tool. In order to quantify the modeling error due to the choice of a relatively simple and less predictive constitutive model, we adopt the use of a posteriori model-form error-estimation techniques. Based on local error indicators in the energymore » norm, an algorithm is developed for reducing the modeling error by spatially adapting the material parameters in the simpler constitutive model. The resulting material parameters are not material properties per se, but depend on the given boundary-value problem. As a first step to the more general nonlinear case, we focus here on linear elasticity in which the “complex” constitutive model is general anisotropic elasticity and the chosen simpler model is isotropic elasticity. As a result, the algorithm for adaptive error reduction is demonstrated using two examples: (1) A transversely-isotropic plate with hole subjected to tension, and (2) a transversely-isotropic tube with two side holes subjected to torsion.« less

A practical method of estimating standard error of age in the fission track dating method

USGS Publications Warehouse

Johnson, N.M.; McGee, V.E.; Naeser, C.W.

1979-01-01

A first-order approximation formula for the propagation of error in the fission track age equation is given by PA = C[P2s+P2i+P2??-2rPsPi] 1 2, where PA, Ps, Pi and P?? are the percentage error of age, of spontaneous track density, of induced track density, and of neutron dose, respectively, and C is a constant. The correlation, r, between spontaneous are induced track densities is a crucial element in the error analysis, acting generally to improve the standard error of age. In addition, the correlation parameter r is instrumental is specifying the level of neutron dose, a controlled variable, which will minimize the standard error of age. The results from the approximation equation agree closely with the results from an independent statistical model for the propagation of errors in the fission-track dating method. ?? 1979.

Global modeling of land water and energy balances. Part I: The land dynamics (LaD) model

USGS Publications Warehouse

Milly, P.C.D.; Shmakin, A.B.

2002-01-01

A simple model of large-scale land (continental) water and energy balances is presented. The model is an extension of an earlier scheme with a record of successful application in climate modeling. The most important changes from the original model include 1) introduction of non-water-stressed stomatal control of transpiration, in order to correct a tendency toward excessive evaporation: 2) conversion from globally constant parameters (with the exception of vegetation-dependent snow-free surface albedo) to more complete vegetation and soil dependence of all parameters, in order to provide more realistic representation of geographic variations in water and energy balances and to enable model-based investigations of land-cover change; 3) introduction of soil sensible heat storage and transport, in order to move toward realistic diurnal-cycle modeling; 4) a groundwater (saturated-zone) storage reservoir, in order to provide more realistic temporal variability of runoff; and 5) a rudimentary runoff-routing scheme for delivery of runoff to the ocean, in order to provide realistic freshwater forcing of the ocean general circulation model component of a global climate model. The new model is tested with forcing from the International Satellite Land Surface Climatology Project Initiative I global dataset and a recently produced observation-based water-balance dataset for major river basins of the world. Model performance is evaluated by comparing computed and observed runoff ratios from many major river basins of the world. Special attention is given to distinguishing between two components of the apparent runoff ratio error: the part due to intrinsic model error and the part due to errors in the assumed precipitation forcing. The pattern of discrepancies between modeled and observed runoff ratios is consistent with results from a companion study of precipitation estimation errors. The new model is tuned by adjustment of a globally constant scale factor for non-water-stressed stomatal resistance. After tuning, significant overestimation of runoff is found in environments where an overall arid climate includes a brief but intense wet season. It is shown that this error may be explained by the neglect of upward soil water diffusion from below the root zone during the dry season. With the exception of such basins, and in the absence of precipitation errors. It is estimated that annual runoff ratios simulated by the model would have a root-mean-square error of about 0.05. The new model matches observations better than its predecessor, which has a negative runoff bias and greater scatter.

Assessment of the pseudo-tracking approach for the calculation of material acceleration and pressure fields from time-resolved PIV: part I. Error propagation

NASA Astrophysics Data System (ADS)

van Gent, P. L.; Schrijer, F. F. J.; van Oudheusden, B. W.

2018-04-01

Pseudo-tracking refers to the construction of imaginary particle paths from PIV velocity fields and the subsequent estimation of the particle (material) acceleration. In view of the variety of existing and possible alternative ways to perform the pseudo-tracking method, it is not straightforward to select a suitable combination of numerical procedures for its implementation. To address this situation, this paper extends the theoretical framework for the approach. The developed theory is verified by applying various implementations of pseudo-tracking to a simulated PIV experiment. The findings of the investigations allow us to formulate the following insights and practical recommendations: (1) the velocity errors along the imaginary particle track are primarily a function of velocity measurement errors and spatial velocity gradients; (2) the particle path may best be calculated with second-order accurate numerical procedures while ensuring that the CFL condition is met; (3) least-square fitting of a first-order polynomial is a suitable method to estimate the material acceleration from the track; and (4) a suitable track length may be selected on the basis of the variation in material acceleration with track length.

Real-time precise orbit determination of LEO satellites using a single-frequency GPS receiver: Preliminary results of Chinese SJ-9A satellite

NASA Astrophysics Data System (ADS)

Sun, Xiucong; Han, Chao; Chen, Pei

2017-10-01

Spaceborne Global Positioning System (GPS) receivers are widely used for orbit determination of low-Earth-orbiting (LEO) satellites. With the improvement of measurement accuracy, single-frequency receivers are recently considered for low-cost small satellite missions. In this paper, a Schmidt-Kalman filter which processes single-frequency GPS measurements and broadcast ephemerides is proposed for real-time precise orbit determination of LEO satellites. The C/A code and L1 phase are linearly combined to eliminate the first-order ionospheric effects. Systematic errors due to ionospheric delay residual, group delay variation, phase center variation, and broadcast ephemeris errors, are lumped together into a noise term, which is modeled as a first-order Gauss-Markov process. In order to reduce computational complexity, the colored noise is considered rather than estimated in the orbit determination process. This ensures that the covariance matrix accurately represents the distribution of estimation errors without increasing the dimension of the state vector. The orbit determination algorithm is tested with actual flight data from the single-frequency GPS receiver onboard China's small satellite Shi Jian-9A (SJ-9A). Preliminary results using a 7-h data arc on October 25, 2012 show that the Schmidt-Kalman filter performs better than the standard Kalman filter in terms of accuracy.

Exploring Discretization Error in Simulation-Based Aerodynamic Databases

NASA Technical Reports Server (NTRS)

Aftosmis, Michael J.; Nemec, Marian

2010-01-01

This work examines the level of discretization error in simulation-based aerodynamic databases and introduces strategies for error control. Simulations are performed using a parallel, multi-level Euler solver on embedded-boundary Cartesian meshes. Discretization errors in user-selected outputs are estimated using the method of adjoint-weighted residuals and we use adaptive mesh refinement to reduce these errors to specified tolerances. Using this framework, we examine the behavior of discretization error throughout a token database computed for a NACA 0012 airfoil consisting of 120 cases. We compare the cost and accuracy of two approaches for aerodynamic database generation. In the first approach, mesh adaptation is used to compute all cases in the database to a prescribed level of accuracy. The second approach conducts all simulations using the same computational mesh without adaptation. We quantitatively assess the error landscape and computational costs in both databases. This investigation highlights sensitivities of the database under a variety of conditions. The presence of transonic shocks or the stiffness in the governing equations near the incompressible limit are shown to dramatically increase discretization error requiring additional mesh resolution to control. Results show that such pathologies lead to error levels that vary by over factor of 40 when using a fixed mesh throughout the database. Alternatively, controlling this sensitivity through mesh adaptation leads to mesh sizes which span two orders of magnitude. We propose strategies to minimize simulation cost in sensitive regions and discuss the role of error-estimation in database quality.

On the error propagation of semi-Lagrange and Fourier methods for advection problems☆

PubMed Central

Einkemmer, Lukas; Ostermann, Alexander

2015-01-01

In this paper we study the error propagation of numerical schemes for the advection equation in the case where high precision is desired. The numerical methods considered are based on the fast Fourier transform, polynomial interpolation (semi-Lagrangian methods using a Lagrange or spline interpolation), and a discontinuous Galerkin semi-Lagrangian approach (which is conservative and has to store more than a single value per cell). We demonstrate, by carrying out numerical experiments, that the worst case error estimates given in the literature provide a good explanation for the error propagation of the interpolation-based semi-Lagrangian methods. For the discontinuous Galerkin semi-Lagrangian method, however, we find that the characteristic property of semi-Lagrangian error estimates (namely the fact that the error increases proportionally to the number of time steps) is not observed. We provide an explanation for this behavior and conduct numerical simulations that corroborate the different qualitative features of the error in the two respective types of semi-Lagrangian methods. The method based on the fast Fourier transform is exact but, due to round-off errors, susceptible to a linear increase of the error in the number of time steps. We show how to modify the Cooley–Tukey algorithm in order to obtain an error growth that is proportional to the square root of the number of time steps. Finally, we show, for a simple model, that our conclusions hold true if the advection solver is used as part of a splitting scheme. PMID:25844018

Self-calibration method without joint iteration for distributed small satellite SAR systems

NASA Astrophysics Data System (ADS)

Xu, Qing; Liao, Guisheng; Liu, Aifei; Zhang, Juan

2013-12-01

The performance of distributed small satellite synthetic aperture radar systems degrades significantly due to the unavoidable array errors, including gain, phase, and position errors, in real operating scenarios. In the conventional method proposed in (IEEE T Aero. Elec. Sys. 42:436-451, 2006), the spectrum components within one Doppler bin are considered as calibration sources. However, it is found in this article that the gain error estimation and the position error estimation in the conventional method can interact with each other. The conventional method may converge to suboptimal solutions in large position errors since it requires the joint iteration between gain-phase error estimation and position error estimation. In addition, it is also found that phase errors can be estimated well regardless of position errors when the zero Doppler bin is chosen. In this article, we propose a method obtained by modifying the conventional one, based on these two observations. In this modified method, gain errors are firstly estimated and compensated, which eliminates the interaction between gain error estimation and position error estimation. Then, by using the zero Doppler bin data, the phase error estimation can be performed well independent of position errors. Finally, position errors are estimated based on the Taylor-series expansion. Meanwhile, the joint iteration between gain-phase error estimation and position error estimation is not required. Therefore, the problem of suboptimal convergence, which occurs in the conventional method, can be avoided with low computational method. The modified method has merits of faster convergence and lower estimation error compared to the conventional one. Theoretical analysis and computer simulation results verified the effectiveness of the modified method.

As-built design specification for proportion estimate software subsystem

NASA Technical Reports Server (NTRS)

Obrien, S. (Principal Investigator)

1980-01-01

The Proportion Estimate Processor evaluates four estimation techniques in order to get an improved estimate of the proportion of a scene that is planted in a selected crop. The four techniques to be evaluated were provided by the techniques development section and are: (1) random sampling; (2) proportional allocation, relative count estimate; (3) proportional allocation, Bayesian estimate; and (4) sequential Bayesian allocation. The user is given two options for computation of the estimated mean square error. These are referred to as the cluster calculation option and the segment calculation option. The software for the Proportion Estimate Processor is operational on the IBM 3031 computer.

Rain radar measurement error estimation using data assimilation in an advection-based nowcasting system

NASA Astrophysics Data System (ADS)

Merker, Claire; Ament, Felix; Clemens, Marco

2017-04-01

The quantification of measurement uncertainty for rain radar data remains challenging. Radar reflectivity measurements are affected, amongst other things, by calibration errors, noise, blocking and clutter, and attenuation. Their combined impact on measurement accuracy is difficult to quantify due to incomplete process understanding and complex interdependencies. An improved quality assessment of rain radar measurements is of interest for applications both in meteorology and hydrology, for example for precipitation ensemble generation, rainfall runoff simulations, or in data assimilation for numerical weather prediction. Especially a detailed description of the spatial and temporal structure of errors is beneficial in order to make best use of the areal precipitation information provided by radars. Radar precipitation ensembles are one promising approach to represent spatially variable radar measurement errors. We present a method combining ensemble radar precipitation nowcasting with data assimilation to estimate radar measurement uncertainty at each pixel. This combination of ensemble forecast and observation yields a consistent spatial and temporal evolution of the radar error field. We use an advection-based nowcasting method to generate an ensemble reflectivity forecast from initial data of a rain radar network. Subsequently, reflectivity data from single radars is assimilated into the forecast using the Local Ensemble Transform Kalman Filter. The spread of the resulting analysis ensemble provides a flow-dependent, spatially and temporally correlated reflectivity error estimate at each pixel. We will present first case studies that illustrate the method using data from a high-resolution X-band radar network.

A measurement-based performability model for a multiprocessor system

NASA Technical Reports Server (NTRS)

Ilsueh, M. C.; Iyer, Ravi K.; Trivedi, K. S.

1987-01-01

A measurement-based performability model based on real error-data collected on a multiprocessor system is described. Model development from the raw errror-data to the estimation of cumulative reward is described. Both normal and failure behavior of the system are characterized. The measured data show that the holding times in key operational and failure states are not simple exponential and that semi-Markov process is necessary to model the system behavior. A reward function, based on the service rate and the error rate in each state, is then defined in order to estimate the performability of the system and to depict the cost of different failure types and recovery procedures.

Developing and Validating Path-Dependent Uncertainty Estimates for use with the Regional Seismic Travel Time (RSTT) Model

NASA Astrophysics Data System (ADS)

Begnaud, M. L.; Anderson, D. N.; Phillips, W. S.; Myers, S. C.; Ballard, S.

2016-12-01

The Regional Seismic Travel Time (RSTT) tomography model has been developed to improve travel time predictions for regional phases (Pn, Sn, Pg, Lg) in order to increase seismic location accuracy, especially for explosion monitoring. The RSTT model is specifically designed to exploit regional phases for location, especially when combined with teleseismic arrivals. The latest RSTT model (version 201404um) has been released (http://www.sandia.gov/rstt). Travel time uncertainty estimates for RSTT are determined using one-dimensional (1D), distance-dependent error models, that have the benefit of being very fast to use in standard location algorithms, but do not account for path-dependent variations in error, and structural inadequacy of the RSTTT model (e.g., model error). Although global in extent, the RSTT tomography model is only defined in areas where data exist. A simple 1D error model does not accurately model areas where RSTT has not been calibrated. We are developing and validating a new error model for RSTT phase arrivals by mathematically deriving this multivariate model directly from a unified model of RSTT embedded into a statistical random effects model that captures distance, path and model error effects. An initial method developed is a two-dimensional path-distributed method using residuals. The goals for any RSTT uncertainty method are for it to be both readily useful for the standard RSTT user as well as improve travel time uncertainty estimates for location. We have successfully tested using the new error model for Pn phases and will demonstrate the method and validation of the error model for Sn, Pg, and Lg phases.

Spatial interpolation schemes of daily precipitation for hydrologic modeling

USGS Publications Warehouse

Hwang, Y.; Clark, M.R.; Rajagopalan, B.; Leavesley, G.

2012-01-01

Distributed hydrologic models typically require spatial estimates of precipitation interpolated from sparsely located observational points to the specific grid points. We compare and contrast the performance of regression-based statistical methods for the spatial estimation of precipitation in two hydrologically different basins and confirmed that widely used regression-based estimation schemes fail to describe the realistic spatial variability of daily precipitation field. The methods assessed are: (1) inverse distance weighted average; (2) multiple linear regression (MLR); (3) climatological MLR; and (4) locally weighted polynomial regression (LWP). In order to improve the performance of the interpolations, the authors propose a two-step regression technique for effective daily precipitation estimation. In this simple two-step estimation process, precipitation occurrence is first generated via a logistic regression model before estimate the amount of precipitation separately on wet days. This process generated the precipitation occurrence, amount, and spatial correlation effectively. A distributed hydrologic model (PRMS) was used for the impact analysis in daily time step simulation. Multiple simulations suggested noticeable differences between the input alternatives generated by three different interpolation schemes. Differences are shown in overall simulation error against the observations, degree of explained variability, and seasonal volumes. Simulated streamflows also showed different characteristics in mean, maximum, minimum, and peak flows. Given the same parameter optimization technique, LWP input showed least streamflow error in Alapaha basin and CMLR input showed least error (still very close to LWP) in Animas basin. All of the two-step interpolation inputs resulted in lower streamflow error compared to the directly interpolated inputs. ?? 2011 Springer-Verlag.

Augmented GNSS Differential Corrections Minimum Mean Square Error Estimation Sensitivity to Spatial Correlation Modeling Errors

PubMed Central

Kassabian, Nazelie; Presti, Letizia Lo; Rispoli, Francesco

2014-01-01

Railway signaling is a safety system that has evolved over the last couple of centuries towards autonomous functionality. Recently, great effort is being devoted in this field, towards the use and exploitation of Global Navigation Satellite System (GNSS) signals and GNSS augmentation systems in view of lower railway track equipments and maintenance costs, that is a priority to sustain the investments for modernizing the local and regional lines most of which lack automatic train protection systems and are still manually operated. The objective of this paper is to assess the sensitivity of the Linear Minimum Mean Square Error (LMMSE) algorithm to modeling errors in the spatial correlation function that characterizes true pseudorange Differential Corrections (DCs). This study is inspired by the railway application; however, it applies to all transportation systems, including the road sector, that need to be complemented by an augmentation system in order to deliver accurate and reliable positioning with integrity specifications. A vector of noisy pseudorange DC measurements are simulated, assuming a Gauss-Markov model with a decay rate parameter inversely proportional to the correlation distance that exists between two points of a certain environment. The LMMSE algorithm is applied on this vector to estimate the true DC, and the estimation error is compared to the noise added during simulation. The results show that for large enough correlation distance to Reference Stations (RSs) distance separation ratio values, the LMMSE brings considerable advantage in terms of estimation error accuracy and precision. Conversely, the LMMSE algorithm may deteriorate the quality of the DC measurements whenever the ratio falls below a certain threshold. PMID:24922454

A selective-update affine projection algorithm with selective input vectors

NASA Astrophysics Data System (ADS)

Kong, NamWoong; Shin, JaeWook; Park, PooGyeon

2011-10-01

This paper proposes an affine projection algorithm (APA) with selective input vectors, which based on the concept of selective-update in order to reduce estimation errors and computations. The algorithm consists of two procedures: input- vector-selection and state-decision. The input-vector-selection procedure determines the number of input vectors by checking with mean square error (MSE) whether the input vectors have enough information for update. The state-decision procedure determines the current state of the adaptive filter by using the state-decision criterion. As the adaptive filter is in transient state, the algorithm updates the filter coefficients with the selected input vectors. On the other hand, as soon as the adaptive filter reaches the steady state, the update procedure is not performed. Through these two procedures, the proposed algorithm achieves small steady-state estimation errors, low computational complexity and low update complexity for colored input signals.

POSTPROCESSING MIXED FINITE ELEMENT METHODS FOR SOLVING CAHN-HILLIARD EQUATION: METHODS AND ERROR ANALYSIS

PubMed Central

Wang, Wansheng; Chen, Long; Zhou, Jie

2015-01-01

A postprocessing technique for mixed finite element methods for the Cahn-Hilliard equation is developed and analyzed. Once the mixed finite element approximations have been computed at a fixed time on the coarser mesh, the approximations are postprocessed by solving two decoupled Poisson equations in an enriched finite element space (either on a finer grid or a higher-order space) for which many fast Poisson solvers can be applied. The nonlinear iteration is only applied to a much smaller size problem and the computational cost using Newton and direct solvers is negligible compared with the cost of the linear problem. The analysis presented here shows that this technique remains the optimal rate of convergence for both the concentration and the chemical potential approximations. The corresponding error estimate obtained in our paper, especially the negative norm error estimates, are non-trivial and different with the existing results in the literatures. PMID:27110063

Expected trace gas and aerosol retrieval accuracy of the Geostationary Environment Monitoring Spectrometer

NASA Astrophysics Data System (ADS)

Jeong, U.; Kim, J.; Liu, X.; Lee, K. H.; Chance, K.; Song, C. H.

2015-12-01

The predicted accuracy of the trace gases and aerosol retrievals from the geostationary environment monitoring spectrometer (GEMS) was investigated. The GEMS is one of the first sensors to monitor NO2, SO2, HCHO, O3, and aerosols onboard geostationary earth orbit (GEO) over Asia. Since the GEMS is not launched yet, the simulated measurements and its precision were used in this study. The random and systematic component of the measurement error was estimated based on the instrument design. The atmospheric profiles were obtained from Model for Ozone And Related chemical Tracers (MOZART) simulations and surface reflectances were obtained from climatology of OMI Lambertian equivalent reflectance. The uncertainties of the GEMS trace gas and aerosol products were estimated based on the OE method using the atmospheric profile and surface reflectance. Most of the estimated uncertainties of NO2, HCHO, stratospheric and total O3 products satisfied the user's requirements with sufficient margin. However, about 26% of the estimated uncertainties of SO2 and about 30% of the estimated uncertainties of tropospheric O3 do not meet the required precision. Particularly the estimated uncertainty of SO2 is high in winter, when the emission is strong in East Asia. Further efforts are necessary in order to improve the retrieval accuracy of SO2 and tropospheric O3 in order to reach the scientific goal of GEMS. Random measurement error of GEMS was important for the NO2, SO2, and HCHO retrieval, while both the random and systematic measurement errors were important for the O3 retrievals. The degree of freedom for signal of tropospheric O3 was 0.8 ± 0.2 and that for stratospheric O3 was 2.9 ± 0.5. The estimated uncertainties of the aerosol retrieval from GEMS measurements were predicted to be lower than the required precision for the SZA range of the trace gas retrievals.

Estimating Discharge in Low-Order Rivers With High-Resolution Aerial Imagery

NASA Astrophysics Data System (ADS)

King, Tyler V.; Neilson, Bethany T.; Rasmussen, Mitchell T.

2018-02-01

Remote sensing of river discharge promises to augment in situ gauging stations, but the majority of research in this field focuses on large rivers (>50 m wide). We present a method for estimating volumetric river discharge in low-order (<50 m wide) rivers from remotely sensed data by coupling high-resolution imagery with one-dimensional hydraulic modeling at so-called virtual gauging stations. These locations were identified as locations where the river contracted under low flows, exposing a substantial portion of the river bed. Topography of the exposed river bed was photogrammetrically extracted from high-resolution aerial imagery while the geometry of the remaining inundated portion of the channel was approximated based on adjacent bank topography and maximum depth assumptions. Full channel bathymetry was used to create hydraulic models that encompassed virtual gauging stations. Discharge for each aerial survey was estimated with the hydraulic model by matching modeled and remotely sensed wetted widths. Based on these results, synthetic width-discharge rating curves were produced for each virtual gauging station. In situ observations were used to determine the accuracy of wetted widths extracted from imagery (mean error 0.36 m), extracted bathymetry (mean vertical RMSE 0.23 m), and discharge (mean percent error 7% with a standard deviation of 6%). Sensitivity analyses were conducted to determine the influence of inundated channel bathymetry and roughness parameters on estimated discharge. Comparison of synthetic rating curves produced through sensitivity analyses show that reasonable ranges of parameter values result in mean percent errors in predicted discharges of 12%-27%.

A Frequency-Domain Multipath Parameter Estimation and Mitigation Method for BOC-Modulated GNSS Signals

PubMed Central

Sun, Chao; Feng, Wenquan; Du, Songlin

2018-01-01

As multipath is one of the dominating error sources for high accuracy Global Navigation Satellite System (GNSS) applications, multipath mitigation approaches are employed to minimize this hazardous error in receivers. Binary offset carrier modulation (BOC), as a modernized signal structure, is adopted to achieve significant enhancement. However, because of its multi-peak autocorrelation function, conventional multipath mitigation techniques for binary phase shift keying (BPSK) signal would not be optimal. Currently, non-parametric and parametric approaches have been studied specifically aiming at multipath mitigation for BOC signals. Non-parametric techniques, such as Code Correlation Reference Waveforms (CCRW), usually have good feasibility with simple structures, but suffer from low universal applicability for different BOC signals. Parametric approaches can thoroughly eliminate multipath error by estimating multipath parameters. The problems with this category are at the high computation complexity and vulnerability to the noise. To tackle the problem, we present a practical parametric multipath estimation method in the frequency domain for BOC signals. The received signal is transferred to the frequency domain to separate out the multipath channel transfer function for multipath parameter estimation. During this process, we take the operations of segmentation and averaging to reduce both noise effect and computational load. The performance of the proposed method is evaluated and compared with the previous work in three scenarios. Results indicate that the proposed averaging-Fast Fourier Transform (averaging-FFT) method achieves good robustness in severe multipath environments with lower computational load for both low-order and high-order BOC signals. PMID:29495589

Estimation of regression laws for ground motion parameters using as case of study the Amatrice earthquake

NASA Astrophysics Data System (ADS)

Tiberi, Lara; Costa, Giovanni

2017-04-01

The possibility to directly associate the damages to the ground motion parameters is always a great challenge, in particular for civil protections. Indeed a ground motion parameter, estimated in near real time that can express the damages occurred after an earthquake, is fundamental to arrange the first assistance after an event. The aim of this work is to contribute to the estimation of the ground motion parameter that better describes the observed intensity, immediately after an event. This can be done calculating for each ground motion parameter estimated in a near real time mode a regression law which correlates the above-mentioned parameter to the observed macro-seismic intensity. This estimation is done collecting high quality accelerometric data in near field, filtering them at different frequency steps. The regression laws are calculated using two different techniques: the non linear least-squares (NLLS) Marquardt-Levenberg algorithm and the orthogonal distance methodology (ODR). The limits of the first methodology are the needed of initial values for the parameters a and b (set 1.0 in this study), and the constraint that the independent variable must be known with greater accuracy than the dependent variable. While the second algorithm is based on the estimation of the errors perpendicular to the line, rather than just vertically. The vertical errors are just the errors in the 'y' direction, so only for the dependent variable whereas the perpendicular errors take into account errors for both the variables, the dependent and the independent. This makes possible also to directly invert the relation, so the a and b values can be used also to express the gmps as function of I. For each law the standard deviation and R2 value are estimated in order to test the quality and the reliability of the found relation. The Amatrice earthquake of 24th August of 2016 is used as case of study to test the goodness of the calculated regression laws.

Modeling, estimation and identification methods for static shape determination of flexible structures. [for large space structure design

NASA Technical Reports Server (NTRS)

Rodriguez, G.; Scheid, R. E., Jr.

1986-01-01

This paper outlines methods for modeling, identification and estimation for static determination of flexible structures. The shape estimation schemes are based on structural models specified by (possibly interconnected) elliptic partial differential equations. The identification techniques provide approximate knowledge of parameters in elliptic systems. The techniques are based on the method of maximum-likelihood that finds parameter values such that the likelihood functional associated with the system model is maximized. The estimation methods are obtained by means of a function-space approach that seeks to obtain the conditional mean of the state given the data and a white noise characterization of model errors. The solutions are obtained in a batch-processing mode in which all the data is processed simultaneously. After methods for computing the optimal estimates are developed, an analysis of the second-order statistics of the estimates and of the related estimation error is conducted. In addition to outlining the above theoretical results, the paper presents typical flexible structure simulations illustrating performance of the shape determination methods.

Field evaluation of distance-estimation error during wetland-dependent bird surveys

USGS Publications Warehouse

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-count surveys. Evaluating distance-estimation errors will allow investigators to better evaluate the accuracy of avian density and trend estimates. Moreover, investigators who evaluate distance-estimation errors could employ recently developed models to incorporate distance-estimation error into analyses. We encourage further development of such models, including the inclusion of such models into distance-analysis software.

Asymptotic stability estimates near an equilibrium point

NASA Astrophysics Data System (ADS)

Dumas, H. Scott; Meyer, Kenneth R.; Palacián, Jesús F.; Yanguas, Patricia

2017-07-01

We use the error bounds for adiabatic invariants found in the work of Chartier, Murua and Sanz-Serna [3] to bound the solutions of a Hamiltonian system near an equilibrium over exponentially long times. Our estimates depend only on the linearized system and not on the higher order terms as in KAM theory, nor do we require any steepness or convexity conditions as in Nekhoroshev theory. We require that the equilibrium point where our estimate applies satisfy a type of formal stability called Lie stability.

Two-dimensional simulation of eccentric photorefraction images for ametropes: factors influencing the measurement.

PubMed

Wu, Yifei; Thibos, Larry N; Candy, T Rowan

2018-05-07

Eccentric photorefraction and Purkinje image tracking are used to estimate refractive state and eye position simultaneously. Beyond vision screening, they provide insight into typical and atypical visual development. Systematic analysis of the effect of refractive error and spectacles on photorefraction data is needed to gauge the accuracy and precision of the technique. Simulation of two-dimensional, double-pass eccentric photorefraction was performed (Zemax). The inward pass included appropriate light sources, lenses and a single surface pupil plane eye model to create an extended retinal image that served as the source for the outward pass. Refractive state, as computed from the luminance gradient in the image of the pupil captured by the model's camera, was evaluated for a range of refractive errors (-15D to +15D), pupil sizes (3 mm to 7 mm) and two sets of higher-order monochromatic aberrations. Instrument calibration was simulated using -8D to +8D trial lenses at the spectacle plane for: (1) vertex distances from 3 mm to 23 mm, (2) uncorrected and corrected hyperopic refractive errors of +4D and +7D, and (3) uncorrected and corrected astigmatism of 4D at four different axes. Empirical calibration of a commercial photorefractor was also compared with a wavefront aberrometer for human eyes. The pupil luminance gradient varied linearly with refractive state for defocus less than approximately 4D (5 mm pupil). For larger errors, the gradient magnitude saturated and then reduced, leading to under-estimation of refractive state. Additional inaccuracy (up to 1D for 8D of defocus) resulted from spectacle magnification in the pupil image, which would reduce precision in situations where vertex distance is variable. The empirical calibration revealed a constant offset between the two clinical instruments. Computational modelling demonstrates the principles and limitations of photorefraction to help users avoid potential measurement errors. Factors that could cause clinically significant errors in photorefraction estimates include high refractive error, vertex distance and magnification effects of a spectacle lens, increased higher-order monochromatic aberrations, and changes in primary spherical aberration with accommodation. The impact of these errors increases with increasing defocus. © 2018 The Authors Ophthalmic & Physiological Optics © 2018 The College of Optometrists.

First-Order System Least-Squares for the Navier-Stokes Equations

NASA Technical Reports Server (NTRS)

Bochev, P.; Cai, Z.; Manteuffel, T. A.; McCormick, S. F.

1996-01-01

This paper develops a least-squares approach to the solution of the incompressible Navier-Stokes equations in primitive variables. As with our earlier work on Stokes equations, we recast the Navier-Stokes equations as a first-order system by introducing a velocity flux variable and associated curl and trace equations. We show that the resulting system is well-posed, and that an associated least-squares principle yields optimal discretization error estimates in the H(sup 1) norm in each variable (including the velocity flux) and optimal multigrid convergence estimates for the resulting algebraic system.

Estimate of B(B¯→Xsγ) at O(αs2)

NASA Astrophysics Data System (ADS)

Misiak, M.; Asatrian, H. M.; Bieri, K.; Czakon, M.; Czarnecki, A.; Ewerth, T.; Ferroglia, A.; Gambino, P.; Gorbahn, M.; Greub, C.; Haisch, U.; Hovhannisyan, A.; Hurth, T.; Mitov, A.; Poghosyan, V.; Ślusarczyk, M.; Steinhauser, M.

2007-01-01

Combining our results for various O(αs2) corrections to the weak radiative B-meson decay, we are able to present the first estimate of the branching ratio at the next-to-next-to-leading order in QCD. We find B(B¯→Xsγ)=(3.15±0.23)×10-4 for Eγ>1.6GeV in the B¯-meson rest frame. The four types of uncertainties: nonperturbative (5%), parametric (3%), higher-order (3%), and mc-interpolation ambiguity (3%) have been added in quadrature to obtain the total error.

Attitude Representations for Kalman Filtering

NASA Technical Reports Server (NTRS)

Markley, F. Landis; Bauer, Frank H. (Technical Monitor)

2001-01-01

The four-component quaternion has the lowest dimensionality possible for a globally nonsingular attitude representation, it represents the attitude matrix as a homogeneous quadratic function, and its dynamic propagation equation is bilinear in the quaternion and the angular velocity. The quaternion is required to obey a unit norm constraint, though, so Kalman filters often employ a quaternion for the global attitude estimate and a three-component representation for small errors about the estimate. We consider these mixed attitude representations for both a first-order Extended Kalman filter and a second-order filter, as well for quaternion-norm-preserving attitude propagation.

The cost-saving effect and prevention of medication errors by clinical pharmacist intervention in a nephrology unit.

PubMed

Chen, Chia-Chi; Hsiao, Fei-Yuan; Shen, Li-Jiuan; Wu, Chien-Chih

2017-08-01

Medication errors may lead to adverse drug events (ADEs), which endangers patient safety and increases healthcare-related costs. The on-ward deployment of clinical pharmacists has been shown to reduce preventable ADEs, and save costs. The purpose of this study was to evaluate the ADEs prevention and cost-saving effects by clinical pharmacist deployment in a nephrology ward.This was a retrospective study, which compared the number of pharmacist interventions 1 year before and after a clinical pharmacist was deployed in a nephrology ward. The clinical pharmacist attended ward rounds, reviewed and revised all medication orders, and gave active recommendations of medication use. For intervention analysis, the numbers and types of the pharmacist's interventions in medication orders and the active recommendations were compared. For cost analysis, both estimated cost saving and avoidance were calculated and compared.The total numbers of pharmacist interventions in medication orders were 824 in 2012 (preintervention), and 1977 in 2013 (postintervention). The numbers of active recommendation were 40 in 2012, and 253 in 2013. The estimated cost savings in 2012 and 2013 were NT$52,072 and NT$144,138, respectively. The estimated cost avoidances of preventable ADEs in 2012 and 2013 were NT$3,383,700 and NT$7,342,200, respectively. The benefit/cost ratio increased from 4.29 to 9.36, and average admission days decreased by 2 days after the on-ward deployment of a clinical pharmacist.The number of pharmacist's interventions increased dramatically after her on-ward deployment. This service could reduce medication errors, preventable ADEs, and costs of both medications and potential ADEs.

Quantifying Carbon Flux Estimation Errors

NASA Astrophysics Data System (ADS)

Wesloh, D.

2017-12-01

Atmospheric Bayesian inversions have been used to estimate surface carbon dioxide (CO2) fluxes from global to sub-continental scales using atmospheric mixing ratio measurements. These inversions use an atmospheric transport model, coupled to a set of fluxes, in order to simulate mixing ratios that can then be compared to the observations. The comparison is then used to update the fluxes to better match the observations in a manner consistent with the uncertainties prescribed for each. However, inversion studies disagree with each other at continental scales, prompting further investigations to examine the causes of these differences. Inter-comparison studies have shown that the errors resulting from atmospheric transport inaccuracies are comparable to those from the errors in the prior fluxes. However, not as much effort has gone into studying the origins of the errors induced by errors in the transport as by errors in the prior distribution. This study uses a mesoscale transport model to evaluate the effects of representation errors in the observations and of incorrect descriptions of the transport. To obtain realizations of these errors, we performed an Observing System Simulation Experiments (OSSEs), with the transport model used for the inversion operating at two resolutions, one typical of a global inversion and the other of a mesoscale, and with various prior flux distributions to. Transport error covariances are inferred from an ensemble of perturbed mesoscale simulations while flux error covariances are computed using prescribed distributions and magnitudes. We examine how these errors can be diagnosed in the inversion process using aircraft, ground-based, and satellite observations of meteorological variables and CO2.

Ice Cores Dating With a New Inverse Method Taking Account of the Flow Modeling Errors

NASA Astrophysics Data System (ADS)

Lemieux-Dudon, B.; Parrenin, F.; Blayo, E.

2007-12-01

Deep ice cores extracted from Antarctica or Greenland recorded a wide range of past climatic events. In order to contribute to the Quaternary climate system understanding, the calculation of an accurate depth-age relationship is a crucial point. Up to now ice chronologies for deep ice cores estimated with inverse approaches are based on quite simplified ice-flow models that fail to reproduce flow irregularities and consequently to respect all available set of age markers. We describe in this paper, a new inverse method that takes into account the model uncertainty in order to circumvent the restrictions linked to the use of simplified flow models. This method uses first guesses on two flow physical entities, the ice thinning function and the accumulation rate and then identifies correction functions on both flow entities. We highlight two major benefits brought by this new method: first of all the ability to respect large set of observations and as a consequence, the feasibility to estimate a synchronized common ice chronology for several cores at the same time. This inverse approach relies on a bayesian framework. To respect the positive constraint on the searched correction functions, we assume lognormal probability distribution on one hand for the background errors, but also for one particular set of the observation errors. We test this new inversion method on three cores simultaneously (the two EPICA cores : DC and DML and the Vostok core) and we assimilate more than 150 observations (e.g.: age markers, stratigraphic links,...). We analyze the sensitivity of the solution with respect to the background information, especially the prior error covariance matrix. The confidence intervals based on the posterior covariance matrix calculation, are estimated on the correction functions and for the first time on the overall output chronologies.

A modified Holly-Preissmann scheme for simulating sharp concentration fronts in streams with steep velocity gradients using RIV1Q

NASA Astrophysics Data System (ADS)

Liu, Zhao-wei; Zhu, De-jun; Chen, Yong-can; Wang, Zhi-gang

2014-12-01

RIV1Q is the stand-alone water quality program of CE-QUAL-RIV1, a hydraulic and water quality model developed by U.S. Army Corps of Engineers Waterways Experiment Station. It utilizes an operator-splitting algorithm and the advection term in governing equation is treated using the explicit two-point, fourth-order accurate, Holly-Preissmann scheme, in order to preserve numerical accuracy for advection of sharp gradients in concentration. In the scheme, the spatial derivative of the transport equation, where the derivative of velocity is included, is introduced to update the first derivative of dependent variable. In the stream with larger cross-sectional variation, steep velocity gradient can be easily found and should be estimated correctly. In the original version of RIV1Q, however, the derivative of velocity is approximated by a finite difference which is first-order accurate. Its leading truncation error leads to the numerical error of concentration which is related with the velocity and concentration gradients and increases with the decreasing Courant number. The simulation may also be unstable when a sharp velocity drop occurs. In the present paper, the derivative of velocity is estimated with a modified second-order accurate scheme and the corresponding numerical error of concentration decreases. Additionally, the stability of the simulation is improved. The modified scheme is verified with a hypothetical channel case and the results demonstrate that satisfactory accuracy and stability can be achieved even when the Courant number is very low. Finally, the applicability of the modified scheme is discussed.

Modeling error distributions of growth curve models through Bayesian methods.

PubMed

Zhang, Zhiyong

2016-06-01

Growth curve models are widely used in social and behavioral sciences. However, typical growth curve models often assume that the errors are normally distributed although non-normal data may be even more common than normal data. In order to avoid possible statistical inference problems in blindly assuming normality, a general Bayesian framework is proposed to flexibly model normal and non-normal data through the explicit specification of the error distributions. A simulation study shows when the distribution of the error is correctly specified, one can avoid the loss in the efficiency of standard error estimates. A real example on the analysis of mathematical ability growth data from the Early Childhood Longitudinal Study, Kindergarten Class of 1998-99 is used to show the application of the proposed methods. Instructions and code on how to conduct growth curve analysis with both normal and non-normal error distributions using the the MCMC procedure of SAS are provided.

Measurement-based reliability/performability models

NASA Technical Reports Server (NTRS)

Hsueh, Mei-Chen

1987-01-01

Measurement-based models based on real error-data collected on a multiprocessor system are described. Model development from the raw error-data to the estimation of cumulative reward is also described. A workload/reliability model is developed based on low-level error and resource usage data collected on an IBM 3081 system during its normal operation in order to evaluate the resource usage/error/recovery process in a large mainframe system. Thus, both normal and erroneous behavior of the system are modeled. The results provide an understanding of the different types of errors and recovery processes. The measured data show that the holding times in key operational and error states are not simple exponentials and that a semi-Markov process is necessary to model the system behavior. A sensitivity analysis is performed to investigate the significance of using a semi-Markov process, as opposed to a Markov process, to model the measured system.

Reducing Uncertainty in the American Community Survey through Data-Driven Regionalization

PubMed Central

Spielman, Seth E.; Folch, David C.

2015-01-01

The American Community Survey (ACS) is the largest survey of US households and is the principal source for neighborhood scale information about the US population and economy. The ACS is used to allocate billions in federal spending and is a critical input to social scientific research in the US. However, estimates from the ACS can be highly unreliable. For example, in over 72% of census tracts, the estimated number of children under 5 in poverty has a margin of error greater than the estimate. Uncertainty of this magnitude complicates the use of social data in policy making, research, and governance. This article presents a heuristic spatial optimization algorithm that is capable of reducing the margins of error in survey data via the creation of new composite geographies, a process called regionalization. Regionalization is a complex combinatorial problem. Here rather than focusing on the technical aspects of regionalization we demonstrate how to use a purpose built open source regionalization algorithm to process survey data in order to reduce the margins of error to a user-specified threshold. PMID:25723176

Improved Spatial Registration and Target Tracking Method for Sensors on Multiple Missiles.

PubMed

Lu, Xiaodong; Xie, Yuting; Zhou, Jun

2018-05-27

Inspired by the problem that the current spatial registration methods are unsuitable for three-dimensional (3-D) sensor on high-dynamic platform, this paper focuses on the estimation for the registration errors of cooperative missiles and motion states of maneuvering target. There are two types of errors being discussed: sensor measurement biases and attitude biases. Firstly, an improved Kalman Filter on Earth-Centered Earth-Fixed (ECEF-KF) coordinate algorithm is proposed to estimate the deviations mentioned above, from which the outcomes are furtherly compensated to the error terms. Secondly, the Pseudo Linear Kalman Filter (PLKF) and the nonlinear scheme the Unscented Kalman Filter (UKF) with modified inputs are employed for target tracking. The convergence of filtering results are monitored by a position-judgement logic, and a low-pass first order filter is selectively introduced before compensation to inhibit the jitter of estimations. In the simulation, the ECEF-KF enhancement is proven to improve the accuracy and robustness of the space alignment, while the conditional-compensation-based PLKF method is demonstrated to be the optimal performance in target tracking.

Reducing uncertainty in the american community survey through data-driven regionalization.

PubMed

Spielman, Seth E; Folch, David C

2015-01-01

The American Community Survey (ACS) is the largest survey of US households and is the principal source for neighborhood scale information about the US population and economy. The ACS is used to allocate billions in federal spending and is a critical input to social scientific research in the US. However, estimates from the ACS can be highly unreliable. For example, in over 72% of census tracts, the estimated number of children under 5 in poverty has a margin of error greater than the estimate. Uncertainty of this magnitude complicates the use of social data in policy making, research, and governance. This article presents a heuristic spatial optimization algorithm that is capable of reducing the margins of error in survey data via the creation of new composite geographies, a process called regionalization. Regionalization is a complex combinatorial problem. Here rather than focusing on the technical aspects of regionalization we demonstrate how to use a purpose built open source regionalization algorithm to process survey data in order to reduce the margins of error to a user-specified threshold.

A New Stratified Sampling Procedure which Decreases Error Estimation of Varroa Mite Number on Sticky Boards.

PubMed

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. © The Authors 2015. Published by Oxford University Press on behalf of Entomological Society of America. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Bias in the Wagner-Nelson estimate of the fraction of drug absorbed.

PubMed

Wang, Yibin; Nedelman, Jerry

2002-04-01

To examine and quantify bias in the Wagner-Nelson estimate of the fraction of drug absorbed resulting from the estimation error of the elimination rate constant (k), measurement error of the drug concentration, and the truncation error in the area under the curve. Bias in the Wagner-Nelson estimate was derived as a function of post-dosing time (t), k, ratio of absorption rate constant to k (r), and the coefficient of variation for estimates of k (CVk), or CV% for the observed concentration, by assuming a one-compartment model and using an independent estimate of k. The derived functions were used for evaluating the bias with r = 0.5, 3, or 6; k = 0.1 or 0.2; CV, = 0.2 or 0.4; and CV, =0.2 or 0.4; for t = 0 to 30 or 60. Estimation error of k resulted in an upward bias in the Wagner-Nelson estimate that could lead to the estimate of the fraction absorbed being greater than unity. The bias resulting from the estimation error of k inflates the fraction of absorption vs. time profiles mainly in the early post-dosing period. The magnitude of the bias in the Wagner-Nelson estimate resulting from estimation error of k was mainly determined by CV,. The bias in the Wagner-Nelson estimate resulting from to estimation error in k can be dramatically reduced by use of the mean of several independent estimates of k, as in studies for development of an in vivo-in vitro correlation. The truncation error in the area under the curve can introduce a negative bias in the Wagner-Nelson estimate. This can partially offset the bias resulting from estimation error of k in the early post-dosing period. Measurement error of concentration does not introduce bias in the Wagner-Nelson estimate. Estimation error of k results in an upward bias in the Wagner-Nelson estimate, mainly in the early drug absorption phase. The truncation error in AUC can result in a downward bias, which may partially offset the upward bias due to estimation error of k in the early absorption phase. Measurement error of concentration does not introduce bias. The joint effect of estimation error of k and truncation error in AUC can result in a non-monotonic fraction-of-drug-absorbed-vs-time profile. However, only estimation error of k can lead to the Wagner-Nelson estimate of fraction of drug absorbed greater than unity.

A negentropy minimization approach to adaptive equalization for digital communication systems.

PubMed

Choi, Sooyong; Lee, Te-Won

2004-07-01

In this paper, we introduce and investigate a new adaptive equalization method based on minimizing approximate negentropy of the estimation error for a finite-length equalizer. We consider an approximate negentropy using nonpolynomial expansions of the estimation error as a new performance criterion to improve performance of a linear equalizer based on minimizing minimum mean squared error (MMSE). Negentropy includes higher order statistical information and its minimization provides improved converge, performance and accuracy compared to traditional methods such as MMSE in terms of bit error rate (BER). The proposed negentropy minimization (NEGMIN) equalizer has two kinds of solutions, the MMSE solution and the other one, depending on the ratio of the normalization parameters. The NEGMIN equalizer has best BER performance when the ratio of the normalization parameters is properly adjusted to maximize the output power(variance) of the NEGMIN equalizer. Simulation experiments show that BER performance of the NEGMIN equalizer with the other solution than the MMSE one has similar characteristics to the adaptive minimum bit error rate (AMBER) equalizer. The main advantage of the proposed equalizer is that it needs significantly fewer training symbols than the AMBER equalizer. Furthermore, the proposed equalizer is more robust to nonlinear distortions than the MMSE equalizer.

Self-adaptive difference method for the effective solution of computationally complex problems of boundary layer theory

NASA Technical Reports Server (NTRS)

Schoenauer, W.; Daeubler, H. G.; Glotz, G.; Gruening, J.

1986-01-01

An implicit difference procedure for the solution of equations for a chemically reacting hypersonic boundary layer is described. Difference forms of arbitrary error order in the x and y coordinate plane were used to derive estimates for discretization error. Computational complexity and time were minimized by the use of this difference method and the iteration of the nonlinear boundary layer equations was regulated by discretization error. Velocity and temperature profiles are presented for Mach 20.14 and Mach 18.5; variables are velocity profiles, temperature profiles, mass flow factor, Stanton number, and friction drag coefficient; three figures include numeric data.

A Fortran 77 computer code for damped least-squares inversion of Slingram electromagnetic anomalies over thin tabular conductors

NASA Astrophysics Data System (ADS)

Dondurur, Derman; Sarı, Coşkun

2004-07-01

A FORTRAN 77 computer code is presented that permits the inversion of Slingram electromagnetic anomalies to an optimal conductor model. Damped least-squares inversion algorithm is used to estimate the anomalous body parameters, e.g. depth, dip and surface projection point of the target. Iteration progress is controlled by maximum relative error value and iteration continued until a tolerance value was satisfied, while the modification of Marquardt's parameter is controlled by sum of the squared errors value. In order to form the Jacobian matrix, the partial derivatives of theoretical anomaly expression with respect to the parameters being optimised are calculated by numerical differentiation by using first-order forward finite differences. A theoretical and two field anomalies are inserted to test the accuracy and applicability of the present inversion program. Inversion of the field data indicated that depth and the surface projection point parameters of the conductor are estimated correctly, however, considerable discrepancies appeared on the estimated dip angles. It is therefore concluded that the most important factor resulting in the misfit between observed and calculated data is due to the fact that the theory used for computing Slingram anomalies is valid for only thin conductors and this assumption might have caused incorrect dip estimates in the case of wide conductors.

Analyses of integrated aircraft cabin contaminant monitoring network based on Kalman consensus filter.

PubMed

Wang, Rui; Li, Yanxiao; Sun, Hui; Chen, Zengqiang

2017-11-01

The modern civil aircrafts use air ventilation pressurized cabins subject to the limited space. In order to monitor multiple contaminants and overcome the hypersensitivity of the single sensor, the paper constructs an output correction integrated sensor configuration using sensors with different measurement theories after comparing to other two different configurations. This proposed configuration works as a node in the contaminant distributed wireless sensor monitoring network. The corresponding measurement error models of integrated sensors are also proposed by using the Kalman consensus filter to estimate states and conduct data fusion in order to regulate the single sensor measurement results. The paper develops the sufficient proof of the Kalman consensus filter stability when considering the system and the observation noises and compares the mean estimation and the mean consensus errors between Kalman consensus filter and local Kalman filter. The numerical example analyses show the effectiveness of the algorithm. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Estimating linear-nonlinear models using Rényi divergences

PubMed Central

Kouh, Minjoon; Sharpee, Tatyana O.

2009-01-01

This paper compares a family of methods for characterizing neural feature selectivity using natural stimuli in the framework of the linear-nonlinear model. In this model, the spike probability depends in a nonlinear way on a small number of stimulus dimensions. The relevant stimulus dimensions can be found by optimizing a Rényi divergence that quantifies a change in the stimulus distribution associated with the arrival of single spikes. Generally, good reconstructions can be obtained based on optimization of Rényi divergence of any order, even in the limit of small numbers of spikes. However, the smallest error is obtained when the Rényi divergence of order 1 is optimized. This type of optimization is equivalent to information maximization, and is shown to saturate the Cramér-Rao bound describing the smallest error allowed for any unbiased method. We also discuss conditions under which information maximization provides a convenient way to perform maximum likelihood estimation of linear-nonlinear models from neural data. PMID:19568981

Estimating linear-nonlinear models using Renyi divergences.

PubMed

Kouh, Minjoon; Sharpee, Tatyana O

2009-01-01

This article compares a family of methods for characterizing neural feature selectivity using natural stimuli in the framework of the linear-nonlinear model. In this model, the spike probability depends in a nonlinear way on a small number of stimulus dimensions. The relevant stimulus dimensions can be found by optimizing a Rényi divergence that quantifies a change in the stimulus distribution associated with the arrival of single spikes. Generally, good reconstructions can be obtained based on optimization of Rényi divergence of any order, even in the limit of small numbers of spikes. However, the smallest error is obtained when the Rényi divergence of order 1 is optimized. This type of optimization is equivalent to information maximization, and is shown to saturate the Cramer-Rao bound describing the smallest error allowed for any unbiased method. We also discuss conditions under which information maximization provides a convenient way to perform maximum likelihood estimation of linear-nonlinear models from neural data.

An iterative kernel based method for fourth order nonlinear equation with nonlinear boundary condition

NASA Astrophysics Data System (ADS)

Azarnavid, Babak; Parand, Kourosh; Abbasbandy, Saeid

2018-06-01

This article discusses an iterative reproducing kernel method with respect to its effectiveness and capability of solving a fourth-order boundary value problem with nonlinear boundary conditions modeling beams on elastic foundations. Since there is no method of obtaining reproducing kernel which satisfies nonlinear boundary conditions, the standard reproducing kernel methods cannot be used directly to solve boundary value problems with nonlinear boundary conditions as there is no knowledge about the existence and uniqueness of the solution. The aim of this paper is, therefore, to construct an iterative method by the use of a combination of reproducing kernel Hilbert space method and a shooting-like technique to solve the mentioned problems. Error estimation for reproducing kernel Hilbert space methods for nonlinear boundary value problems have yet to be discussed in the literature. In this paper, we present error estimation for the reproducing kernel method to solve nonlinear boundary value problems probably for the first time. Some numerical results are given out to demonstrate the applicability of the method.

Uncertainty Analysis and Order-by-Order Optimization of Chiral Nuclear Interactions

DOE PAGES

Carlsson, Boris; Forssen, Christian; Fahlin Strömberg, D.; ...

2016-02-24

Chiral effective field theory ( ΧEFT) provides a systematic approach to describe low-energy nuclear forces. Moreover, EFT is able to provide well-founded estimates of statistical and systematic uncertainties | although this unique advantage has not yet been fully exploited. We ll this gap by performing an optimization and statistical analysis of all the low-energy constants (LECs) up to next-to-next-to-leading order. Our optimization protocol corresponds to a simultaneous t to scattering and bound-state observables in the pion-nucleon, nucleon-nucleon, and few-nucleon sectors, thereby utilizing the full model capabilities of EFT. Finally, we study the effect on other observables by demonstrating forward-error-propagation methodsmore » that can easily be adopted by future works. We employ mathematical optimization and implement automatic differentiation to attain e cient and machine-precise first- and second-order derivatives of the objective function with respect to the LECs. This is also vital for the regression analysis. We use power-counting arguments to estimate the systematic uncertainty that is inherent to EFT and we construct chiral interactions at different orders with quantified uncertainties. Statistical error propagation is compared with Monte Carlo sampling showing that statistical errors are in general small compared to systematic ones. In conclusion, we find that a simultaneous t to different sets of data is critical to (i) identify the optimal set of LECs, (ii) capture all relevant correlations, (iii) reduce the statistical uncertainty, and (iv) attain order-by-order convergence in EFT. Furthermore, certain systematic uncertainties in the few-nucleon sector are shown to get substantially magnified in the many-body sector; in particlar when varying the cutoff in the chiral potentials. The methodology and results presented in this Paper open a new frontier for uncertainty quantification in ab initio nuclear theory.« less

Refining Field Measurements of Methane Flux Rates from Abandoned Oil and Gas Wells

NASA Astrophysics Data System (ADS)

Lagron, C. S.; Kang, M.; Riqueros, N. S.; Jackson, R. B.

2015-12-01

Recent studies in Pennsylvania demonstrate the potential for significant methane emissions from abandoned oil and gas wells. A subset of tested wells was high emitting, with methane flux rates up to seven orders of magnitude greater than natural fluxes (up to 105 mg CH4/hour, or about 2.5LPM). These wells contribute disproportionately to the total methane emissions from abandoned oil and gas wells. The principles guiding the chamber design have been developed for lower flux rates, typically found in natural environments, and chamber design modifications may reduce uncertainty in flux rates associated with high-emitting wells. Kang et al. estimate errors of a factor of two in measured values based on previous studies. We conduct controlled releases of methane to refine error estimates and improve chamber design with a focus on high-emitters. Controlled releases of methane are conducted at 0.05 LPM, 0.50 LPM, 1.0 LPM, 2.0 LPM, 3.0 LPM, and 5.0 LPM, and at two chamber dimensions typically used in field measurements studies of abandoned wells. As most sources of error tabulated by Kang et al. tend to bias the results toward underreporting of methane emissions, a flux-targeted chamber design modification can reduce error margins and/or provide grounds for a potential upward revision of emission estimates.

Optimal estimation of the optomechanical coupling strength

NASA Astrophysics Data System (ADS)

Bernád, József Zsolt; Sanavio, Claudio; Xuereb, André

2018-06-01

We apply the formalism of quantum estimation theory to obtain information about the value of the nonlinear optomechanical coupling strength. In particular, we discuss the minimum mean-square error estimator and a quantum Cramér-Rao-type inequality for the estimation of the coupling strength. Our estimation strategy reveals some cases where quantum statistical inference is inconclusive and merely results in the reinforcement of prior expectations. We show that these situations also involve the highest expected information losses. We demonstrate that interaction times on the order of one time period of mechanical oscillations are the most suitable for our estimation scenario, and compare situations involving different photon and phonon excitations.

A New Quaternion-Based Kalman Filter for Real-Time Attitude Estimation Using the Two-Step Geometrically-Intuitive Correction Algorithm.

PubMed

Feng, Kaiqiang; Li, Jie; Zhang, Xiaoming; Shen, Chong; Bi, Yu; Zheng, Tao; Liu, Jun

2017-09-19

In order to reduce the computational complexity, and improve the pitch/roll estimation accuracy of the low-cost attitude heading reference system (AHRS) under conditions of magnetic-distortion, a novel linear Kalman filter, suitable for nonlinear attitude estimation, is proposed in this paper. The new algorithm is the combination of two-step geometrically-intuitive correction (TGIC) and the Kalman filter. In the proposed algorithm, the sequential two-step geometrically-intuitive correction scheme is used to make the current estimation of pitch/roll immune to magnetic distortion. Meanwhile, the TGIC produces a computed quaternion input for the Kalman filter, which avoids the linearization error of measurement equations and reduces the computational complexity. Several experiments have been carried out to validate the performance of the filter design. The results demonstrate that the mean time consumption and the root mean square error (RMSE) of pitch/roll estimation under magnetic disturbances are reduced by 45.9% and 33.8%, respectively, when compared with a standard filter. In addition, the proposed filter is applicable for attitude estimation under various dynamic conditions.

A New Quaternion-Based Kalman Filter for Real-Time Attitude Estimation Using the Two-Step Geometrically-Intuitive Correction Algorithm

PubMed Central

Feng, Kaiqiang; Li, Jie; Zhang, Xiaoming; Shen, Chong; Bi, Yu; Zheng, Tao; Liu, Jun

2017-01-01

In order to reduce the computational complexity, and improve the pitch/roll estimation accuracy of the low-cost attitude heading reference system (AHRS) under conditions of magnetic-distortion, a novel linear Kalman filter, suitable for nonlinear attitude estimation, is proposed in this paper. The new algorithm is the combination of two-step geometrically-intuitive correction (TGIC) and the Kalman filter. In the proposed algorithm, the sequential two-step geometrically-intuitive correction scheme is used to make the current estimation of pitch/roll immune to magnetic distortion. Meanwhile, the TGIC produces a computed quaternion input for the Kalman filter, which avoids the linearization error of measurement equations and reduces the computational complexity. Several experiments have been carried out to validate the performance of the filter design. The results demonstrate that the mean time consumption and the root mean square error (RMSE) of pitch/roll estimation under magnetic disturbances are reduced by 45.9% and 33.8%, respectively, when compared with a standard filter. In addition, the proposed filter is applicable for attitude estimation under various dynamic conditions. PMID:28925979

Lunar gravitational field estimation and the effects of mismodeling upon lunar satellite orbit prediction. M.S. Thesis

NASA Technical Reports Server (NTRS)

Davis, John H.

1993-01-01

Lunar spherical harmonic gravity coefficients are estimated from simulated observations of a near-circular low altitude polar orbiter disturbed by lunar mascons. Lunar gravity sensing missions using earth-based nearside observations with and without satellite-based far-side observations are simulated and least squares maximum likelihood estimates are developed for spherical harmonic expansion fit models. Simulations and parameter estimations are performed by a modified version of the Smithsonian Astrophysical Observatory's Planetary Ephemeris Program. Two different lunar spacecraft mission phases are simulated to evaluate the estimated fit models. Results for predicting state covariances one orbit ahead are presented along with the state errors resulting from the mismodeled gravity field. The position errors from planning a lunar landing maneuver with a mismodeled gravity field are also presented. These simulations clearly demonstrate the need to include observations of satellite motion over the far side in estimating the lunar gravity field. The simulations also illustrate that the eighth degree and order expansions used in the simulated fits were unable to adequately model lunar mascons.

Evaluation of causes and frequency of medication errors during information technology downtime.

PubMed

Hanuscak, Tara L; Szeinbach, Sheryl L; Seoane-Vazquez, Enrique; Reichert, Brendan J; McCluskey, Charles F

2009-06-15

The causes and frequency of medication errors occurring during information technology downtime were evaluated. Individuals from a convenience sample of 78 hospitals who were directly responsible for supporting and maintaining clinical information systems (CISs) and automated dispensing systems (ADSs) were surveyed using an online tool between February 2007 and May 2007 to determine if medication errors were reported during periods of system downtime. The errors were classified using the National Coordinating Council for Medication Error Reporting and Prevention severity scoring index. The percentage of respondents reporting downtime was estimated. Of the 78 eligible hospitals, 32 respondents with CIS and ADS responsibilities completed the online survey for a response rate of 41%. For computerized prescriber order entry, patch installations and system upgrades caused an average downtime of 57% over a 12-month period. Lost interface and interface malfunction were reported for centralized and decentralized ADSs, with an average downtime response of 34% and 29%, respectively. The average downtime response was 31% for software malfunctions linked to clinical decision-support systems. Although patient harm did not result from 30 (54%) medication errors, the potential for harm was present for 9 (16%) of these errors. Medication errors occurred during CIS and ADS downtime despite the availability of backup systems and standard protocols to handle periods of system downtime. Efforts should be directed to reduce the frequency and length of down-time in order to minimize medication errors during such downtime.

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 different number of input products. For the globe the calculated relative error estimate from this study is about 9%, which is also probably a slight overestimate. These tropical and global estimated bias errors provide one estimate of the current state of knowledge of the planet's mean precipitation.

Observer-based output consensus of a class of heterogeneous multi-agent systems with unmatched disturbances

NASA Astrophysics Data System (ADS)

Zhang, Jiancheng; Zhu, Fanglai

2018-03-01

In this paper, the output consensus of a class of linear heterogeneous multi-agent systems with unmatched disturbances is considered. Firstly, based on the relative output information among neighboring agents, we propose an asymptotic sliding-mode based consensus control scheme, under which, the output consensus error can converge to zero by removing the disturbances from output channels. Secondly, in order to reach the consensus goal, we design a novel high-order unknown input observer for each agent. It can estimate not only each agent's states and disturbances, but also the disturbances' high-order derivatives, which are required in the control scheme aforementioned above. The observer-based consensus control laws and the convergence analysis of the consensus error dynamics are given. Finally, a simulation example is provided to verify the validity of our methods.

The Effect of Random Error on Diagnostic Accuracy Illustrated with the Anthropometric Diagnosis of Malnutrition

PubMed Central

2016-01-01

Background It is often thought that random measurement error has a minor effect upon the results of an epidemiological survey. Theoretically, errors of measurement should always increase the spread of a distribution. Defining an illness by having a measurement outside an established healthy range will lead to an inflated prevalence of that condition if there are measurement errors. Methods and results A Monte Carlo simulation was conducted of anthropometric assessment of children with malnutrition. Random errors of increasing magnitude were imposed upon the populations and showed that there was an increase in the standard deviation with each of the errors that became exponentially greater with the magnitude of the error. The potential magnitude of the resulting error of reported prevalence of malnutrition were compared with published international data and found to be of sufficient magnitude to make a number of surveys and the numerous reports and analyses that used these data unreliable. Conclusions The effect of random error in public health surveys and the data upon which diagnostic cut-off points are derived to define “health” has been underestimated. Even quite modest random errors can more than double the reported prevalence of conditions such as malnutrition. Increasing sample size does not address this problem, and may even result in less accurate estimates. More attention needs to be paid to the selection, calibration and maintenance of instruments, measurer selection, training & supervision, routine estimation of the likely magnitude of errors using standardization tests, use of statistical likelihood of error to exclude data from analysis and full reporting of these procedures in order to judge the reliability of survey reports. PMID:28030627

Autonomous frequency domain identification: Theory and experiment

NASA Technical Reports Server (NTRS)

Yam, Yeung; Bayard, D. S.; Hadaegh, F. Y.; Mettler, E.; Milman, M. H.; Scheid, R. E.

1989-01-01

The analysis, design, and on-orbit tuning of robust controllers require more information about the plant than simply a nominal estimate of the plant transfer function. Information is also required concerning the uncertainty in the nominal estimate, or more generally, the identification of a model set within which the true plant is known to lie. The identification methodology that was developed and experimentally demonstrated makes use of a simple but useful characterization of the model uncertainty based on the output error. This is a characterization of the additive uncertainty in the plant model, which has found considerable use in many robust control analysis and synthesis techniques. The identification process is initiated by a stochastic input u which is applied to the plant p giving rise to the output. Spectral estimation (h = P sub uy/P sub uu) is used as an estimate of p and the model order is estimated using the produce moment matrix (PMM) method. A parametric model unit direction vector p is then determined by curve fitting the spectral estimate to a rational transfer function. The additive uncertainty delta sub m = p - unit direction vector p is then estimated by the cross spectral estimate delta = P sub ue/P sub uu where e = y - unit direction vectory y is the output error, and unit direction vector y = unit direction vector pu is the computed output of the parametric model subjected to the actual input u. The experimental results demonstrate the curve fitting algorithm produces the reduced-order plant model which minimizes the additive uncertainty. The nominal transfer function estimate unit direction vector p and the estimate delta of the additive uncertainty delta sub m are subsequently available to be used for optimization of robust controller performance and stability.

Direct discontinuous Galerkin method and its variations for second order elliptic equations

DOE PAGES

Huang, Hongying; Chen, Zheng; Li, Jin; ...

2016-08-23

In this study, we study direct discontinuous Galerkin method (Liu and Yan in SIAM J Numer Anal 47(1):475–698, 2009) and its variations (Liu and Yan in Commun Comput Phys 8(3):541–564, 2010; Vidden and Yan in J Comput Math 31(6):638–662, 2013; Yan in J Sci Comput 54(2–3):663–683, 2013) for 2nd order elliptic problems. A priori error estimate under energy norm is established for all four methods. Optimal error estimate under L 2 norm is obtained for DDG method with interface correction (Liu and Yan in Commun Comput Phys 8(3):541–564, 2010) and symmetric DDG method (Vidden and Yan in J Comput Mathmore » 31(6):638–662, 2013). A series of numerical examples are carried out to illustrate the accuracy and capability of the schemes. Numerically we obtain optimal (k+1)th order convergence for DDG method with interface correction and symmetric DDG method on nonuniform and unstructured triangular meshes. An interface problem with discontinuous diffusion coefficients is investigated and optimal (k+1)th order accuracy is obtained. Peak solutions with sharp transitions are captured well. Highly oscillatory wave solutions of Helmholz equation are well resolved.« less

Direct discontinuous Galerkin method and its variations for second order elliptic equations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Huang, Hongying; Chen, Zheng; Li, Jin

In this study, we study direct discontinuous Galerkin method (Liu and Yan in SIAM J Numer Anal 47(1):475–698, 2009) and its variations (Liu and Yan in Commun Comput Phys 8(3):541–564, 2010; Vidden and Yan in J Comput Math 31(6):638–662, 2013; Yan in J Sci Comput 54(2–3):663–683, 2013) for 2nd order elliptic problems. A priori error estimate under energy norm is established for all four methods. Optimal error estimate under L 2 norm is obtained for DDG method with interface correction (Liu and Yan in Commun Comput Phys 8(3):541–564, 2010) and symmetric DDG method (Vidden and Yan in J Comput Mathmore » 31(6):638–662, 2013). A series of numerical examples are carried out to illustrate the accuracy and capability of the schemes. Numerically we obtain optimal (k+1)th order convergence for DDG method with interface correction and symmetric DDG method on nonuniform and unstructured triangular meshes. An interface problem with discontinuous diffusion coefficients is investigated and optimal (k+1)th order accuracy is obtained. Peak solutions with sharp transitions are captured well. Highly oscillatory wave solutions of Helmholz equation are well resolved.« less

The Combined Effects of Measurement Error and Omitting Confounders in the Single-Mediator Model

PubMed Central

Fritz, Matthew S.; Kenny, David A.; MacKinnon, David P.

2016-01-01

Mediation analysis requires a number of strong assumptions be met in order to make valid causal inferences. Failing to account for violations of these assumptions, such as not modeling measurement error or omitting a common cause of the effects in the model, can bias the parameter estimates of the mediated effect. When the independent variable is perfectly reliable, for example when participants are randomly assigned to levels of treatment, measurement error in the mediator tends to underestimate the mediated effect, while the omission of a confounding variable of the mediator to outcome relation tends to overestimate the mediated effect. Violations of these two assumptions often co-occur, however, in which case the mediated effect could be overestimated, underestimated, or even, in very rare circumstances, unbiased. In order to explore the combined effect of measurement error and omitted confounders in the same model, the impact of each violation on the single-mediator model is first examined individually. Then the combined effect of having measurement error and omitted confounders in the same model is discussed. Throughout, an empirical example is provided to illustrate the effect of violating these assumptions on the mediated effect. PMID:27739903

An assessment of the accuracy of a novel weight estimation device for children.

PubMed

Jung, Jae Yun; Kwak, Young Ho; Kim, Do Kyun; Suh, Dongbum; Chang, Ikwan; Yoon, Chiyul; Lee, Jung Chan; Kim, Hee Chan; Choi, Jae Yeon; Ahn, HeeJeong

2017-03-01

We sought to validate the accuracy and assess the efficacy of a newly developed electronic weight estimation device (ie, the rolling tape) for paediatric weight estimation. We enrolled a convenience sample of children aged <17 years presenting to our emergency department who volunteered to participate in the study. The children's heights and weights were measured, and three researchers estimated these values using the rolling tape and Broselow tape at 5 min intervals. The weight estimates of researcher 1, researcher 2 and the Broselow tape were compared with measured values, and mean percentage error (MPE), root mean square error (RMSE) and percentage of estimates within 10% of the actual measured values were calculated. For 30 randomly selected subjects, we compared the time interval from the start of the measurement to the time that orders for epinephrine, defibrillation dose and instrument size could be given in a simulated arrest scenario. We enrolled 906 children (median age 4.0 years). For researcher 1, researcher 2 and the Broselow tape, MPE values were 0.11% (RMSE 2.61 kg), 1.41% (RMSE, 2.61 kg) and 1.72% (RMSE 5.41 kg), respectively, and the percentages of children with predictions within 10% of their actual weight were 75.1%, 75.7% and 60.6%, respectively. In the 30 simulated cases, the mean time for measurement to ordering was significantly shorter (25.8 s vs 35.5 s, p<0.001) for the rolling tape compared with the Broselow tape method. The rolling tape is a good weight estimation tool for children compared with other methods. The rolling tape method significantly decreased the time from weight estimation to orders for essential drug dose, instrument size and defibrillation dose for resuscitation. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/.

Optimization of planar PIV-based pressure estimates in laminar and turbulent wakes

NASA Astrophysics Data System (ADS)

McClure, Jeffrey; Yarusevych, Serhiy

2017-05-01

The performance of four pressure estimation techniques using Eulerian material acceleration estimates from planar, two-component Particle Image Velocimetry (PIV) data were evaluated in a bluff body wake. To allow for the ground truth comparison of the pressure estimates, direct numerical simulations of flow over a circular cylinder were used to obtain synthetic velocity fields. Direct numerical simulations were performed for Re_D = 100, 300, and 1575, spanning laminar, transitional, and turbulent wake regimes, respectively. A parametric study encompassing a range of temporal and spatial resolutions was performed for each Re_D. The effect of random noise typical of experimental velocity measurements was also evaluated. The results identified optimal temporal and spatial resolutions that minimize the propagation of random and truncation errors to the pressure field estimates. A model derived from linear error propagation through the material acceleration central difference estimators was developed to predict these optima, and showed good agreement with the results from common pressure estimation techniques. The results of the model are also shown to provide acceptable first-order approximations for sampling parameters that reduce error propagation when Lagrangian estimations of material acceleration are employed. For pressure integration based on planar PIV, the effect of flow three-dimensionality was also quantified, and shown to be most pronounced at higher Reynolds numbers downstream of the vortex formation region, where dominant vortices undergo substantial three-dimensional deformations. The results of the present study provide a priori recommendations for the use of pressure estimation techniques from experimental PIV measurements in vortex dominated laminar and turbulent wake flows.

Towards a detailed anthropometric body characterization using the Microsoft Kinect.

PubMed

Domingues, Ana; Barbosa, Filipa; Pereira, Eduardo M; Santos, Márcio Borgonovo; Seixas, Adérito; Vilas-Boas, João; Gabriel, Joaquim; Vardasca, Ricardo

2016-01-01

Anthropometry has been widely used in different fields, providing relevant information for medicine, ergonomics and biometric applications. However, the existent solutions present marked disadvantages, reducing the employment of this type of evaluation. Studies have been conducted in order to easily determine anthropometric measures considering data provided by low-cost sensors, such as the Microsoft Kinect. In this work, a methodology is proposed and implemented for estimating anthropometric measures considering the information acquired with this sensor. The measures obtained with this method were compared with the ones from a validation system, Qualisys. Comparing the relative errors determined with state-of-art references, for some of the estimated measures, lower errors were verified and a more complete characterization of the whole body structure was achieved.

Evaluation of a load cell model for dynamic calibration of the rotor systems research aircraft

NASA Technical Reports Server (NTRS)

Duval, R. W.; Bahrami, H.; Wellman, B.

1985-01-01

The Rotor Systems Research Aircraft uses load cells to isolate the rotor/transmission system from the fuselage. An analytical model of the relationship between applied rotor loads and the resulting load cell measurements is derived by applying a force-and-moment balance to the isolated rotor/transmission system. The model is then used to estimate the applied loads from measured load cell data, as obtained from a ground-based shake test. Using nominal design values for the parameters, the estimation errors, for the case of lateral forcing, were shown to be on the order of the sensor measurement noise in all but the roll axis. An unmodeled external load appears to be the source of the error in this axis.

Does manipulating the speed of visual flow in virtual reality change distance estimation while walking in Parkinson's disease?

PubMed

Ehgoetz Martens, Kaylena A; Ellard, Colin G; Almeida, Quincy J

2015-03-01

Although dopaminergic replacement therapy is believed to improve sensory processing in PD, while delayed perceptual speed is thought to be caused by a predominantly cholinergic deficit, it is unclear whether sensory-perceptual deficits are a result of corrupt sensory processing, or a delay in updating perceived feedback during movement. The current study aimed to examine these two hypotheses by manipulating visual flow speed and dopaminergic medication to examine which influenced distance estimation in PD. Fourteen PD and sixteen HC participants were instructed to estimate the distance of a remembered target by walking to the position the target formerly occupied. This task was completed in virtual reality in order to manipulate the visual flow (VF) speed in real time. Three conditions were carried out: (1) BASELINE: VF speed was equal to participants' real-time movement speed; (2) SLOW: VF speed was reduced by 50 %; (2) FAST: VF speed was increased by 30 %. Individuals with PD performed the experiment in their ON and OFF state. PD demonstrated significantly greater judgement error during BASELINE and FAST conditions compared to HC, although PD did not improve their judgement error during the SLOW condition. Additionally, PD had greater variable error during baseline compared to HC; however, during the SLOW conditions, PD had significantly less variable error compared to baseline and similar variable error to HC participants. Overall, dopaminergic medication did not significantly influence judgement error. Therefore, these results suggest that corrupt processing of sensory information is the main contributor to sensory-perceptual deficits during movement in PD rather than delayed updating of sensory feedback.

Errors Associated with IOLMaster Biometry as a Function of Internal Ocular Dimensions

PubMed Central

Faria-Ribeiro, Miguel; Lopes-Ferreira, Daniela; López-Gil, Norberto; Jorge, Jorge; González-Méijome, José Manuel

2014-01-01

Purpose To evaluate the error in the estimation of axial length (AL) with the IOLMaster partial coherence interferometry (PCI) biometer and obtain a correction factor that varies as a function of AL and crystalline lens thickness (LT). Methods Optical simulations were produced for theoretical eyes using Zemax-EE software. Thirty-three combinations including eleven different AL (from 20 mm to 30 mm in 1 mm steps) and three different LT (3.6 mm, 4.2 mm and 4.8 mm) were used. Errors were obtained comparing the AL measured for a constant equivalent refractive index of 1.3549 and for the actual combinations of indices and intra-ocular dimensions of LT and AL in each model eye. Results In the range from 20 mm to 30 mm AL and 3.6–4.8 mm LT, the instrument measurements yielded an error between −0.043 mm and +0.089 mm. Regression analyses for the three LT condition were combined in order to derive a correction factor as a function of the instrument measured AL for each combination of AL and LT in the theoretical eye. Conclusions The assumption of a single “average” refractive index in the estimation of AL by the IOLMaster PCI biometer only induces very small errors in a wide range of combinations of ocular dimensions. Even so, the accurate estimation of those errors may help to improve accuracy of intra-ocular lens calculations through exact ray tracing, particularly in longer eyes and eyes with thicker or thinner crystalline lenses. PMID:24766863

Errors associated with IOLMaster biometry as a function of internal ocular dimensions.

PubMed

Faria-Ribeiro, Miguel; Lopes-Ferreira, Daniela; López-Gil, Norberto; Jorge, Jorge; González-Méijome, José Manuel

2014-01-01

To evaluate the error in the estimation of axial length (AL) with the IOLMaster partial coherence interferometry (PCI) biometer and obtain a correction factor that varies as a function of AL and crystalline lens thickness (LT). Optical simulations were produced for theoretical eyes using Zemax-EE software. Thirty-three combinations including eleven different AL (from 20mm to 30mm in 1mm steps) and three different LT (3.6mm, 4.2mm and 4.8mm) were used. Errors were obtained comparing the AL measured for a constant equivalent refractive index of 1.3549 and for the actual combinations of indices and intra-ocular dimensions of LT and AL in each model eye. In the range from 20mm to 30mm AL and 3.6-4.8mm LT, the instrument measurements yielded an error between -0.043mm and +0.089mm. Regression analyses for the three LT condition were combined in order to derive a correction factor as a function of the instrument measured AL for each combination of AL and LT in the theoretical eye. The assumption of a single "average" refractive index in the estimation of AL by the IOLMaster PCI biometer only induces very small errors in a wide range of combinations of ocular dimensions. Even so, the accurate estimation of those errors may help to improve accuracy of intra-ocular lens calculations through exact ray tracing, particularly in longer eyes and eyes with thicker or thinner crystalline lenses. Copyright © 2013 Spanish General Council of Optometry. Published by Elsevier Espana. All rights reserved.

Robust Mean and Covariance Structure Analysis through Iteratively Reweighted Least Squares.

ERIC Educational Resources Information Center

Yuan, Ke-Hai; Bentler, Peter M.

2000-01-01

Adapts robust schemes to mean and covariance structures, providing an iteratively reweighted least squares approach to robust structural equation modeling. Each case is weighted according to its distance, based on first and second order moments. Test statistics and standard error estimators are given. (SLD)

Description and Sensitivity Analysis of the SOLSE/LORE-2 and SAGE III Limb Scattering Ozone Retrieval Algorithms

NASA Technical Reports Server (NTRS)

Loughman, R.; Flittner, D.; Herman, B.; Bhartia, P.; Hilsenrath, E.; McPeters, R.; Rault, D.

2002-01-01

The SOLSE (Shuttle Ozone Limb Sounding Experiment) and LORE (Limb Ozone Retrieval Experiment) instruments are scheduled for reflight on Space Shuttle flight STS-107 in July 2002. In addition, the SAGE III (Stratospheric Aerosol and Gas Experiment) instrument will begin to make limb scattering measurements during Spring 2002. The optimal estimation technique is used to analyze visible and ultraviolet limb scattered radiances and produce a retrieved ozone profile. The algorithm used to analyze data from the initial flight of the SOLSE/LORE instruments (on Space Shuttle flight STS-87 in November 1997) forms the basis of the current algorithms, with expansion to take advantage of the increased multispectral information provided by SOLSE/LORE-2 and SAGE III. We also present detailed sensitivity analysis for these ozone retrieval algorithms. The primary source of ozone retrieval error is tangent height misregistration (i.e., instrument pointing error), which is relevant throughout the altitude range of interest, and can produce retrieval errors on the order of 10-20 percent due to a tangent height registration error of 0.5 km at the tangent point. Other significant sources of error are sensitivity to stratospheric aerosol and sensitivity to error in the a priori ozone estimate (given assumed instrument signal-to-noise = 200). These can produce errors up to 10 percent for the ozone retrieval at altitudes less than 20 km, but produce little error above that level.

Numerical Analysis of an H 1-Galerkin Mixed Finite Element Method for Time Fractional Telegraph Equation

PubMed Central

Wang, Jinfeng; Zhao, Meng; Zhang, Min; Liu, Yang; Li, Hong

2014-01-01

We discuss and analyze an H 1-Galerkin mixed finite element (H 1-GMFE) method to look for the numerical solution of time fractional telegraph equation. We introduce an auxiliary variable to reduce the original equation into lower-order coupled equations and then formulate an H 1-GMFE scheme with two important variables. We discretize the Caputo time fractional derivatives using the finite difference methods and approximate the spatial direction by applying the H 1-GMFE method. Based on the discussion on the theoretical error analysis in L 2-norm for the scalar unknown and its gradient in one dimensional case, we obtain the optimal order of convergence in space-time direction. Further, we also derive the optimal error results for the scalar unknown in H 1-norm. Moreover, we derive and analyze the stability of H 1-GMFE scheme and give the results of a priori error estimates in two- or three-dimensional cases. In order to verify our theoretical analysis, we give some results of numerical calculation by using the Matlab procedure. PMID:25184148

Comparison of Low Cost Photogrammetric Survey with Tls and Leica Pegasus Backpack 3d Modelss

NASA Astrophysics Data System (ADS)

Masiero, A.; Fissore, F.; Guarnieri, A.; Piragnolo, M.; Vettore, A.

2017-11-01

This paper considers Leica backpack and photogrammetric surveys of a mediaeval bastion in Padua, Italy. Furhtermore, terrestrial laser scanning (TLS) survey is considered in order to provide a state of the art reconstruction of the bastion. Despite control points are typically used to avoid deformations in photogrammetric surveys and ensure correct scaling of the reconstruction, in this paper a different approach is considered: this work is part of a project aiming at the development of a system exploiting ultra-wide band (UWB) devices to provide correct scaling of the reconstruction. In particular, low cost Pozyx UWB devices are used to estimate camera positions during image acquisitions. Then, in order to obtain a metric reconstruction, scale factor in the photogrammetric survey is estimated by comparing camera positions obtained from UWB measurements with those obtained from photogrammetric reconstruction. Compared with the TLS survey, the considered photogrammetric model of the bastion results in a RMSE of 21.9cm, average error 13.4cm, and standard deviation 13.5cm. Excluding the final part of the bastion left wing, where the presence of several poles make reconstruction more difficult, (RMSE) fitting error is 17.3cm, average error 11.5cm, and standard deviation 9.5cm. Instead, comparison of Leica backpack and TLS surveys leads to an average error of 4.7cm and standard deviation 0.6cm (4.2cm and 0.3cm, respectively, by excluding the final part of the left wing).

Correcting the Standard Errors of 2-Stage Residual Inclusion Estimators for Mendelian Randomization Studies

PubMed Central

Palmer, Tom M; Holmes, Michael V; Keating, Brendan J; Sheehan, Nuala A

2017-01-01

Abstract Mendelian randomization studies use genotypes as instrumental variables to test for and estimate the causal effects of modifiable risk factors on outcomes. Two-stage residual inclusion (TSRI) estimators have been used when researchers are willing to make parametric assumptions. However, researchers are currently reporting uncorrected or heteroscedasticity-robust standard errors for these estimates. We compared several different forms of the standard error for linear and logistic TSRI estimates in simulations and in real-data examples. Among others, we consider standard errors modified from the approach of Newey (1987), Terza (2016), and bootstrapping. In our simulations Newey, Terza, bootstrap, and corrected 2-stage least squares (in the linear case) standard errors gave the best results in terms of coverage and type I error. In the real-data examples, the Newey standard errors were 0.5% and 2% larger than the unadjusted standard errors for the linear and logistic TSRI estimators, respectively. We show that TSRI estimators with modified standard errors have correct type I error under the null. Researchers should report TSRI estimates with modified standard errors instead of reporting unadjusted or heteroscedasticity-robust standard errors. PMID:29106476

Errors and error rates in surgical pathology: an Association of Directors of Anatomic and Surgical Pathology survey.

PubMed

Cooper, Kumarasen

2006-05-01

This survey on errors in surgical pathology was commissioned by the Association of Directors of Anatomic and Surgical Pathology Council to explore broad perceptions and definitions of error in surgical pathology among its membership and to get some estimate of the perceived frequency of such errors. Overall, 41 laboratories were surveyed, with 34 responding to a confidential questionnaire. Six small, 13 medium, and 10 large laboratories (based on specimen volume), predominantly located in the United States, were surveyed (the remaining 5 laboratories did not provide this particular information). The survey questions, responses, and associated comments are presented. It is clear from this survey that we lack uniformity and consistency with respect to terminology, definitions, and the identification/documentation of errors in surgical pathology. An appeal is made for the urgent need to reach some consensus in order to address these discrepancies as we prepare to combat the issue of errors in surgical pathology.

Nonlinear least squares regression for single image scanning electron microscope signal-to-noise ratio estimation.

PubMed

Sim, K S; Norhisham, S

2016-11-01

A new method based on nonlinear least squares regression (NLLSR) is formulated to estimate signal-to-noise ratio (SNR) of scanning electron microscope (SEM) images. The estimation of SNR value based on NLLSR method is compared with the three existing methods of nearest neighbourhood, first-order interpolation and the combination of both nearest neighbourhood and first-order interpolation. Samples of SEM images with different textures, contrasts and edges were used to test the performance of NLLSR method in estimating the SNR values of the SEM images. It is shown that the NLLSR method is able to produce better estimation accuracy as compared to the other three existing methods. According to the SNR results obtained from the experiment, the NLLSR method is able to produce approximately less than 1% of SNR error difference as compared to the other three existing methods. © 2016 The Authors Journal of Microscopy © 2016 Royal Microscopical Society.

Development of a Nonlinear Soft-Sensor Using a GMDH Network for a Refinery Crude Distillation Tower

NASA Astrophysics Data System (ADS)

Fujii, Kenzo; Yamamoto, Toru

In atmospheric distillation processes, the stabilization of processes is required in order to optimize the crude-oil composition that corresponds to product market conditions. However, the process control systems sometimes fall into unstable states in the case where unexpected disturbances are introduced, and these unusual phenomena have had an undesirable affect on certain products. Furthermore, a useful chemical engineering model has not yet been established for these phenomena. This remains a serious problem in the atmospheric distillation process. This paper describes a new modeling scheme to predict unusual phenomena in the atmospheric distillation process using the GMDH (Group Method of Data Handling) network which is one type of network model. According to the GMDH network, the model structure can be determined systematically. However, the least squares method has been commonly utilized in determining weight coefficients (model parameters). Estimation accuracy is not entirely expected, because the sum of squared errors between the measured values and estimates is evaluated. Therefore, instead of evaluating the sum of squared errors, the sum of absolute value of errors is introduced and the Levenberg-Marquardt method is employed in order to determine model parameters. The effectiveness of the proposed method is evaluated by the foaming prediction in the crude oil switching operation in the atmospheric distillation process.

Feasibility of Coherent and Incoherent Backscatter Experiments from the AMPS Laboratory. Technical Section

NASA Technical Reports Server (NTRS)

Mozer, F. S.

1976-01-01

A computer program simulated the spectrum which resulted when a radar signal was transmitted into the ionosphere for a finite time and received for an equal finite interval. The spectrum derived from this signal is statistical in nature because the signal is scattered from the ionosphere, which is statistical in nature. Many estimates of any property of the ionosphere can be made. Their average value will approach the average property of the ionosphere which is being measured. Due to the statistical nature of the spectrum itself, the estimators will vary about this average. The square root of the variance about this average is called the standard deviation, an estimate of the error which exists in any particular radar measurement. In order to determine the feasibility of the space shuttle radar, the magnitude of these errors for measurements of physical interest must be understood.

Hyper-X Post-Flight Trajectory Reconstruction

NASA Technical Reports Server (NTRS)

Karlgaard, Christopher D.; Tartabini, Paul V.; Blanchard, RobertC.; Kirsch, Michael; Toniolo, Matthew D.

2004-01-01

This paper discusses the formulation and development of a trajectory reconstruction tool for the NASA X{43A/Hyper{X high speed research vehicle, and its implementation for the reconstruction and analysis of ight test data. Extended Kalman ltering techniques are employed to reconstruct the trajectory of the vehicle, based upon numerical integration of inertial measurement data along with redundant measurements of the vehicle state. The equations of motion are formulated in order to include the effects of several systematic error sources, whose values may also be estimated by the ltering routines. Additionally, smoothing algorithms have been implemented in which the nal value of the state (or an augmented state that includes other systematic error parameters to be estimated) and covariance are propagated back to the initial time to generate the best-estimated trajectory, based upon all available data. The methods are applied to the problem of reconstructing the trajectory of the Hyper-X vehicle from ight data.

Aeroelastic Modeling of X-56A Stiff-Wing Configuration Flight Test Data

NASA Technical Reports Server (NTRS)

Grauer, Jared A.; Boucher, Matthew J.

2017-01-01

Aeroelastic stability and control derivatives for the X-56A Multi-Utility Technology Testbed (MUTT), in the stiff-wing configuration, were estimated from flight test data using the output-error method. Practical aspects of the analysis are discussed. The orthogonal phase-optimized multisine inputs provided excellent data information for aeroelastic modeling. Consistent parameter estimates were determined using output error in both the frequency and time domains. The frequency domain analysis converged faster and was less sensitive to starting values for the model parameters, which was useful for determining the aeroelastic model structure and obtaining starting values for the time domain analysis. Including a modal description of the structure from a finite element model reduced the complexity of the estimation problem and improved the modeling results. Effects of reducing the model order on the short period stability and control derivatives were investigated.

An assessment of the Nguyen and Pinder method for slug test analysis. [In situ estimates of ground water contamination

DOE Office of Scientific and Technical Information (OSTI.GOV)

Butler, J.J. Jr.; Hyder, Z.

The Nguyen and Pinder method is one of four techniques commonly used for analysis of response data from slug tests. Limited field research has raised questions about the reliability of the parameter estimates obtained with this method. A theoretical evaluation of this technique reveals that errors were made in the derivation of the analytical solution upon which the technique is based. Simulation and field examples show that the errors result in parameter estimates that can differ from actual values by orders of magnitude. These findings indicate that the Nguyen and Pinder method should no longer be a tool in themore » repertoire of the field hydrogeologist. If data from a slug test performed in a partially penetrating well in a confined aquifer need to be analyzed, recent work has shown that the Hvorslev method is the best alternative among the commonly used techniques.« less

Quantification of sewer system infiltration using delta(18)O hydrograph separation.

PubMed

Prigiobbe, V; Giulianelli, M

2009-01-01

The infiltration of parasitical water into two sewer systems in Rome (Italy) was quantified during a dry weather period. Infiltration was estimated using the hydrograph separation method with two water components and delta(18)O as a conservative tracer. The two water components were groundwater, the possible source of parasitical water within the sewer, and drinking water discharged into the sewer system. This method was applied at an urban catchment scale in order to test the effective water-tightness of two different sewer networks. The sampling strategy was based on an uncertainty analysis and the errors have been propagated using Monte Carlo random sampling. Our field applications showed that the method can be applied easily and quickly, but the error in the estimated infiltration rate can be up to 20%. The estimated infiltration into the recent sewer in Torraccia is 14% and can be considered negligible given the precision of the method, while the old sewer in Infernetto has an estimated infiltration of 50%.

The orbit of asteroid (99942) Apophis as determined from optical and radar observations

NASA Astrophysics Data System (ADS)

Vinogradova, T. A.; Kochetova, O. M.; Chernetenko, Yu. A.; Shor, V. A.; Yagudina, E. I.

2008-08-01

The results of improving the orbit accuracy for the asteroid Apophis and the circumstances of its approach to Earth in 2029 are described. Gravitational perturbations from all of the major planets and Pluto, Ceres, Pallas, and Vesta are taken into account in the equations of motion of the asteroid. Relativistic perturbations from the Sun and perturbations due to the oblateness of the Sun and Earth and due to the light pressure are also included in the model. Perturbations from the Earth and Moon are considered separately. The coordinates of the perturbing bodies are calculated using DE405. The phase correction and the gravitational deflection of light are taken into account. The numerical integration of the equations of motion and equations in variations is performed by the 15th-order Everhart method. The error of the numerical integration over the 2005 2029 interval, estimated using forward and backward computations, is not more than 3 × 10-11 AU. Improved coordinates and velocities at epoch JD2454200.5 (April 10, 2007) were obtained applying the weighted leastsquares fit. For the period from March 15, 2004, to August 16, 2006, 989 optical and 7 radar observations were used. The resulting system represents the optical observations with an error of 0.37 (66 conditional equations were rejected). The residuals of the radar observations are an order, or more, smaller than their errors. The system of Apophis’ elements and the estimates of their precision obtained in this study are in perfect agreement with the results published by other authors. The minimum Apophis-Earth distance is about 38 200 km on April 13, 2029. This estimate agrees to within 20 km with those calculated based on other published systems of elements. The effect of some model components on the minimum distance is estimated.

Application of age estimation methods based on teeth eruption: how easy is Olze method to use?

PubMed

De Angelis, D; Gibelli, D; Merelli, V; Botto, M; Ventura, F; Cattaneo, C

2014-09-01

The development of new methods for age estimation has become with time an urgent issue because of the increasing immigration, in order to estimate accurately the age of those subjects who lack valid identity documents. Methods of age estimation are divided in skeletal and dental ones, and among the latter, Olze's method is one of the most recent, since it was introduced in 2010 with the aim to identify the legal age of 18 and 21 years by evaluating the different stages of development of the periodontal ligament of the third molars with closed root apices. The present study aims at verifying the applicability of the method to the daily forensic practice, with special focus on the interobserver repeatability. Olze's method was applied by three different observers (two physicians and one dentist without a specific training in Olze's method) to 61 orthopantomograms from subjects of mixed ethnicity aged between 16 and 51 years. The analysis took into consideration the lower third molars. The results provided by the different observers were then compared in order to verify the interobserver error. Results showed that interobserver error varies between 43 and 57 % for the right lower third molar (M48) and between 23 and 49 % for the left lower third molar (M38). Chi-square test did not show significant differences according to the side of teeth and type of professional figure. The results prove that Olze's method is not easy to apply when used by not adequately trained personnel, because of an intrinsic interobserver error. Since it is however a crucial method in age determination, it should be used only by experienced observers after an intensive and specific training.

Model error estimation for distributed systems described by elliptic equations

NASA Technical Reports Server (NTRS)

Rodriguez, G.

1983-01-01

A function space approach is used to develop a theory for estimation of the errors inherent in an elliptic partial differential equation model for a distributed parameter system. By establishing knowledge of the inevitable deficiencies in the model, the error estimates provide a foundation for updating the model. The function space solution leads to a specification of a method for computation of the model error estimates and development of model error analysis techniques for comparison between actual and estimated errors. The paper summarizes the model error estimation approach as well as an application arising in the area of modeling for static shape determination of large flexible systems.

Development of advanced methods for analysis of experimental data in diffusion

NASA Astrophysics Data System (ADS)

Jaques, Alonso V.

There are numerous experimental configurations and data analysis techniques for the characterization of diffusion phenomena. However, the mathematical methods for estimating diffusivities traditionally do not take into account the effects of experimental errors in the data, and often require smooth, noiseless data sets to perform the necessary analysis steps. The current methods used for data smoothing require strong assumptions which can introduce numerical "artifacts" into the data, affecting confidence in the estimated parameters. The Boltzmann-Matano method is used extensively in the determination of concentration - dependent diffusivities, D(C), in alloys. In the course of analyzing experimental data, numerical integrations and differentiations of the concentration profile are performed. These methods require smoothing of the data prior to analysis. We present here an approach to the Boltzmann-Matano method that is based on a regularization method to estimate a differentiation operation on the data, i.e., estimate the concentration gradient term, which is important in the analysis process for determining the diffusivity. This approach, therefore, has the potential to be less subjective, and in numerical simulations shows an increased accuracy in the estimated diffusion coefficients. We present a regression approach to estimate linear multicomponent diffusion coefficients that eliminates the need pre-treat or pre-condition the concentration profile. This approach fits the data to a functional form of the mathematical expression for the concentration profile, and allows us to determine the diffusivity matrix directly from the fitted parameters. Reformulation of the equation for the analytical solution is done in order to reduce the size of the problem and accelerate the convergence. The objective function for the regression can incorporate point estimations for error in the concentration, improving the statistical confidence in the estimated diffusivity matrix. Case studies are presented to demonstrate the reliability and the stability of the method. To the best of our knowledge there is no published analysis of the effects of experimental errors on the reliability of the estimates for the diffusivities. For the case of linear multicomponent diffusion, we analyze the effects of the instrument analytical spot size, positioning uncertainty, and concentration uncertainty on the resulting values of the diffusivities. These effects are studied using Monte Carlo method on simulated experimental data. Several useful scaling relationships were identified which allow more rigorous and quantitative estimates of the errors in the measured data, and are valuable for experimental design. To further analyze anomalous diffusion processes, where traditional diffusional transport equations do not hold, we explore the use of fractional calculus in analytically representing these processes is proposed. We use the fractional calculus approach for anomalous diffusion processes occurring through a finite plane sheet with one face held at a fixed concentration, the other held at zero, and the initial concentration within the sheet equal to zero. This problem is related to cases in nature where diffusion is enhanced relative to the classical process, and the order of differentiation is not necessarily a second--order differential equation. That is, differentiation is of fractional order alpha, where 1 ≤ alpha < 2. For alpha = 2, the presented solutions reduce to the classical second-order diffusion solution for the conditions studied. The solution obtained allows the analysis of permeation experiments. Frequently, hydrogen diffusion is analyzed using electrochemical permeation methods using the traditional, Fickian-based theory. Experimental evidence shows the latter analytical approach is not always appropiate, because reported data shows qualitative (and quantitative) deviation from its theoretical scaling predictions. Preliminary analysis of data shows better agreement with fractional diffusion analysis when compared to traditional square-root scaling. Although there is a large amount of work in the estimation of the diffusivity from experimental data, reported studies typically present only the analytical description for the diffusivity, without scattering. However, because these studies do not consider effects produced by instrument analysis, their direct applicability is limited. We propose alternatives to address these, and to evaluate their influence on the final resulting diffusivity values.

Cascade Error Projection with Low Bit Weight Quantization for High Order Correlation Data

NASA Technical Reports Server (NTRS)

Duong, Tuan A.; Daud, Taher

1998-01-01

In this paper, we reinvestigate the solution for chaotic time series prediction problem using neural network approach. The nature of this problem is such that the data sequences are never repeated, but they are rather in chaotic region. However, these data sequences are correlated between past, present, and future data in high order. We use Cascade Error Projection (CEP) learning algorithm to capture the high order correlation between past and present data to predict a future data using limited weight quantization constraints. This will help to predict a future information that will provide us better estimation in time for intelligent control system. In our earlier work, it has been shown that CEP can sufficiently learn 5-8 bit parity problem with 4- or more bits, and color segmentation problem with 7- or more bits of weight quantization. In this paper, we demonstrate that chaotic time series can be learned and generalized well with as low as 4-bit weight quantization using round-off and truncation techniques. The results show that generalization feature will suffer less as more bit weight quantization is available and error surfaces with the round-off technique are more symmetric around zero than error surfaces with the truncation technique. This study suggests that CEP is an implementable learning technique for hardware consideration.

Local error estimates for adaptive simulation of the Reaction–Diffusion Master Equation via operator splitting

PubMed Central

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

Local error estimates for adaptive simulation of the Reaction-Diffusion Master Equation via operator splitting.

PubMed

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 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.

Neural Correlates of User-initiated Motor Success and Failure - A Brain-Computer Interface Perspective.

PubMed

Yazmir, Boris; Reiner, Miriam

2018-05-15

Any motor action is, by nature, potentially accompanied by human errors. In order to facilitate development of error-tailored Brain-Computer Interface (BCI) correction systems, we focused on internal, human-initiated errors, and investigated EEG correlates of user outcome successes and errors during a continuous 3D virtual tennis game against a computer player. We used a multisensory, 3D, highly immersive environment. Missing and repelling the tennis ball were considered, as 'error' (miss) and 'success' (repel). Unlike most previous studies, where the environment "encouraged" the participant to perform a mistake, here errors happened naturally, resulting from motor-perceptual-cognitive processes of incorrect estimation of the ball kinematics, and can be regarded as user internal, self-initiated errors. Results show distinct and well-defined Event-Related Potentials (ERPs), embedded in the ongoing EEG, that differ across conditions by waveforms, scalp signal distribution maps, source estimation results (sLORETA) and time-frequency patterns, establishing a series of typical features that allow valid discrimination between user internal outcome success and error. The significant delay in latency between positive peaks of error- and success-related ERPs, suggests a cross-talk between top-down and bottom-up processing, represented by an outcome recognition process, in the context of the game world. Success-related ERPs had a central scalp distribution, while error-related ERPs were centro-parietal. The unique characteristics and sharp differences between EEG correlates of error/success provide the crucial components for an improved BCI system. The features of the EEG waveform can be used to detect user action outcome, to be fed into the BCI correction system. Copyright © 2016 IBRO. Published by Elsevier Ltd. All rights reserved.

Adaptive Window Zero-Crossing-Based Instantaneous Frequency Estimation

NASA Astrophysics Data System (ADS)

Sekhar, S. Chandra; Sreenivas, TV

2004-12-01

We address the problem of estimating instantaneous frequency (IF) of a real-valued constant amplitude time-varying sinusoid. Estimation of polynomial IF is formulated using the zero-crossings of the signal. We propose an algorithm to estimate nonpolynomial IF by local approximation using a low-order polynomial, over a short segment of the signal. This involves the choice of window length to minimize the mean square error (MSE). The optimal window length found by directly minimizing the MSE is a function of the higher-order derivatives of the IF which are not available a priori. However, an optimum solution is formulated using an adaptive window technique based on the concept of intersection of confidence intervals. The adaptive algorithm enables minimum MSE-IF (MMSE-IF) estimation without requiring a priori information about the IF. Simulation results show that the adaptive window zero-crossing-based IF estimation method is superior to fixed window methods and is also better than adaptive spectrogram and adaptive Wigner-Ville distribution (WVD)-based IF estimators for different signal-to-noise ratio (SNR).

Effects of vibration on inertial wind-tunnel model attitude measurement devices

NASA Technical Reports Server (NTRS)

Young, Clarence P., Jr.; Buehrle, Ralph D.; Balakrishna, S.; Kilgore, W. Allen

1994-01-01

Results of an experimental study of a wind tunnel model inertial angle-of-attack sensor response to a simulated dynamic environment are presented. The inertial device cannot distinguish between the gravity vector and the centrifugal accelerations associated with wind tunnel model vibration, this situation results in a model attitude measurement bias error. Significant bias error in model attitude measurement was found for the model system tested. The model attitude bias error was found to be vibration mode and amplitude dependent. A first order correction model was developed and used for estimating attitude measurement bias error due to dynamic motion. A method for correcting the output of the model attitude inertial sensor in the presence of model dynamics during on-line wind tunnel operation is proposed.

Self-evaluation of decision-making: A general Bayesian framework for metacognitive computation.

PubMed

Fleming, Stephen M; Daw, Nathaniel D

2017-01-01

People are often aware of their mistakes, and report levels of confidence in their choices that correlate with objective performance. These metacognitive assessments of decision quality are important for the guidance of behavior, particularly when external feedback is absent or sporadic. However, a computational framework that accounts for both confidence and error detection is lacking. In addition, accounts of dissociations between performance and metacognition have often relied on ad hoc assumptions, precluding a unified account of intact and impaired self-evaluation. Here we present a general Bayesian framework in which self-evaluation is cast as a "second-order" inference on a coupled but distinct decision system, computationally equivalent to inferring the performance of another actor. Second-order computation may ensue whenever there is a separation between internal states supporting decisions and confidence estimates over space and/or time. We contrast second-order computation against simpler first-order models in which the same internal state supports both decisions and confidence estimates. Through simulations we show that second-order computation provides a unified account of different types of self-evaluation often considered in separate literatures, such as confidence and error detection, and generates novel predictions about the contribution of one's own actions to metacognitive judgments. In addition, the model provides insight into why subjects' metacognition may sometimes be better or worse than task performance. We suggest that second-order computation may underpin self-evaluative judgments across a range of domains. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

Physician Utilization of a Hospital Information System: A Computer Simulation Model

PubMed Central

Anderson, James G.; Jay, Stephen J.; Clevenger, Stephen J.; Kassing, David R.; Perry, Jane; Anderson, Marilyn M.

1988-01-01

The purpose of this research was to develop a computer simulation model that represents the process through which physicians enter orders into a hospital information system (HIS). Computer simulation experiments were performed to estimate the effects of two methods of order entry on outcome variables. The results of the computer simulation experiments were used to perform a cost-benefit analysis to compare the two different means of entering medical orders into the HIS. The results indicate that the use of personal order sets to enter orders into the HIS will result in a significant reduction in manpower, salaries and fringe benefits, and errors in order entry.

Intelligent complementary sliding-mode control for LUSMS-based X-Y-theta motion control stage.

PubMed

Lin, Faa-Jeng; Chen, Syuan-Yi; Shyu, Kuo-Kai; Liu, Yen-Hung

2010-07-01

An intelligent complementary sliding-mode control (ICSMC) system using a recurrent wavelet-based Elman neural network (RWENN) estimator is proposed in this study to control the mover position of a linear ultrasonic motors (LUSMs)-based X-Y-theta motion control stage for the tracking of various contours. By the addition of a complementary generalized error transformation, the complementary sliding-mode control (CSMC) can efficiently reduce the guaranteed ultimate bound of the tracking error by half compared with the slidingmode control (SMC) while using the saturation function. To estimate a lumped uncertainty on-line and replace the hitting control of the CSMC directly, the RWENN estimator is adopted in the proposed ICSMC system. In the RWENN, each hidden neuron employs a different wavelet function as an activation function to improve both the convergent precision and the convergent time compared with the conventional Elman neural network (ENN). The estimation laws of the RWENN are derived using the Lyapunov stability theorem to train the network parameters on-line. A robust compensator is also proposed to confront the uncertainties including approximation error, optimal parameter vectors, and higher-order terms in Taylor series. Finally, some experimental results of various contours tracking show that the tracking performance of the ICSMC system is significantly improved compared with the SMC and CSMC systems.

Spatial uncertainty of a geoid undulation model in Guayaquil, Ecuador

NASA Astrophysics Data System (ADS)

Chicaiza, E. G.; Leiva, C. A.; Arranz, J. J.; Buenańo, X. E.

2017-06-01

Geostatistics is a discipline that deals with the statistical analysis of regionalized variables. In this case study, geostatistics is used to estimate geoid undulation in the rural area of Guayaquil town in Ecuador. The geostatistical approach was chosen because the estimation error of prediction map is getting. Open source statistical software R and mainly geoR, gstat and RGeostats libraries were used. Exploratory data analysis (EDA), trend and structural analysis were carried out. An automatic model fitting by Iterative Least Squares and other fitting procedures were employed to fit the variogram. Finally, Kriging using gravity anomaly of Bouguer as external drift and Universal Kriging were used to get a detailed map of geoid undulation. The estimation uncertainty was reached in the interval [-0.5; +0.5] m for errors and a maximum estimation standard deviation of 2 mm in relation with the method of interpolation applied. The error distribution of the geoid undulation map obtained in this study provides a better result than Earth gravitational models publicly available for the study area according the comparison with independent validation points. The main goal of this paper is to confirm the feasibility to use geoid undulations from Global Navigation Satellite Systems and leveling field measurements and geostatistical techniques methods in order to use them in high-accuracy engineering projects.

Bias in error estimation when using cross-validation for model selection.

PubMed

Varma, Sudhir; Simon, Richard

2006-02-23

Cross-validation (CV) is an effective method for estimating the prediction error of a classifier. Some recent articles have proposed methods for optimizing classifiers by choosing classifier parameter values that minimize the CV error estimate. We have evaluated the validity of using the CV error estimate of the optimized classifier as an estimate of the true error expected on independent data. We used CV to optimize the classification parameters for two kinds of classifiers; Shrunken Centroids and Support Vector Machines (SVM). Random training datasets were created, with no difference in the distribution of the features between the two classes. Using these "null" datasets, we selected classifier parameter values that minimized the CV error estimate. 10-fold CV was used for Shrunken Centroids while Leave-One-Out-CV (LOOCV) was used for the SVM. Independent test data was created to estimate the true error. With "null" and "non null" (with differential expression between the classes) data, we also tested a nested CV procedure, where an inner CV loop is used to perform the tuning of the parameters while an outer CV is used to compute an estimate of the error. The CV error estimate for the classifier with the optimal parameters was found to be a substantially biased estimate of the true error that the classifier would incur on independent data. Even though there is no real difference between the two classes for the "null" datasets, the CV error estimate for the Shrunken Centroid with the optimal parameters was less than 30% on 18.5% of simulated training data-sets. For SVM with optimal parameters the estimated error rate was less than 30% on 38% of "null" data-sets. Performance of the optimized classifiers on the independent test set was no better than chance. The nested CV procedure reduces the bias considerably and gives an estimate of the error that is very close to that obtained on the independent testing set for both Shrunken Centroids and SVM classifiers for "null" and "non-null" data distributions. We show that using CV to compute an error estimate for a classifier that has itself been tuned using CV gives a significantly biased estimate of the true error. Proper use of CV for estimating true error of a classifier developed using a well defined algorithm requires that all steps of the algorithm, including classifier parameter tuning, be repeated in each CV loop. A nested CV procedure provides an almost unbiased estimate of the true error.

Application of a Reduced Order Kalman Filter to Initialize a Coupled Atmosphere-Ocean Model: Impact on the Prediction of El Nino

NASA Technical Reports Server (NTRS)

Ballabrera-Poy, J.; Busalacchi, A.; Murtugudde, R.

2000-01-01

A reduced order Kalman Filter, based on a simplification of the Singular Evolutive Extended Kalman (SEEK) filter equations, is used to assimilate observed fields of the surface wind stress, sea surface temperature and sea level into the nonlinear coupled ocean-atmosphere model of Zebiak and Cane. The SEEK filter projects the Kalman Filter equations onto a subspace defined by the eigenvalue decomposition of the error forecast matrix, allowing its application to high dimensional systems. The Zebiak and Cane model couples a linear reduced gravity ocean model with a single vertical mode atmospheric model of Zebiak. The compatibility between the simplified physics of the model and each observed variable is studied separately and together. The results show the ability of the model to represent the simultaneous value of the wind stress, SST and sea level, when the fields are limited to the latitude band 10 deg S - 10 deg N In this first application of the Kalman Filter to a coupled ocean-atmosphere prediction model, the sea level fields are assimilated in terms of the Kelvin and Rossby modes of the thermocline depth anomaly. An estimation of the error of these modes is derived from the projection of an estimation of the sea level error over such modes. This method gives a value of 12 for the error of the Kelvin amplitude, and 6 m of error for the Rossby component of the thermocline depth. The ability of the method to reconstruct the state of the equatorial Pacific and predict its time evolution is demonstrated. The method is shown to be quite robust for predictions up to six months, and able to predict the onset of the 1997 warm event fifteen months before its occurrence.

Application of a Reduced Order Kalman Filter to Initialize a Coupled Atmosphere-Ocean Model: Impact on the Prediction of El Nino

NASA Technical Reports Server (NTRS)

Ballabrera-Poy, Joaquim; Busalacchi, Antonio J.; Murtugudde, Ragu

2000-01-01

A reduced order Kalman Filter, based on a simplification of the Singular Evolutive Extended Kalman (SEEK) filter equations, is used to assimilate observed fields of the surface wind stress, sea surface temperature and sea level into the nonlinear coupled ocean-atmosphere model. The SEEK filter projects the Kalman Filter equations onto a subspace defined by the eigenvalue decomposition of the error forecast matrix, allowing its application to high dimensional systems. The Zebiak and Cane model couples a linear reduced gravity ocean model with a single vertical mode atmospheric model of Zebiak. The compatibility between the simplified physics of the model and each observed variable is studied separately and together. The results show the ability of the model to represent the simultaneous value of the wind stress, SST and sea level, when the fields are limited to the latitude band 10 deg S - 10 deg N. In this first application of the Kalman Filter to a coupled ocean-atmosphere prediction model, the sea level fields are assimilated in terms of the Kelvin and Rossby modes of the thermocline depth anomaly. An estimation of the error of these modes is derived from the projection of an estimation of the sea level error over such modes. This method gives a value of 12 for the error of the Kelvin amplitude, and 6 m of error for the Rossby component of the thermocline depth. The ability of the method to reconstruct the state of the equatorial Pacific and predict its time evolution is demonstrated. The method is shown to be quite robust for predictions I up to six months, and able to predict the onset of the 1997 warm event fifteen months before its occurrence.

Parallel computers - Estimate errors caused by imprecise data

NASA Technical Reports Server (NTRS)

Kreinovich, Vladik; Bernat, Andrew; Villa, Elsa; Mariscal, Yvonne

1991-01-01

A new approach to the problem of estimating errors caused by imprecise data is proposed in the context of software engineering. A software device is used to produce an ideal solution to the problem, when the computer is capable of computing errors of arbitrary programs. The software engineering aspect of this problem is to describe a device for computing the error estimates in software terms and then to provide precise numbers with error estimates to the user. The feasibility of the program capable of computing both some quantity and its error estimate in the range of possible measurement errors is demonstrated.

Correcting the Standard Errors of 2-Stage Residual Inclusion Estimators for Mendelian Randomization Studies.

PubMed

Palmer, Tom M; Holmes, Michael V; Keating, Brendan J; Sheehan, Nuala A

2017-11-01

Mendelian randomization studies use genotypes as instrumental variables to test for and estimate the causal effects of modifiable risk factors on outcomes. Two-stage residual inclusion (TSRI) estimators have been used when researchers are willing to make parametric assumptions. However, researchers are currently reporting uncorrected or heteroscedasticity-robust standard errors for these estimates. We compared several different forms of the standard error for linear and logistic TSRI estimates in simulations and in real-data examples. Among others, we consider standard errors modified from the approach of Newey (1987), Terza (2016), and bootstrapping. In our simulations Newey, Terza, bootstrap, and corrected 2-stage least squares (in the linear case) standard errors gave the best results in terms of coverage and type I error. In the real-data examples, the Newey standard errors were 0.5% and 2% larger than the unadjusted standard errors for the linear and logistic TSRI estimators, respectively. We show that TSRI estimators with modified standard errors have correct type I error under the null. Researchers should report TSRI estimates with modified standard errors instead of reporting unadjusted or heteroscedasticity-robust standard errors. © The Author(s) 2017. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health.

GEOS-2 refraction program summary document. [ionospheric and tropospheric propagation errors in satellite tracking instruments

NASA Technical Reports Server (NTRS)

Mallinckrodt, A. J.

1977-01-01

Data from an extensive array of collocated instrumentation at the Wallops Island test facility were intercompared in order to (1) determine the practical achievable accuracy limitations of various tropospheric and ionospheric correction techniques; (2) examine the theoretical bases and derivation of improved refraction correction techniques; and (3) estimate internal systematic and random error levels of the various tracking stations. The GEOS 2 satellite was used as the target vehicle. Data were obtained regarding the ionospheric and tropospheric propagation errors, the theoretical and data analysis of which was documented in some 30 separate reports over the last 6 years. An overview of project results is presented.

High-order computer-assisted estimates of topological entropy

NASA Astrophysics Data System (ADS)

Grote, Johannes

The concept of Taylor Models is introduced, which offers highly accurate C0-estimates for the enclosures of functional dependencies, combining high-order Taylor polynomial approximation of functions and rigorous estimates of the truncation error, performed using verified interval arithmetic. The focus of this work is on the application of Taylor Models in algorithms for strongly nonlinear dynamical systems. A method to obtain sharp rigorous enclosures of Poincare maps for certain types of flows and surfaces is developed and numerical examples are presented. Differential algebraic techniques allow the efficient and accurate computation of polynomial approximations for invariant curves of certain planar maps around hyperbolic fixed points. Subsequently we introduce a procedure to extend these polynomial curves to verified Taylor Model enclosures of local invariant manifolds with C0-errors of size 10-10--10 -14, and proceed to generate the global invariant manifold tangle up to comparable accuracy through iteration in Taylor Model arithmetic. Knowledge of the global manifold structure up to finite iterations of the local manifold pieces enables us to find all homoclinic and heteroclinic intersections in the generated manifold tangle. Combined with the mapping properties of the homoclinic points and their ordering we are able to construct a subshift of finite type as a topological factor of the original planar system to obtain rigorous lower bounds for its topological entropy. This construction is fully automatic and yields homoclinic tangles with several hundred homoclinic points. As an example rigorous lower bounds for the topological entropy of the Henon map are computed, which to the best knowledge of the authors yield the largest such estimates published so far.

Moments and Root-Mean-Square Error of the Bayesian MMSE Estimator of Classification Error in the Gaussian Model.

PubMed

Zollanvari, Amin; Dougherty, Edward R

2014-06-01

The most important aspect of any classifier is its error rate, because this quantifies its predictive capacity. Thus, the accuracy of error estimation is critical. Error estimation is problematic in small-sample classifier design because the error must be estimated using the same data from which the classifier has been designed. Use of prior knowledge, in the form of a prior distribution on an uncertainty class of feature-label distributions to which the true, but unknown, feature-distribution belongs, can facilitate accurate error estimation (in the mean-square sense) in circumstances where accurate completely model-free error estimation is impossible. This paper provides analytic asymptotically exact finite-sample approximations for various performance metrics of the resulting Bayesian Minimum Mean-Square-Error (MMSE) error estimator in the case of linear discriminant analysis (LDA) in the multivariate Gaussian model. These performance metrics include the first, second, and cross moments of the Bayesian MMSE error estimator with the true error of LDA, and therefore, the Root-Mean-Square (RMS) error of the estimator. We lay down the theoretical groundwork for Kolmogorov double-asymptotics in a Bayesian setting, which enables us to derive asymptotic expressions of the desired performance metrics. From these we produce analytic finite-sample approximations and demonstrate their accuracy via numerical examples. Various examples illustrate the behavior of these approximations and their use in determining the necessary sample size to achieve a desired RMS. The Supplementary Material contains derivations for some equations and added figures.

Errors in retarding potential analyzers caused by nonuniformity of the grid-plane potential.

NASA Technical Reports Server (NTRS)

Hanson, W. B.; Frame, D. R.; Midgley, J. E.

1972-01-01

One aspect of the degradation in performance of retarding potential analyzers caused by potential depressions in the retarding grid is quantitatively estimated from laboratory measurements and theoretical calculations. A simple expression is obtained that permits the use of laboratory measurements of grid properties to make first-order corrections to flight data. Systematic positive errors in ion temperature of approximately 16% for the Ogo 4 instrument and 3% for the Ogo 6 instrument are deduced. The effects of the transverse electric fields arising from the grid potential depressions are not treated.

Variability of rainfall over small areas

NASA Technical Reports Server (NTRS)

Runnels, R. C.

1983-01-01

A preliminary investigation was made to determine estimates of the number of raingauges needed in order to measure the variability of rainfall in time and space over small areas (approximately 40 sq miles). The literature on rainfall variability was examined and the types of empirical relationships used to relate rainfall variations to meteorological and catchment-area characteristics were considered. Relations between the coefficient of variation and areal-mean rainfall and area have been used by several investigators. These parameters seemed reasonable ones to use in any future study of rainfall variations. From a knowledge of an appropriate coefficient of variation (determined by the above-mentioned relations) the number rain gauges needed for the precise determination of areal-mean rainfall may be calculated by statistical estimation theory. The number gauges needed to measure the coefficient of variation over a 40 sq miles area, with varying degrees of error, was found to range from 264 (10% error, mean precipitation = 0.1 in) to about 2 (100% error, mean precipitation = 0.1 in).

A comparison of two estimates of standard error for a ratio-of-means estimator for a mapped-plot sample design in southeast Alaska.

Treesearch

Willem W.S. van Hees

2002-01-01

Comparisons of estimated standard error for a ratio-of-means (ROM) estimator are presented for forest resource inventories conducted in southeast Alaska between 1995 and 2000. Estimated standard errors for the ROM were generated by using a traditional variance estimator and also approximated by bootstrap methods. Estimates of standard error generated by both...

A physics-based fractional order model and state of energy estimation for lithium ion batteries. Part II: Parameter identification and state of energy estimation for LiFePO4 battery

NASA Astrophysics Data System (ADS)

Li, Xiaoyu; Pan, Ke; Fan, Guodong; Lu, Rengui; Zhu, Chunbo; Rizzoni, Giorgio; Canova, Marcello

2017-11-01

State of energy (SOE) is an important index for the electrochemical energy storage system in electric vehicles. In this paper, a robust state of energy estimation method in combination with a physical model parameter identification method is proposed to achieve accurate battery state estimation at different operating conditions and different aging stages. A physics-based fractional order model with variable solid-state diffusivity (FOM-VSSD) is used to characterize the dynamic performance of a LiFePO4/graphite battery. In order to update the model parameter automatically at different aging stages, a multi-step model parameter identification method based on the lexicographic optimization is especially designed for the electric vehicle operating conditions. As the battery available energy changes with different applied load current profiles, the relationship between the remaining energy loss and the state of charge, the average current as well as the average squared current is modeled. The SOE with different operating conditions and different aging stages are estimated based on an adaptive fractional order extended Kalman filter (AFEKF). Validation results show that the overall SOE estimation error is within ±5%. The proposed method is suitable for the electric vehicle online applications.

Error correcting coding-theory for structured light illumination systems

NASA Astrophysics Data System (ADS)

Porras-Aguilar, Rosario; Falaggis, Konstantinos; Ramos-Garcia, Ruben

2017-06-01

Intensity discrete structured light illumination systems project a series of projection patterns for the estimation of the absolute fringe order using only the temporal grey-level sequence at each pixel. This work proposes the use of error-correcting codes for pixel-wise correction of measurement errors. The use of an error correcting code is advantageous in many ways: it allows reducing the effect of random intensity noise, it corrects outliners near the border of the fringe commonly present when using intensity discrete patterns, and it provides a robustness in case of severe measurement errors (even for burst errors where whole frames are lost). The latter aspect is particular interesting in environments with varying ambient light as well as in critical safety applications as e.g. monitoring of deformations of components in nuclear power plants, where a high reliability is ensured even in case of short measurement disruptions. A special form of burst errors is the so-called salt and pepper noise, which can largely be removed with error correcting codes using only the information of a given pixel. The performance of this technique is evaluated using both simulations and experiments.

A Particle Smoother with Sequential Importance Resampling for soil hydraulic parameter estimation: A lysimeter experiment

NASA Astrophysics Data System (ADS)

Montzka, Carsten; Hendricks Franssen, Harrie-Jan; Moradkhani, Hamid; Pütz, Thomas; Han, Xujun; Vereecken, Harry

2013-04-01

An adequate description of soil hydraulic properties is essential for a good performance of hydrological forecasts. So far, several studies showed that data assimilation could reduce the parameter uncertainty by considering soil moisture observations. However, these observations and also the model forcings were recorded with a specific measurement error. It seems a logical step to base state updating and parameter estimation on observations made at multiple time steps, in order to reduce the influence of outliers at single time steps given measurement errors and unknown model forcings. Such outliers could result in erroneous state estimation as well as inadequate parameters. This has been one of the reasons to use a smoothing technique as implemented for Bayesian data assimilation methods such as the Ensemble Kalman Filter (i.e. Ensemble Kalman Smoother). Recently, an ensemble-based smoother has been developed for state update with a SIR particle filter. However, this method has not been used for dual state-parameter estimation. In this contribution we present a Particle Smoother with sequentially smoothing of particle weights for state and parameter resampling within a time window as opposed to the single time step data assimilation used in filtering techniques. This can be seen as an intermediate variant between a parameter estimation technique using global optimization with estimation of single parameter sets valid for the whole period, and sequential Monte Carlo techniques with estimation of parameter sets evolving from one time step to another. The aims are i) to improve the forecast of evaporation and groundwater recharge by estimating hydraulic parameters, and ii) to reduce the impact of single erroneous model inputs/observations by a smoothing method. In order to validate the performance of the proposed method in a real world application, the experiment is conducted in a lysimeter environment.

Retrieval of ice cloud properties using an optimal estimation algorithm and MODIS infrared observations: 1. Forward model, error analysis, and information content

NASA Astrophysics Data System (ADS)

Wang, Chenxi; Platnick, Steven; Zhang, Zhibo; Meyer, Kerry; Yang, Ping

2016-05-01

An optimal estimation (OE) retrieval method is developed to infer three ice cloud properties simultaneously: optical thickness (τ), effective radius (reff), and cloud top height (h). This method is based on a fast radiative transfer (RT) model and infrared (IR) observations from the MODerate resolution Imaging Spectroradiometer (MODIS). This study conducts thorough error and information content analyses to understand the error propagation and performance of retrievals from various MODIS band combinations under different cloud/atmosphere states. Specifically, the algorithm takes into account four error sources: measurement uncertainty, fast RT model uncertainty, uncertainties in ancillary data sets (e.g., atmospheric state), and assumed ice crystal habit uncertainties. It is found that the ancillary and ice crystal habit error sources dominate the MODIS IR retrieval uncertainty and cannot be ignored. The information content analysis shows that for a given ice cloud, the use of four MODIS IR observations is sufficient to retrieve the three cloud properties. However, the selection of MODIS IR bands that provide the most information and their order of importance varies with both the ice cloud properties and the ambient atmospheric and the surface states. As a result, this study suggests the inclusion of all MODIS IR bands in practice since little a priori information is available.

Retrieval of ice cloud properties using an optimal estimation algorithm and MODIS infrared observations. Part I: Forward model, error analysis, and information content.

PubMed

Wang, Chenxi; Platnick, Steven; Zhang, Zhibo; Meyer, Kerry; Yang, Ping

2016-05-27

An optimal estimation (OE) retrieval method is developed to infer three ice cloud properties simultaneously: optical thickness ( τ ), effective radius ( r eff ), and cloud-top height ( h ). This method is based on a fast radiative transfer (RT) model and infrared (IR) observations from the MODerate resolution Imaging Spectroradiometer (MODIS). This study conducts thorough error and information content analyses to understand the error propagation and performance of retrievals from various MODIS band combinations under different cloud/atmosphere states. Specifically, the algorithm takes into account four error sources: measurement uncertainty, fast RT model uncertainty, uncertainties in ancillary datasets (e.g., atmospheric state), and assumed ice crystal habit uncertainties. It is found that the ancillary and ice crystal habit error sources dominate the MODIS IR retrieval uncertainty and cannot be ignored. The information content analysis shows that, for a given ice cloud, the use of four MODIS IR observations is sufficient to retrieve the three cloud properties. However, the selection of MODIS IR bands that provide the most information and their order of importance varies with both the ice cloud properties and the ambient atmospheric and the surface states. As a result, this study suggests the inclusion of all MODIS IR bands in practice since little a priori information is available.

A spline-based parameter estimation technique for static models of elastic structures

NASA Technical Reports Server (NTRS)

Dutt, P.; Taasan, S.

1986-01-01

The problem of identifying the spatially varying coefficient of elasticity using an observed solution to the forward problem is considered. Under appropriate conditions this problem can be treated as a first order hyperbolic equation in the unknown coefficient. Some continuous dependence results are developed for this problem and a spline-based technique is proposed for approximating the unknown coefficient, based on these results. The convergence of the numerical scheme is established and error estimates obtained.

Recruiter Stress: An Experiment Using Text-messages as a Stress Intervention

DTIC Science & Technology

2013-02-01

messages covered a number of areas (e.g., physiological, cognitive , social) and were generally self-contained with 140 characters or less (to meet text...although most differences here are small and within margins of errors. The two most noticeable differences are for the impact of work stress on job...order to compute estimated hour and day losses. Using recruiter population estimates, over 4,500 man-hours were lost due to late arrivals and over 9,500

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.

Estimating long-run equilibrium real exchange rates: short-lived shocks with long-lived impacts on Pakistan.

PubMed

Zardad, Asma; Mohsin, Asma; Zaman, Khalid

2013-12-01

The purpose of this study is to investigate the factors that affect real exchange rate volatility for Pakistan through the co-integration and error correction model over a 30-year time period, i.e. between 1980 and 2010. The study employed the autoregressive conditional heteroskedasticity (ARCH), generalized autoregressive conditional heteroskedasticity (GARCH) and Vector Error Correction model (VECM) to estimate the changes in the volatility of real exchange rate series, while an error correction model was used to determine the short-run dynamics of the system. The study is limited to a few variables i.e., productivity differential (i.e., real GDP per capita relative to main trading partner); terms of trade; trade openness and government expenditures in order to manage robust data. The result indicates that real effective exchange rate (REER) has been volatile around its equilibrium level; while, the speed of adjustment is relatively slow. VECM results confirm long run convergence of real exchange rate towards its equilibrium level. Results from ARCH and GARCH estimation shows that real shocks volatility persists, so that shocks die out rather slowly, and lasting misalignment seems to have occurred.

The modal surface interpolation method for damage localization

NASA Astrophysics Data System (ADS)

Pina Limongelli, Maria

2017-05-01

The Interpolation Method (IM) has been previously proposed and successfully applied for damage localization in plate like structures. The method is based on the detection of localized reductions of smoothness in the Operational Deformed Shapes (ODSs) of the structure. The IM can be applied to any type of structure provided the ODSs are estimated accurately in the original and in the damaged configurations. If the latter circumstance fails to occur, for example when the structure is subjected to an unknown input(s) or if the structural responses are strongly corrupted by noise, both false and missing alarms occur when the IM is applied to localize a concentrated damage. In order to overcome these drawbacks a modification of the method is herein investigated. An ODS is the deformed shape of a structure subjected to a harmonic excitation: at resonances the ODS are dominated by the relevant mode shapes. The effect of noise at resonance is usually lower with respect to other frequency values hence the relevant ODS are estimated with higher reliability. Several methods have been proposed to reliably estimate modal shapes in case of unknown input. These two circumstances can be exploited to improve the reliability of the IM. In order to reduce or eliminate the drawbacks related to the estimation of the ODSs in case of noisy signals, in this paper is investigated a modified version of the method based on a damage feature calculated considering the interpolation error relevant only to the modal shapes and not to all the operational shapes in the significant frequency range. Herein will be reported the comparison between the results of the IM in its actual version (with the interpolation error calculated summing up the contributions of all the operational shapes) and in the new proposed version (with the estimation of the interpolation error limited to the modal shapes).

Bayesian assessment of overtriage and undertriage at a level I trauma centre.

PubMed

DiDomenico, Paul B; Pietzsch, Jan B; Paté-Cornell, M Elisabeth

2008-07-13

We analysed the trauma triage system at a specific level I trauma centre to assess rates of over- and undertriage and to support recommendations for system improvements. The triage process is designed to estimate the severity of patient injury and allocate resources accordingly, with potential errors of overestimation (overtriage) consuming excess resources and underestimation (undertriage) potentially leading to medical errors.We first modelled the overall trauma system using risk analysis methods to understand interdependencies among the actions of the participants. We interviewed six experienced trauma surgeons to obtain their expert opinion of the over- and undertriage rates occurring in the trauma centre. We then assessed actual over- and undertriage rates in a random sample of 86 trauma cases collected over a six-week period at the same centre. We employed Bayesian analysis to quantitatively combine the data with the prior probabilities derived from expert opinion in order to obtain posterior distributions. The results were estimates of overtriage and undertriage in 16.1 and 4.9% of patients, respectively. This Bayesian approach, which provides a quantitative assessment of the error rates using both case data and expert opinion, provides a rational means of obtaining a best estimate of the system's performance. The overall approach that we describe in this paper can be employed more widely to analyse complex health care delivery systems, with the objective of reduced errors, patient risk and excess costs.

Dynamic Programming and Error Estimates for Stochastic Control Problems with Maximum Cost

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bokanowski, Olivier, E-mail: boka@math.jussieu.fr; Picarelli, Athena, E-mail: athena.picarelli@inria.fr; Zidani, Hasnaa, E-mail: hasnaa.zidani@ensta.fr

2015-02-15

This work is concerned with stochastic optimal control for a running maximum cost. A direct approach based on dynamic programming techniques is studied leading to the characterization of the value function as the unique viscosity solution of a second order Hamilton–Jacobi–Bellman (HJB) equation with an oblique derivative boundary condition. A general numerical scheme is proposed and a convergence result is provided. Error estimates are obtained for the semi-Lagrangian scheme. These results can apply to the case of lookback options in finance. Moreover, optimal control problems with maximum cost arise in the characterization of the reachable sets for a system ofmore » controlled stochastic differential equations. Some numerical simulations on examples of reachable analysis are included to illustrate our approach.« less

Robust adaptive uniform exact tracking control for uncertain Euler-Lagrange system

NASA Astrophysics Data System (ADS)

Yang, Yana; Hua, Changchun; Li, Junpeng; Guan, Xinping

2017-12-01

This paper offers a solution to the robust adaptive uniform exact tracking control for uncertain nonlinear Euler-Lagrange (EL) system. An adaptive finite-time tracking control algorithm is designed by proposing a novel nonsingular integral terminal sliding-mode surface. Moreover, a new adaptive parameter tuning law is also developed by making good use of the system tracking errors and the adaptive parameter estimation errors. Thus, both the trajectory tracking and the parameter estimation can be achieved in a guaranteed time adjusted arbitrarily based on practical demands, simultaneously. Additionally, the control result for the EL system proposed in this paper can be extended to high-order nonlinear systems easily. Finally, a test-bed 2-DOF robot arm is set-up to demonstrate the performance of the new control algorithm.

Electrochemical state and internal variables estimation using a reduced-order physics-based model of a lithium-ion cell and an extended Kalman filter

DOE Office of Scientific and Technical Information (OSTI.GOV)

Stetzel, KD; Aldrich, LL; Trimboli, MS

2015-03-15

This paper addresses the problem of estimating the present value of electrochemical internal variables in a lithium-ion cell in real time, using readily available measurements of cell voltage, current, and temperature. The variables that can be estimated include any desired set of reaction flux and solid and electrolyte potentials and concentrations at any set of one-dimensional spatial locations, in addition to more standard quantities such as state of charge. The method uses an extended Kalman filter along with a one-dimensional physics-based reduced-order model of cell dynamics. Simulations show excellent and robust predictions having dependable error bounds for most internal variables.more » (C) 2014 Elsevier B.V. All rights reserved.« less

Error estimation of deformable image registration of pulmonary CT scans using convolutional neural networks.

PubMed

Eppenhof, Koen A J; Pluim, Josien P W

2018-04-01

Error estimation in nonlinear medical image registration is a nontrivial problem that is important for validation of registration methods. We propose a supervised method for estimation of registration errors in nonlinear registration of three-dimensional (3-D) images. The method is based on a 3-D convolutional neural network that learns to estimate registration errors from a pair of image patches. By applying the network to patches centered around every voxel, we construct registration error maps. The network is trained using a set of representative images that have been synthetically transformed to construct a set of image pairs with known deformations. The method is evaluated on deformable registrations of inhale-exhale pairs of thoracic CT scans. Using ground truth target registration errors on manually annotated landmarks, we evaluate the method's ability to estimate local registration errors. Estimation of full domain error maps is evaluated using a gold standard approach. The two evaluation approaches show that we can train the network to robustly estimate registration errors in a predetermined range, with subvoxel accuracy. We achieved a root-mean-square deviation of 0.51 mm from gold standard registration errors and of 0.66 mm from ground truth landmark registration errors.

Updated Magmatic Flux Rate Estimates for the Hawaii Plume

NASA Astrophysics Data System (ADS)

Wessel, P.

2013-12-01

Several studies have estimated the magmatic flux rate along the Hawaiian-Emperor Chain using a variety of methods and arriving at different results. These flux rate estimates have weaknesses because of incomplete data sets and different modeling assumptions, especially for the youngest portion of the chain (<3 Ma). While they generally agree on the 1st order features, there is less agreement on the magnitude and relative size of secondary flux variations. Some of these differences arise from the use of different methodologies, but the significance of this variability is difficult to assess due to a lack of confidence bounds on the estimates obtained with these disparate methods. All methods introduce some error, but to date there has been little or no quantification of error estimates for the inferred melt flux, making an assessment problematic. Here we re-evaluate the melt flux for the Hawaii plume with the latest gridded data sets (SRTM30+ and FAA 21.1) using several methods, including the optimal robust separator (ORS) and directional median filtering techniques (DiM). We also compute realistic confidence limits on the results. In particular, the DiM technique was specifically developed to aid in the estimation of surface loads that are superimposed on wider bathymetric swells and it provides error estimates on the optimal residuals. Confidence bounds are assigned separately for the estimated surface load (obtained from the ORS regional/residual separation techniques) and the inferred subsurface volume (from gravity-constrained isostasy and plate flexure optimizations). These new and robust estimates will allow us to assess which secondary features in the resulting melt flux curve are significant and should be incorporated when correlating melt flux variations with other geophysical and geochemical observations.

A simultaneously calibration approach for installation and attitude errors of an INS/GPS/LDS target tracker.

PubMed

Cheng, Jianhua; Chen, Daidai; Sun, Xiangyu; Wang, Tongda

2015-02-04

To obtain the absolute position of a target is one of the basic topics for non-cooperated target tracking problems. In this paper, we present a simultaneously calibration method for an Inertial navigation system (INS)/Global position system (GPS)/Laser distance scanner (LDS) integrated system based target positioning approach. The INS/GPS integrated system provides the attitude and position of observer, and LDS offers the distance between the observer and the target. The two most significant errors are taken into jointly consideration and analyzed: (1) the attitude measure error of INS/GPS; (2) the installation error between INS/GPS and LDS subsystems. Consequently, a INS/GPS/LDS based target positioning approach considering these two errors is proposed. In order to improve the performance of this approach, a novel calibration method is designed to simultaneously estimate and compensate these two main errors. Finally, simulations are conducted to access the performance of the proposed target positioning approach and the designed simultaneously calibration method.

The GEnes in Myopia (GEM) study in understanding the aetiology of refractive errors.

PubMed

Baird, Paul N; Schäche, Maria; Dirani, Mohamed

2010-11-01

Refractive errors represent the leading cause of correctable vision impairment and blindness in the world with an estimated 2 billion people affected. Refractive error refers to a group of refractive conditions including hypermetropia, myopia, astigmatism and presbyopia but relatively little is known about their aetiology. In order to explore the potential role of genetic determinants in refractive error the "GEnes in Myopia (GEM) study" was established in 2004. The findings that have resulted from this study have not only provided greater insight into the role of genes and other factors involved in myopia but have also gone some way to uncovering the aetiology of other refractive errors. This review will describe some of the major findings of the GEM study and their relative contribution to the literature, illuminate where the deficiencies are in our understanding of the development of refractive errors and how we will advance this field in the future. Copyright © 2010 Elsevier Ltd. All rights reserved.

Estimating error statistics for Chambon-la-Forêt observatory definitive data

NASA Astrophysics Data System (ADS)

Lesur, Vincent; Heumez, Benoît; Telali, Abdelkader; Lalanne, Xavier; Soloviev, Anatoly

2017-08-01

We propose a new algorithm for calibrating definitive observatory data with the goal of providing users with estimates of the data error standard deviations (SDs). The algorithm has been implemented and tested using Chambon-la-Forêt observatory (CLF) data. The calibration process uses all available data. It is set as a large, weakly non-linear, inverse problem that ultimately provides estimates of baseline values in three orthogonal directions, together with their expected standard deviations. For this inverse problem, absolute data error statistics are estimated from two series of absolute measurements made within a day. Similarly, variometer data error statistics are derived by comparing variometer data time series between different pairs of instruments over few years. The comparisons of these time series led us to use an autoregressive process of order 1 (AR1 process) as a prior for the baselines. Therefore the obtained baselines do not vary smoothly in time. They have relatively small SDs, well below 300 pT when absolute data are recorded twice a week - i.e. within the daily to weekly measures recommended by INTERMAGNET. The algorithm was tested against the process traditionally used to derive baselines at CLF observatory, suggesting that statistics are less favourable when this latter process is used. Finally, two sets of definitive data were calibrated using the new algorithm. Their comparison shows that the definitive data SDs are less than 400 pT and may be slightly overestimated by our process: an indication that more work is required to have proper estimates of absolute data error statistics. For magnetic field modelling, the results show that even on isolated sites like CLF observatory, there are very localised signals over a large span of temporal frequencies that can be as large as 1 nT. The SDs reported here encompass signals of a few hundred metres and less than a day wavelengths.

A heteroskedastic error covariance matrix estimator using a first-order conditional autoregressive Markov simulation for deriving asympotical efficient estimates from ecological sampled Anopheles arabiensis aquatic habitat covariates

PubMed Central

Jacob, Benjamin G; Griffith, Daniel A; Muturi, Ephantus J; Caamano, Erick X; Githure, John I; Novak, Robert J

2009-01-01

Background Autoregressive regression coefficients for Anopheles arabiensis aquatic habitat models are usually assessed using global error techniques and are reported as error covariance matrices. A global statistic, however, will summarize error estimates from multiple habitat locations. This makes it difficult to identify where there are clusters of An. arabiensis aquatic habitats of acceptable prediction. It is therefore useful to conduct some form of spatial error analysis to detect clusters of An. arabiensis aquatic habitats based on uncertainty residuals from individual sampled habitats. In this research, a method of error estimation for spatial simulation models was demonstrated using autocorrelation indices and eigenfunction spatial filters to distinguish among the effects of parameter uncertainty on a stochastic simulation of ecological sampled Anopheles aquatic habitat covariates. A test for diagnostic checking error residuals in an An. arabiensis aquatic habitat model may enable intervention efforts targeting productive habitats clusters, based on larval/pupal productivity, by using the asymptotic distribution of parameter estimates from a residual autocovariance matrix. The models considered in this research extends a normal regression analysis previously considered in the literature. Methods Field and remote-sampled data were collected during July 2006 to December 2007 in Karima rice-village complex in Mwea, Kenya. SAS 9.1.4® was used to explore univariate statistics, correlations, distributions, and to generate global autocorrelation statistics from the ecological sampled datasets. A local autocorrelation index was also generated using spatial covariance parameters (i.e., Moran's Indices) in a SAS/GIS® database. The Moran's statistic was decomposed into orthogonal and uncorrelated synthetic map pattern components using a Poisson model with a gamma-distributed mean (i.e. negative binomial regression). The eigenfunction values from the spatial configuration matrices were then used to define expectations for prior distributions using a Markov chain Monte Carlo (MCMC) algorithm. A set of posterior means were defined in WinBUGS 1.4.3®. After the model had converged, samples from the conditional distributions were used to summarize the posterior distribution of the parameters. Thereafter, a spatial residual trend analyses was used to evaluate variance uncertainty propagation in the model using an autocovariance error matrix. Results By specifying coefficient estimates in a Bayesian framework, the covariate number of tillers was found to be a significant predictor, positively associated with An. arabiensis aquatic habitats. The spatial filter models accounted for approximately 19% redundant locational information in the ecological sampled An. arabiensis aquatic habitat data. In the residual error estimation model there was significant positive autocorrelation (i.e., clustering of habitats in geographic space) based on log-transformed larval/pupal data and the sampled covariate depth of habitat. Conclusion An autocorrelation error covariance matrix and a spatial filter analyses can prioritize mosquito control strategies by providing a computationally attractive and feasible description of variance uncertainty estimates for correctly identifying clusters of prolific An. arabiensis aquatic habitats based on larval/pupal productivity. PMID:19772590

Parameter estimation method that directly compares gravitational wave observations to numerical relativity

NASA Astrophysics Data System (ADS)

Lange, J.; O'Shaughnessy, R.; Boyle, M.; Calderón Bustillo, J.; Campanelli, M.; Chu, T.; Clark, J. A.; Demos, N.; Fong, H.; Healy, J.; Hemberger, D. A.; Hinder, I.; Jani, K.; Khamesra, B.; Kidder, L. E.; Kumar, P.; Laguna, P.; Lousto, C. O.; Lovelace, G.; Ossokine, S.; Pfeiffer, H.; Scheel, M. A.; Shoemaker, D. M.; Szilagyi, B.; Teukolsky, S.; Zlochower, Y.

2017-11-01

We present and assess a Bayesian method to interpret gravitational wave signals from binary black holes. Our method directly compares gravitational wave data to numerical relativity (NR) simulations. In this study, we present a detailed investigation of the systematic and statistical parameter estimation errors of this method. This procedure bypasses approximations used in semianalytical models for compact binary coalescence. In this work, we use the full posterior parameter distribution for only generic nonprecessing binaries, drawing inferences away from the set of NR simulations used, via interpolation of a single scalar quantity (the marginalized log likelihood, ln L ) evaluated by comparing data to nonprecessing binary black hole simulations. We also compare the data to generic simulations, and discuss the effectiveness of this procedure for generic sources. We specifically assess the impact of higher order modes, repeating our interpretation with both l ≤2 as well as l ≤3 harmonic modes. Using the l ≤3 higher modes, we gain more information from the signal and can better constrain the parameters of the gravitational wave signal. We assess and quantify several sources of systematic error that our procedure could introduce, including simulation resolution and duration; most are negligible. We show through examples that our method can recover the parameters for equal mass, zero spin, GW150914-like, and unequal mass, precessing spin sources. Our study of this new parameter estimation method demonstrates that we can quantify and understand the systematic and statistical error. This method allows us to use higher order modes from numerical relativity simulations to better constrain the black hole binary parameters.

Understanding and comparisons of different sampling approaches for the Fourier Amplitudes Sensitivity Test (FAST)

PubMed Central

Xu, Chonggang; Gertner, George

2013-01-01

Fourier Amplitude Sensitivity Test (FAST) is one of the most popular uncertainty and sensitivity analysis techniques. It uses a periodic sampling approach and a Fourier transformation to decompose the variance of a model output into partial variances contributed by different model parameters. Until now, the FAST analysis is mainly confined to the estimation of partial variances contributed by the main effects of model parameters, but does not allow for those contributed by specific interactions among parameters. In this paper, we theoretically show that FAST analysis can be used to estimate partial variances contributed by both main effects and interaction effects of model parameters using different sampling approaches (i.e., traditional search-curve based sampling, simple random sampling and random balance design sampling). We also analytically calculate the potential errors and biases in the estimation of partial variances. Hypothesis tests are constructed to reduce the effect of sampling errors on the estimation of partial variances. Our results show that compared to simple random sampling and random balance design sampling, sensitivity indices (ratios of partial variances to variance of a specific model output) estimated by search-curve based sampling generally have higher precision but larger underestimations. Compared to simple random sampling, random balance design sampling generally provides higher estimation precision for partial variances contributed by the main effects of parameters. The theoretical derivation of partial variances contributed by higher-order interactions and the calculation of their corresponding estimation errors in different sampling schemes can help us better understand the FAST method and provide a fundamental basis for FAST applications and further improvements. PMID:24143037

Understanding and comparisons of different sampling approaches for the Fourier Amplitudes Sensitivity Test (FAST).

PubMed

Xu, Chonggang; Gertner, George

2011-01-01

Fourier Amplitude Sensitivity Test (FAST) is one of the most popular uncertainty and sensitivity analysis techniques. It uses a periodic sampling approach and a Fourier transformation to decompose the variance of a model output into partial variances contributed by different model parameters. Until now, the FAST analysis is mainly confined to the estimation of partial variances contributed by the main effects of model parameters, but does not allow for those contributed by specific interactions among parameters. In this paper, we theoretically show that FAST analysis can be used to estimate partial variances contributed by both main effects and interaction effects of model parameters using different sampling approaches (i.e., traditional search-curve based sampling, simple random sampling and random balance design sampling). We also analytically calculate the potential errors and biases in the estimation of partial variances. Hypothesis tests are constructed to reduce the effect of sampling errors on the estimation of partial variances. Our results show that compared to simple random sampling and random balance design sampling, sensitivity indices (ratios of partial variances to variance of a specific model output) estimated by search-curve based sampling generally have higher precision but larger underestimations. Compared to simple random sampling, random balance design sampling generally provides higher estimation precision for partial variances contributed by the main effects of parameters. The theoretical derivation of partial variances contributed by higher-order interactions and the calculation of their corresponding estimation errors in different sampling schemes can help us better understand the FAST method and provide a fundamental basis for FAST applications and further improvements.

Quantifying soil carbon loss and uncertainty from a peatland wildfire using multi-temporal LiDAR

USGS Publications Warehouse

Reddy, Ashwan D.; Hawbaker, Todd J.; Wurster, F.; Zhu, Zhiliang; Ward, S.; Newcomb, Doug; Murray, R.

2015-01-01

Peatlands are a major reservoir of global soil carbon, yet account for just 3% of global land cover. Human impacts like draining can hinder the ability of peatlands to sequester carbon and expose their soils to fire under dry conditions. Estimating soil carbon loss from peat fires can be challenging due to uncertainty about pre-fire surface elevations. This study uses multi-temporal LiDAR to obtain pre- and post-fire elevations and estimate soil carbon loss caused by the 2011 Lateral West fire in the Great Dismal Swamp National Wildlife Refuge, VA, USA. We also determine how LiDAR elevation error affects uncertainty in our carbon loss estimate by randomly perturbing the LiDAR point elevations and recalculating elevation change and carbon loss, iterating this process 1000 times. We calculated a total loss using LiDAR of 1.10 Tg C across the 25 km2 burned area. The fire burned an average of 47 cm deep, equivalent to 44 kg C/m2, a value larger than the 1997 Indonesian peat fires (29 kg C/m2). Carbon loss via the First-Order Fire Effects Model (FOFEM) was estimated to be 0.06 Tg C. Propagating the LiDAR elevation error to the carbon loss estimates, we calculated a standard deviation of 0.00009 Tg C, equivalent to 0.008% of total carbon loss. We conclude that LiDAR elevation error is not a significant contributor to uncertainty in soil carbon loss under severe fire conditions with substantial peat consumption. However, uncertainties may be more substantial when soil elevation loss is of a similar or smaller magnitude than the reported LiDAR error.

Estimation of Handling Qualities Parameters of the Tu-144 Supersonic Transport Aircraft from Flight Test Data

NASA Technical Reports Server (NTRS)

Curry, Timothy J.; Batterson, James G. (Technical Monitor)

2000-01-01

Low order equivalent system (LOES) models for the Tu-144 supersonic transport aircraft were identified from flight test data. The mathematical models were given in terms of transfer functions with a time delay by the military standard MIL-STD-1797A, "Flying Qualities of Piloted Aircraft," and the handling qualities were predicted from the estimated transfer function coefficients. The coefficients and the time delay in the transfer functions were estimated using a nonlinear equation error formulation in the frequency domain. Flight test data from pitch, roll, and yaw frequency sweeps at various flight conditions were used for parameter estimation. Flight test results are presented in terms of the estimated parameter values, their standard errors, and output fits in the time domain. Data from doublet maneuvers at the same flight conditions were used to assess the predictive capabilities of the identified models. The identified transfer function models fit the measured data well and demonstrated good prediction capabilities. The Tu-144 was predicted to be between level 2 and 3 for all longitudinal maneuvers and level I for all lateral maneuvers. High estimates of the equivalent time delay in the transfer function model caused the poor longitudinal rating.

Optimal heavy tail estimation - Part 1: Order selection

NASA Astrophysics Data System (ADS)

Mudelsee, Manfred; Bermejo, Miguel A.

2017-12-01

The tail probability, P, of the distribution of a variable is important for risk analysis of extremes. Many variables in complex geophysical systems show heavy tails, where P decreases with the value, x, of a variable as a power law with a characteristic exponent, α. Accurate estimation of α on the basis of data is currently hindered by the problem of the selection of the order, that is, the number of largest x values to utilize for the estimation. This paper presents a new, widely applicable, data-adaptive order selector, which is based on computer simulations and brute force search. It is the first in a set of papers on optimal heavy tail estimation. The new selector outperforms competitors in a Monte Carlo experiment, where simulated data are generated from stable distributions and AR(1) serial dependence. We calculate error bars for the estimated α by means of simulations. We illustrate the method on an artificial time series. We apply it to an observed, hydrological time series from the River Elbe and find an estimated characteristic exponent of 1.48 ± 0.13. This result indicates finite mean but infinite variance of the statistical distribution of river runoff.

Sensitivity analysis of non-cohesive sediment transport formulae

NASA Astrophysics Data System (ADS)

Pinto, Lígia; Fortunato, André B.; Freire, Paula

2006-10-01

Sand transport models are often based on semi-empirical equilibrium transport formulae that relate sediment fluxes to physical properties such as velocity, depth and characteristic sediment grain sizes. In engineering applications, errors in these physical properties affect the accuracy of the sediment fluxes. The present analysis quantifies error propagation from the input physical properties to the sediment fluxes, determines which ones control the final errors, and provides insight into the relative strengths, weaknesses and limitations of four total load formulae (Ackers and White, Engelund and Hansen, van Rijn, and Karim and Kennedy) and one bed load formulation (van Rijn). The various sources of uncertainty are first investigated individually, in order to pinpoint the key physical properties that control the errors. Since the strong non-linearity of most sand transport formulae precludes analytical approaches, a Monte Carlo method is validated and used in the analysis. Results show that the accuracy in total sediment transport evaluations is mainly determined by errors in the current velocity and in the sediment median grain size. For the bed load transport using the van Rijn formula, errors in the current velocity alone control the final accuracy. In a final set of tests, all physical properties are allowed to vary simultaneously in order to analyze the combined effect of errors. The combined effect of errors in all the physical properties is then compared to an estimate of the errors due to the intrinsic limitations of the formulae. Results show that errors in the physical properties can be dominant for typical uncertainties associated with these properties, particularly for small depths. A comparison between the various formulae reveals that the van Rijn formula is more sensitive to basic physical properties. Hence, it should only be used when physical properties are known with precision.

First-Order System Least Squares for the Stokes Equations, with Application to Linear Elasticity

NASA Technical Reports Server (NTRS)

Cai, Z.; Manteuffel, T. A.; McCormick, S. F.

1996-01-01

Following our earlier work on general second-order scalar equations, here we develop a least-squares functional for the two- and three-dimensional Stokes equations, generalized slightly by allowing a pressure term in the continuity equation. By introducing a velocity flux variable and associated curl and trace equations, we are able to establish ellipticity in an H(exp 1) product norm appropriately weighted by the Reynolds number. This immediately yields optimal discretization error estimates for finite element spaces in this norm and optimal algebraic convergence estimates for multiplicative and additive multigrid methods applied to the resulting discrete systems. Both estimates are uniform in the Reynolds number. Moreover, our pressure-perturbed form of the generalized Stokes equations allows us to develop an analogous result for the Dirichlet problem for linear elasticity with estimates that are uniform in the Lame constants.

Fault tolerance issues in nanoelectronics

NASA Astrophysics Data System (ADS)

Spagocci, S. M.

The astonishing success story of microelectronics cannot go on indefinitely. In fact, once devices reach the few-atom scale (nanoelectronics), transient quantum effects are expected to impair their behaviour. Fault tolerant techniques will then be required. The aim of this thesis is to investigate the problem of transient errors in nanoelectronic devices. Transient error rates for a selection of nanoelectronic gates, based upon quantum cellular automata and single electron devices, in which the electrostatic interaction between electrons is used to create Boolean circuits, are estimated. On the bases of such results, various fault tolerant solutions are proposed, for both logic and memory nanochips. As for logic chips, traditional techniques are found to be unsuitable. A new technique, in which the voting approach of triple modular redundancy (TMR) is extended by cascading TMR units composed of nanogate clusters, is proposed and generalised to other voting approaches. For memory chips, an error correcting code approach is found to be suitable. Various codes are considered and a lookup table approach is proposed for encoding and decoding. We are then able to give estimations for the redundancy level to be provided on nanochips, so as to make their mean time between failures acceptable. It is found that, for logic chips, space redundancies up to a few tens are required, if mean times between failures have to be of the order of a few years. Space redundancy can also be traded for time redundancy. As for memory chips, mean times between failures of the order of a few years are found to imply both space and time redundancies of the order of ten.

Simulation and mitigation of higher-order ionospheric errors in PPP

NASA Astrophysics Data System (ADS)

Zus, Florian; Deng, Zhiguo; Wickert, Jens

2017-04-01

We developed a rapid and precise algorithm to compute ionospheric phase advances in a realistic electron density field. The electron density field is derived from a plasmaspheric extension of the International Reference Ionosphere (Gulyaeva and Bilitza, 2012) and the magnetic field stems from the International Geomagnetic Reference Field. For specific station locations, elevation and azimuth angles the ionospheric phase advances are stored in a look-up table. The higher-order ionospheric residuals are computed by forming the standard linear combination of the ionospheric phase advances. In a simulation study we examine how the higher-order ionospheric residuals leak into estimated station coordinates, clocks, zenith delays and tropospheric gradients in precise point positioning. The simulation study includes a few hundred globally distributed stations and covers the time period 1990-2015. We take a close look on the estimated zenith delays and tropospheric gradients as they are considered a data source for meteorological and climate related research. We also show how the by product of this simulation study, the look-up tables, can be used to mitigate higher-order ionospheric errors in practise. Gulyaeva, T.L., and Bilitza, D. Towards ISO Standard Earth Ionosphere and Plasmasphere Model. In: New Developments in the Standard Model, edited by R.J. Larsen, pp. 1-39, NOVA, Hauppauge, New York, 2012, available at https://www.novapublishers.com/catalog/product_info.php?products_id=35812

Tests for qualitative treatment-by-centre interaction using a 'pushback' procedure.

PubMed

Ciminera, J L; Heyse, J F; Nguyen, H H; Tukey, J W

1993-06-15

In multicentre clinical trials using a common protocol, the centres are usually regarded as being a fixed factor, thus allowing any treatment-by-centre interaction to be omitted from the error term for the effect of treatment. However, we feel it necessary to use the treatment-by-centre interaction as the error term if there is substantial evidence that the interaction with centres is qualitative instead of quantitative. To make allowance for the estimated uncertainties of the centre means, we propose choosing a reference value (for example, the median of the ordered array of centre means) and converting the individual centre results into standardized deviations from the reference value. The deviations are then reordered, and the results 'pushed back' by amounts appropriate for the corresponding order statistics in a sample from the relevant distribution. The pushed-back standardized deviations are then restored to the original scale. The appearance of opposite signs among the destandardized values for the various centres is then taken as 'substantial evidence' of qualitative interaction. Procedures are presented using, in any combination: (i) Gaussian, or Student's t-distribution; (ii) order-statistic medians or outward 90 per cent points of the corresponding order statistic distributions; (iii) pooling or grouping and pooling the internally estimated standard deviations of the centre means. The use of the least conservative combination--Student's t, outward 90 per cent points, grouping and pooling--is recommended.

Comparing Parameter Estimation Techniques for an Electrical Power Transformer Oil Temperature Prediction Model

NASA Technical Reports Server (NTRS)

Morris, A. Terry

1999-01-01

This paper examines various sources of error in MIT's improved top oil temperature rise over ambient temperature model and estimation process. The sources of error are the current parameter estimation technique, quantization noise, and post-processing of the transformer data. Results from this paper will show that an output error parameter estimation technique should be selected to replace the current least squares estimation technique. The output error technique obtained accurate predictions of transformer behavior, revealed the best error covariance, obtained consistent parameter estimates, and provided for valid and sensible parameters. This paper will also show that the output error technique should be used to minimize errors attributed to post-processing (decimation) of the transformer data. Models used in this paper are validated using data from a large transformer in service.

On the impact of a refined stochastic model for airborne LiDAR measurements

NASA Astrophysics Data System (ADS)

Bolkas, Dimitrios; Fotopoulos, Georgia; Glennie, Craig

2016-09-01

Accurate topographic information is critical for a number of applications in science and engineering. In recent years, airborne light detection and ranging (LiDAR) has become a standard tool for acquiring high quality topographic information. The assessment of airborne LiDAR derived DEMs is typically based on (i) independent ground control points and (ii) forward error propagation utilizing the LiDAR geo-referencing equation. The latter approach is dependent on the stochastic model information of the LiDAR observation components. In this paper, the well-known statistical tool of variance component estimation (VCE) is implemented for a dataset in Houston, Texas, in order to refine the initial stochastic information. Simulations demonstrate the impact of stochastic-model refinement for two practical applications, namely coastal inundation mapping and surface displacement estimation. Results highlight scenarios where erroneous stochastic information is detrimental. Furthermore, the refined stochastic information provides insights on the effect of each LiDAR measurement in the airborne LiDAR error budget. The latter is important for targeting future advancements in order to improve point cloud accuracy.

Uncertainty Analysis of Instrument Calibration and Application

NASA Technical Reports Server (NTRS)

Tripp, John S.; Tcheng, Ping

1999-01-01

Experimental aerodynamic researchers require estimated precision and bias uncertainties of measured physical quantities, typically at 95 percent confidence levels. Uncertainties of final computed aerodynamic parameters are obtained by propagation of individual measurement uncertainties through the defining functional expressions. In this paper, rigorous mathematical techniques are extended to determine precision and bias uncertainties of any instrument-sensor system. Through this analysis, instrument uncertainties determined through calibration are now expressed as functions of the corresponding measurement for linear and nonlinear univariate and multivariate processes. Treatment of correlated measurement precision error is developed. During laboratory calibration, calibration standard uncertainties are assumed to be an order of magnitude less than those of the instrument being calibrated. Often calibration standards do not satisfy this assumption. This paper applies rigorous statistical methods for inclusion of calibration standard uncertainty and covariance due to the order of their application. The effects of mathematical modeling error on calibration bias uncertainty are quantified. The effects of experimental design on uncertainty are analyzed. The importance of replication is emphasized, techniques for estimation of both bias and precision uncertainties using replication are developed. Statistical tests for stationarity of calibration parameters over time are obtained.

Statistical analysis of nonlinearly reconstructed near-infrared tomographic images: Part I--Theory and simulations.

PubMed

Pogue, Brian W; Song, Xiaomei; Tosteson, Tor D; McBride, Troy O; Jiang, Shudong; Paulsen, Keith D

2002-07-01

Near-infrared (NIR) diffuse tomography is an emerging method for imaging the interior of tissues to quantify concentrations of hemoglobin and exogenous chromophores non-invasively in vivo. It often exploits an optical diffusion model-based image reconstruction algorithm to estimate spatial property values from measurements of the light flux at the surface of the tissue. In this study, mean-squared error (MSE) over the image is used to evaluate methods for regularizing the ill-posed inverse image reconstruction problem in NIR tomography. Estimates of image bias and image standard deviation were calculated based upon 100 repeated reconstructions of a test image with randomly distributed noise added to the light flux measurements. It was observed that the bias error dominates at high regularization parameter values while variance dominates as the algorithm is allowed to approach the optimal solution. This optimum does not necessarily correspond to the minimum projection error solution, but typically requires further iteration with a decreasing regularization parameter to reach the lowest image error. Increasing measurement noise causes a need to constrain the minimum regularization parameter to higher values in order to achieve a minimum in the overall image MSE.

Analysis of Sources of Large Positioning Errors in Deterministic Fingerprinting

PubMed Central

2017-01-01

Wi-Fi fingerprinting is widely used for indoor positioning and indoor navigation due to the ubiquity of wireless networks, high proliferation of Wi-Fi-enabled mobile devices, and its reasonable positioning accuracy. The assumption is that the position can be estimated based on the received signal strength intensity from multiple wireless access points at a given point. The positioning accuracy, within a few meters, enables the use of Wi-Fi fingerprinting in many different applications. However, it has been detected that the positioning error might be very large in a few cases, which might prevent its use in applications with high accuracy positioning requirements. Hybrid methods are the new trend in indoor positioning since they benefit from multiple diverse technologies (Wi-Fi, Bluetooth, and Inertial Sensors, among many others) and, therefore, they can provide a more robust positioning accuracy. In order to have an optimal combination of technologies, it is crucial to identify when large errors occur and prevent the use of extremely bad positioning estimations in hybrid algorithms. This paper investigates why large positioning errors occur in Wi-Fi fingerprinting and how to detect them by using the received signal strength intensities. PMID:29186921

Stochastic goal-oriented error estimation with memory

NASA Astrophysics Data System (ADS)

Ackmann, Jan; Marotzke, Jochem; Korn, Peter

2017-11-01

We propose a stochastic dual-weighted error estimator for the viscous shallow-water equation with boundaries. For this purpose, previous work on memory-less stochastic dual-weighted error estimation is extended by incorporating memory effects. The memory is introduced by describing the local truncation error as a sum of time-correlated random variables. The random variables itself represent the temporal fluctuations in local truncation errors and are estimated from high-resolution information at near-initial times. The resulting error estimator is evaluated experimentally in two classical ocean-type experiments, the Munk gyre and the flow around an island. In these experiments, the stochastic process is adapted locally to the respective dynamical flow regime. Our stochastic dual-weighted error estimator is shown to provide meaningful error bounds for a range of physically relevant goals. We prove, as well as show numerically, that our approach can be interpreted as a linearized stochastic-physics ensemble.

A Sensor Fusion Method Based on an Integrated Neural Network and Kalman Filter for Vehicle Roll Angle Estimation.

PubMed

Vargas-Meléndez, Leandro; Boada, Beatriz L; Boada, María Jesús L; Gauchía, Antonio; Díaz, Vicente

2016-08-31

This article presents a novel estimator based on sensor fusion, which combines the Neural Network (NN) with a Kalman filter in order to estimate the vehicle roll angle. The NN estimates a "pseudo-roll angle" through variables that are easily measured from Inertial Measurement Unit (IMU) sensors. An IMU is a device that is commonly used for vehicle motion detection, and its cost has decreased during recent years. The pseudo-roll angle is introduced in the Kalman filter in order to filter noise and minimize the variance of the norm and maximum errors' estimation. The NN has been trained for J-turn maneuvers, double lane change maneuvers and lane change maneuvers at different speeds and road friction coefficients. The proposed method takes into account the vehicle non-linearities, thus yielding good roll angle estimation. Finally, the proposed estimator has been compared with one that uses the suspension deflections to obtain the pseudo-roll angle. Experimental results show the effectiveness of the proposed NN and Kalman filter-based estimator.

Self-Evaluation of Decision-Making: A General Bayesian Framework for Metacognitive Computation

PubMed Central

2017-01-01

People are often aware of their mistakes, and report levels of confidence in their choices that correlate with objective performance. These metacognitive assessments of decision quality are important for the guidance of behavior, particularly when external feedback is absent or sporadic. However, a computational framework that accounts for both confidence and error detection is lacking. In addition, accounts of dissociations between performance and metacognition have often relied on ad hoc assumptions, precluding a unified account of intact and impaired self-evaluation. Here we present a general Bayesian framework in which self-evaluation is cast as a “second-order” inference on a coupled but distinct decision system, computationally equivalent to inferring the performance of another actor. Second-order computation may ensue whenever there is a separation between internal states supporting decisions and confidence estimates over space and/or time. We contrast second-order computation against simpler first-order models in which the same internal state supports both decisions and confidence estimates. Through simulations we show that second-order computation provides a unified account of different types of self-evaluation often considered in separate literatures, such as confidence and error detection, and generates novel predictions about the contribution of one’s own actions to metacognitive judgments. In addition, the model provides insight into why subjects’ metacognition may sometimes be better or worse than task performance. We suggest that second-order computation may underpin self-evaluative judgments across a range of domains. PMID:28004960

The use of kernel density estimators in breakthrough curve reconstruction and advantages in risk analysis

NASA Astrophysics Data System (ADS)

Siirila, E. R.; Fernandez-Garcia, D.; Sanchez-Vila, X.

2014-12-01

Particle tracking (PT) techniques, often considered favorable over Eulerian techniques due to artificial smoothening in breakthrough curves (BTCs), are evaluated in a risk-driven framework. Recent work has shown that given a relatively few number of particles (np), PT methods can yield well-constructed BTCs with kernel density estimators (KDEs). This work compares KDE and non-KDE BTCs simulated as a function of np (102-108) and averaged as a function of the exposure duration, ED. Results show that regardless of BTC shape complexity, un-averaged PT BTCs show a large bias over several orders of magnitude in concentration (C) when compared to the KDE results, remarkably even when np is as low as 102. With the KDE, several orders of magnitude less np are required to obtain the same global error in BTC shape as the PT technique. PT and KDE BTCs are averaged as a function of the ED with standard and new methods incorporating the optimal h (ANA). The lowest error curve is obtained through the ANA method, especially for smaller EDs. Percent error of peak of averaged-BTCs, important in a risk framework, is approximately zero for all scenarios and all methods for np ≥105, but vary between the ANA and PT methods, when np is lower. For fewer np, the ANA solution provides a lower error fit except when C oscillations are present during a short time frame. We show that obtaining a representative average exposure concentration is reliant on an accurate representation of the BTC, especially when data is scarce.

Wind power error estimation in resource assessments.

PubMed

Rodríguez, Osvaldo; Del Río, Jesús A; Jaramillo, Oscar A; Martínez, Manuel

2015-01-01

Estimating the power output is one of the elements that determine the techno-economic feasibility of a renewable project. At present, there is a need to develop reliable methods that achieve this goal, thereby contributing to wind power penetration. In this study, we propose a method for wind power error estimation based on the wind speed measurement error, probability density function, and wind turbine power curves. This method uses the actual wind speed data without prior statistical treatment based on 28 wind turbine power curves, which were fitted by Lagrange's method, to calculate the estimate wind power output and the corresponding error propagation. We found that wind speed percentage errors of 10% were propagated into the power output estimates, thereby yielding an error of 5%. The proposed error propagation complements the traditional power resource assessments. The wind power estimation error also allows us to estimate intervals for the power production leveled cost or the investment time return. The implementation of this method increases the reliability of techno-economic resource assessment studies.

Wind Power Error Estimation in Resource Assessments

PubMed Central

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

Quantifying error of lidar and sodar Doppler beam swinging measurements of wind turbine wakes using computational fluid dynamics

DOE PAGES

Lundquist, J. K.; Churchfield, M. J.; Lee, S.; ...

2015-02-23

Wind-profiling lidars are now regularly used in boundary-layer meteorology and in applications such as wind energy and air quality. Lidar wind profilers exploit the Doppler shift of laser light backscattered from particulates carried by the wind to measure a line-of-sight (LOS) velocity. The Doppler beam swinging (DBS) technique, used by many commercial systems, considers measurements of this LOS velocity in multiple radial directions in order to estimate horizontal and vertical winds. The method relies on the assumption of homogeneous flow across the region sampled by the beams. Using such a system in inhomogeneous flow, such as wind turbine wakes ormore » complex terrain, will result in errors. To quantify the errors expected from such violation of the assumption of horizontal homogeneity, we simulate inhomogeneous flow in the atmospheric boundary layer, notably stably stratified flow past a wind turbine, with a mean wind speed of 6.5 m s -1 at the turbine hub-height of 80 m. This slightly stable case results in 15° of wind direction change across the turbine rotor disk. The resulting flow field is sampled in the same fashion that a lidar samples the atmosphere with the DBS approach, including the lidar range weighting function, enabling quantification of the error in the DBS observations. The observations from the instruments located upwind have small errors, which are ameliorated with time averaging. However, the downwind observations, particularly within the first two rotor diameters downwind from the wind turbine, suffer from errors due to the heterogeneity of the wind turbine wake. Errors in the stream-wise component of the flow approach 30% of the hub-height inflow wind speed close to the rotor disk. Errors in the cross-stream and vertical velocity components are also significant: cross-stream component errors are on the order of 15% of the hub-height inflow wind speed (1.0 m s −1) and errors in the vertical velocity measurement exceed the actual vertical velocity. By three rotor diameters downwind, DBS-based assessments of wake wind speed deficits based on the stream-wise velocity can be relied on even within the near wake within 1.0 s -1 (or 15% of the hub-height inflow wind speed), and the cross-stream velocity error is reduced to 8% while vertical velocity estimates are compromised. Furthermore, measurements of inhomogeneous flow such as wind turbine wakes are susceptible to these errors, and interpretations of field observations should account for this uncertainty.« less

Quantifying error of lidar and sodar Doppler beam swinging measurements of wind turbine wakes using computational fluid dynamics

NASA Astrophysics Data System (ADS)

Lundquist, J. K.; Churchfield, M. J.; Lee, S.; Clifton, A.

2015-02-01

Wind-profiling lidars are now regularly used in boundary-layer meteorology and in applications such as wind energy and air quality. Lidar wind profilers exploit the Doppler shift of laser light backscattered from particulates carried by the wind to measure a line-of-sight (LOS) velocity. The Doppler beam swinging (DBS) technique, used by many commercial systems, considers measurements of this LOS velocity in multiple radial directions in order to estimate horizontal and vertical winds. The method relies on the assumption of homogeneous flow across the region sampled by the beams. Using such a system in inhomogeneous flow, such as wind turbine wakes or complex terrain, will result in errors. To quantify the errors expected from such violation of the assumption of horizontal homogeneity, we simulate inhomogeneous flow in the atmospheric boundary layer, notably stably stratified flow past a wind turbine, with a mean wind speed of 6.5 m s-1 at the turbine hub-height of 80 m. This slightly stable case results in 15° of wind direction change across the turbine rotor disk. The resulting flow field is sampled in the same fashion that a lidar samples the atmosphere with the DBS approach, including the lidar range weighting function, enabling quantification of the error in the DBS observations. The observations from the instruments located upwind have small errors, which are ameliorated with time averaging. However, the downwind observations, particularly within the first two rotor diameters downwind from the wind turbine, suffer from errors due to the heterogeneity of the wind turbine wake. Errors in the stream-wise component of the flow approach 30% of the hub-height inflow wind speed close to the rotor disk. Errors in the cross-stream and vertical velocity components are also significant: cross-stream component errors are on the order of 15% of the hub-height inflow wind speed (1.0 m s-1) and errors in the vertical velocity measurement exceed the actual vertical velocity. By three rotor diameters downwind, DBS-based assessments of wake wind speed deficits based on the stream-wise velocity can be relied on even within the near wake within 1.0 m s-1 (or 15% of the hub-height inflow wind speed), and the cross-stream velocity error is reduced to 8% while vertical velocity estimates are compromised. Measurements of inhomogeneous flow such as wind turbine wakes are susceptible to these errors, and interpretations of field observations should account for this uncertainty.

Using artificial intelligence to predict the equilibrated postdialysis blood urea concentration.

PubMed

Fernández, E A; Valtuille, R; Willshaw, P; Perazzo, C A

2001-01-01

Total dialysis dose (Kt/V) is considered to be a major determinant of morbidity and mortality in hemodialyzed patients. The continuous growth of the blood urea concentration over the 30- to 60-min period following dialysis, a phenomenon known as urea rebound, is a critical factor in determining the true dose of hemodialysis. The misestimation of the equilibrated (true) postdialysis blood urea or equilibrated Kt/V results in an inadequate hemodialysis prescription, with predictably poor clinical outcomes for the patients. The estimation of the equilibrated postdialysis blood urea (eqU) is therefore crucial in order to estimate the equilibrated (true) Kt/V. In this work we propose a supervised neural network to predict the eqU at 60 min after the end of hemodialysis. The use of this model is new in this field and is shown to be better than the currently accepted methods (Smye for eqU and Daugirdas for eqKt/V). With this approach we achieve a mean difference error of 0.22 +/- 7.71 mg/ml (mean % error: 1.88 +/- 13.46) on the eqU prediction and a mean difference error for eqKt/V of -0.01 +/- 0.15 (mean % error: -0.95 +/- 14.73). The equilibrated Kt/V estimated with the eqU calculated using the Smye formula is not appropriate because it showed a great dispersion. The Daugirdas double-pool Kt/V estimation formula appeared to be accurate and in agreement with the results of the HEMO study. Copyright 2001 S. Karger AG, Basel.

Accuracy of indirect estimation of power output from uphill performance in cycling.

PubMed

Millet, Grégoire P; Tronche, Cyrille; Grappe, Frédéric

2014-09-01

To use measurement by cycling power meters (Pmes) to evaluate the accuracy of commonly used models for estimating uphill cycling power (Pest). Experiments were designed to explore the influence of wind speed and steepness of climb on accuracy of Pest. The authors hypothesized that the random error in Pest would be largely influenced by the windy conditions, the bias would be diminished in steeper climbs, and windy conditions would induce larger bias in Pest. Sixteen well-trained cyclists performed 15 uphill-cycling trials (range: length 1.3-6.3 km, slope 4.4-10.7%) in a random order. Trials included different riding position in a group (lead or follow) and different wind speeds. Pmes was quantified using a power meter, and Pest was calculated with a methodology used by journalists reporting on the Tour de France. Overall, the difference between Pmes and Pest was -0.95% (95%CI: -10.4%, +8.5%) for all trials and 0.24% (-6.1%, +6.6%) in conditions without wind (<2 m/s). The relationship between percent slope and the error between Pest and Pmes were considered trivial. Aerodynamic drag (affected by wind velocity and orientation, frontal area, drafting, and speed) is the most confounding factor. The mean estimated values are close to the power-output values measured by power meters, but the random error is between ±6% and ±10%. Moreover, at the power outputs (>400 W) produced by professional riders, this error is likely to be higher. This observation calls into question the validity of releasing individual values without reporting the range of random errors.

Decorrelation of the true and estimated classifier errors in high-dimensional settings.

PubMed

Hanczar, Blaise; Hua, Jianping; Dougherty, Edward R

2007-01-01

The aim of many microarray experiments is to build discriminatory diagnosis and prognosis models. Given the huge number of features and the small number of examples, model validity which refers to the precision of error estimation is a critical issue. Previous studies have addressed this issue via the deviation distribution (estimated error minus true error), in particular, the deterioration of cross-validation precision in high-dimensional settings where feature selection is used to mitigate the peaking phenomenon (overfitting). Because classifier design is based upon random samples, both the true and estimated errors are sample-dependent random variables, and one would expect a loss of precision if the estimated and true errors are not well correlated, so that natural questions arise as to the degree of correlation and the manner in which lack of correlation impacts error estimation. We demonstrate the effect of correlation on error precision via a decomposition of the variance of the deviation distribution, observe that the correlation is often severely decreased in high-dimensional settings, and show that the effect of high dimensionality on error estimation tends to result more from its decorrelating effects than from its impact on the variance of the estimated error. We consider the correlation between the true and estimated errors under different experimental conditions using both synthetic and real data, several feature-selection methods, different classification rules, and three error estimators commonly used (leave-one-out cross-validation, k-fold cross-validation, and .632 bootstrap). Moreover, three scenarios are considered: (1) feature selection, (2) known-feature set, and (3) all features. Only the first is of practical interest; however, the other two are needed for comparison purposes. We will observe that the true and estimated errors tend to be much more correlated in the case of a known feature set than with either feature selection or using all features, with the better correlation between the latter two showing no general trend, but differing for different models.

Spectral combination of spherical gravitational curvature boundary-value problems

NASA Astrophysics Data System (ADS)

PitoÅák, Martin; Eshagh, Mehdi; Šprlák, Michal; Tenzer, Robert; Novák, Pavel

2018-04-01

Four solutions of the spherical gravitational curvature boundary-value problems can be exploited for the determination of the Earth's gravitational potential. In this article we discuss the combination of simulated satellite gravitational curvatures, i.e., components of the third-order gravitational tensor, by merging these solutions using the spectral combination method. For this purpose, integral estimators of biased- and unbiased-types are derived. In numerical studies, we investigate the performance of the developed mathematical models for the gravitational field modelling in the area of Central Europe based on simulated satellite measurements. Firstly, we verify the correctness of the integral estimators for the spectral downward continuation by a closed-loop test. Estimated errors of the combined solution are about eight orders smaller than those from the individual solutions. Secondly, we perform a numerical experiment by considering the Gaussian noise with the standard deviation of 6.5× 10-17 m-1s-2 in the input data at the satellite altitude of 250 km above the mean Earth sphere. This value of standard deviation is equivalent to a signal-to-noise ratio of 10. Superior results with respect to the global geopotential model TIM-r5 are obtained by the spectral downward continuation of the vertical-vertical-vertical component with the standard deviation of 2.104 m2s-2, but the root mean square error is the largest and reaches 9.734 m2s-2. Using the spectral combination of all gravitational curvatures the root mean square error is more than 400 times smaller but the standard deviation reaches 17.234 m2s-2. The combination of more components decreases the root mean square error of the corresponding solutions while the standard deviations of the combined solutions do not improve as compared to the solution from the vertical-vertical-vertical component. The presented method represents a weight mean in the spectral domain that minimizes the root mean square error of the combined solutions and improves standard deviation of the solution based only on the least accurate components.

Error estimates for ice discharge calculated using the flux gate approach

NASA Astrophysics Data System (ADS)

Navarro, F. J.; Sánchez Gámez, P.

2017-12-01

Ice discharge to the ocean is usually estimated using the flux gate approach, in which ice flux is calculated through predefined flux gates close to the marine glacier front. However, published results usually lack a proper error estimate. In the flux calculation, both errors in cross-sectional area and errors in velocity are relevant. While for estimating the errors in velocity there are well-established procedures, the calculation of the error in the cross-sectional area requires the availability of ground penetrating radar (GPR) profiles transverse to the ice-flow direction. In this contribution, we use IceBridge operation GPR profiles collected in Ellesmere and Devon Islands, Nunavut, Canada, to compare the cross-sectional areas estimated using various approaches with the cross-sections estimated from GPR ice-thickness data. These error estimates are combined with those for ice-velocities calculated from Sentinel-1 SAR data, to get the error in ice discharge. Our preliminary results suggest, regarding area, that the parabolic cross-section approaches perform better than the quartic ones, which tend to overestimate the cross-sectional area for flight lines close to the central flowline. Furthermore, the results show that regional ice-discharge estimates made using parabolic approaches provide reasonable results, but estimates for individual glaciers can have large errors, up to 20% in cross-sectional area.

Finite Element A Posteriori Error Estimation for Heat Conduction. Degree awarded by George Washington Univ.

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.

Expeditious reconciliation for practical quantum key distribution

NASA Astrophysics Data System (ADS)

Nakassis, Anastase; Bienfang, Joshua C.; Williams, Carl J.

2004-08-01

The paper proposes algorithmic and environmental modifications to the extant reconciliation algorithms within the BB84 protocol so as to speed up reconciliation and privacy amplification. These algorithms have been known to be a performance bottleneck 1 and can process data at rates that are six times slower than the quantum channel they serve2. As improvements in single-photon sources and detectors are expected to improve the quantum channel throughput by two or three orders of magnitude, it becomes imperative to improve the performance of the classical software. We developed a Cascade-like algorithm that relies on a symmetric formulation of the problem, error estimation through the segmentation process, outright elimination of segments with many errors, Forward Error Correction, recognition of the distinct data subpopulations that emerge as the algorithm runs, ability to operate on massive amounts of data (of the order of 1 Mbit), and a few other minor improvements. The data from the experimental algorithm we developed show that by operating on massive arrays of data we can improve software performance by better than three orders of magnitude while retaining nearly as many bits (typically more than 90%) as the algorithms that were designed for optimal bit retention.

Investigation of error sources in regional inverse estimates of greenhouse gas emissions in Canada

NASA Astrophysics Data System (ADS)

Chan, E.; Chan, D.; Ishizawa, M.; Vogel, F.; Brioude, J.; Delcloo, A.; Wu, Y.; Jin, B.

2015-08-01

Inversion models can use atmospheric concentration measurements to estimate surface fluxes. This study is an evaluation of the errors in a regional flux inversion model for different provinces of Canada, Alberta (AB), Saskatchewan (SK) and Ontario (ON). Using CarbonTracker model results as the target, the synthetic data experiment analyses examined the impacts of the errors from the Bayesian optimisation method, prior flux distribution and the atmospheric transport model, as well as their interactions. The scaling factors for different sub-regions were estimated by the Markov chain Monte Carlo (MCMC) simulation and cost function minimization (CFM) methods. The CFM method results are sensitive to the relative size of the assumed model-observation mismatch and prior flux error variances. Experiment results show that the estimation error increases with the number of sub-regions using the CFM method. For the region definitions that lead to realistic flux estimates, the numbers of sub-regions for the western region of AB/SK combined and the eastern region of ON are 11 and 4 respectively. The corresponding annual flux estimation errors for the western and eastern regions using the MCMC (CFM) method are -7 and -3 % (0 and 8 %) respectively, when there is only prior flux error. The estimation errors increase to 36 and 94 % (40 and 232 %) resulting from transport model error alone. When prior and transport model errors co-exist in the inversions, the estimation errors become 5 and 85 % (29 and 201 %). This result indicates that estimation errors are dominated by the transport model error and can in fact cancel each other and propagate to the flux estimates non-linearly. In addition, it is possible for the posterior flux estimates having larger differences than the prior compared to the target fluxes, and the posterior uncertainty estimates could be unrealistically small that do not cover the target. The systematic evaluation of the different components of the inversion model can help in the understanding of the posterior estimates and percentage errors. Stable and realistic sub-regional and monthly flux estimates for western region of AB/SK can be obtained, but not for the eastern region of ON. This indicates that it is likely a real observation-based inversion for the annual provincial emissions will work for the western region whereas; improvements are needed with the current inversion setup before real inversion is performed for the eastern region.

On the Mean Squared Error of Nonparametric Quantile Estimators under Random Right-Censorship.

DTIC Science & Technology

1986-09-01

SECURITY CI.ASSIFICATION lb. RESTRICTIVE MARKINGS UNCLASSIFIED 2a, SECURITY CLASSIFICATION AUTHORITY 3 . OISTRIBUTIONIAVAILASIL.ITY OF REPORT P16e 2b...UNCLASSIPIEO/UNLIMITEO 3 SAME AS RPT". 0 OTIC USERS 1 UNCLASSIFIED p." " 22. NAME OP RESPONSIBLE INOIVIOUAL 22b. TELEPHONE NUMBER 22c. OFFICE SYMBOL...in Section 3 , and the result for the kernel estimator Qn is derived in Section 4. It should be k. mentioned that the order statistic methods used by

A Posteriori Correction of Forecast and Observation Error Variances

NASA Technical Reports Server (NTRS)

Rukhovets, Leonid

2005-01-01

Proposed method of total observation and forecast error variance correction is based on the assumption about normal distribution of "observed-minus-forecast" residuals (O-F), where O is an observed value and F is usually a short-term model forecast. This assumption can be accepted for several types of observations (except humidity) which are not grossly in error. Degree of nearness to normal distribution can be estimated by the symmetry or skewness (luck of symmetry) a(sub 3) = mu(sub 3)/sigma(sup 3) and kurtosis a(sub 4) = mu(sub 4)/sigma(sup 4) - 3 Here mu(sub i) = i-order moment, sigma is a standard deviation. It is well known that for normal distribution a(sub 3) = a(sub 4) = 0.

Optimized tomography of continuous variable systems using excitation counting

NASA Astrophysics Data System (ADS)

Shen, Chao; Heeres, Reinier W.; Reinhold, Philip; Jiang, Luyao; Liu, Yi-Kai; Schoelkopf, Robert J.; Jiang, Liang

2016-11-01

We propose a systematic procedure to optimize quantum state tomography protocols for continuous variable systems based on excitation counting preceded by a displacement operation. Compared with conventional tomography based on Husimi or Wigner function measurement, the excitation counting approach can significantly reduce the number of measurement settings. We investigate both informational completeness and robustness, and provide a bound of reconstruction error involving the condition number of the sensing map. We also identify the measurement settings that optimize this error bound, and demonstrate that the improved reconstruction robustness can lead to an order-of-magnitude reduction of estimation error with given resources. This optimization procedure is general and can incorporate prior information of the unknown state to further simplify the protocol.

ADAPTIVE METHODS FOR STOCHASTIC DIFFERENTIAL EQUATIONS VIA NATURAL EMBEDDINGS AND REJECTION SAMPLING WITH MEMORY.

PubMed

Rackauckas, Christopher; Nie, Qing

2017-01-01

Adaptive time-stepping with high-order embedded Runge-Kutta pairs and rejection sampling provides efficient approaches for solving differential equations. While many such methods exist for solving deterministic systems, little progress has been made for stochastic variants. One challenge in developing adaptive methods for stochastic differential equations (SDEs) is the construction of embedded schemes with direct error estimates. We present a new class of embedded stochastic Runge-Kutta (SRK) methods with strong order 1.5 which have a natural embedding of strong order 1.0 methods. This allows for the derivation of an error estimate which requires no additional function evaluations. Next we derive a general method to reject the time steps without losing information about the future Brownian path termed Rejection Sampling with Memory (RSwM). This method utilizes a stack data structure to do rejection sampling, costing only a few floating point calculations. We show numerically that the methods generate statistically-correct and tolerance-controlled solutions. Lastly, we show that this form of adaptivity can be applied to systems of equations, and demonstrate that it solves a stiff biological model 12.28x faster than common fixed timestep algorithms. Our approach only requires the solution to a bridging problem and thus lends itself to natural generalizations beyond SDEs.

ADAPTIVE METHODS FOR STOCHASTIC DIFFERENTIAL EQUATIONS VIA NATURAL EMBEDDINGS AND REJECTION SAMPLING WITH MEMORY

PubMed Central

Rackauckas, Christopher

2017-01-01

Adaptive time-stepping with high-order embedded Runge-Kutta pairs and rejection sampling provides efficient approaches for solving differential equations. While many such methods exist for solving deterministic systems, little progress has been made for stochastic variants. One challenge in developing adaptive methods for stochastic differential equations (SDEs) is the construction of embedded schemes with direct error estimates. We present a new class of embedded stochastic Runge-Kutta (SRK) methods with strong order 1.5 which have a natural embedding of strong order 1.0 methods. This allows for the derivation of an error estimate which requires no additional function evaluations. Next we derive a general method to reject the time steps without losing information about the future Brownian path termed Rejection Sampling with Memory (RSwM). This method utilizes a stack data structure to do rejection sampling, costing only a few floating point calculations. We show numerically that the methods generate statistically-correct and tolerance-controlled solutions. Lastly, we show that this form of adaptivity can be applied to systems of equations, and demonstrate that it solves a stiff biological model 12.28x faster than common fixed timestep algorithms. Our approach only requires the solution to a bridging problem and thus lends itself to natural generalizations beyond SDEs. PMID:29527134

Experimental demonstration of laser tomographic adaptive optics on a 30-meter telescope at 800 nm

NASA Astrophysics Data System (ADS)

Ammons, S., Mark; Johnson, Luke; Kupke, Renate; Gavel, Donald T.; Max, Claire E.

2010-07-01

A critical goal in the next decade is to develop techniques that will extend Adaptive Optics correction to visible wavelengths on Extremely Large Telescopes (ELTs). We demonstrate in the laboratory the highly accurate atmospheric tomography necessary to defeat the cone effect on ELTs, an essential milestone on the path to this capability. We simulate a high-order Laser Tomographic AO System for a 30-meter telescope with the LTAO/MOAO testbed at UCSC. Eight Sodium Laser Guide Stars (LGSs) are sensed by 99x99 Shack-Hartmann wavefront sensors over 75". The AO system is diffraction-limited at a science wavelength of 800 nm (S ~ 6-9%) over a field of regard of 20" diameter. Openloop WFS systematic error is observed to be proportional to the total input atmospheric disturbance and is nearly the dominant error budget term (81 nm RMS), exceeded only by tomographic wavefront estimation error (92 nm RMS). The total residual wavefront error for this experiment is comparable to that expected for wide-field tomographic adaptive optics systems of similar wavefront sensor order and LGS constellation geometry planned for Extremely Large Telescopes.

Methods for the computation of detailed geoids and their accuracy

NASA Technical Reports Server (NTRS)

Rapp, R. H.; Rummel, R.

1975-01-01

Two methods for the computation of geoid undulations using potential coefficients and 1 deg x 1 deg terrestrial anomaly data are examined. It was found that both methods give the same final result but that one method allows a more simplified error analysis. Specific equations were considered for the effect of the mass of the atmosphere and a cap dependent zero-order undulation term was derived. Although a correction to a gravity anomaly for the effect of the atmosphere is only about -0.87 mgal, this correction causes a fairly large undulation correction that was not considered previously. The accuracy of a geoid undulation computed by these techniques was estimated considering anomaly data errors, potential coefficient errors, and truncation (only a finite set of potential coefficients being used) errors. It was found that an optimum cap size of 20 deg should be used. The geoid and its accuracy were computed in the Geos 3 calibration area using the GEM 6 potential coefficients and 1 deg x 1 deg terrestrial anomaly data. The accuracy of the computed geoid is on the order of plus or minus 2 m with respect to an unknown set of best earth parameter constants.

Sparse Method for Direction of Arrival Estimation Using Denoised Fourth-Order Cumulants Vector.

PubMed

Fan, Yangyu; Wang, Jianshu; Du, Rui; Lv, Guoyun

2018-06-04

Fourth-order cumulants (FOCs) vector-based direction of arrival (DOA) estimation methods of non-Gaussian sources may suffer from poor performance for limited snapshots or difficulty in setting parameters. In this paper, a novel FOCs vector-based sparse DOA estimation method is proposed. Firstly, by utilizing the concept of a fourth-order difference co-array (FODCA), an advanced FOCs vector denoising or dimension reduction procedure is presented for arbitrary array geometries. Then, a novel single measurement vector (SMV) model is established by the denoised FOCs vector, and efficiently solved by an off-grid sparse Bayesian inference (OGSBI) method. The estimation errors of FOCs are integrated in the SMV model, and are approximately estimated in a simple way. A necessary condition regarding the number of identifiable sources of our method is presented that, in order to uniquely identify all sources, the number of sources K must fulfill K ≤ ( M 4 - 2 M 3 + 7 M 2 - 6 M ) / 8 . The proposed method suits any geometry, does not need prior knowledge of the number of sources, is insensitive to associated parameters, and has maximum identifiability O ( M 4 ) , where M is the number of sensors in the array. Numerical simulations illustrate the superior performance of the proposed method.

Continuum limit of Bk from 2+1 flavor domain wall QCD

DOE Office of Scientific and Technical Information (OSTI.GOV)

Soni, A.; T. Izubuchi, et al.

2011-07-01

We determine the neutral kaon mixing matrix element B{sub K} in the continuum limit with 2+1 flavors of domain wall fermions, using the Iwasaki gauge action at two different lattice spacings. These lattice fermions have near exact chiral symmetry and therefore avoid artificial lattice operator mixing. We introduce a significant improvement to the conventional nonperturbative renormalization (NPR) method in which the bare matrix elements are renormalized nonperturbatively in the regularization invariant momentum scheme (RI-MOM) and are then converted into the MS{sup -} scheme using continuum perturbation theory. In addition to RI-MOM, we introduce and implement four nonexceptional intermediate momentum schemesmore » that suppress infrared nonperturbative uncertainties in the renormalization procedure. We compute the conversion factors relating the matrix elements in this family of regularization invariant symmetric momentum schemes (RI-SMOM) and MS{sup -} at one-loop order. Comparison of the results obtained using these different intermediate schemes allows for a more reliable estimate of the unknown higher-order contributions and hence for a correspondingly more robust estimate of the systematic error. We also apply a recently proposed approach in which twisted boundary conditions are used to control the Symanzik expansion for off-shell vertex functions leading to a better control of the renormalization in the continuum limit. We control chiral extrapolation errors by considering both the next-to-leading order SU(2) chiral effective theory, and an analytic mass expansion. We obtain B{sub K}{sup MS{sup -}} (3 GeV) = 0.529(5){sub stat}(15){sub {chi}}(2){sub FV}(11){sub NPR}. This corresponds to B{sup -}{sub K}{sup RGI{sup -}} = 0.749(7){sub stat}(21){sub {chi}}(3){sub FV}(15){sub NPR}. Adding all sources of error in quadrature, we obtain B{sup -}{sub K}{sup RGI{sup -}} = 0.749(27){sub combined}, with an overall combined error of 3.6%.« less

Micro CT based truth estimation of nodule volume

NASA Astrophysics Data System (ADS)

Kinnard, L. M.; Gavrielides, M. A.; Myers, K. J.; Zeng, R.; Whiting, B.; Lin-Gibson, S.; Petrick, N.

2010-03-01

With the advent of high-resolution CT, three-dimensional (3D) methods for nodule volumetry have been introduced, with the hope that such methods will be more accurate and consistent than currently used planar measures of size. However, the error associated with volume estimation methods still needs to be quantified. Volume estimation error is multi-faceted in the sense that there is variability associated with the patient, the software tool and the CT system. A primary goal of our current research efforts is to quantify the various sources of measurement error and, when possible, minimize their effects. In order to assess the bias of an estimate, the actual value, or "truth," must be known. In this work we investigate the reliability of micro CT to determine the "true" volume of synthetic nodules. The advantage of micro CT over other truthing methods is that it can provide both absolute volume and shape information in a single measurement. In the current study we compare micro CT volume truth to weight-density truth for spherical, elliptical, spiculated and lobulated nodules with diameters from 5 to 40 mm, and densities of -630 and +100 HU. The percent differences between micro CT and weight-density volume for -630 HU nodules range from [-21.7%, -0.6%] (mean= -11.9%) and the differences for +100 HU nodules range from [-0.9%, 3.0%] (mean=1.7%).

Non-contact estimation of heart rate and oxygen saturation using ambient light.

PubMed

Bal, Ufuk

2015-01-01

We propose a robust method for automated computation of heart rate (HR) from digital color video recordings of the human face. In order to extract photoplethysmographic signals, two orthogonal vectors of RGB color space are used. We used a dual tree complex wavelet transform based denoising algorithm to reduce artifacts (e.g. artificial lighting, movement, etc.). Most of the previous work on skin color based HR estimation performed experiments with healthy volunteers and focused to solve motion artifacts. In addition to healthy volunteers we performed experiments with child patients in pediatric intensive care units. In order to investigate the possible factors that affect the non-contact HR monitoring in a clinical environment, we studied the relation between hemoglobin levels and HR estimation errors. Low hemoglobin causes underestimation of HR. Nevertheless, we conclude that our method can provide acceptable accuracy to estimate mean HR of patients in a clinical environment, where the measurements can be performed remotely. In addition to mean heart rate estimation, we performed experiments to estimate oxygen saturation. We observed strong correlations between our SpO2 estimations and the commercial oximeter readings.

Non-contact estimation of heart rate and oxygen saturation using ambient light

PubMed Central

Bal, Ufuk

2014-01-01

We propose a robust method for automated computation of heart rate (HR) from digital color video recordings of the human face. In order to extract photoplethysmographic signals, two orthogonal vectors of RGB color space are used. We used a dual tree complex wavelet transform based denoising algorithm to reduce artifacts (e.g. artificial lighting, movement, etc.). Most of the previous work on skin color based HR estimation performed experiments with healthy volunteers and focused to solve motion artifacts. In addition to healthy volunteers we performed experiments with child patients in pediatric intensive care units. In order to investigate the possible factors that affect the non-contact HR monitoring in a clinical environment, we studied the relation between hemoglobin levels and HR estimation errors. Low hemoglobin causes underestimation of HR. Nevertheless, we conclude that our method can provide acceptable accuracy to estimate mean HR of patients in a clinical environment, where the measurements can be performed remotely. In addition to mean heart rate estimation, we performed experiments to estimate oxygen saturation. We observed strong correlations between our SpO2 estimations and the commercial oximeter readings PMID:25657877

Methods for estimation of radiation risk in epidemiological studies accounting for classical and Berkson errors in doses.

PubMed

Kukush, Alexander; Shklyar, Sergiy; Masiuk, Sergii; Likhtarov, Illya; Kovgan, Lina; Carroll, Raymond J; Bouville, Andre

2011-02-16

With a binary response Y, the dose-response model under consideration is logistic in flavor with pr(Y=1 | D) = R (1+R)(-1), R = λ(0) + EAR D, where λ(0) is the baseline incidence rate and EAR is the excess absolute risk per gray. The calculated thyroid dose of a person i is expressed as Dimes=fiQi(mes)/Mi(mes). Here, Qi(mes) is the measured content of radioiodine in the thyroid gland of person i at time t(mes), Mi(mes) is the estimate of the thyroid mass, and f(i) is the normalizing multiplier. The Q(i) and M(i) are measured with multiplicative errors Vi(Q) and ViM, so that Qi(mes)=Qi(tr)Vi(Q) (this is classical measurement error model) and Mi(tr)=Mi(mes)Vi(M) (this is Berkson measurement error model). Here, Qi(tr) is the true content of radioactivity in the thyroid gland, and Mi(tr) is the true value of the thyroid mass. The error in f(i) is much smaller than the errors in ( Qi(mes), Mi(mes)) and ignored in the analysis. By means of Parametric Full Maximum Likelihood and Regression Calibration (under the assumption that the data set of true doses has lognormal distribution), Nonparametric Full Maximum Likelihood, Nonparametric Regression Calibration, and by properly tuned SIMEX method we study the influence of measurement errors in thyroid dose on the estimates of λ(0) and EAR. The simulation study is presented based on a real sample from the epidemiological studies. The doses were reconstructed in the framework of the Ukrainian-American project on the investigation of Post-Chernobyl thyroid cancers in Ukraine, and the underlying subpolulation was artificially enlarged in order to increase the statistical power. The true risk parameters were given by the values to earlier epidemiological studies, and then the binary response was simulated according to the dose-response model.

Full Field and Anomaly Initialisation using a low order climate model: a comparison, and proposals for advanced formulations

NASA Astrophysics Data System (ADS)

Weber, Robin; Carrassi, Alberto; Guemas, Virginie; Doblas-Reyes, Francisco; Volpi, Danila

2014-05-01

Full Field (FFI) and Anomaly Initialisation (AI) are two schemes used to initialise seasonal-to-decadal (s2d) prediction. FFI initialises the model on the best estimate of the actual climate state and minimises the initial error. However, due to inevitable model deficiencies, the trajectories drift away from the observations towards the model's own attractor, inducing a bias in the forecast. AI has been devised to tackle the impact of drift through the addition of this bias onto the observations, in the hope of gaining an initial state closer to the model attractor. Its goal is to forecast climate anomalies. The large variety of experimental setups, global coupled models, and observational networks adopted world-wide have led to varying results with regards to the relative performance of AI and FFI. Our research is firstly motivated in a comparison of these two initialisation approaches under varying circumstances of observational errors, observational distributions, and model errors. We also propose and compare two advanced schemes for s2d prediction. Least Square Initialisation (LSI) intends to propagate observational information of partially initialized systems to the whole model domain, based on standard practices in data assimilation and using the covariance of the model anomalies. Exploring the Parameters Uncertainty (EPU) is an online drift correction technique applied during the forecast run after initialisation. It is designed to estimate, and subtract, the bias in the forecast related to parametric error. Experiments are carried out using an idealized coupled dynamics in order to facilitate better control and robust statistical inference. Results show that an improvement of FFI will necessitate refinements in the observations, whereas improvements in AI are subject to model advances. A successful approximation of the model attractor using AI is guaranteed only when the differences between model and nature probability distribution functions (PDFs) are limited to the first order. Significant higher order differences can lead to an initial conditions distribution for AI that is less representative of the model PDF and lead to a degradation of the initalisation skill. Finally, both ad- vanced schemes lead to significantly improved skill scores, encouraging their implementation for models of higher complexity.

Simultaneous treatment of unspecified heteroskedastic model error distribution and mismeasured covariates for restricted moment models.

PubMed

Garcia, Tanya P; Ma, Yanyuan

2017-10-01

We develop consistent and efficient estimation of parameters in general regression models with mismeasured covariates. We assume the model error and covariate distributions are unspecified, and the measurement error distribution is a general parametric distribution with unknown variance-covariance. We construct root- n consistent, asymptotically normal and locally efficient estimators using the semiparametric efficient score. We do not estimate any unknown distribution or model error heteroskedasticity. Instead, we form the estimator under possibly incorrect working distribution models for the model error, error-prone covariate, or both. Empirical results demonstrate robustness to different incorrect working models in homoscedastic and heteroskedastic models with error-prone covariates.

Methods for calculating the electrode position Jacobian for impedance imaging.

PubMed

Boyle, A; Crabb, M G; Jehl, M; Lionheart, W R B; Adler, A

2017-03-01

Electrical impedance tomography (EIT) or electrical resistivity tomography (ERT) current and measure voltages at the boundary of a domain through electrodes. The movement or incorrect placement of electrodes may lead to modelling errors that result in significant reconstructed image artifacts. These errors may be accounted for by allowing for electrode position estimates in the model. Movement may be reconstructed through a first-order approximation, the electrode position Jacobian. A reconstruction that incorporates electrode position estimates and conductivity can significantly reduce image artifacts. Conversely, if electrode position is ignored it can be difficult to distinguish true conductivity changes from reconstruction artifacts which may increase the risk of a flawed interpretation. In this work, we aim to determine the fastest, most accurate approach for estimating the electrode position Jacobian. Four methods of calculating the electrode position Jacobian were evaluated on a homogeneous halfspace. Results show that Fréchet derivative and rank-one update methods are competitive in computational efficiency but achieve different solutions for certain values of contact impedance and mesh density.

Hyper-X Mach 10 Trajectory Reconstruction

NASA Technical Reports Server (NTRS)

Karlgaard, Christopher D.; Martin, John G.; Tartabini, Paul V.; Thornblom, Mark N.

2005-01-01

This paper discusses the formulation and development of a trajectory reconstruction tool for the NASA X-43A/Hyper-X high speed research vehicle, and its implementation for the reconstruction and analysis of flight test data. Extended Kalman filtering techniques are employed to reconstruct the trajectory of the vehicle, based upon numerical integration of inertial measurement data along with redundant measurements of the vehicle state. The equations of motion are formulated in order to include the effects of several systematic error sources, whose values may also be estimated by the filtering routines. Additionally, smoothing algorithms have been implemented in which the final value of the state (or an augmented state that includes other systematic error parameters to be estimated) and covariance are propagated back to the initial time to generate the best-estimated trajectory, based upon all available data. The methods are applied to the problem of reconstructing the trajectory of the Hyper-X vehicle from data obtained during the Mach 10 test flight, which occurred on November 16th 2004.

Kalman-Filter-Based Orientation Determination Using Inertial/Magnetic Sensors: Observability Analysis and Performance Evaluation

PubMed Central

Sabatini, Angelo Maria

2011-01-01

In this paper we present a quaternion-based Extended Kalman Filter (EKF) for estimating the three-dimensional orientation of a rigid body. The EKF exploits the measurements from an Inertial Measurement Unit (IMU) that is integrated with a tri-axial magnetic sensor. Magnetic disturbances and gyro bias errors are modeled and compensated by including them in the filter state vector. We employ the observability rank criterion based on Lie derivatives to verify the conditions under which the nonlinear system that describes the process of motion tracking by the IMU is observable, namely it may provide sufficient information for performing the estimation task with bounded estimation errors. The observability conditions are that the magnetic field, perturbed by first-order Gauss-Markov magnetic variations, and the gravity vector are not collinear and that the IMU is subject to some angular motions. Computer simulations and experimental testing are presented to evaluate the algorithm performance, including when the observability conditions are critical. PMID:22163689

Semiblind channel estimation for MIMO-OFDM systems

NASA Astrophysics Data System (ADS)

Chen, Yi-Sheng; Song, Jyu-Han

2012-12-01

This article proposes a semiblind channel estimation method for multiple-input multiple-output orthogonal frequency-division multiplexing systems based on circular precoding. Relying on the precoding scheme at the transmitters, the autocorrelation matrix of the received data induces a structure relating the outer product of the channel frequency response matrix and precoding coefficients. This structure makes it possible to extract information about channel product matrices, which can be used to form a Hermitian matrix whose positive eigenvalues and corresponding eigenvectors yield the channel impulse response matrix. This article also tests the resistance of the precoding design to finite-sample estimation errors, and explores the effects of the precoding scheme on channel equalization by performing pairwise error probability analysis. The proposed method is immune to channel zero locations, and is reasonably robust to channel order overestimation. The proposed method is applicable to the scenarios in which the number of transmitters exceeds that of the receivers. Simulation results demonstrate the performance of the proposed method and compare it with some existing methods.

A method to estimate statistical errors of properties derived from charge-density modelling

PubMed Central

Lecomte, Claude

2018-01-01

Estimating uncertainties of property values derived from a charge-density model is not straightforward. A methodology, based on calculation of sample standard deviations (SSD) of properties using randomly deviating charge-density models, is proposed with the MoPro software. The parameter shifts applied in the deviating models are generated in order to respect the variance–covariance matrix issued from the least-squares refinement. This ‘SSD methodology’ procedure can be applied to estimate uncertainties of any property related to a charge-density model obtained by least-squares fitting. This includes topological properties such as critical point coordinates, electron density, Laplacian and ellipticity at critical points and charges integrated over atomic basins. Errors on electrostatic potentials and interaction energies are also available now through this procedure. The method is exemplified with the charge density of compound (E)-5-phenylpent-1-enylboronic acid, refined at 0.45 Å resolution. The procedure is implemented in the freely available MoPro program dedicated to charge-density refinement and modelling. PMID:29724964

On the prompt identification of traces of explosives

NASA Astrophysics Data System (ADS)

Trobajo, M. T.; López-Cabeceira, M. M.; Carriegos, M. V.; Díez-Machío, H.

2014-12-01

Some recent results in the use of Raman spectroscopy for recognition of explosives are reviewed. Experimental study using spectra data base has been developed. In order to simulate a more real situation, both blank substances and explosives substances have been considered in this research. Statistic classification techniques have been performed. Estimations of prediction errors were obtained by cross-validation methods. These results can be applied in airport security systems in order to prevent terror acts (by the detection of explosive/flammable substances).

On the superconvergence of Galerkin methods for hyperbolic IBVP

NASA Technical Reports Server (NTRS)

Gottlieb, David; Gustafsson, Bertil; Olsson, Pelle; Strand, BO

1993-01-01

Finite element Galerkin methods for periodic first order hyperbolic equations exhibit superconvergence on uniform grids at the nodes, i.e., there is an error estimate 0(h(sup 2r)) instead of the expected approximation order 0(h(sup r)). It will be shown that no matter how the approximating subspace S(sup h) is chosen, the superconvergence property is lost if there are characteristics leaving the domain. The implications of this result when constructing compact implicit difference schemes is also discussed.

An Enhanced Non-Coherent Pre-Filter Design for Tracking Error Estimation in GNSS Receivers.

PubMed

Luo, Zhibin; Ding, Jicheng; Zhao, Lin; Wu, Mouyan

2017-11-18

Tracking error estimation is of great importance in global navigation satellite system (GNSS) receivers. Any inaccurate estimation for tracking error will decrease the signal tracking ability of signal tracking loops and the accuracies of position fixing, velocity determination, and timing. Tracking error estimation can be done by traditional discriminator, or Kalman filter-based pre-filter. The pre-filter can be divided into two categories: coherent and non-coherent. This paper focuses on the performance improvements of non-coherent pre-filter. Firstly, the signal characteristics of coherent and non-coherent integration-which are the basis of tracking error estimation-are analyzed in detail. After that, the probability distribution of estimation noise of four-quadrant arctangent (ATAN2) discriminator is derived according to the mathematical model of coherent integration. Secondly, the statistical property of observation noise of non-coherent pre-filter is studied through Monte Carlo simulation to set the observation noise variance matrix correctly. Thirdly, a simple fault detection and exclusion (FDE) structure is introduced to the non-coherent pre-filter design, and thus its effective working range for carrier phase error estimation extends from (-0.25 cycle, 0.25 cycle) to (-0.5 cycle, 0.5 cycle). Finally, the estimation accuracies of discriminator, coherent pre-filter, and the enhanced non-coherent pre-filter are evaluated comprehensively through the carefully designed experiment scenario. The pre-filter outperforms traditional discriminator in estimation accuracy. In a highly dynamic scenario, the enhanced non-coherent pre-filter provides accuracy improvements of 41.6%, 46.4%, and 50.36% for carrier phase error, carrier frequency error, and code phase error estimation, respectively, when compared with coherent pre-filter. The enhanced non-coherent pre-filter outperforms the coherent pre-filter in code phase error estimation when carrier-to-noise density ratio is less than 28.8 dB-Hz, in carrier frequency error estimation when carrier-to-noise density ratio is less than 20 dB-Hz, and in carrier phase error estimation when carrier-to-noise density belongs to (15, 23) dB-Hz ∪ (26, 50) dB-Hz.

Are Low-order Covariance Estimates Useful in Error Analyses?

NASA Astrophysics Data System (ADS)

Baker, D. F.; Schimel, D.

2005-12-01

Atmospheric trace gas inversions, using modeled atmospheric transport to infer surface sources and sinks from measured concentrations, are most commonly done using least-squares techniques that return not only an estimate of the state (the surface fluxes) but also the covariance matrix describing the uncertainty in that estimate. Besides allowing one to place error bars around the estimate, the covariance matrix may be used in simulation studies to learn what uncertainties would be expected from various hypothetical observing strategies. This error analysis capability is routinely used in designing instrumentation, measurement campaigns, and satellite observing strategies. For example, Rayner, et al (2002) examined the ability of satellite-based column-integrated CO2 measurements to constrain monthly-average CO2 fluxes for about 100 emission regions using this approach. Exact solutions for both state vector and covariance matrix become computationally infeasible, however, when the surface fluxes are solved at finer resolution (e.g., daily in time, under 500 km in space). It is precisely at these finer scales, however, that one would hope to be able to estimate fluxes using high-density satellite measurements. Non-exact estimation methods such as variational data assimilation or the ensemble Kalman filter could be used, but they achieve their computational savings by obtaining an only approximate state estimate and a low-order approximation of the true covariance. One would like to be able to use this covariance matrix to do the same sort of error analyses as are done with the full-rank covariance, but is it correct to do so? Here we compare uncertainties and `information content' derived from full-rank covariance matrices obtained from a direct, batch least squares inversion to those from the incomplete-rank covariance matrices given by a variational data assimilation approach solved with a variable metric minimization technique (the Broyden-Fletcher- Goldfarb-Shanno algorithm). Two cases are examined: a toy problem in which CO2 fluxes for 3 latitude bands are estimated for only 2 time steps per year, and for the monthly fluxes for 22 regions across 1988-2003 solved for in the TransCom3 interannual flux inversion of Baker, et al (2005). The usefulness of the uncertainty estimates will be assessed as a function of the number of minimization steps used in the variational approach; this will help determine whether they will also be useful in the high-resolution cases that we would most like to apply the non-exact methods to. Baker, D.F., et al., TransCom3 inversion intercomparison: Impact of transport model errors on the interannual variability of regional CO2 fluxes, 1988-2003, Glob. Biogeochem. Cycles, doi:10.1029/2004GB002439, 2005, in press. Rayner, P.J., R.M. Law, D.M. O'Brien, T.M. Butler, and A.C. Dilley, Global observations of the carbon budget, 3, Initial assessment of the impact of satellite orbit, scan geometry, and cloud on measuring CO2 from space, J. Geophys. Res., 107(D21), 4557, doi:10.1029/2001JD000618, 2002.

Evaluation of the kinetic oxidation of aqueous volatile organic compounds by permanganate.

PubMed

Mahmoodlu, Mojtaba G; Hassanizadeh, S Majid; Hartog, Niels

2014-07-01

The use of permanganate solutions for in-situ chemical oxidation (ISCO) is a well-established groundwater remediation technology, particularly for targeting chlorinated ethenes. The kinetics of oxidation reactions is an important ISCO remediation design aspect that affects the efficiency and oxidant persistence. The overall rate of the ISCO reaction between oxidant and contaminant is typically described using a second-order kinetic model while the second-order rate constant is determined experimentally by means of a pseudo first order approach. However, earlier studies of chlorinated hydrocarbons have yielded a wide range of values for the second-order rate constants. Also, there is limited insight in the kinetics of permanganate reactions with fuel-derived groundwater contaminants such as toluene and ethanol. In this study, batch experiments were carried out to investigate and compare the oxidation kinetics of aqueous trichloroethylene (TCE), ethanol, and toluene in an aqueous potassium permanganate solution. The overall second-order rate constants were determined directly by fitting a second-order model to the data, instead of typically using the pseudo-first-order approach. The second-order reaction rate constants (M(-1) s(-1)) for TCE, toluene, and ethanol were 8.0×10(-1), 2.5×10(-4), and 6.5×10(-4), respectively. Results showed that the inappropriate use of the pseudo-first-order approach in several previous studies produced biased estimates of the second-order rate constants. In our study, this error was expressed as a function of the extent (P/N) in which the reactant concentrations deviated from the stoichiometric ratio of each oxidation reaction. The error associated with the inappropriate use of the pseudo-first-order approach is negatively correlated with the P/N ratio and reached up to 25% of the estimated second-order rate constant in some previous studies of TCE oxidation. Based on our results, a similar relation is valid for the other volatile organic compounds studied. Copyright © 2013 Elsevier B.V. All rights reserved.

Technical Note: Millimeter precision in ultrasound based patient positioning: Experimental quantification of inherent technical limitations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ballhausen, Hendrik, E-mail: hendrik.ballhausen@med.uni-muenchen.de; Hieber, Sheila; Li, Minglun

2014-08-15

Purpose: To identify the relevant technical sources of error of a system based on three-dimensional ultrasound (3D US) for patient positioning in external beam radiotherapy. To quantify these sources of error in a controlled laboratory setting. To estimate the resulting end-to-end geometric precision of the intramodality protocol. Methods: Two identical free-hand 3D US systems at both the planning-CT and the treatment room were calibrated to the laboratory frame of reference. Every step of the calibration chain was repeated multiple times to estimate its contribution to overall systematic and random error. Optimal margins were computed given the identified and quantified systematicmore » and random errors. Results: In descending order of magnitude, the identified and quantified sources of error were: alignment of calibration phantom to laser marks 0.78 mm, alignment of lasers in treatment vs planning room 0.51 mm, calibration and tracking of 3D US probe 0.49 mm, alignment of stereoscopic infrared camera to calibration phantom 0.03 mm. Under ideal laboratory conditions, these errors are expected to limit ultrasound-based positioning to an accuracy of 1.05 mm radially. Conclusions: The investigated 3D ultrasound system achieves an intramodal accuracy of about 1 mm radially in a controlled laboratory setting. The identified systematic and random errors require an optimal clinical tumor volume to planning target volume margin of about 3 mm. These inherent technical limitations do not prevent clinical use, including hypofractionation or stereotactic body radiation therapy.« less

Physical Validation of TRMM TMI and PR Monthly Rain Products Over Oklahoma

NASA Technical Reports Server (NTRS)

Fisher, Brad L.

2004-01-01

The Tropical Rainfall Measuring Mission (TRMM) provides monthly rainfall estimates using data collected by the TRMM satellite. These estimates cover a substantial fraction of the earth's surface. The physical validation of TRMM estimates involves corroborating the accuracy of spaceborne estimates of areal rainfall by inferring errors and biases from ground-based rain estimates. The TRMM error budget consists of two major sources of error: retrieval and sampling. Sampling errors are intrinsic to the process of estimating monthly rainfall and occur because the satellite extrapolates monthly rainfall from a small subset of measurements collected only during satellite overpasses. Retrieval errors, on the other hand, are related to the process of collecting measurements while the satellite is overhead. One of the big challenges confronting the TRMM validation effort is how to best estimate these two main components of the TRMM error budget, which are not easily decoupled. This four-year study computed bulk sampling and retrieval errors for the TRMM microwave imager (TMI) and the precipitation radar (PR) by applying a technique that sub-samples gauge data at TRMM overpass times. Gridded monthly rain estimates are then computed from the monthly bulk statistics of the collected samples, providing a sensor-dependent gauge rain estimate that is assumed to include a TRMM equivalent sampling error. The sub-sampled gauge rain estimates are then used in conjunction with the monthly satellite and gauge (without sub- sampling) estimates to decouple retrieval and sampling errors. The computed mean sampling errors for the TMI and PR were 5.9% and 7.796, respectively, in good agreement with theoretical predictions. The PR year-to-year retrieval biases exceeded corresponding TMI biases, but it was found that these differences were partially due to negative TMI biases during cold months and positive TMI biases during warm months.

Smooth empirical Bayes estimation of observation error variances in linear systems

NASA Technical Reports Server (NTRS)

Martz, H. F., Jr.; Lian, M. W.

1972-01-01

A smooth empirical Bayes estimator was developed for estimating the unknown random scale component of each of a set of observation error variances. It is shown that the estimator possesses a smaller average squared error loss than other estimators for a discrete time linear system.

Elimination of Emergency Department Medication Errors Due To Estimated Weights.

PubMed

Greenwalt, Mary; Griffen, David; Wilkerson, Jim

2017-01-01

From 7/2014 through 6/2015, 10 emergency department (ED) medication dosing errors were reported through the electronic incident reporting system of an urban academic medical center. Analysis of these medication errors identified inaccurate estimated weight on patients as the root cause. The goal of this project was to reduce weight-based dosing medication errors due to inaccurate estimated weights on patients presenting to the ED. Chart review revealed that 13.8% of estimated weights documented on admitted ED patients varied more than 10% from subsequent actual admission weights recorded. A random sample of 100 charts containing estimated weights revealed 2 previously unreported significant medication dosage errors (.02 significant error rate). Key improvements included removing barriers to weighing ED patients, storytelling to engage staff and change culture, and removal of the estimated weight documentation field from the ED electronic health record (EHR) forms. With these improvements estimated weights on ED patients, and the resulting medication errors, were eliminated.

An error-based micro-sensor capture system for real-time motion estimation

NASA Astrophysics Data System (ADS)

Yang, Lin; Ye, Shiwei; Wang, Zhibo; Huang, Zhipei; Wu, Jiankang; Kong, Yongmei; Zhang, Li

2017-10-01

A wearable micro-sensor motion capture system with 16 IMUs and an error-compensatory complementary filter algorithm for real-time motion estimation has been developed to acquire accurate 3D orientation and displacement in real life activities. In the proposed filter algorithm, the gyroscope bias error, orientation error and magnetic disturbance error are estimated and compensated, significantly reducing the orientation estimation error due to sensor noise and drift. Displacement estimation, especially for activities such as jumping, has been the challenge in micro-sensor motion capture. An adaptive gait phase detection algorithm has been developed to accommodate accurate displacement estimation in different types of activities. The performance of this system is benchmarked with respect to the results of VICON optical capture system. The experimental results have demonstrated effectiveness of the system in daily activities tracking, with estimation error 0.16 ± 0.06 m for normal walking and 0.13 ± 0.11 m for jumping motions. Research supported by the National Natural Science Foundation of China (Nos. 61431017, 81272166).

An Empirical State Error Covariance Matrix Orbit Determination Example

NASA Technical Reports Server (NTRS)

Frisbee, Joseph H., Jr.

2015-01-01

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. First, consider that an actual observation contains only measurement error and that an estimated observation contains all other errors, known and unknown. Then it 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 of the estimate will result in an empirical state error covariance matrix. This empirical state error covariance matrix will fully include all of the errors in the state estimate. The empirical error covariance matrix is determined from a literal reinterpretation of the equations involved in the weighted least squares estimation algorithm. It is a formally correct, empirical state error covariance matrix obtained through use of the average form of the weighted measurement residual variance performance index rather than the usual total weighted residual form. Based on its formulation, this matrix will contain the total uncertainty in the state estimate, regardless as to the source of the uncertainty and whether the source is anticipated or not. It is expected that the empirical error covariance matrix will give a better, statistical representation of the state error in poorly modeled systems or when sensor performance is suspect. In its most straight forward form, the technique only requires supplemental calculations to be added to existing batch estimation algorithms. In the current problem being studied a truth model making use of gravity with spherical, J2 and J4 terms plus a standard exponential type atmosphere with simple diurnal and random walk components is used. The ability of the empirical state error covariance matrix to account for errors is investigated under four scenarios during orbit estimation. These scenarios are: exact modeling under known measurement errors, exact modeling under corrupted measurement errors, inexact modeling under known measurement errors, and inexact modeling under corrupted measurement errors. For this problem a simple analog of a distributed space surveillance network is used. The sensors in this network make only range measurements and with simple normally distributed measurement errors. The sensors are assumed to have full horizon to horizon viewing at any azimuth. For definiteness, an orbit at the approximate altitude and inclination of the International Space Station is used for the study. The comparison analyses of the data involve only total vectors. No investigation of specific orbital elements is undertaken. The total vector analyses will look at the chisquare values of the error in the difference between the estimated state and the true modeled state using both the empirical and theoretical error covariance matrices for each of scenario.

An estimator-predictor approach to PLL loop filter design

NASA Technical Reports Server (NTRS)

Statman, J. I.; Hurd, W. J.

1986-01-01

An approach to the design of digital phase locked loops (DPLLs), using estimation theory concepts in the selection of a loop filter, is presented. The key concept is that the DPLL closed-loop transfer function is decomposed into an estimator and a predictor. The estimator provides recursive estimates of phase, frequency, and higher order derivatives, while the predictor compensates for the transport lag inherent in the loop. This decomposition results in a straightforward loop filter design procedure, enabling use of techniques from optimal and sub-optimal estimation theory. A design example for a particular choice of estimator is presented, followed by analysis of the associated bandwidth, gain margin, and steady state errors caused by unmodeled dynamics. This approach is under consideration for the design of the Deep Space Network (DSN) Advanced Receiver Carrier DPLL.

Parameterization of clear-sky surface irradiance and its implications for estimation of aerosol direct radiative effect and aerosol optical depth

PubMed Central

Xia, Xiangao

2015-01-01

Aerosols impact clear-sky surface irradiance () through the effects of scattering and absorption. Linear or nonlinear relationships between aerosol optical depth (τa) and have been established to describe the aerosol direct radiative effect on (ADRE). However, considerable uncertainties remain associated with ADRE due to the incorrect estimation of (τa in the absence of aerosols). Based on data from the Aerosol Robotic Network, the effects of τa, water vapor content (w) and the cosine of the solar zenith angle (μ) on are thoroughly considered, leading to an effective parameterization of as a nonlinear function of these three quantities. The parameterization is proven able to estimate with a mean bias error of 0.32 W m−2, which is one order of magnitude smaller than that derived using earlier linear or nonlinear functions. Applications of this new parameterization to estimate τa from , or vice versa, show that the root-mean-square errors were 0.08 and 10.0 Wm−2, respectively. Therefore, this study establishes a straightforward method to derive from τa or estimate τa from measurements if water vapor measurements are available. PMID:26395310

Using nonlinear programming to correct leakage and estimate mass change from GRACE observation and its application to Antarctica

NASA Astrophysics Data System (ADS)

Tang, Jingshi; Cheng, Haowen; Liu, Lin

2012-11-01

The Gravity Recovery And Climate Experiment (GRACE) mission has been providing high quality observations since its launch in 2002. Over the years, fruitful achievements have been obtained and the temporal gravity field has revealed the ongoing geophysical, hydrological and other processes. These discoveries help the scientists better understand various aspects of the Earth. However, errors exist in high degree and order spherical harmonics, which need to be processed before use. Filtering is one of the most commonly used techniques to smooth errors, yet it attenuates signals and also causes leakage of gravity signal into surrounding areas. This paper reports a new method to estimate the true mass change on the grid (expressed in equivalent water height or surface density). The mass change over the grid can be integrated to estimate regional or global mass change. This method assumes the GRACE-observed apparent mass change is only caused by the mass change on land. By comparing the computed and observed apparent mass change, the true mass change can be iteratively adjusted and estimated. The problem is solved with nonlinear programming (NLP) and yields solutions which are in good agreement with other GRACE-based estimates.

A comparison of abundance estimates from extended batch-marking and Jolly–Seber-type experiments

PubMed Central

Cowen, Laura L E; Besbeas, Panagiotis; Morgan, Byron J T; Schwarz, Carl J

2014-01-01

Little attention has been paid to the use of multi-sample batch-marking studies, as it is generally assumed that an individual's capture history is necessary for fully efficient estimates. However, recently, Huggins et al. (2010) present a pseudo-likelihood for a multi-sample batch-marking study where they used estimating equations to solve for survival and capture probabilities and then derived abundance estimates using a Horvitz–Thompson-type estimator. We have developed and maximized the likelihood for batch-marking studies. We use data simulated from a Jolly–Seber-type study and convert this to what would have been obtained from an extended batch-marking study. We compare our abundance estimates obtained from the Crosbie–Manly–Arnason–Schwarz (CMAS) model with those of the extended batch-marking model to determine the efficiency of collecting and analyzing batch-marking data. We found that estimates of abundance were similar for all three estimators: CMAS, Huggins, and our likelihood. Gains are made when using unique identifiers and employing the CMAS model in terms of precision; however, the likelihood typically had lower mean square error than the pseudo-likelihood method of Huggins et al. (2010). When faced with designing a batch-marking study, researchers can be confident in obtaining unbiased abundance estimators. Furthermore, they can design studies in order to reduce mean square error by manipulating capture probabilities and sample size. PMID:24558576

The Effect of Response Time on Conjoint Analysis Estimates of Rainforest Protection Values

Treesearch

Thomas Holmes; Keith Alger; Christian Zinkhan; D. Evan Mercer

1998-01-01

This paper reports the first estimutes of willingness to pay (WTP) for rain forest protection in the threatened Atlantic Coastal Forest ecosystem in northeastern Brazil. Conjoint analysis data were collected from Brazilian tourists for recreational bundles with complex prices. An ordered probit model with time-varying parameters and heteroskedastic errors was...

Validating the Modeling and Simulation of a Generic Tracking Radar

DTIC Science & Technology

2009-07-28

order Gauss-Markov time series with CTGM = 250 units and rGM = 10 s is shown in the top panel of Figure 1. The time series, ifr , can represent any...are shared among the sensors. The total position and velocity estimation errors valid at time index k are given by < fr *|fc = rk\\k - rk and

Phobos mass estimations from MEX and Viking 1 data: influence of different noise sources and estimation strategies

NASA Astrophysics Data System (ADS)

Kudryashova, M.; Rosenblatt, P.; Marty, J.-C.

2015-08-01

The mass of Phobos is an important parameter which, together with second-order gravity field coefficients and libration amplitude, constrains internal structure and nature of the moon. And thus, it needs to be known with high precision. Nevertheless, Phobos mass (GM, more precisely) estimated by different authors based on diverse data-sets and methods, varies by more than their 1-sigma error. The most complete lists of GM values are presented in the works of R. Jacobson (2010) and M. Paetzold et al. (2014) and include the estimations in the interval from (5.39 ± 0:03).10^5 (Smith et al., 1995) till (8.5 ± 0.7).10^5[m^3/s^2] (Williams et al., 1988). Furthermore, even the comparison of the estimations coming from the same estimation procedure applied to the consecutive flybys of the same spacecraft (s/c) shows big variations in GMs. The indicated behavior is very pronounced in the GM estimations stemming from the Viking1 flybys in February 1977 (as well as from MEX flybys, though in a smaller amplitude) and in this work we made an attempt to figure out its roots. The errors of Phobos GM estimations depend on the precision of the model (e.g. accuracy of Phobos a priori ephemeris and its a priori GM value) as well as on the radio-tracking measurements quality (noise, coverage, flyby distance). In the present work we are testing the impact of mentioned above error sources by means of simulations. We also consider the effect of the uncertainties in a priori Phobos positions on the GM estimations from real observations. Apparently, the strategy (i.e. splitting real observations in data-arcs, whether they stem from the close approaches of Phobos by spacecraft or from analysis of the s/c orbit evolution around Mars) of the estimations has an impact on the Phobos GM estimation.

A Novel Error Model of Optical Systems and an On-Orbit Calibration Method for Star Sensors.

PubMed

Wang, Shuang; Geng, Yunhai; Jin, Rongyu

2015-12-12

In order to improve the on-orbit measurement accuracy of star sensors, the effects of image-plane rotary error, image-plane tilt error and distortions of optical systems resulting from the on-orbit thermal environment were studied in this paper. Since these issues will affect the precision of star image point positions, in this paper, a novel measurement error model based on the traditional error model is explored. Due to the orthonormal characteristics of image-plane rotary-tilt errors and the strong nonlinearity among these error parameters, it is difficult to calibrate all the parameters simultaneously. To solve this difficulty, for the new error model, a modified two-step calibration method based on the Extended Kalman Filter (EKF) and Least Square Methods (LSM) is presented. The former one is used to calibrate the main point drift, focal length error and distortions of optical systems while the latter estimates the image-plane rotary-tilt errors. With this calibration method, the precision of star image point position influenced by the above errors is greatly improved from 15.42% to 1.389%. Finally, the simulation results demonstrate that the presented measurement error model for star sensors has higher precision. Moreover, the proposed two-step method can effectively calibrate model error parameters, and the calibration precision of on-orbit star sensors is also improved obviously.

Depth-of-interaction estimates in pixelated scintillator sensors using Monte Carlo techniques

NASA Astrophysics Data System (ADS)

Sharma, Diksha; Sze, Christina; Bhandari, Harish; Nagarkar, Vivek; Badano, Aldo

2017-01-01

Image quality in thick scintillator detectors can be improved by minimizing parallax errors through depth-of-interaction (DOI) estimation. A novel sensor for low-energy single photon imaging having a thick, transparent, crystalline pixelated micro-columnar CsI:Tl scintillator structure has been described, with possible future application in small-animal single photon emission computed tomography (SPECT) imaging when using thicker structures under development. In order to understand the fundamental limits of this new structure, we introduce cartesianDETECT2, an open-source optical transport package that uses Monte Carlo methods to obtain estimates of DOI for improving spatial resolution of nuclear imaging applications. Optical photon paths are calculated as a function of varying simulation parameters such as columnar surface roughness, bulk, and top-surface absorption. We use scanning electron microscope images to estimate appropriate surface roughness coefficients. Simulation results are analyzed to model and establish patterns between DOI and photon scattering. The effect of varying starting locations of optical photons on the spatial response is studied. Bulk and top-surface absorption fractions were varied to investigate their effect on spatial response as a function of DOI. We investigated the accuracy of our DOI estimation model for a particular screen with various training and testing sets, and for all cases the percent error between the estimated and actual DOI over the majority of the detector thickness was ±5% with a maximum error of up to ±10% at deeper DOIs. In addition, we found that cartesianDETECT2 is computationally five times more efficient than MANTIS. Findings indicate that DOI estimates can be extracted from a double-Gaussian model of the detector response. We observed that our model predicts DOI in pixelated scintillator detectors reasonably well.

Towards the 1 mm/y stability of the radial orbit error at regional scales

NASA Astrophysics Data System (ADS)

Couhert, Alexandre; Cerri, Luca; Legeais, Jean-François; Ablain, Michael; Zelensky, Nikita P.; Haines, Bruce J.; Lemoine, Frank G.; Bertiger, William I.; Desai, Shailen D.; Otten, Michiel

2015-01-01

An estimated orbit error budget for the Jason-1 and Jason-2 GDR-D solutions is constructed, using several measures of orbit error. The focus is on the long-term stability of the orbit time series for mean sea level applications on a regional scale. We discuss various issues related to the assessment of radial orbit error trends; in particular this study reviews orbit errors dependent on the tracking technique, with an aim to monitoring the long-term stability of all available tracking systems operating on Jason-1 and Jason-2 (GPS, DORIS, SLR). The reference frame accuracy and its effect on Jason orbit is assessed. We also examine the impact of analysis method on the inference of Geographically Correlated Errors as well as the significance of estimated radial orbit error trends versus the time span of the analysis. Thus a long-term error budget of the 10-year Jason-1 and Envisat GDR-D orbit time series is provided for two time scales: interannual and decadal. As the temporal variations of the geopotential remain one of the primary limitations in the Precision Orbit Determination modeling, the overall accuracy of the Jason-1 and Jason-2 GDR-D solutions is evaluated through comparison with external orbits based on different time-variable gravity models. This contribution is limited to an East-West “order-1” pattern at the 2 mm/y level (secular) and 4 mm level (seasonal), over the Jason-2 lifetime. The possibility of achieving sub-mm/y radial orbit stability over interannual and decadal periods at regional scales and the challenge of evaluating such an improvement using in situ independent data is discussed.

Interpreting SBUV Smoothing Errors: an Example Using the Quasi-biennial Oscillation

NASA Technical Reports Server (NTRS)

Kramarova, N. A.; Bhartia, Pawan K.; Frith, S. M.; McPeters, R. D.; Stolarski, R. S.

2013-01-01

The Solar Backscattered Ultraviolet (SBUV) observing system consists of a series of instruments that have been measuring both total ozone and the ozone profile since 1970. SBUV measures the profile in the upper stratosphere with a resolution that is adequate to resolve most of the important features of that region. In the lower stratosphere the limited vertical resolution of the SBUV system means that there are components of the profile variability that SBUV cannot measure. The smoothing error, as defined in the optimal estimation retrieval method, describes the components of the profile variability that the SBUV observing system cannot measure. In this paper we provide a simple visual interpretation of the SBUV smoothing error by comparing SBUV ozone anomalies in the lower tropical stratosphere associated with the quasi-biennial oscillation (QBO) to anomalies obtained from the Aura Microwave Limb Sounder (MLS). We describe a methodology for estimating the SBUV smoothing error for monthly zonal mean (mzm) profiles. We construct covariance matrices that describe the statistics of the inter-annual ozone variability using a 6 yr record of Aura MLS and ozonesonde data. We find that the smoothing error is of the order of 1percent between 10 and 1 hPa, increasing up to 15-20 percent in the troposphere and up to 5 percent in the mesosphere. The smoothing error for total ozone columns is small, mostly less than 0.5 percent. We demonstrate that by merging the partial ozone columns from several layers in the lower stratosphere/troposphere into one thick layer, we can minimize the smoothing error. We recommend using the following layer combinations to reduce the smoothing error to about 1 percent: surface to 25 hPa (16 hPa) outside (inside) of the narrow equatorial zone 20 S-20 N.

Towards the 1 mm/y Stability of the Radial Orbit Error at Regional Scales

NASA Technical Reports Server (NTRS)

Couhert, Alexandre; Cerri, Luca; Legeais, Jean-Francois; Ablain, Michael; Zelensky, Nikita P.; Haines, Bruce J.; Lemoine, Frank G.; Bertiger, William I.; Desai, Shailen D.; Otten, Michiel

2015-01-01

An estimated orbit error budget for the Jason-1 and Jason-2 GDR-D solutions is constructed, using several measures of orbit error. The focus is on the long-term stability of the orbit time series for mean sea level applications on a regional scale. We discuss various issues related to the assessment of radial orbit error trends; in particular this study reviews orbit errors dependent on the tracking technique, with an aim to monitoring the long-term stability of all available tracking systems operating on Jason-1 and Jason-2 (GPS, DORIS, SLR). The reference frame accuracy and its effect on Jason orbit is assessed. We also examine the impact of analysis method on the inference of Geographically Correlated Errors as well as the significance of estimated radial orbit error trends versus the time span of the analysis. Thus a long-term error budget of the 10-year Jason-1 and Envisat GDR-D orbit time series is provided for two time scales: interannual and decadal. As the temporal variations of the geopotential remain one of the primary limitations in the Precision Orbit Determination modeling, the overall accuracy of the Jason-1 and Jason-2 GDR-D solutions is evaluated through comparison with external orbits based on different time-variable gravity models. This contribution is limited to an East-West "order-1" pattern at the 2 mm/y level (secular) and 4 mm level (seasonal), over the Jason-2 lifetime. The possibility of achieving sub-mm/y radial orbit stability over interannual and decadal periods at regional scales and the challenge of evaluating such an improvement using in situ independent data is discussed.

Towards the 1 mm/y Stability of the Radial Orbit Error at Regional Scales

NASA Technical Reports Server (NTRS)

Couhert, Alexandre; Cerri, Luca; Legeais, Jean-Francois; Ablain, Michael; Zelensky, Nikita P.; Haines, Bruce J.; Lemoine, Frank G.; Bertiger, William I.; Desai, Shailen D.; Otten, Michiel

2014-01-01

An estimated orbit error budget for the Jason-1 and Jason-2 GDR-D solutions is constructed, using several measures of orbit error. The focus is on the long-term stability of the orbit time series for mean sea level applications on a regional scale. We discuss various issues related to the assessment of radial orbit error trends; in particular this study reviews orbit errors dependent on the tracking technique, with an aim to monitoring the long-term stability of all available tracking systems operating on Jason-1 and Jason-2 (GPS, DORIS,SLR). The reference frame accuracy and its effect on Jason orbit is assessed. We also examine the impact of analysis method on the inference of Geographically Correlated Errors as well as the significance of estimated radial orbit error trends versus the time span of the analysis. Thus a long-term error budget of the 10-year Jason-1 and Envisat GDR-D orbit time series is provided for two time scales: interannual and decadal. As the temporal variations of the geopotential remain one of the primary limitations in the Precision Orbit Determination modeling, the overall accuracy of the Jason-1 and Jason-2 GDR-D solutions is evaluated through comparison with external orbits based on different time-variable gravity models. This contribution is limited to an East-West "order-1" pattern at the 2 mm/y level (secular) and 4 mm level (seasonal), over the Jason-2 lifetime. The possibility of achieving sub-mm/y radial orbit stability over interannual and decadal periods at regional scales and the challenge of evaluating such an improvement using in situ independent data is discussed.

Retrieval of ice cloud properties using an optimal estimation algorithm and MODIS infrared observations. Part I: Forward model, error analysis, and information content

PubMed Central

Wang, Chenxi; Platnick, Steven; Zhang, Zhibo; Meyer, Kerry; Yang, Ping

2018-01-01

An optimal estimation (OE) retrieval method is developed to infer three ice cloud properties simultaneously: optical thickness (τ), effective radius (reff), and cloud-top height (h). This method is based on a fast radiative transfer (RT) model and infrared (IR) observations from the MODerate resolution Imaging Spectroradiometer (MODIS). This study conducts thorough error and information content analyses to understand the error propagation and performance of retrievals from various MODIS band combinations under different cloud/atmosphere states. Specifically, the algorithm takes into account four error sources: measurement uncertainty, fast RT model uncertainty, uncertainties in ancillary datasets (e.g., atmospheric state), and assumed ice crystal habit uncertainties. It is found that the ancillary and ice crystal habit error sources dominate the MODIS IR retrieval uncertainty and cannot be ignored. The information content analysis shows that, for a given ice cloud, the use of four MODIS IR observations is sufficient to retrieve the three cloud properties. However, the selection of MODIS IR bands that provide the most information and their order of importance varies with both the ice cloud properties and the ambient atmospheric and the surface states. As a result, this study suggests the inclusion of all MODIS IR bands in practice since little a priori information is available. PMID:29707470

Retrieval of Ice Cloud Properties Using an Optimal Estimation Algorithm and MODIS Infrared Observations. Part I: Forward Model, Error Analysis, and Information Content

NASA Technical Reports Server (NTRS)

Wang, Chenxi; Platnick, Steven; Zhang, Zhibo; Meyer, Kerry; Yang, Ping

2016-01-01

An optimal estimation (OE) retrieval method is developed to infer three ice cloud properties simultaneously: optical thickness (tau), effective radius (r(sub eff)), and cloud-top height (h). This method is based on a fast radiative transfer (RT) model and infrared (IR) observations from the MODerate resolution Imaging Spectroradiometer (MODIS). This study conducts thorough error and information content analyses to understand the error propagation and performance of retrievals from various MODIS band combinations under different cloud/atmosphere states. Specifically, the algorithm takes into account four error sources: measurement uncertainty, fast RT model uncertainty, uncertainties in ancillary datasets (e.g., atmospheric state), and assumed ice crystal habit uncertainties. It is found that the ancillary and ice crystal habit error sources dominate the MODIS IR retrieval uncertainty and cannot be ignored. The information content analysis shows that, for a given ice cloud, the use of four MODIS IR observations is sufficient to retrieve the three cloud properties. However, the selection of MODIS IR bands that provide the most information and their order of importance varies with both the ice cloud properties and the ambient atmospheric and the surface states. As a result, this study suggests the inclusion of all MODIS IR bands in practice since little a priori information is available.

Retrieval of Ice Cloud Properties Using an Optimal Estimation Algorithm and MODIS Infrared Observations. Part I: Forward Model, Error Analysis, and Information Content

NASA Technical Reports Server (NTRS)

Wang, Chenxi; Platnick, Steven; Zhang, Zhibo; Meyer, Kerry; Yang, Ping

2016-01-01

An optimal estimation (OE) retrieval method is developed to infer three ice cloud properties simultaneously: optical thickness (tau), effective radius (r(sub eff)), and cloud top height (h). This method is based on a fast radiative transfer (RT) model and infrared (IR) observations from the MODerate resolution Imaging Spectroradiometer (MODIS). This study conducts thorough error and information content analyses to understand the error propagation and performance of retrievals from various MODIS band combinations under different cloud/atmosphere states. Specifically, the algorithm takes into account four error sources: measurement uncertainty, fast RT model uncertainty, uncertainties in ancillary data sets (e.g., atmospheric state), and assumed ice crystal habit uncertainties. It is found that the ancillary and ice crystal habit error sources dominate the MODIS IR retrieval uncertainty and cannot be ignored. The information content analysis shows that for a given ice cloud, the use of four MODIS IR observations is sufficient to retrieve the three cloud properties. However, the selection of MODIS IR bands that provide the most information and their order of importance varies with both the ice cloud properties and the ambient atmospheric and the surface states. As a result, this study suggests the inclusion of all MODIS IR bands in practice since little a priori information is available.

Maximum inflation of the type 1 error rate when sample size and allocation rate are adapted in a pre-planned interim look.

PubMed

Graf, Alexandra C; Bauer, Peter

2011-06-30

We calculate the maximum type 1 error rate of the pre-planned conventional fixed sample size test for comparing the means of independent normal distributions (with common known variance) which can be yielded when sample size and allocation rate to the treatment arms can be modified in an interim analysis. Thereby it is assumed that the experimenter fully exploits knowledge of the unblinded interim estimates of the treatment effects in order to maximize the conditional type 1 error rate. The 'worst-case' strategies require knowledge of the unknown common treatment effect under the null hypothesis. Although this is a rather hypothetical scenario it may be approached in practice when using a standard control treatment for which precise estimates are available from historical data. The maximum inflation of the type 1 error rate is substantially larger than derived by Proschan and Hunsberger (Biometrics 1995; 51:1315-1324) for design modifications applying balanced samples before and after the interim analysis. Corresponding upper limits for the maximum type 1 error rate are calculated for a number of situations arising from practical considerations (e.g. restricting the maximum sample size, not allowing sample size to decrease, allowing only increase in the sample size in the experimental treatment). The application is discussed for a motivating example. Copyright © 2011 John Wiley & Sons, Ltd.

Reduction in chemotherapy order errors with computerized physician order entry.

PubMed

Meisenberg, Barry R; Wright, Robert R; Brady-Copertino, Catherine J

2014-01-01

To measure the number and type of errors associated with chemotherapy order composition associated with three sequential methods of ordering: handwritten orders, preprinted orders, and computerized physician order entry (CPOE) embedded in the electronic health record. From 2008 to 2012, a sample of completed chemotherapy orders were reviewed by a pharmacist for the number and type of errors as part of routine performance improvement monitoring. Error frequencies for each of the three distinct methods of composing chemotherapy orders were compared using statistical methods. The rate of problematic order sets-those requiring significant rework for clarification-was reduced from 30.6% with handwritten orders to 12.6% with preprinted orders (preprinted v handwritten, P < .001) to 2.2% with CPOE (preprinted v CPOE, P < .001). The incidence of errors capable of causing harm was reduced from 4.2% with handwritten orders to 1.5% with preprinted orders (preprinted v handwritten, P < .001) to 0.1% with CPOE (CPOE v preprinted, P < .001). The number of problem- and error-containing chemotherapy orders was reduced sequentially by preprinted order sets and then by CPOE. CPOE is associated with low error rates, but it did not eliminate all errors, and the technology can introduce novel types of errors not seen with traditional handwritten or preprinted orders. Vigilance even with CPOE is still required to avoid patient harm.

Evaluation of centroiding algorithm error for Nano-JASMINE

NASA Astrophysics Data System (ADS)

Hara, Takuji; Gouda, Naoteru; Yano, Taihei; Yamada, Yoshiyuki

2014-08-01

The Nano-JASMINE mission has been designed to perform absolute astrometric measurements with unprecedented accuracy; the end-of-mission parallax standard error is required to be of the order of 3 milli arc seconds for stars brighter than 7.5 mag in the zw-band(0.6μm-1.0μm) .These requirements set a stringent constraint on the accuracy of the estimation of the location of the stellar image on the CCD for each observation. However each stellar images have individual shape depend on the spectral energy distribution of the star, the CCD properties, and the optics and its associated wave front errors. So it is necessity that the centroiding algorithm performs a high accuracy in any observables. Referring to the study of Gaia, we use LSF fitting method for centroiding algorithm, and investigate systematic error of the algorithm for Nano-JASMINE. Furthermore, we found to improve the algorithm by restricting sample LSF when we use a Principle Component Analysis. We show that centroiding algorithm error decrease after adapted the method.

Time-to-contact estimation of accelerated stimuli is based on first-order information.

PubMed

Benguigui, Nicolas; Ripoll, Hubert; Broderick, Michael P

2003-12-01

The goal of this study was to test whether 1st-order information, which does not account for acceleration, is used (a) to estimate the time to contact (TTC) of an accelerated stimulus after the occlusion of a final part of its trajectory and (b) to indirectly intercept an accelerated stimulus with a thrown projectile. Both tasks require the production of an action on the basis of predictive information acquired before the arrival of the stimulus at the target and allow the experimenter to make quantitative predictions about the participants' use (or nonuse) of 1st-order information. The results show that participants do not use information about acceleration and that they commit errors that rely quantitatively on 1st-order information even when acceleration is psychophysically detectable. In the indirect interceptive task, action is planned about 200 ms before the initiation of the movement, at which time the 1st-order TTC attains a critical value. ((c) 2003 APA, all rights reserved)

Evaluation of three lidar scanning strategies for turbulence measurements

NASA Astrophysics Data System (ADS)

Newman, J. F.; Klein, P. M.; Wharton, S.; Sathe, A.; Bonin, T. A.; Chilson, P. B.; Muschinski, A.

2015-11-01

Several errors occur when a traditional Doppler-beam swinging (DBS) or velocity-azimuth display (VAD) strategy is used to measure turbulence with a lidar. To mitigate some of these errors, a scanning strategy was recently developed which employs six beam positions to independently estimate the u, v, and w velocity variances and covariances. In order to assess the ability of these different scanning techniques to measure turbulence, a Halo scanning lidar, WindCube v2 pulsed lidar and ZephIR continuous wave lidar were deployed at field sites in Oklahoma and Colorado with collocated sonic anemometers. Results indicate that the six-beam strategy mitigates some of the errors caused by VAD and DBS scans, but the strategy is strongly affected by errors in the variance measured at the different beam positions. The ZephIR and WindCube lidars overestimated horizontal variance values by over 60 % under unstable conditions as a result of variance contamination, where additional variance components contaminate the true value of the variance. A correction method was developed for the WindCube lidar that uses variance calculated from the vertical beam position to reduce variance contamination in the u and v variance components. The correction method reduced WindCube variance estimates by over 20 % at both the Oklahoma and Colorado sites under unstable conditions, when variance contamination is largest. This correction method can be easily applied to other lidars that contain a vertical beam position and is a promising method for accurately estimating turbulence with commercially available lidars.

Evaluation of three lidar scanning strategies for turbulence measurements

NASA Astrophysics Data System (ADS)

Newman, Jennifer F.; Klein, Petra M.; Wharton, Sonia; Sathe, Ameya; Bonin, Timothy A.; Chilson, Phillip B.; Muschinski, Andreas

2016-05-01

Several errors occur when a traditional Doppler beam swinging (DBS) or velocity-azimuth display (VAD) strategy is used to measure turbulence with a lidar. To mitigate some of these errors, a scanning strategy was recently developed which employs six beam positions to independently estimate the u, v, and w velocity variances and covariances. In order to assess the ability of these different scanning techniques to measure turbulence, a Halo scanning lidar, WindCube v2 pulsed lidar, and ZephIR continuous wave lidar were deployed at field sites in Oklahoma and Colorado with collocated sonic anemometers.Results indicate that the six-beam strategy mitigates some of the errors caused by VAD and DBS scans, but the strategy is strongly affected by errors in the variance measured at the different beam positions. The ZephIR and WindCube lidars overestimated horizontal variance values by over 60 % under unstable conditions as a result of variance contamination, where additional variance components contaminate the true value of the variance. A correction method was developed for the WindCube lidar that uses variance calculated from the vertical beam position to reduce variance contamination in the u and v variance components. The correction method reduced WindCube variance estimates by over 20 % at both the Oklahoma and Colorado sites under unstable conditions, when variance contamination is largest. This correction method can be easily applied to other lidars that contain a vertical beam position and is a promising method for accurately estimating turbulence with commercially available lidars.

Optimal full motion video registration with rigorous error propagation

NASA Astrophysics Data System (ADS)

Dolloff, John; Hottel, Bryant; Doucette, Peter; Theiss, Henry; Jocher, Glenn

2014-06-01

Optimal full motion video (FMV) registration is a crucial need for the Geospatial community. It is required for subsequent and optimal geopositioning with simultaneous and reliable accuracy prediction. An overall approach being developed for such registration is presented that models relevant error sources in terms of the expected magnitude and correlation of sensor errors. The corresponding estimator is selected based on the level of accuracy of the a priori information of the sensor's trajectory and attitude (pointing) information, in order to best deal with non-linearity effects. Estimator choices include near real-time Kalman Filters and batch Weighted Least Squares. Registration solves for corrections to the sensor a priori information for each frame. It also computes and makes available a posteriori accuracy information, i.e., the expected magnitude and correlation of sensor registration errors. Both the registered sensor data and its a posteriori accuracy information are then made available to "down-stream" Multi-Image Geopositioning (MIG) processes. An object of interest is then measured on the registered frames and a multi-image optimal solution, including reliable predicted solution accuracy, is then performed for the object's 3D coordinates. This paper also describes a robust approach to registration when a priori information of sensor attitude is unavailable. It makes use of structure-from-motion principles, but does not use standard Computer Vision techniques, such as estimation of the Essential Matrix which can be very sensitive to noise. The approach used instead is a novel, robust, direct search-based technique.

Learning time-dependent noise to reduce logical errors: real time error rate estimation in quantum error correction

NASA Astrophysics Data System (ADS)

Huo, Ming-Xia; Li, Ying

2017-12-01

Quantum error correction is important to quantum information processing, which allows us to reliably process information encoded in quantum error correction codes. Efficient quantum error correction benefits from the knowledge of error rates. We propose a protocol for monitoring error rates in real time without interrupting the quantum error correction. Any adaptation of the quantum error correction code or its implementation circuit is not required. The protocol can be directly applied to the most advanced quantum error correction techniques, e.g. surface code. A Gaussian processes algorithm is used to estimate and predict error rates based on error correction data in the past. We find that using these estimated error rates, the probability of error correction failures can be significantly reduced by a factor increasing with the code distance.

Improvement in error propagation in the Shack-Hartmann-type zonal wavefront sensors.

PubMed

Pathak, Biswajit; Boruah, Bosanta R

2017-12-01

Estimation of the wavefront from measured slope values is an essential step in a Shack-Hartmann-type wavefront sensor. Using an appropriate estimation algorithm, these measured slopes are converted into wavefront phase values. Hence, accuracy in wavefront estimation lies in proper interpretation of these measured slope values using the chosen estimation algorithm. There are two important sources of errors associated with the wavefront estimation process, namely, the slope measurement error and the algorithm discretization error. The former type is due to the noise in the slope measurements or to the detector centroiding error, and the latter is a consequence of solving equations of a basic estimation algorithm adopted onto a discrete geometry. These errors deserve particular attention, because they decide the preference of a specific estimation algorithm for wavefront estimation. In this paper, we investigate these two important sources of errors associated with the wavefront estimation algorithms of Shack-Hartmann-type wavefront sensors. We consider the widely used Southwell algorithm and the recently proposed Pathak-Boruah algorithm [J. Opt.16, 055403 (2014)JOOPDB0150-536X10.1088/2040-8978/16/5/055403] and perform a comparative study between the two. We find that the latter algorithm is inherently superior to the Southwell algorithm in terms of the error propagation performance. We also conduct experiments that further establish the correctness of the comparative study between the said two estimation algorithms.

A comparative study of shear wave speed estimation techniques in optical coherence elastography applications

NASA Astrophysics Data System (ADS)

Zvietcovich, Fernando; Yao, Jianing; Chu, Ying-Ju; Meemon, Panomsak; Rolland, Jannick P.; Parker, Kevin J.

2016-03-01

Optical Coherence Elastography (OCE) is a widely investigated noninvasive technique for estimating the mechanical properties of tissue. In particular, vibrational OCE methods aim to estimate the shear wave velocity generated by an external stimulus in order to calculate the elastic modulus of tissue. In this study, we compare the performance of five acquisition and processing techniques for estimating the shear wave speed in simulations and experiments using tissue-mimicking phantoms. Accuracy, contrast-to-noise ratio, and resolution are measured for all cases. The first two techniques make the use of one piezoelectric actuator for generating a continuous shear wave propagation (SWP) and a tone-burst propagation (TBP) of 400 Hz over the gelatin phantom. The other techniques make use of one additional actuator located on the opposite side of the region of interest in order to create an interference pattern. When both actuators have the same frequency, a standing wave (SW) pattern is generated. Otherwise, when there is a frequency difference df between both actuators, a crawling wave (CrW) pattern is generated and propagates with less speed than a shear wave, which makes it suitable for being detected by the 2D cross-sectional OCE imaging. If df is not small compared to the operational frequency, the CrW travels faster and a sampled version of it (SCrW) is acquired by the system. Preliminary results suggest that TBP (error < 4.1%) and SWP (error < 6%) techniques are more accurate when compared to mechanical measurement test results.

Fuel Burn Estimation Model

NASA Technical Reports Server (NTRS)

Chatterji, Gano

2011-01-01

Conclusions: Validated the fuel estimation procedure using flight test data. A good fuel model can be created if weight and fuel data are available. Error in assumed takeoff weight results in similar amount of error in the fuel estimate. Fuel estimation error bounds can be determined.

An Enhanced Non-Coherent Pre-Filter Design for Tracking Error Estimation in GNSS Receivers

PubMed Central

Luo, Zhibin; Ding, Jicheng; Zhao, Lin; Wu, Mouyan

2017-01-01

Tracking error estimation is of great importance in global navigation satellite system (GNSS) receivers. Any inaccurate estimation for tracking error will decrease the signal tracking ability of signal tracking loops and the accuracies of position fixing, velocity determination, and timing. Tracking error estimation can be done by traditional discriminator, or Kalman filter-based pre-filter. The pre-filter can be divided into two categories: coherent and non-coherent. This paper focuses on the performance improvements of non-coherent pre-filter. Firstly, the signal characteristics of coherent and non-coherent integration—which are the basis of tracking error estimation—are analyzed in detail. After that, the probability distribution of estimation noise of four-quadrant arctangent (ATAN2) discriminator is derived according to the mathematical model of coherent integration. Secondly, the statistical property of observation noise of non-coherent pre-filter is studied through Monte Carlo simulation to set the observation noise variance matrix correctly. Thirdly, a simple fault detection and exclusion (FDE) structure is introduced to the non-coherent pre-filter design, and thus its effective working range for carrier phase error estimation extends from (−0.25 cycle, 0.25 cycle) to (−0.5 cycle, 0.5 cycle). Finally, the estimation accuracies of discriminator, coherent pre-filter, and the enhanced non-coherent pre-filter are evaluated comprehensively through the carefully designed experiment scenario. The pre-filter outperforms traditional discriminator in estimation accuracy. In a highly dynamic scenario, the enhanced non-coherent pre-filter provides accuracy improvements of 41.6%, 46.4%, and 50.36% for carrier phase error, carrier frequency error, and code phase error estimation, respectively, when compared with coherent pre-filter. The enhanced non-coherent pre-filter outperforms the coherent pre-filter in code phase error estimation when carrier-to-noise density ratio is less than 28.8 dB-Hz, in carrier frequency error estimation when carrier-to-noise density ratio is less than 20 dB-Hz, and in carrier phase error estimation when carrier-to-noise density belongs to (15, 23) dB-Hz ∪ (26, 50) dB-Hz. PMID:29156581

Monitoring Method of Cutting Force by Using Additional Spindle Sensors

NASA Astrophysics Data System (ADS)

Sarhan, Ahmed Aly Diaa; Matsubara, Atsushi; Sugihara, Motoyuki; Saraie, Hidenori; Ibaraki, Soichi; Kakino, Yoshiaki

This paper describes a monitoring method of cutting forces for end milling process by using displacement sensors. Four eddy-current displacement sensors are installed on the spindle housing of a machining center so that they can detect the radial motion of the rotating spindle. Thermocouples are also attached to the spindle structure in order to examine the thermal effect in the displacement sensing. The change in the spindle stiffness due to the spindle temperature and the speed is investigated as well. Finally, the estimation performance of cutting forces using the spindle displacement sensors is experimentally investigated by machining tests on carbon steel in end milling operations under different cutting conditions. It is found that the monitoring errors are attributable to the thermal displacement of the spindle, the time lag of the sensing system, and the modeling error of the spindle stiffness. It is also shown that the root mean square errors between estimated and measured amplitudes of cutting forces are reduced to be less than 20N with proper selection of the linear stiffness.

Thermospheric density variations: Observability using precision satellite orbits and effects on orbit propagation

NASA Astrophysics Data System (ADS)

Lechtenberg, Travis; McLaughlin, Craig A.; Locke, Travis; Krishna, Dhaval Mysore

2013-01-01

paper examines atmospheric density estimated using precision orbit ephemerides (POE) from the CHAMP and GRACE satellites during short periods of greater atmospheric density variability. The results of the calibration of CHAMP densities derived using POEs with those derived using accelerometers are examined for three different types of density perturbations, [traveling atmospheric disturbances (TADs), geomagnetic cusp phenomena, and midnight density maxima] in order to determine the temporal resolution of POE solutions. In addition, the densities are compared to High-Accuracy Satellite Drag Model (HASDM) densities to compare temporal resolution for both types of corrections. The resolution for these models of thermospheric density was found to be inadequate to sufficiently characterize the short-term density variations examined here. Also examined in this paper is the effect of differing density estimation schemes by propagating an initial orbit state forward in time and examining induced errors. The propagated POE-derived densities incurred errors of a smaller magnitude than the empirical models and errors on the same scale or better than those incurred using the HASDM model.

A model of the 1.6 GHz scatterometer. [performance of airborne scatterometer used as microwave remote sensor of soil moisture

NASA Technical Reports Server (NTRS)

Wang, J. R.

1977-01-01

The performance was studied of the 1.6 GHz airborne scatterometer system which is used as one of several Johnson Space Center (JSC) microwave remote sensors to detect moisture content of soil. The system is analyzed with respect to its antenna pattern and coupling, the signal flow in the receiver data channels, and the errors in the signal outputs. The operational principle and the sensitivity of the system, as well as data handling are also described. The finite cross-polarized gains of all four 1.6 GHz scatterometer antennae are found to have profound influence on the cross-polarized backscattered signal returns. If these signals are not analyzed properly, large errors could result in the estimate of the cross-polarized coefficient. It is also found necessary to make corrections to the variations of the aircraft parameters during data reduction in order to minimize the error in the coefficient estimate. Finally, a few recommendations are made to improve the overall performance of the scatterometer system.

Radar and Lidar Radar DEM

NASA Technical Reports Server (NTRS)

Liskovich, Diana; Simard, Marc

2011-01-01

Using radar and lidar data, the aim is to improve 3D rendering of terrain, including digital elevation models (DEM) and estimates of vegetation height and biomass in a variety of forest types and terrains. The 3D mapping of vegetation structure and the analysis are useful to determine the role of forest in climate change (carbon cycle), in providing habitat and as a provider of socio-economic services. This in turn will lead to potential for development of more effective land-use management. The first part of the project was to characterize the Shuttle Radar Topography Mission DEM error with respect to ICESat/GLAS point estimates of elevation. We investigated potential trends with latitude, canopy height, signal to noise ratio (SNR), number of LiDAR waveform peaks, and maximum peak width. Scatter plots were produced for each variable and were fitted with 1st and 2nd degree polynomials. Higher order trends were visually inspected through filtering with a mean and median filter. We also assessed trends in the DEM error variance. Finally, a map showing how DEM error was geographically distributed globally was created.

A simulation test of the effectiveness of several methods for error-checking non-invasive genetic data

USGS Publications Warehouse

Roon, David A.; Waits, L.P.; Kendall, K.C.

2005-01-01

Non-invasive genetic sampling (NGS) is becoming a popular tool for population estimation. However, multiple NGS studies have demonstrated that polymerase chain reaction (PCR) genotyping errors can bias demographic estimates. These errors can be detected by comprehensive data filters such as the multiple-tubes approach, but this approach is expensive and time consuming as it requires three to eight PCR replicates per locus. Thus, researchers have attempted to correct PCR errors in NGS datasets using non-comprehensive error checking methods, but these approaches have not been evaluated for reliability. We simulated NGS studies with and without PCR error and 'filtered' datasets using non-comprehensive approaches derived from published studies and calculated mark-recapture estimates using CAPTURE. In the absence of data-filtering, simulated error resulted in serious inflations in CAPTURE estimates; some estimates exceeded N by ??? 200%. When data filters were used, CAPTURE estimate reliability varied with per-locus error (E??). At E?? = 0.01, CAPTURE estimates from filtered data displayed < 5% deviance from error-free estimates. When E?? was 0.05 or 0.09, some CAPTURE estimates from filtered data displayed biases in excess of 10%. Biases were positive at high sampling intensities; negative biases were observed at low sampling intensities. We caution researchers against using non-comprehensive data filters in NGS studies, unless they can achieve baseline per-locus error rates below 0.05 and, ideally, near 0.01. However, we suggest that data filters can be combined with careful technique and thoughtful NGS study design to yield accurate demographic information. ?? 2005 The Zoological Society of London.

Spatial regression test for ensuring temperature data quality in southern Spain

NASA Astrophysics Data System (ADS)

Estévez, J.; Gavilán, P.; García-Marín, A. P.

2018-01-01

Quality assurance of meteorological data is crucial for ensuring the reliability of applications and models that use such data as input variables, especially in the field of environmental sciences. Spatial validation of meteorological data is based on the application of quality control procedures using data from neighbouring stations to assess the validity of data from a candidate station (the station of interest). These kinds of tests, which are referred to in the literature as spatial consistency tests, take data from neighbouring stations in order to estimate the corresponding measurement at the candidate station. These estimations can be made by weighting values according to the distance between the stations or to the coefficient of correlation, among other methods. The test applied in this study relies on statistical decision-making and uses a weighting based on the standard error of the estimate. This paper summarizes the results of the application of this test to maximum, minimum and mean temperature data from the Agroclimatic Information Network of Andalusia (southern Spain). This quality control procedure includes a decision based on a factor f, the fraction of potential outliers for each station across the region. Using GIS techniques, the geographic distribution of the errors detected has been also analysed. Finally, the performance of the test was assessed by evaluating its effectiveness in detecting known errors.

Research on SOC Calibration of Large Capacity Lead Acid Battery

NASA Astrophysics Data System (ADS)

Ye, W. Q.; Guo, Y. X.

2018-05-01

Large capacity lead-acid battery is used in track electric locomotive, and State of Charge (SOC) is an important quantitative parameter of locomotive power output and operating mileage of power emergency recovery vehicle. But State of Charge estimation has been a difficult part in the battery management system. In order to reduce the SOC estimation error better, this paper uses the linear relationship of Open Circuit Voltage (OCV) and State of Charge to fit the SOC-OCV curve equation by MATLAB. The method proposed in this paper is small, easy to implement and can be used in the battery non-working state SOC estimation correction, improve the estimation accuracy of SOC.

Estimation of the simple correlation coefficient.

PubMed

Shieh, Gwowen

2010-11-01

This article investigates some unfamiliar properties of the Pearson product-moment correlation coefficient for the estimation of simple correlation coefficient. Although Pearson's r is biased, except for limited situations, and the minimum variance unbiased estimator has been proposed in the literature, researchers routinely employ the sample correlation coefficient in their practical applications, because of its simplicity and popularity. In order to support such practice, this study examines the mean squared errors of r and several prominent formulas. The results reveal specific situations in which the sample correlation coefficient performs better than the unbiased and nearly unbiased estimators, facilitating recommendation of r as an effect size index for the strength of linear association between two variables. In addition, related issues of estimating the squared simple correlation coefficient are also considered.

Adaptive robust motion trajectory tracking control of pneumatic cylinders with LuGre model-based friction compensation

NASA Astrophysics Data System (ADS)

Meng, Deyuan; Tao, Guoliang; Liu, Hao; Zhu, Xiaocong

2014-07-01

Friction compensation is particularly important for motion trajectory tracking control of pneumatic cylinders at low speed movement. However, most of the existing model-based friction compensation schemes use simple classical models, which are not enough to address applications with high-accuracy position requirements. Furthermore, the friction force in the cylinder is time-varying, and there exist rather severe unmodelled dynamics and unknown disturbances in the pneumatic system. To deal with these problems effectively, an adaptive robust controller with LuGre model-based dynamic friction compensation is constructed. The proposed controller employs on-line recursive least squares estimation (RLSE) to reduce the extent of parametric uncertainties, and utilizes the sliding mode control method to attenuate the effects of parameter estimation errors, unmodelled dynamics and disturbances. In addition, in order to realize LuGre model-based friction compensation, the modified dual-observer structure for estimating immeasurable friction internal state is developed. Therefore, a prescribed motion tracking transient performance and final tracking accuracy can be guaranteed. Since the system model uncertainties are unmatched, the recursive backstepping design technology is applied. In order to solve the conflicts between the sliding mode control design and the adaptive control design, the projection mapping is used to condition the RLSE algorithm so that the parameter estimates are kept within a known bounded convex set. Finally, the proposed controller is tested for tracking sinusoidal trajectories and smooth square trajectory under different loads and sudden disturbance. The testing results demonstrate that the achievable performance of the proposed controller is excellent and is much better than most other studies in literature. Especially when a 0.5 Hz sinusoidal trajectory is tracked, the maximum tracking error is 0.96 mm and the average tracking error is 0.45 mm. This paper constructs an adaptive robust controller which can compensate the friction force in the cylinder.

Analyses of global sea surface temperature 1856-1991

NASA Astrophysics Data System (ADS)

Kaplan, Alexey; Cane, Mark A.; Kushnir, Yochanan; Clement, Amy C.; Blumenthal, M. Benno; Rajagopalan, Balaji

1998-08-01

Global analyses of monthly sea surface temperature (SST) anomalies from 1856 to 1991 are produced using three statistically based methods: optimal smoothing (OS), the Kaiman filter (KF) and optimal interpolation (OI). Each of these is accompanied by estimates of the error covariance of the analyzed fields. The spatial covariance function these methods require is estimated from the available data; the timemarching model is a first-order autoregressive model again estimated from data. The data input for the analyses are monthly anomalies from the United Kingdom Meteorological Office historical sea surface temperature data set (MOHSST5) [Parker et al., 1994] of the Global Ocean Surface Temperature Atlas (GOSTA) [Bottomley et al., 1990]. These analyses are compared with each other, with GOSTA, and with an analysis generated by projection (P) onto a set of empirical orthogonal functions (as in Smith et al. [1996]). In theory, the quality of the analyses should rank in the order OS, KF, OI, P, and GOSTA. It is found that the first four give comparable results in the data-rich periods (1951-1991), but at times when data is sparse the first three differ significantly from P and GOSTA. At these times the latter two often have extreme and fluctuating values, prima facie evidence of error. The statistical schemes are also verified against data not used in any of the analyses (proxy records derived from corals and air temperature records from coastal and island stations). We also present evidence that the analysis error estimates are indeed indicative of the quality of the products. At most times the OS and KF products are close to the OI product, but at times of especially poor coverage their use of information from other times is advantageous. The methods appear to reconstruct the major features of the global SST field from very sparse data. Comparison with other indications of the El Niño-Southern Oscillation cycle show that the analyses provide usable information on interannual variability as far back as the 1860s.

Impact of fitting algorithms on errors of parameter estimates in dynamic contrast-enhanced MRI

NASA Astrophysics Data System (ADS)

Debus, C.; Floca, R.; Nörenberg, D.; Abdollahi, A.; Ingrisch, M.

2017-12-01

Parameter estimation in dynamic contrast-enhanced MRI (DCE MRI) is usually performed by non-linear least square (NLLS) fitting of a pharmacokinetic model to a measured concentration-time curve. The two-compartment exchange model (2CXM) describes the compartments ‘plasma’ and ‘interstitial volume’ and their exchange in terms of plasma flow and capillary permeability. The model function can be defined by either a system of two coupled differential equations or a closed-form analytical solution. The aim of this study was to compare these two representations in terms of accuracy, robustness and computation speed, depending on parameter combination and temporal sampling. The impact on parameter estimation errors was investigated by fitting the 2CXM to simulated concentration-time curves. Parameter combinations representing five tissue types were used, together with two arterial input functions, a measured and a theoretical population based one, to generate 4D concentration images at three different temporal resolutions. Images were fitted by NLLS techniques, where the sum of squared residuals was calculated by either numeric integration with the Runge-Kutta method or convolution. Furthermore two example cases, a prostate carcinoma and a glioblastoma multiforme patient, were analyzed in order to investigate the validity of our findings in real patient data. The convolution approach yields improved results in precision and robustness of determined parameters. Precision and stability are limited in curves with low blood flow. The model parameter ve shows great instability and little reliability in all cases. Decreased temporal resolution results in significant errors for the differential equation approach in several curve types. The convolution excelled in computational speed by three orders of magnitude. Uncertainties in parameter estimation at low temporal resolution cannot be compensated by usage of the differential equations. Fitting with the convolution approach is superior in computational time, with better stability and accuracy at the same time.

Reducing errors in the GRACE gravity solutions using regularization

NASA Astrophysics Data System (ADS)

Save, Himanshu; Bettadpur, Srinivas; Tapley, Byron D.

2012-09-01

The nature of the gravity field inverse problem amplifies the noise in the GRACE data, which creeps into the mid and high degree and order harmonic coefficients of the Earth's monthly gravity fields provided by GRACE. Due to the use of imperfect background models and data noise, these errors are manifested as north-south striping in the monthly global maps of equivalent water heights. In order to reduce these errors, this study investigates the use of the L-curve method with Tikhonov regularization. L-curve is a popular aid for determining a suitable value of the regularization parameter when solving linear discrete ill-posed problems using Tikhonov regularization. However, the computational effort required to determine the L-curve is prohibitively high for a large-scale problem like GRACE. This study implements a parameter-choice method, using Lanczos bidiagonalization which is a computationally inexpensive approximation to L-curve. Lanczos bidiagonalization is implemented with orthogonal transformation in a parallel computing environment and projects a large estimation problem on a problem of the size of about 2 orders of magnitude smaller for computing the regularization parameter. Errors in the GRACE solution time series have certain characteristics that vary depending on the ground track coverage of the solutions. These errors increase with increasing degree and order. In addition, certain resonant and near-resonant harmonic coefficients have higher errors as compared with the other coefficients. Using the knowledge of these characteristics, this study designs a regularization matrix that provides a constraint on the geopotential coefficients as a function of its degree and order. This regularization matrix is then used to compute the appropriate regularization parameter for each monthly solution. A 7-year time-series of the candidate regularized solutions (Mar 2003-Feb 2010) show markedly reduced error stripes compared with the unconstrained GRACE release 4 solutions (RL04) from the Center for Space Research (CSR). Post-fit residual analysis shows that the regularized solutions fit the data to within the noise level of GRACE. A time series of filtered hydrological model is used to confirm that signal attenuation for basins in the Total Runoff Integrating Pathways (TRIP) database over 320 km radii is less than 1 cm equivalent water height RMS, which is within the noise level of GRACE.

Error due to unresolved scales in estimation problems for atmospheric data assimilation

NASA Astrophysics Data System (ADS)

Janjic, Tijana

The error arising due to unresolved scales in data assimilation procedures is examined. The problem of estimating the projection of the state of a passive scalar undergoing advection at a sequence of times is considered. The projection belongs to a finite- dimensional function space and is defined on the continuum. Using the continuum projection of the state of a passive scalar, a mathematical definition is obtained for the error arising due to the presence, in the continuum system, of scales unresolved by the discrete dynamical model. This error affects the estimation procedure through point observations that include the unresolved scales. In this work, two approximate methods for taking into account the error due to unresolved scales and the resulting correlations are developed and employed in the estimation procedure. The resulting formulas resemble the Schmidt-Kalman filter and the usual discrete Kalman filter, respectively. For this reason, the newly developed filters are called the Schmidt-Kalman filter and the traditional filter. In order to test the assimilation methods, a two- dimensional advection model with nonstationary spectrum was developed for passive scalar transport in the atmosphere. An analytical solution on the sphere was found depicting the model dynamics evolution. Using this analytical solution the model error is avoided, and the error due to unresolved scales is the only error left in the estimation problem. It is demonstrated that the traditional and the Schmidt- Kalman filter work well provided the exact covariance function of the unresolved scales is known. However, this requirement is not satisfied in practice, and the covariance function must be modeled. The Schmidt-Kalman filter cannot be computed in practice without further approximations. Therefore, the traditional filter is better suited for practical use. Also, the traditional filter does not require modeling of the full covariance function of the unresolved scales, but only modeling of the covariance matrix obtained by evaluating the covariance function at the observation points. We first assumed that this covariance matrix is stationary and that the unresolved scales are not correlated between the observation points, i.e., the matrix is diagonal, and that the values along the diagonal are constant. Tests with these assumptions were unsuccessful, indicating that a more sophisticated model of the covariance is needed for assimilation of data with nonstationary spectrum. A new method for modeling the covariance matrix based on an extended set of modeling assumptions is proposed. First, it is assumed that the covariance matrix is diagonal, that is, that the unresolved scales are not correlated between the observation points. It is postulated that the values on the diagonal depend on a wavenumber that is characteristic for the unresolved part of the spectrum. It is further postulated that this characteristic wavenumber can be diagnosed from the observations and from the estimate of the projection of the state that is being estimated. It is demonstrated that the new method successfully overcomes previously encountered difficulties.

A non-contact method based on multiple signal classification algorithm to reduce the measurement time for accurately heart rate detection

NASA Astrophysics Data System (ADS)

Bechet, P.; Mitran, R.; Munteanu, M.

2013-08-01

Non-contact methods for the assessment of vital signs are of great interest for specialists due to the benefits obtained in both medical and special applications, such as those for surveillance, monitoring, and search and rescue. This paper investigates the possibility of implementing a digital processing algorithm based on the MUSIC (Multiple Signal Classification) parametric spectral estimation in order to reduce the observation time needed to accurately measure the heart rate. It demonstrates that, by proper dimensioning the signal subspace, the MUSIC algorithm can be optimized in order to accurately assess the heart rate during an 8-28 s time interval. The validation of the processing algorithm performance was achieved by minimizing the mean error of the heart rate after performing simultaneous comparative measurements on several subjects. In order to calculate the error the reference value of heart rate was measured using a classic measurement system through direct contact.

Gravity gradient preprocessing at the GOCE HPF

NASA Astrophysics Data System (ADS)

Bouman, J.; Rispens, S.; Gruber, T.; Schrama, E.; Visser, P.; Tscherning, C. C.; Veicherts, M.

2009-04-01

One of the products derived from the GOCE observations are the gravity gradients. These gravity gradients are provided in the Gradiometer Reference Frame (GRF) and are calibrated in-flight using satellite shaking and star sensor data. In order to use these gravity gradients for application in Earth sciences and gravity field analysis, additional pre-processing needs to be done, including corrections for temporal gravity field signals to isolate the static gravity field part, screening for outliers, calibration by comparison with existing external gravity field information and error assessment. The temporal gravity gradient corrections consist of tidal and non-tidal corrections. These are all generally below the gravity gradient error level, which is predicted to show a 1/f behaviour for low frequencies. In the outlier detection the 1/f error is compensated for by subtracting a local median from the data, while the data error is assessed using the median absolute deviation. The local median acts as a high-pass filter and it is robust as is the median absolute deviation. Three different methods have been implemented for the calibration of the gravity gradients. All three methods use a high-pass filter to compensate for the 1/f gravity gradient error. The baseline method uses state-of-the-art global gravity field models and the most accurate results are obtained if star sensor misalignments are estimated along with the calibration parameters. A second calibration method uses GOCE GPS data to estimate a low degree gravity field model as well as gravity gradient scale factors. Both methods allow to estimate gravity gradient scale factors down to the 10-3 level. The third calibration method uses high accurate terrestrial gravity data in selected regions to validate the gravity gradient scale factors, focussing on the measurement band. Gravity gradient scale factors may be estimated down to the 10-2 level with this method.

Accounting for uncertain fault geometry in earthquake source inversions - I: theory and simplified application

NASA Astrophysics Data System (ADS)

Ragon, Théa; Sladen, Anthony; Simons, Mark

2018-05-01

The ill-posed nature of earthquake source estimation derives from several factors including the quality and quantity of available observations and the fidelity of our forward theory. Observational errors are usually accounted for in the inversion process. Epistemic errors, which stem from our simplified description of the forward problem, are rarely dealt with despite their potential to bias the estimate of a source model. In this study, we explore the impact of uncertainties related to the choice of a fault geometry in source inversion problems. The geometry of a fault structure is generally reduced to a set of parameters, such as position, strike and dip, for one or a few planar fault segments. While some of these parameters can be solved for, more often they are fixed to an uncertain value. We propose a practical framework to address this limitation by following a previously implemented method exploring the impact of uncertainties on the elastic properties of our models. We develop a sensitivity analysis to small perturbations of fault dip and position. The uncertainties in fault geometry are included in the inverse problem under the formulation of the misfit covariance matrix that combines both prediction and observation uncertainties. We validate this approach with the simplified case of a fault that extends infinitely along strike, using both Bayesian and optimization formulations of a static inversion. If epistemic errors are ignored, predictions are overconfident in the data and source parameters are not reliably estimated. In contrast, inclusion of uncertainties in fault geometry allows us to infer a robust posterior source model. Epistemic uncertainties can be many orders of magnitude larger than observational errors for great earthquakes (Mw > 8). Not accounting for uncertainties in fault geometry may partly explain observed shallow slip deficits for continental earthquakes. Similarly, ignoring the impact of epistemic errors can also bias estimates of near surface slip and predictions of tsunamis induced by megathrust earthquakes. (Mw > 8)

Estimation of water level and steam temperature using ensemble Kalman filter square root (EnKF-SR)

NASA Astrophysics Data System (ADS)

Herlambang, T.; Mufarrikoh, Z.; Karya, D. F.; Rahmalia, D.

2018-04-01

The equipment unit which has the most vital role in the steam-powered electric power plant is boiler. Steam drum boiler is a tank functioning to separate fluida into has phase and liquid phase. The existence in boiler system has a vital role. The controlled variables in the steam drum boiler are water level and the steam temperature. If the water level is higher than the determined level, then the gas phase resulted will contain steam endangering the following process and making the resulted steam going to turbine get less, and the by causing damages to pipes in the boiler. On the contrary, if less than the height of determined water level, the resulted height will result in dry steam likely to endanger steam drum. Thus an error was observed between the determined. This paper studied the implementation of the Ensemble Kalman Filter Square Root (EnKF-SR) method in nonlinear model of the steam drum boiler equation. The computation to estimate the height of water level and the temperature of steam was by simulation using Matlab software. Thus an error was observed between the determined water level and the steam temperature, and that of estimated water level and steam temperature. The result of simulation by Ensemble Kalman Filter Square Root (EnKF-SR) on the nonlinear model of steam drum boiler showed that the error was less than 2%. The implementation of EnKF-SR on the steam drum boiler r model comprises of three simulations, each of which generates 200, 300 and 400 ensembles. The best simulation exhibited the error between the real condition and the estimated result, by generating 400 ensemble. The simulation in water level in order of 0.00002145 m, whereas in the steam temperature was some 0.00002121 kelvin.

Derivation of respiration rate from ambulatory ECG and PPG using Ensemble Empirical Mode Decomposition: Comparison and fusion.

PubMed

Orphanidou, Christina

2017-02-01

A new method for extracting the respiratory rate from ECG and PPG obtained via wearable sensors is presented. The proposed technique employs Ensemble Empirical Mode Decomposition in order to identify the respiration "mode" from the noise-corrupted Heart Rate Variability/Pulse Rate Variability and Amplitude Modulation signals extracted from ECG and PPG signals. The technique was validated with respect to a Respiratory Impedance Pneumography (RIP) signal using the mean absolute and the average relative errors for a group ambulatory hospital patients. We compared approaches using single respiration-induced modulations on the ECG and PPG signals with approaches fusing the different modulations. Additionally, we investigated whether the presence of both the simultaneously recorded ECG and PPG signals provided a benefit in the overall system performance. Our method outperformed state-of-the-art ECG- and PPG-based algorithms and gave the best results over the whole database with a mean error of 1.8bpm for 1min estimates when using the fused ECG modulations, which was a relative error of 10.3%. No statistically significant differences were found when comparing the ECG-, PPG- and ECG/PPG-based approaches, indicating that the PPG can be used as a valid alternative to the ECG for applications using wearable sensors. While the presence of both the ECG and PPG signals did not provide an improvement in the estimation error, it increased the proportion of windows for which an estimate was obtained by at least 9%, indicating that the use of two simultaneously recorded signals might be desirable in high-acuity cases where an RR estimate is required more frequently. Copyright © 2016 Elsevier Ltd. All rights reserved.

SIVA/DIVA- INITIAL VALUE ORDINARY DIFFERENTIAL EQUATION SOLUTION VIA A VARIABLE ORDER ADAMS METHOD

NASA Technical Reports Server (NTRS)

Krogh, F. T.

1994-01-01

The SIVA/DIVA package is a collection of subroutines for the solution of ordinary differential equations. There are versions for single precision and double precision arithmetic. These solutions are applicable to stiff or nonstiff differential equations of first or second order. SIVA/DIVA requires fewer evaluations of derivatives than other variable order Adams predictor-corrector methods. There is an option for the direct integration of second order equations which can make integration of trajectory problems significantly more efficient. Other capabilities of SIVA/DIVA include: monitoring a user supplied function which can be separate from the derivative; dynamically controlling the step size; displaying or not displaying output at initial, final, and step size change points; saving the estimated local error; and reverse communication where subroutines return to the user for output or computation of derivatives instead of automatically performing calculations. The user must supply SIVA/DIVA with: 1) the number of equations; 2) initial values for the dependent and independent variables, integration stepsize, error tolerance, etc.; and 3) the driver program and operational parameters necessary for subroutine execution. SIVA/DIVA contains an extensive diagnostic message library should errors occur during execution. SIVA/DIVA is written in FORTRAN 77 for batch execution and is machine independent. It has a central memory requirement of approximately 120K of 8 bit bytes. This program was developed in 1983 and last updated in 1987.

Calculating radiotherapy margins based on Bayesian modelling of patient specific random errors

NASA Astrophysics Data System (ADS)

Herschtal, A.; te Marvelde, L.; Mengersen, K.; Hosseinifard, Z.; Foroudi, F.; Devereux, T.; Pham, D.; Ball, D.; Greer, P. B.; Pichler, P.; Eade, T.; Kneebone, A.; Bell, L.; Caine, H.; Hindson, B.; Kron, T.

2015-02-01

Collected real-life clinical target volume (CTV) displacement data show that some patients undergoing external beam radiotherapy (EBRT) demonstrate significantly more fraction-to-fraction variability in their displacement (‘random error’) than others. This contrasts with the common assumption made by historical recipes for margin estimation for EBRT, that the random error is constant across patients. In this work we present statistical models of CTV displacements in which random errors are characterised by an inverse gamma (IG) distribution in order to assess the impact of random error variability on CTV-to-PTV margin widths, for eight real world patient cohorts from four institutions, and for different sites of malignancy. We considered a variety of clinical treatment requirements and penumbral widths. The eight cohorts consisted of a total of 874 patients and 27 391 treatment sessions. Compared to a traditional margin recipe that assumes constant random errors across patients, for a typical 4 mm penumbral width, the IG based margin model mandates that in order to satisfy the common clinical requirement that 90% of patients receive at least 95% of prescribed RT dose to the entire CTV, margins be increased by a median of 10% (range over the eight cohorts -19% to +35%). This substantially reduces the proportion of patients for whom margins are too small to satisfy clinical requirements.

Scatter and veiling glare corrections for quantitative digital subtraction angiography

NASA Astrophysics Data System (ADS)

Ersahin, Atila; Molloi, Sabee Y.; Qian, Yao-Jin

1994-05-01

In order to quantitate anatomical and physiological parameters such as vessel dimensions and volumetric blood flow, it is necessary to make corrections for scatter and veiling glare (SVG), which are the major sources of nonlinearities in videodensitometric digital subtraction angiography (DSA). A convolution filtering technique has been investigated to estimate SVG distribution in DSA images without the need to sample the SVG for each patient. This technique utilizes exposure parameters and image gray levels to estimate SVG intensity by predicting the total thickness for every pixel in the image. At this point, corrections were also made for variation of SVG fraction with beam energy and field size. To test its ability to estimate SVG intensity, the correction technique was applied to images of a Lucite step phantom, anthropomorphic chest phantom, head phantom, and animal models at different thicknesses, projections, and beam energies. The root-mean-square (rms) percentage error of these estimates were obtained by comparison with direct SVG measurements made behind a lead strip. The average rms percentage errors in the SVG estimate for the 25 phantom studies and for the 17 animal studies were 6.22% and 7.96%, respectively. These results indicate that the SVG intensity can be estimated for a wide range of thicknesses, projections, and beam energies.

EQUILGAS: Program to estimate temperatures and in situ two-phase conditions in geothermal reservoirs using three combined FT-HSH gas equilibria models

NASA Astrophysics Data System (ADS)

Barragán, Rosa María; Núñez, José; Arellano, Víctor Manuel; Nieva, David

2016-03-01

Exploration and exploitation of geothermal resources require the estimation of important physical characteristics of reservoirs including temperatures, pressures and in situ two-phase conditions, in order to evaluate possible uses and/or investigate changes due to exploitation. As at relatively high temperatures (>150 °C) reservoir fluids usually attain chemical equilibrium in contact with hot rocks, different models based on the chemistry of fluids have been developed that allow deep conditions to be estimated. Currently either in water-dominated or steam-dominated reservoirs the chemistry of steam has been useful for working out reservoir conditions. In this context, three methods based on the Fischer-Tropsch (FT) and combined H2S-H2 (HSH) mineral-gas reactions have been developed for estimating temperatures and the quality of the in situ two-phase mixture prevailing in the reservoir. For these methods the mineral buffers considered to be controlling H2S-H2 composition of fluids are as follows. The pyrite-magnetite buffer (FT-HSH1); the pyrite-hematite buffer (FT-HSH2) and the pyrite-pyrrhotite buffer (FT-HSH3). Currently from such models the estimations of both, temperature and steam fraction in the two-phase fluid are obtained graphically by using a blank diagram with a background theoretical solution as reference. Thus large errors are involved since the isotherms are highly nonlinear functions while reservoir steam fractions are taken from a logarithmic scale. In order to facilitate the use of the three FT-HSH methods and minimize visual interpolation errors, the EQUILGAS program that numerically solves the equations of the FT-HSH methods was developed. In this work the FT-HSH methods and the EQUILGAS program are described. Illustrative examples for Mexican fields are also given in order to help the users in deciding which method could be more suitable for every specific data set.

The incidence and severity of errors in pharmacist-written discharge medication orders.

PubMed

Onatade, Raliat; Sawieres, Sara; Veck, Alexandra; Smith, Lindsay; Gore, Shivani; Al-Azeib, Sumiah

2017-08-01

Background Errors in discharge prescriptions are problematic. When hospital pharmacists write discharge prescriptions improvements are seen in the quality and efficiency of discharge. There is limited information on the incidence of errors in pharmacists' medication orders. Objective To investigate the extent and clinical significance of errors in pharmacist-written discharge medication orders. Setting 1000-bed teaching hospital in London, UK. Method Pharmacists in this London hospital routinely write discharge medication orders as part of the clinical pharmacy service. Convenient days, based on researcher availability, between October 2013 and January 2014 were selected. Pre-registration pharmacists reviewed all discharge medication orders written by pharmacists on these days and identified discrepancies between the medication history, inpatient chart, patient records and discharge summary. A senior clinical pharmacist confirmed the presence of an error. Each error was assigned a potential clinical significance rating (based on the NCCMERP scale) by a physician and an independent senior clinical pharmacist, working separately. Main outcome measure Incidence of errors in pharmacist-written discharge medication orders. Results 509 prescriptions, written by 51 pharmacists, containing 4258 discharge medication orders were assessed (8.4 orders per prescription). Ten prescriptions (2%), contained a total of ten erroneous orders (order error rate-0.2%). The pharmacist considered that one error had the potential to cause temporary harm (0.02% of all orders). The physician did not rate any of the errors with the potential to cause harm. Conclusion The incidence of errors in pharmacists' discharge medication orders was low. The quality, safety and policy implications of pharmacists routinely writing discharge medication orders should be further explored.

Adjusting for radiotelemetry error to improve estimates of habitat use.

Treesearch

Scott L. Findholt; Bruce K. Johnson; Lyman L. McDonald; John W. Kern; Alan Ager; Rosemary J. Stussy; Larry D. Bryant

2002-01-01

Animal locations estimated from radiotelemetry have traditionally been treated as error-free when analyzed in relation to habitat variables. Location error lowers the power of statistical tests of habitat selection. We describe a method that incorporates the error surrounding point estimates into measures of environmental variables determined from a geographic...

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.

Moisture Forecast Bias Correction in GEOS DAS

NASA Technical Reports Server (NTRS)

Dee, D.

1999-01-01

Data assimilation methods rely on numerous assumptions about the errors involved in measuring and forecasting atmospheric fields. One of the more disturbing of these is that short-term model forecasts are assumed to be unbiased. In case of atmospheric moisture, for example, observational evidence shows that the systematic component of errors in forecasts and analyses is often of the same order of magnitude as the random component. we have implemented a sequential algorithm for estimating forecast moisture bias from rawinsonde data in the Goddard Earth Observing System Data Assimilation System (GEOS DAS). The algorithm is designed to remove the systematic component of analysis errors and can be easily incorporated in an existing statistical data assimilation system. We will present results of initial experiments that show a significant reduction of bias in the GEOS DAS moisture analyses.

Kalman Filter for Mass Property and Thrust Identification (MMS)

NASA Technical Reports Server (NTRS)

Queen, Steven

2015-01-01

The Magnetospheric Multiscale (MMS) mission consists of four identically instrumented, spin-stabilized observatories, elliptically orbiting the Earth in a tetrahedron formation. For the operational success of the mission, on-board systems must be able to deliver high-precision orbital adjustment maneuvers. On MMS, this is accomplished using feedback from on-board star sensors in tandem with accelerometers whose measurements are dynamically corrected for errors associated with a spinning platform. In order to determine the required corrections to the measured acceleration, precise estimates of attitude, rate, and mass-properties is necessary. To this end, both an on-board and ground-based Multiplicative Extended Kalman Filter (MEKF) were formulated and implemented in order to estimate the dynamic and quasi-static properties of the spacecraft.

A comparison of reduced-order modelling techniques for application in hyperthermia control and estimation.

PubMed

Bailey, E A; Dutton, A W; Mattingly, M; Devasia, S; Roemer, R B

1998-01-01

Reduced-order modelling techniques can make important contributions in the control and state estimation of large systems. In hyperthermia, reduced-order modelling can provide a useful tool by which a large thermal model can be reduced to the most significant subset of its full-order modes, making real-time control and estimation possible. Two such reduction methods, one based on modal decomposition and the other on balanced realization, are compared in the context of simulated hyperthermia heat transfer problems. The results show that the modal decomposition reduction method has three significant advantages over that of balanced realization. First, modal decomposition reduced models result in less error, when compared to the full-order model, than balanced realization reduced models of similar order in problems with low or moderate advective heat transfer. Second, because the balanced realization based methods require a priori knowledge of the sensor and actuator placements, the reduced-order model is not robust to changes in sensor or actuator locations, a limitation not present in modal decomposition. Third, the modal decomposition transformation is less demanding computationally. On the other hand, in thermal problems dominated by advective heat transfer, numerical instabilities make modal decomposition based reduction problematic. Modal decomposition methods are therefore recommended for reduction of models in which advection is not dominant and research continues into methods to render balanced realization based reduction more suitable for real-time clinical hyperthermia control and estimation.

Accuracy assessment: The statistical approach to performance evaluation in LACIE. [Great Plains corridor, United States

NASA Technical Reports Server (NTRS)

Houston, A. G.; Feiveson, A. H.; Chhikara, R. S.; Hsu, E. M. (Principal Investigator)

1979-01-01

A statistical methodology was developed to check the accuracy of the products of the experimental operations throughout crop growth and to determine whether the procedures are adequate to accomplish the desired accuracy and reliability goals. It has allowed the identification and isolation of key problems in wheat area yield estimation, some of which have been corrected and some of which remain to be resolved. The major unresolved problem in accuracy assessment is that of precisely estimating the bias of the LACIE production estimator. Topics covered include: (1) evaluation techniques; (2) variance and bias estimation for the wheat production estimate; (3) the 90/90 evaluation; (4) comparison of the LACIE estimate with reference standards; and (5) first and second order error source investigations.

Systematic review of statistical approaches to quantify, or correct for, measurement error in a continuous exposure in nutritional epidemiology.

PubMed

Bennett, Derrick A; Landry, Denise; Little, Julian; Minelli, Cosetta

2017-09-19

Several statistical approaches have been proposed to assess and correct for exposure measurement error. We aimed to provide a critical overview of the most common approaches used in nutritional epidemiology. MEDLINE, EMBASE, BIOSIS and CINAHL were searched for reports published in English up to May 2016 in order to ascertain studies that described methods aimed to quantify and/or correct for measurement error for a continuous exposure in nutritional epidemiology using a calibration study. We identified 126 studies, 43 of which described statistical methods and 83 that applied any of these methods to a real dataset. The statistical approaches in the eligible studies were grouped into: a) approaches to quantify the relationship between different dietary assessment instruments and "true intake", which were mostly based on correlation analysis and the method of triads; b) approaches to adjust point and interval estimates of diet-disease associations for measurement error, mostly based on regression calibration analysis and its extensions. Two approaches (multiple imputation and moment reconstruction) were identified that can deal with differential measurement error. For regression calibration, the most common approach to correct for measurement error used in nutritional epidemiology, it is crucial to ensure that its assumptions and requirements are fully met. Analyses that investigate the impact of departures from the classical measurement error model on regression calibration estimates can be helpful to researchers in interpreting their findings. With regard to the possible use of alternative methods when regression calibration is not appropriate, the choice of method should depend on the measurement error model assumed, the availability of suitable calibration study data and the potential for bias due to violation of the classical measurement error model assumptions. On the basis of this review, we provide some practical advice for the use of methods to assess and adjust for measurement error in nutritional epidemiology.

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.

Videopanorama Frame Rate Requirements Derived from Visual Discrimination of Deceleration During Simulated Aircraft Landing

NASA Technical Reports Server (NTRS)

Furnstenau, Norbert; Ellis, Stephen R.

2015-01-01

In order to determine the required visual frame rate (FR) for minimizing prediction errors with out-the-window video displays at remote/virtual airport towers, thirteen active air traffic controllers viewed high dynamic fidelity simulations of landing aircraft and decided whether aircraft would stop as if to be able to make a turnoff or whether a runway excursion would be expected. The viewing conditions and simulation dynamics replicated visual rates and environments of transport aircraft landing at small commercial airports. The required frame rate was estimated using Bayes inference on prediction errors by linear FRextrapolation of event probabilities conditional on predictions (stop, no-stop). Furthermore estimates were obtained from exponential model fits to the parametric and non-parametric perceptual discriminabilities d' and A (average area under ROC-curves) as dependent on FR. Decision errors are biased towards preference of overshoot and appear due to illusionary increase in speed at low frames rates. Both Bayes and A - extrapolations yield a framerate requirement of 35 < FRmin < 40 Hz. When comparing with published results [12] on shooter game scores the model based d'(FR)-extrapolation exhibits the best agreement and indicates even higher FRmin > 40 Hz for minimizing decision errors. Definitive recommendations require further experiments with FR > 30 Hz.

ClonEvol: clonal ordering and visualization in cancer sequencing.

PubMed

Dang, H X; White, B S; Foltz, S M; Miller, C A; Luo, J; Fields, R C; Maher, C A

2017-12-01

Reconstruction of clonal evolution is critical for understanding tumor progression and implementing personalized therapies. This is often done by clustering somatic variants based on their cellular prevalence estimated via bulk tumor sequencing of multiple samples. The clusters, consisting of the clonal marker variants, are then ordered based on their estimated cellular prevalence to reconstruct clonal evolution trees, a process referred to as 'clonal ordering'. However, cellular prevalence estimate is confounded by statistical variability and errors in sequencing/data analysis, and therefore inhibits accurate reconstruction of the clonal evolution. This problem is further complicated by intra- and inter-tumor heterogeneity. Furthermore, the field lacks a comprehensive visualization tool to facilitate the interpretation of complex clonal relationships. To address these challenges we developed ClonEvol, a unified software tool for clonal ordering, visualization, and interpretation. ClonEvol uses a bootstrap resampling technique to estimate the cellular fraction of the clones and probabilistically models the clonal ordering constraints to account for statistical variability. The bootstrapping allows identification of the sample founding- and sub-clones, thus enabling interpretation of clonal seeding. ClonEvol automates the generation of multiple widely used visualizations for reconstructing and interpreting clonal evolution. ClonEvol outperformed three of the state of the art tools (LICHeE, Canopy and PhyloWGS) for clonal evolution inference, showing more robust error tolerance and producing more accurate trees in a simulation. Building upon multiple recent publications that utilized ClonEvol to study metastasis and drug resistance in solid cancers, here we show that ClonEvol rediscovered relapsed subclones in two published acute myeloid leukemia patients. Furthermore, we demonstrated that through noninvasive monitoring ClonEvol recapitulated the emerging subclones throughout metastatic progression observed in the tumors of a published breast cancer patient. ClonEvol has broad applicability for longitudinal monitoring of clonal populations in tumor biopsies, or noninvasively, to guide precision medicine. ClonEvol is written in R and is available at https://github.com/ChrisMaherLab/ClonEvol. © The Author 2017. Published by Oxford University Press on behalf of the European Society for Medical Oncology. All rights reserved. For permissions, please email: journals.permissions@oup.com.

A new method for distortion magnetic field compensation of a geomagnetic vector measurement system

NASA Astrophysics Data System (ADS)

Liu, Zhongyan; Pan, Mengchun; Tang, Ying; Zhang, Qi; Geng, Yunling; Wan, Chengbiao; Chen, Dixiang; Tian, Wugang

2016-12-01

The geomagnetic vector measurement system mainly consists of three-axis magnetometer and an INS (inertial navigation system), which have many ferromagnetic parts on them. The magnetometer is always distorted by ferromagnetic parts and other electric equipments such as INS and power circuit module within the system, which can lead to geomagnetic vector measurement error of thousands of nT. Thus, the geomagnetic vector measurement system has to be compensated in order to guarantee the measurement accuracy. In this paper, a new distortion magnetic field compensation method is proposed, in which a permanent magnet with different relative positions is used to change the ambient magnetic field to construct equations of the error model parameters, and the parameters can be accurately estimated by solving linear equations. In order to verify effectiveness of the proposed method, the experiment is conducted, and the results demonstrate that, after compensation, the components errors of measured geomagnetic field are reduced significantly. It demonstrates that the proposed method can effectively improve the accuracy of the geomagnetic vector measurement system.

Statistical design and analysis for plant cover studies with multiple sources of observation errors

USGS Publications Warehouse

Wright, Wilson; Irvine, Kathryn M.; Warren, Jeffrey M .; Barnett, Jenny K.

2017-01-01

Effective wildlife habitat management and conservation requires understanding the factors influencing distribution and abundance of plant species. Field studies, however, have documented observation errors in visually estimated plant cover including measurements which differ from the true value (measurement error) and not observing a species that is present within a plot (detection error). Unlike the rapid expansion of occupancy and N-mixture models for analysing wildlife surveys, development of statistical models accounting for observation error in plants has not progressed quickly. Our work informs development of a monitoring protocol for managed wetlands within the National Wildlife Refuge System.Zero-augmented beta (ZAB) regression is the most suitable method for analysing areal plant cover recorded as a continuous proportion but assumes no observation errors. We present a model extension that explicitly includes the observation process thereby accounting for both measurement and detection errors. Using simulations, we compare our approach to a ZAB regression that ignores observation errors (naïve model) and an “ad hoc” approach using a composite of multiple observations per plot within the naïve model. We explore how sample size and within-season revisit design affect the ability to detect a change in mean plant cover between 2 years using our model.Explicitly modelling the observation process within our framework produced unbiased estimates and nominal coverage of model parameters. The naïve and “ad hoc” approaches resulted in underestimation of occurrence and overestimation of mean cover. The degree of bias was primarily driven by imperfect detection and its relationship with cover within a plot. Conversely, measurement error had minimal impacts on inferences. We found >30 plots with at least three within-season revisits achieved reasonable posterior probabilities for assessing change in mean plant cover.For rapid adoption and application, code for Bayesian estimation of our single-species ZAB with errors model is included. Practitioners utilizing our R-based simulation code can explore trade-offs among different survey efforts and parameter values, as we did, but tuned to their own investigation. Less abundant plant species of high ecological interest may warrant the additional cost of gathering multiple independent observations in order to guard against erroneous conclusions.

On the Estimation of Errors in Sparse Bathymetric Geophysical Data Sets

NASA Astrophysics Data System (ADS)

Jakobsson, M.; Calder, B.; Mayer, L.; Armstrong, A.

2001-05-01

There is a growing demand in the geophysical community for better regional representations of the world ocean's bathymetry. However, given the vastness of the oceans and the relative limited coverage of even the most modern mapping systems, it is likely that many of the older data sets will remain part of our cumulative database for several more decades. Therefore, regional bathymetrical compilations that are based on a mixture of historic and contemporary data sets will have to remain the standard. This raises the problem of assembling bathymetric compilations and utilizing data sets not only with a heterogeneous cover but also with a wide range of accuracies. In combining these data to regularly spaced grids of bathymetric values, which the majority of numerical procedures in earth sciences require, we are often forced to use a complex interpolation scheme due to the sparseness and irregularity of the input data points. Consequently, we are faced with the difficult task of assessing the confidence that we can assign to the final grid product, a task that is not usually addressed in most bathymetric compilations. We approach the problem of assessing the confidence via a direct-simulation Monte Carlo method. We start with a small subset of data from the International Bathymetric Chart of the Arctic Ocean (IBCAO) grid model [Jakobsson et al., 2000]. This grid is compiled from a mixture of data sources ranging from single beam soundings with available metadata to spot soundings with no available metadata, to digitized contours; the test dataset shows examples of all of these types. From this database, we assign a priori error variances based on available meta-data, and when this is not available, based on a worst-case scenario in an essentially heuristic manner. We then generate a number of synthetic datasets by randomly perturbing the base data using normally distributed random variates, scaled according to the predicted error model. These datasets are then re-gridded using the same methodology as the original product, generating a set of plausible grid models of the regional bathymetry that we can use for standard error estimates. Finally, we repeat the entire random estimation process and analyze each run's standard error grids in order to examine sampling bias and variance in the predictions. The final products of the estimation are a collection of standard error grids, which we combine with the source data density in order to create a grid that contains information about the bathymetry model's reliability. Jakobsson, M., Cherkis, N., Woodward, J., Coakley, B., and Macnab, R., 2000, A new grid of Arctic bathymetry: A significant resource for scientists and mapmakers, EOS Transactions, American Geophysical Union, v. 81, no. 9, p. 89, 93, 96.

Using EHR Data to Detect Prescribing Errors in Rapidly Discontinued Medication Orders.

PubMed

Burlison, Jonathan D; McDaniel, Robert B; Baker, Donald K; Hasan, Murad; Robertson, Jennifer J; Howard, Scott C; Hoffman, James M

2018-01-01

Previous research developed a new method for locating prescribing errors in rapidly discontinued electronic medication orders. Although effective, the prospective design of that research hinders its feasibility for regular use. Our objectives were to assess a method to retrospectively detect prescribing errors, to characterize the identified errors, and to identify potential improvement opportunities. Electronically submitted medication orders from 28 randomly selected days that were discontinued within 120 minutes of submission were reviewed and categorized as most likely errors, nonerrors, or not enough information to determine status. Identified errors were evaluated by amount of time elapsed from original submission to discontinuation, error type, staff position, and potential clinical significance. Pearson's chi-square test was used to compare rates of errors across prescriber types. In all, 147 errors were identified in 305 medication orders. The method was most effective for orders that were discontinued within 90 minutes. Duplicate orders were most common; physicians in training had the highest error rate ( p  < 0.001), and 24 errors were potentially clinically significant. None of the errors were voluntarily reported. It is possible to identify prescribing errors in rapidly discontinued medication orders by using retrospective methods that do not require interrupting prescribers to discuss order details. Future research could validate our methods in different clinical settings. Regular use of this measure could help determine the causes of prescribing errors, track performance, and identify and evaluate interventions to improve prescribing systems and processes. Schattauer GmbH Stuttgart.

Imaging and dosimetric errors in 4D PET/CT-guided radiotherapy from patient-specific respiratory patterns: a dynamic motion phantom end-to-end study

NASA Astrophysics Data System (ADS)

Bowen, S. R.; Nyflot, M. J.; Herrmann, C.; Groh, C. M.; Meyer, J.; Wollenweber, S. D.; Stearns, C. W.; Kinahan, P. E.; Sandison, G. A.

2015-05-01

Effective positron emission tomography / computed tomography (PET/CT) guidance in radiotherapy of lung cancer requires estimation and mitigation of errors due to respiratory motion. An end-to-end workflow was developed to measure patient-specific motion-induced uncertainties in imaging, treatment planning, and radiation delivery with respiratory motion phantoms and dosimeters. A custom torso phantom with inserts mimicking normal lung tissue and lung lesion was filled with [18F]FDG. The lung lesion insert was driven by six different patient-specific respiratory patterns or kept stationary. PET/CT images were acquired under motionless ground truth, tidal breathing motion-averaged (3D), and respiratory phase-correlated (4D) conditions. Target volumes were estimated by standardized uptake value (SUV) thresholds that accurately defined the ground-truth lesion volume. Non-uniform dose-painting plans using volumetrically modulated arc therapy were optimized for fixed normal lung and spinal cord objectives and variable PET-based target objectives. Resulting plans were delivered to a cylindrical diode array at rest, in motion on a platform driven by the same respiratory patterns (3D), or motion-compensated by a robotic couch with an infrared camera tracking system (4D). Errors were estimated relative to the static ground truth condition for mean target-to-background (T/Bmean) ratios, target volumes, planned equivalent uniform target doses, and 2%-2 mm gamma delivery passing rates. Relative to motionless ground truth conditions, PET/CT imaging errors were on the order of 10-20%, treatment planning errors were 5-10%, and treatment delivery errors were 5-30% without motion compensation. Errors from residual motion following compensation methods were reduced to 5-10% in PET/CT imaging, <5% in treatment planning, and <2% in treatment delivery. We have demonstrated that estimation of respiratory motion uncertainty and its propagation from PET/CT imaging to RT planning, and RT delivery under a dose painting paradigm is feasible within an integrated respiratory motion phantom workflow. For a limited set of cases, the magnitude of errors was comparable during PET/CT imaging and treatment delivery without motion compensation. Errors were moderately mitigated during PET/CT imaging and significantly mitigated during RT delivery with motion compensation. This dynamic motion phantom end-to-end workflow provides a method for quality assurance of 4D PET/CT-guided radiotherapy, including evaluation of respiratory motion compensation methods during imaging and treatment delivery.

Imaging and dosimetric errors in 4D PET/CT-guided radiotherapy from patient-specific respiratory patterns: a dynamic motion phantom end-to-end study.

PubMed

Bowen, S R; Nyflot, M J; Herrmann, C; Groh, C M; Meyer, J; Wollenweber, S D; Stearns, C W; Kinahan, P E; Sandison, G A

2015-05-07

Effective positron emission tomography / computed tomography (PET/CT) guidance in radiotherapy of lung cancer requires estimation and mitigation of errors due to respiratory motion. An end-to-end workflow was developed to measure patient-specific motion-induced uncertainties in imaging, treatment planning, and radiation delivery with respiratory motion phantoms and dosimeters. A custom torso phantom with inserts mimicking normal lung tissue and lung lesion was filled with [(18)F]FDG. The lung lesion insert was driven by six different patient-specific respiratory patterns or kept stationary. PET/CT images were acquired under motionless ground truth, tidal breathing motion-averaged (3D), and respiratory phase-correlated (4D) conditions. Target volumes were estimated by standardized uptake value (SUV) thresholds that accurately defined the ground-truth lesion volume. Non-uniform dose-painting plans using volumetrically modulated arc therapy were optimized for fixed normal lung and spinal cord objectives and variable PET-based target objectives. Resulting plans were delivered to a cylindrical diode array at rest, in motion on a platform driven by the same respiratory patterns (3D), or motion-compensated by a robotic couch with an infrared camera tracking system (4D). Errors were estimated relative to the static ground truth condition for mean target-to-background (T/Bmean) ratios, target volumes, planned equivalent uniform target doses, and 2%-2 mm gamma delivery passing rates. Relative to motionless ground truth conditions, PET/CT imaging errors were on the order of 10-20%, treatment planning errors were 5-10%, and treatment delivery errors were 5-30% without motion compensation. Errors from residual motion following compensation methods were reduced to 5-10% in PET/CT imaging, <5% in treatment planning, and <2% in treatment delivery. We have demonstrated that estimation of respiratory motion uncertainty and its propagation from PET/CT imaging to RT planning, and RT delivery under a dose painting paradigm is feasible within an integrated respiratory motion phantom workflow. For a limited set of cases, the magnitude of errors was comparable during PET/CT imaging and treatment delivery without motion compensation. Errors were moderately mitigated during PET/CT imaging and significantly mitigated during RT delivery with motion compensation. This dynamic motion phantom end-to-end workflow provides a method for quality assurance of 4D PET/CT-guided radiotherapy, including evaluation of respiratory motion compensation methods during imaging and treatment delivery.

Imaging and dosimetric errors in 4D PET/CT-guided radiotherapy from patient-specific respiratory patterns: a dynamic motion phantom end-to-end study

PubMed Central

Bowen, S R; Nyflot, M J; Hermann, C; Groh, C; Meyer, J; Wollenweber, S D; Stearns, C W; Kinahan, P E; Sandison, G A

2015-01-01

Effective positron emission tomography/computed tomography (PET/CT) guidance in radiotherapy of lung cancer requires estimation and mitigation of errors due to respiratory motion. An end-to-end workflow was developed to measure patient-specific motion-induced uncertainties in imaging, treatment planning, and radiation delivery with respiratory motion phantoms and dosimeters. A custom torso phantom with inserts mimicking normal lung tissue and lung lesion was filled with [18F]FDG. The lung lesion insert was driven by 6 different patient-specific respiratory patterns or kept stationary. PET/CT images were acquired under motionless ground truth, tidal breathing motion-averaged (3D), and respiratory phase-correlated (4D) conditions. Target volumes were estimated by standardized uptake value (SUV) thresholds that accurately defined the ground-truth lesion volume. Non-uniform dose-painting plans using volumetrically modulated arc therapy (VMAT) were optimized for fixed normal lung and spinal cord objectives and variable PET-based target objectives. Resulting plans were delivered to a cylindrical diode array at rest, in motion on a platform driven by the same respiratory patterns (3D), or motion-compensated by a robotic couch with an infrared camera tracking system (4D). Errors were estimated relative to the static ground truth condition for mean target-to-background (T/Bmean) ratios, target volumes, planned equivalent uniform target doses (EUD), and 2%-2mm gamma delivery passing rates. Relative to motionless ground truth conditions, PET/CT imaging errors were on the order of 10–20%, treatment planning errors were 5–10%, and treatment delivery errors were 5–30% without motion compensation. Errors from residual motion following compensation methods were reduced to 5–10% in PET/CT imaging, < 5% in treatment planning, and < 2% in treatment delivery. We have demonstrated that estimation of respiratory motion uncertainty and its propagation from PET/CT imaging to RT planning, and RT delivery under a dose painting paradigm is feasible within an integrated respiratory motion phantom workflow. For a limited set of cases, the magnitude of errors was comparable during PET/CT imaging and treatment delivery without motion compensation. Errors were moderately mitigated during PET/CT imaging and significantly mitigated during RT delivery with motion compensation. This dynamic motion phantom end-to-end workflow provides a method for quality assurance of 4D PET/CT-guided radiotherapy, including evaluation of respiratory motion compensation methods during imaging and treatment delivery. PMID:25884892

Software for Quantifying and Simulating Microsatellite Genotyping Error

PubMed Central

Johnson, Paul C.D.; Haydon, Daniel T.

2007-01-01

Microsatellite genetic marker data are exploited in a variety of fields, including forensics, gene mapping, kinship inference and population genetics. In all of these fields, inference can be thwarted by failure to quantify and account for data errors, and kinship inference in particular can benefit from separating errors into two distinct classes: allelic dropout and false alleles. Pedant is MS Windows software for estimating locus-specific maximum likelihood rates of these two classes of error. Estimation is based on comparison of duplicate error-prone genotypes: neither reference genotypes nor pedigree data are required. Other functions include: plotting of error rate estimates and confidence intervals; simulations for performing power analysis and for testing the robustness of error rate estimates to violation of the underlying assumptions; and estimation of expected heterozygosity, which is a required input. The program, documentation and source code are available from http://www.stats.gla.ac.uk/~paulj/pedant.html. PMID:20066126

hp-Adaptive time integration based on the BDF for viscous flows

NASA Astrophysics Data System (ADS)

Hay, A.; Etienne, S.; Pelletier, D.; Garon, A.

2015-06-01

This paper presents a procedure based on the Backward Differentiation Formulas of order 1 to 5 to obtain efficient time integration of the incompressible Navier-Stokes equations. The adaptive algorithm performs both stepsize and order selections to control respectively the solution accuracy and the computational efficiency of the time integration process. The stepsize selection (h-adaptivity) is based on a local error estimate and an error controller to guarantee that the numerical solution accuracy is within a user prescribed tolerance. The order selection (p-adaptivity) relies on the idea that low-accuracy solutions can be computed efficiently by low order time integrators while accurate solutions require high order time integrators to keep computational time low. The selection is based on a stability test that detects growing numerical noise and deems a method of order p stable if there is no method of lower order that delivers the same solution accuracy for a larger stepsize. Hence, it guarantees both that (1) the used method of integration operates inside of its stability region and (2) the time integration procedure is computationally efficient. The proposed time integration procedure also features a time-step rejection and quarantine mechanisms, a modified Newton method with a predictor and dense output techniques to compute solution at off-step points.

Adjoint-Based, Three-Dimensional Error Prediction and Grid Adaptation

NASA Technical Reports Server (NTRS)

Park, Michael A.

2002-01-01

Engineering computational fluid dynamics (CFD) analysis and design applications focus on output functions (e.g., lift, drag). Errors in these output functions are generally unknown and conservatively accurate solutions may be computed. Computable error estimates can offer the possibility to minimize computational work for a prescribed error tolerance. Such an estimate can be computed by solving the flow equations and the linear adjoint problem for the functional of interest. The computational mesh can be modified to minimize the uncertainty of a computed error estimate. This robust mesh-adaptation procedure automatically terminates when the simulation is within a user specified error tolerance. This procedure for estimating and adapting to error in a functional is demonstrated for three-dimensional Euler problems. An adaptive mesh procedure that links to a Computer Aided Design (CAD) surface representation is demonstrated for wing, wing-body, and extruded high lift airfoil configurations. The error estimation and adaptation procedure yielded corrected functions that are as accurate as functions calculated on uniformly refined grids with ten times as many grid points.

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.

Crack Turning and Arrest Mechanisms for Integral Structure

NASA Technical Reports Server (NTRS)

Pettit, Richard; Ingraffea, Anthony

1999-01-01

In the course of several years of research efforts to predict crack turning and flapping in aircraft fuselage structures and other problems related to crack turning, the 2nd order maximum tangential stress theory has been identified as the theory most capable of predicting the observed test results. This theory requires knowledge of a material specific characteristic length, and also a computation of the stress intensity factors and the T-stress, or second order term in the asymptotic stress field in the vicinity of the crack tip. A characteristic length, r(sub c), is proposed for ductile materials pertaining to the onset of plastic instability, as opposed to the void spacing theories espoused by previous investigators. For the plane stress case, an approximate estimate of r(sub c), is obtained from the asymptotic field for strain hardening materials given by Hutchinson, Rice and Rosengren (HRR). A previous study using of high order finite element methods to calculate T-stresses by contour integrals resulted in extremely high accuracy values obtained for selected test specimen geometries, and a theoretical error estimation parameter was defined. In the present study, it is shown that a large portion of the error in finite element computations of both K and T are systematic, and can be corrected after the initial solution if the finite element implementation utilizes a similar crack tip discretization scheme for all problems. This scheme is applied for two-dimensional problems to a both a p-version finite element code, showing that sufficiently accurate values of both K(sub I) and T can be obtained with fairly low order elements if correction is used. T-stress correction coefficients are also developed for the singular crack tip rosette utilized in the adaptive mesh finite element code FRANC2D, and shown to reduce the error in the computed T-stress significantly. Stress intensity factor correction was not attempted for FRANC2D because it employs a highly accurate quarter-point scheme to obtain stress intensity factors.

The impact of reflectivity correction and conversion methods to improve precipitation estimation by weather radar for an extreme low-land Mesoscale Convective System

NASA Astrophysics Data System (ADS)

Hazenberg, Pieter; Leijnse, Hidde; Uijlenhoet, Remko

2014-05-01

Between 25 and 27 August 2010 a long-duration mesoscale convective system was observed above the Netherlands. For most of the country this led to over 15 hours of near-continuous precipitation, which resulted in total event accumulations exceeding 150 mm in the eastern part of the Netherlands. Such accumulations belong to the largest sums ever recorded in this country and gave rise to local flooding. Measuring precipitation by weather radar within such mesoscale convective systems is known to be a challenge, since measurements are affected by multiple sources of error. For the current event the operational weather radar rainfall product only estimated about 30% of the actual amount of precipitation as measured by rain gauges. In the current presentation we will try to identify what gave rise to such large underestimations. In general weather radar measurement errors can be subdivided into two different groups: 1) errors affecting the volumetric reflectivity measurements taken, and 2) errors related to the conversion of reflectivity values in rainfall intensity and attenuation estimates. To correct for the first group of errors, the quality of the weather radar reflectivity data was improved by successively correcting for 1) clutter and anomalous propagation, 2) radar calibration, 3) wet radome attenuation, 4) signal attenuation and 5) the vertical profile of reflectivity. Such consistent corrections are generally not performed by operational meteorological services. Results show a large improvement in the quality of the precipitation data, however still only ~65% of the actual observed accumulations was estimated. To further improve the quality of the precipitation estimates, the second group of errors are corrected for by making use of disdrometer measurements taken in close vicinity of the radar. Based on these data the parameters of a normalized drop size distribution are estimated for the total event as well as for each precipitation type separately (convective, stratiform and undefined). These are then used to obtain coherent parameter sets for the radar reflectivity-rainfall rate (Z-R) and radar reflectivity-attenuation (Z-k) relationship, specifically applicable for this event. By applying a single parameter set to correct for both sources of errors, the quality of the rainfall product improves further, leading to >80% of the observed accumulations. However, by differentiating between precipitation type no better results are obtained as when using the operational relationships. This leads to the question: how representative are local disdrometer observations to correct large scale weather radar measurements? In order to tackle this question a Monte Carlo approach was used to generate >10000 sets of the normalized dropsize distribution parameters and to assess their impact on the estimated precipitation amounts. Results show that a large number of parameter sets result in improved precipitation estimated by the weather radar closely resembling observations. However, these optimal sets vary considerably as compared to those obtained from the local disdrometer measurements.

Correlation- and covariance-supported normalization method for estimating orthodontic trainer treatment for clenching activity.

PubMed

Akdenur, B; Okkesum, S; Kara, S; Günes, S

2009-11-01

In this study, electromyography signals sampled from children undergoing orthodontic treatment were used to estimate the effect of an orthodontic trainer on the anterior temporal muscle. A novel data normalization method, called the correlation- and covariance-supported normalization method (CCSNM), based on correlation and covariance between features in a data set, is proposed to provide predictive guidance to the orthodontic technique. The method was tested in two stages: first, data normalization using the CCSNM; second, prediction of normalized values of anterior temporal muscles using an artificial neural network (ANN) with a Levenberg-Marquardt learning algorithm. The data set consists of electromyography signals from right anterior temporal muscles, recorded from 20 children aged 8-13 years with class II malocclusion. The signals were recorded at the start and end of a 6-month treatment. In order to train and test the ANN, two-fold cross-validation was used. The CCSNM was compared with four normalization methods: minimum-maximum normalization, z score, decimal scaling, and line base normalization. In order to demonstrate the performance of the proposed method, prevalent performance-measuring methods, and the mean square error and mean absolute error as mathematical methods, the statistical relation factor R2 and the average deviation have been examined. The results show that the CCSNM was the best normalization method among other normalization methods for estimating the effect of the trainer.

Model-based spectral estimation of Doppler signals using parallel genetic algorithms.

PubMed

Solano González, J; Rodríguez Vázquez, K; García Nocetti, D F

2000-05-01

Conventional spectral analysis methods use a fast Fourier transform (FFT) on consecutive or overlapping windowed data segments. For Doppler ultrasound signals, this approach suffers from an inadequate frequency resolution due to the time segment duration and the non-stationarity characteristics of the signals. Parametric or model-based estimators can give significant improvements in the time-frequency resolution at the expense of a higher computational complexity. This work describes an approach which implements in real-time a parametric spectral estimator method using genetic algorithms (GAs) in order to find the optimum set of parameters for the adaptive filter that minimises the error function. The aim is to reduce the computational complexity of the conventional algorithm by using the simplicity associated to GAs and exploiting its parallel characteristics. This will allow the implementation of higher order filters, increasing the spectrum resolution, and opening a greater scope for using more complex methods.

Evaluation of selection index: application to the choice of an indirect multitrait selection index for soybean breeding.

PubMed

Bouchez, A; Goffinet, B

1990-02-01

Selection indices can be used to predict one trait from information available on several traits in order to improve the prediction accuracy. Plant or animal breeders are interested in selecting only the best individuals, and need to compare the efficiency of different trait combinations in order to choose the index ensuring the best prediction quality for individual values. As the usual tools for index evaluation do not remain unbiased in all cases, we propose a robust way of evaluation by means of an estimator of the mean-square error of prediction (EMSEP). This estimator remains valid even when parameters are not known, as usually assumed, but are estimated. EMSEP is applied to the choice of an indirect multitrait selection index at the F5 generation of a classical breeding scheme for soybeans. Best predictions for precocity are obtained by means of indices using only part of the available information.

Trends and Correlation Estimation in Climate Sciences: Effects of Timescale Errors

NASA Astrophysics Data System (ADS)

Mudelsee, M.; Bermejo, M. A.; Bickert, T.; Chirila, D.; Fohlmeister, J.; Köhler, P.; Lohmann, G.; Olafsdottir, K.; Scholz, D.

2012-12-01

Trend describes time-dependence in the first moment of a stochastic process, and correlation measures the linear relation between two random variables. Accurately estimating the trend and correlation, including uncertainties, from climate time series data in the uni- and bivariate domain, respectively, allows first-order insights into the geophysical process that generated the data. Timescale errors, ubiquitious in paleoclimatology, where archives are sampled for proxy measurements and dated, poses a problem to the estimation. Statistical science and the various applied research fields, including geophysics, have almost completely ignored this problem due to its theoretical almost-intractability. However, computational adaptations or replacements of traditional error formulas have become technically feasible. This contribution gives a short overview of such an adaptation package, bootstrap resampling combined with parametric timescale simulation. We study linear regression, parametric change-point models and nonparametric smoothing for trend estimation. We introduce pairwise-moving block bootstrap resampling for correlation estimation. Both methods share robustness against autocorrelation and non-Gaussian distributional shape. We shortly touch computing-intensive calibration of bootstrap confidence intervals and consider options to parallelize the related computer code. Following examples serve not only to illustrate the methods but tell own climate stories: (1) the search for climate drivers of the Agulhas Current on recent timescales, (2) the comparison of three stalagmite-based proxy series of regional, western German climate over the later part of the Holocene, and (3) trends and transitions in benthic oxygen isotope time series from the Cenozoic. Financial support by Deutsche Forschungsgemeinschaft (FOR 668, FOR 1070, MU 1595/4-1) and the European Commission (MC ITN 238512, MC ITN 289447) is acknowledged.

Travel Time Estimation Using Freeway Point Detector Data Based on Evolving Fuzzy Neural Inference System.

PubMed

Tang, Jinjun; Zou, Yajie; Ash, John; Zhang, Shen; Liu, Fang; Wang, Yinhai

2016-01-01

Travel time is an important measurement used to evaluate the extent of congestion within road networks. This paper presents a new method to estimate the travel time based on an evolving fuzzy neural inference system. The input variables in the system are traffic flow data (volume, occupancy, and speed) collected from loop detectors located at points both upstream and downstream of a given link, and the output variable is the link travel time. A first order Takagi-Sugeno fuzzy rule set is used to complete the inference. For training the evolving fuzzy neural network (EFNN), two learning processes are proposed: (1) a K-means method is employed to partition input samples into different clusters, and a Gaussian fuzzy membership function is designed for each cluster to measure the membership degree of samples to the cluster centers. As the number of input samples increases, the cluster centers are modified and membership functions are also updated; (2) a weighted recursive least squares estimator is used to optimize the parameters of the linear functions in the Takagi-Sugeno type fuzzy rules. Testing datasets consisting of actual and simulated data are used to test the proposed method. Three common criteria including mean absolute error (MAE), root mean square error (RMSE), and mean absolute relative error (MARE) are utilized to evaluate the estimation performance. Estimation results demonstrate the accuracy and effectiveness of the EFNN method through comparison with existing methods including: multiple linear regression (MLR), instantaneous model (IM), linear model (LM), neural network (NN), and cumulative plots (CP).

Travel Time Estimation Using Freeway Point Detector Data Based on Evolving Fuzzy Neural Inference System

PubMed Central

Tang, Jinjun; Zou, Yajie; Ash, John; Zhang, Shen; Liu, Fang; Wang, Yinhai

2016-01-01

Travel time is an important measurement used to evaluate the extent of congestion within road networks. This paper presents a new method to estimate the travel time based on an evolving fuzzy neural inference system. The input variables in the system are traffic flow data (volume, occupancy, and speed) collected from loop detectors located at points both upstream and downstream of a given link, and the output variable is the link travel time. A first order Takagi-Sugeno fuzzy rule set is used to complete the inference. For training the evolving fuzzy neural network (EFNN), two learning processes are proposed: (1) a K-means method is employed to partition input samples into different clusters, and a Gaussian fuzzy membership function is designed for each cluster to measure the membership degree of samples to the cluster centers. As the number of input samples increases, the cluster centers are modified and membership functions are also updated; (2) a weighted recursive least squares estimator is used to optimize the parameters of the linear functions in the Takagi-Sugeno type fuzzy rules. Testing datasets consisting of actual and simulated data are used to test the proposed method. Three common criteria including mean absolute error (MAE), root mean square error (RMSE), and mean absolute relative error (MARE) are utilized to evaluate the estimation performance. Estimation results demonstrate the accuracy and effectiveness of the EFNN method through comparison with existing methods including: multiple linear regression (MLR), instantaneous model (IM), linear model (LM), neural network (NN), and cumulative plots (CP). PMID:26829639

Investigation of Primary Mirror Segment's Residual Errors for the Thirty Meter Telescope

NASA Technical Reports Server (NTRS)

Seo, Byoung-Joon; Nissly, Carl; Angeli, George; MacMynowski, Doug; Sigrist, Norbert; Troy, Mitchell; Williams, Eric

2009-01-01

The primary mirror segment aberrations after shape corrections with warping harness have been identified as the single largest error term in the Thirty Meter Telescope (TMT) image quality error budget. In order to better understand the likely errors and how they will impact the telescope performance we have performed detailed simulations. We first generated unwarped primary mirror segment surface shapes that met TMT specifications. Then we used the predicted warping harness influence functions and a Shack-Hartmann wavefront sensor model to determine estimates for the 492 corrected segment surfaces that make up the TMT primary mirror. Surface and control parameters, as well as the number of subapertures were varied to explore the parameter space. The corrected segment shapes were then passed to an optical TMT model built using the Jet Propulsion Laboratory (JPL) developed Modeling and Analysis for Controlled Optical Systems (MACOS) ray-trace simulator. The generated exit pupil wavefront error maps provided RMS wavefront error and image-plane characteristics like the Normalized Point Source Sensitivity (PSSN). The results have been used to optimize the segment shape correction and wavefront sensor designs as well as provide input to the TMT systems engineering error budgets.

Intrinsic errors in transporting a single-spin qubit through a double quantum dot

NASA Astrophysics Data System (ADS)

Li, Xiao; Barnes, Edwin; Kestner, J. P.; Das Sarma, S.

2017-07-01

Coherent spatial transport or shuttling of a single electron spin through semiconductor nanostructures is an important ingredient in many spintronic and quantum computing applications. In this work we analyze the possible errors in solid-state quantum computation due to leakage in transporting a single-spin qubit through a semiconductor double quantum dot. In particular, we consider three possible sources of leakage errors associated with such transport: finite ramping times, spin-dependent tunneling rates between quantum dots induced by finite spin-orbit couplings, and the presence of multiple valley states. In each case we present quantitative estimates of the leakage errors, and discuss how they can be minimized. The emphasis of this work is on how to deal with the errors intrinsic to the ideal semiconductor structure, such as leakage due to spin-orbit couplings, rather than on errors due to defects or noise sources. In particular, we show that in order to minimize leakage errors induced by spin-dependent tunnelings, it is necessary to apply pulses to perform certain carefully designed spin rotations. We further develop a formalism that allows one to systematically derive constraints on the pulse shapes and present a few examples to highlight the advantage of such an approach.

Convergence Rates for Multivariate Smoothing Spline Functions.

DTIC Science & Technology

1982-10-01

GAI (,T) g (T)dT - g In order to show convergence of the series and obtain bounds on the terms, we need to estimate £ Now (1 + Ay v) AyV ( g ,#V...Cox* Technical Summary Report #2437 October 1982 ABSTRACT Given data z i - g (ti ) + ci, 1 4 i 4 n, where g is the unknown function, the ti are unknown...d-dimensional variables in a domain fl, and the ei are i.i.d. random errors, the smoothing spline estimate g n is defined to be the

Refined energetic ordering for sulphate-water (n = 3-6) clusters using high-level electronic structure calculations

NASA Astrophysics Data System (ADS)

Lambrecht, Daniel S.; McCaslin, Laura; Xantheas, Sotiris S.; Epifanovsky, Evgeny; Head-Gordon, Martin

2012-10-01

This work reports refinements of the energetic ordering of the known low-energy structures of sulphate-water clusters ? (n = 3-6) using high-level electronic structure methods. Coupled cluster singles and doubles with perturbative triples (CCSD(T)) is used in combination with an estimate of basis set effects up to the complete basis set limit using second-order Møller-Plesset theory. Harmonic zero-point energy (ZPE), included at the B3LYP/6-311 + + G(3df,3pd) level, was found to have a significant effect on the energetic ordering. In fact, we show that the energetic ordering is a result of a delicate balance between the electronic and vibrational energies. Limitations of the ZPE calculations, both due to electronic structure errors, and use of the harmonic approximation, probably constitute the largest remaining errors. Due to the often small energy differences between cluster isomers, and the significant role of ZPE, deuteration can alter the relative energies of low-lying structures, and, when it is applied in conjunction with calculated harmonic ZPEs, even alters the global minimum for n = 5. Experiments on deuterated clusters, as well as more sophisticated vibrational calculations, may therefore be quite interesting.

Medication errors as malpractice-a qualitative content analysis of 585 medication errors by nurses in Sweden.

PubMed

Björkstén, Karin Sparring; Bergqvist, Monica; Andersén-Karlsson, Eva; Benson, Lina; Ulfvarson, Johanna

2016-08-24

Many studies address the prevalence of medication errors but few address medication errors serious enough to be regarded as malpractice. Other studies have analyzed the individual and system contributory factor leading to a medication error. Nurses have a key role in medication administration, and there are contradictory reports on the nurses' work experience in relation to the risk and type for medication errors. All medication errors where a nurse was held responsible for malpractice (n = 585) during 11 years in Sweden were included. A qualitative content analysis and classification according to the type and the individual and system contributory factors was made. In order to test for possible differences between nurses' work experience and associations within and between the errors and contributory factors, Fisher's exact test was used, and Cohen's kappa (k) was performed to estimate the magnitude and direction of the associations. There were a total of 613 medication errors in the 585 cases, the most common being "Wrong dose" (41 %), "Wrong patient" (13 %) and "Omission of drug" (12 %). In 95 % of the cases, an average of 1.4 individual contributory factors was found; the most common being "Negligence, forgetfulness or lack of attentiveness" (68 %), "Proper protocol not followed" (25 %), "Lack of knowledge" (13 %) and "Practice beyond scope" (12 %). In 78 % of the cases, an average of 1.7 system contributory factors was found; the most common being "Role overload" (36 %), "Unclear communication or orders" (30 %) and "Lack of adequate access to guidelines or unclear organisational routines" (30 %). The errors "Wrong patient due to mix-up of patients" and "Wrong route" and the contributory factors "Lack of knowledge" and "Negligence, forgetfulness or lack of attentiveness" were more common in less experienced nurses. The experienced nurses were more prone to "Practice beyond scope of practice" and to make errors in spite of "Lack of adequate access to guidelines or unclear organisational routines". Medication errors regarded as malpractice in Sweden were of the same character as medication errors worldwide. A complex interplay between individual and system factors often contributed to the errors.

An Application of Semi-parametric Estimator with Weighted Matrix of Data Depth in Variance Component Estimation

NASA Astrophysics Data System (ADS)

Pan, X. G.; Wang, J. Q.; Zhou, H. Y.

2013-05-01

The variance component estimation (VCE) based on semi-parametric estimator with weighted matrix of data depth has been proposed, because the coupling system model error and gross error exist in the multi-source heterogeneous measurement data of space and ground combined TT&C (Telemetry, Tracking and Command) technology. The uncertain model error has been estimated with the semi-parametric estimator model, and the outlier has been restrained with the weighted matrix of data depth. On the basis of the restriction of the model error and outlier, the VCE can be improved and used to estimate weighted matrix for the observation data with uncertain model error or outlier. Simulation experiment has been carried out under the circumstance of space and ground combined TT&C. The results show that the new VCE based on the model error compensation can determine the rational weight of the multi-source heterogeneous data, and restrain the outlier data.

A novel navigation method used in a ballistic missile

NASA Astrophysics Data System (ADS)

Qian, Hua-ming; Sun, Long; Cai, Jia-nan; Peng, Yu

2013-10-01

The traditional strapdown inertial/celestial integrated navigation method used in a ballistic missile cannot accurately estimate the accelerometer bias. It might cause a divergence of navigation errors. To solve this problem, a new navigation method named strapdown inertial/starlight refractive celestial integrated navigation is proposed. To verify the feasibility of the proposed method, a simulated program of a ballistic missile is presented. The simulation results indicated that, when multiple refraction stars are used, the proposed method can accurately estimate the accelerometer bias, and suppress the divergence of navigation errors completely. Specifically, in order to apply this method to a ballistic missile, a novel measurement equation based on stellar refraction was developed. Furthermore a method to calculate the number of refraction stars observed by the stellar sensor was given. Finally, the relationship between the number of refraction stars used and the navigation accuracy is analysed.

Development of adaptive observation strategy using retrospective optimal interpolation

NASA Astrophysics Data System (ADS)

Noh, N.; Kim, S.; Song, H.; Lim, G.

2011-12-01

Retrospective optimal interpolation (ROI) is a method that is used to minimize cost functions with multiple minima without using adjoint models. Song and Lim (2011) perform the experiments to reduce the computational costs for implementing ROI by transforming the control variables into eigenvectors of background error covariance. We adapt the ROI algorithm to compute sensitivity estimates of severe weather events over the Korean peninsula. The eigenvectors of the ROI algorithm is modified every time the observations are assimilated. This implies that the modified eigenvectors shows the error distribution of control variables which are updated by assimilating observations. So, We can estimate the effects of the specific observations. In order to verify the adaptive observation strategy, High-impact weather over the Korean peninsula is simulated and interpreted using WRF modeling system and sensitive regions for each high-impact weather is calculated. The effects of assimilation for each observation type is discussed.

Effect of Correlated Precision Errors on Uncertainty of a Subsonic Venturi Calibration

NASA Technical Reports Server (NTRS)

Hudson, S. T.; Bordelon, W. J., Jr.; Coleman, H. W.

1996-01-01

An uncertainty analysis performed in conjunction with the calibration of a subsonic venturi for use in a turbine test facility produced some unanticipated results that may have a significant impact in a variety of test situations. Precision uncertainty estimates using the preferred propagation techniques in the applicable American National Standards Institute/American Society of Mechanical Engineers standards were an order of magnitude larger than precision uncertainty estimates calculated directly from a sample of results (discharge coefficient) obtained at the same experimental set point. The differences were attributable to the effect of correlated precision errors, which previously have been considered negligible. An analysis explaining this phenomenon is presented. The article is not meant to document the venturi calibration, but rather to give a real example of results where correlated precision terms are important. The significance of the correlated precision terms could apply to many test situations.

Cloud-based shaft torque estimation for electric vehicle equipped with integrated motor-transmission system

NASA Astrophysics Data System (ADS)

Zhu, Xiaoyuan; Zhang, Hui; Yang, Bo; Zhang, Guichen

2018-01-01

In order to improve oscillation damping control performance as well as gear shift quality of electric vehicle equipped with integrated motor-transmission system, a cloud-based shaft torque estimation scheme is proposed in this paper by using measurable motor and wheel speed signals transmitted by wireless network. It can help reduce computational burden of onboard controllers and also relief network bandwidth requirement of individual vehicle. Considering possible delays during signal wireless transmission, delay-dependent full-order observer design is proposed to estimate the shaft torque in cloud server. With these random delays modeled by using homogenous Markov chain, robust H∞ performance is adopted to minimize the effect of wireless network-induced delays, signal measurement noise as well as system modeling uncertainties on shaft torque estimation error. Observer parameters are derived by solving linear matrix inequalities, and simulation results using acceleration test and tip-in, tip-out test demonstrate the effectiveness of proposed shaft torque observer design.

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…

Satellite Sampling and Retrieval Errors in Regional Monthly Rain Estimates from TMI AMSR-E, SSM/I, AMSU-B and the TRMM PR

NASA Technical Reports Server (NTRS)

Fisher, Brad; Wolff, David B.

2010-01-01

Passive and active microwave rain sensors onboard earth-orbiting satellites estimate monthly rainfall from the instantaneous rain statistics collected during satellite overpasses. It is well known that climate-scale rain estimates from meteorological satellites incur sampling errors resulting from the process of discrete temporal sampling and statistical averaging. Sampling and retrieval errors ultimately become entangled in the estimation of the mean monthly rain rate. The sampling component of the error budget effectively introduces statistical noise into climate-scale rain estimates that obscure the error component associated with the instantaneous rain retrieval. Estimating the accuracy of the retrievals on monthly scales therefore necessitates a decomposition of the total error budget into sampling and retrieval error quantities. This paper presents results from a statistical evaluation of the sampling and retrieval errors for five different space-borne rain sensors on board nine orbiting satellites. Using an error decomposition methodology developed by one of the authors, sampling and retrieval errors were estimated at 0.25 resolution within 150 km of ground-based weather radars located at Kwajalein, Marshall Islands and Melbourne, Florida. Error and bias statistics were calculated according to the land, ocean and coast classifications of the surface terrain mask developed for the Goddard Profiling (GPROF) rain algorithm. Variations in the comparative error statistics are attributed to various factors related to differences in the swath geometry of each rain sensor, the orbital and instrument characteristics of the satellite and the regional climatology. The most significant result from this study found that each of the satellites incurred negative longterm oceanic retrieval biases of 10 to 30%.

Potential benefit of electronic pharmacy claims data to prevent medication history errors and resultant inpatient order errors

PubMed Central

Palmer, Katherine A; Shane, Rita; Wu, Cindy N; Bell, Douglas S; Diaz, Frank; Cook-Wiens, Galen; Jackevicius, Cynthia A

2016-01-01

Objective We sought to assess the potential of a widely available source of electronic medication data to prevent medication history errors and resultant inpatient order errors. Methods We used admission medication history (AMH) data from a recent clinical trial that identified 1017 AMH errors and 419 resultant inpatient order errors among 194 hospital admissions of predominantly older adult patients on complex medication regimens. Among the subset of patients for whom we could access current Surescripts electronic pharmacy claims data (SEPCD), two pharmacists independently assessed error severity and our main outcome, which was whether SEPCD (1) was unrelated to the medication error; (2) probably would not have prevented the error; (3) might have prevented the error; or (4) probably would have prevented the error. Results Seventy patients had both AMH errors and current, accessible SEPCD. SEPCD probably would have prevented 110 (35%) of 315 AMH errors and 46 (31%) of 147 resultant inpatient order errors. When we excluded the least severe medication errors, SEPCD probably would have prevented 99 (47%) of 209 AMH errors and 37 (61%) of 61 resultant inpatient order errors. SEPCD probably would have prevented at least one AMH error in 42 (60%) of 70 patients. Conclusion When current SEPCD was available for older adult patients on complex medication regimens, it had substantial potential to prevent AMH errors and resultant inpatient order errors, with greater potential to prevent more severe errors. Further study is needed to measure the benefit of SEPCD in actual use at hospital admission. PMID:26911817

Error-Rate Bounds for Coded PPM on a Poisson Channel

NASA Technical Reports Server (NTRS)

Moision, Bruce; Hamkins, Jon

2009-01-01

Equations for computing tight bounds on error rates for coded pulse-position modulation (PPM) on a Poisson channel at high signal-to-noise ratio have been derived. These equations and elements of the underlying theory are expected to be especially useful in designing codes for PPM optical communication systems. The equations and the underlying theory apply, more specifically, to a case in which a) At the transmitter, a linear outer code is concatenated with an inner code that includes an accumulator and a bit-to-PPM-symbol mapping (see figure) [this concatenation is known in the art as "accumulate-PPM" (abbreviated "APPM")]; b) The transmitted signal propagates on a memoryless binary-input Poisson channel; and c) At the receiver, near-maximum-likelihood (ML) decoding is effected through an iterative process. Such a coding/modulation/decoding scheme is a variation on the concept of turbo codes, which have complex structures, such that an exact analytical expression for the performance of a particular code is intractable. However, techniques for accurately estimating the performances of turbo codes have been developed. The performance of a typical turbo code includes (1) a "waterfall" region consisting of a steep decrease of error rate with increasing signal-to-noise ratio (SNR) at low to moderate SNR, and (2) an "error floor" region with a less steep decrease of error rate with increasing SNR at moderate to high SNR. The techniques used heretofore for estimating performance in the waterfall region have differed from those used for estimating performance in the error-floor region. For coded PPM, prior to the present derivations, equations for accurate prediction of the performance of coded PPM at high SNR did not exist, so that it was necessary to resort to time-consuming simulations in order to make such predictions. The present derivation makes it unnecessary to perform such time-consuming simulations.

Previous Estimates of Mitochondrial DNA Mutation Level Variance Did Not Account for Sampling Error: Comparing the mtDNA Genetic Bottleneck in Mice and Humans

PubMed Central

Wonnapinij, Passorn; Chinnery, Patrick F.; Samuels, David C.

2010-01-01

In cases of inherited pathogenic mitochondrial DNA (mtDNA) mutations, a mother and her offspring generally have large and seemingly random differences in the amount of mutated mtDNA that they carry. Comparisons of measured mtDNA mutation level variance values have become an important issue in determining the mechanisms that cause these large random shifts in mutation level. These variance measurements have been made with samples of quite modest size, which should be a source of concern because higher-order statistics, such as variance, are poorly estimated from small sample sizes. We have developed an analysis of the standard error of variance from a sample of size n, and we have defined error bars for variance measurements based on this standard error. We calculate variance error bars for several published sets of measurements of mtDNA mutation level variance and show how the addition of the error bars alters the interpretation of these experimental results. We compare variance measurements from human clinical data and from mouse models and show that the mutation level variance is clearly higher in the human data than it is in the mouse models at both the primary oocyte and offspring stages of inheritance. We discuss how the standard error of variance can be used in the design of experiments measuring mtDNA mutation level variance. Our results show that variance measurements based on fewer than 20 measurements are generally unreliable and ideally more than 50 measurements are required to reliably compare variances with less than a 2-fold difference. PMID:20362273

Use of Numerical Groundwater Model and Analytical Empirical Orthogonal Function for Calibrating Spatiotemporal pattern of Pumpage, Recharge and Parameter

NASA Astrophysics Data System (ADS)

Huang, C. L.; Hsu, N. S.; Hsu, F. C.; Liu, H. J.

2016-12-01

This study develops a novel methodology for the spatiotemporal groundwater calibration of mega-quantitative recharge and parameters by coupling a specialized numerical model and analytical empirical orthogonal function (EOF). The actual spatiotemporal patterns of groundwater pumpage are estimated by an originally developed back propagation neural network-based response matrix with the electrical consumption analysis. The spatiotemporal patterns of the recharge from surface water and hydrogeological parameters (i.e. horizontal hydraulic conductivity and vertical leakance) are calibrated by EOF with the simulated error hydrograph of groundwater storage, in order to qualify the multiple error sources and quantify the revised volume. The objective function of the optimization model is minimizing the root mean square error of the simulated storage error percentage across multiple aquifers, meanwhile subject to mass balance of groundwater budget and the governing equation in transient state. The established method was applied on the groundwater system of Chou-Shui River Alluvial Fan. The simulated period is from January 2012 to December 2014. The total numbers of hydraulic conductivity, vertical leakance and recharge from surface water among four aquifers are 126, 96 and 1080, respectively. Results showed that the RMSE during the calibration process was decreased dramatically and can quickly converse within 6th iteration, because of efficient filtration of the transmission induced by the estimated error and recharge across the boundary. Moreover, the average simulated error percentage according to groundwater level corresponding to the calibrated budget variables and parameters of aquifer one is as small as 0.11%. It represent that the developed methodology not only can effectively detect the flow tendency and error source in all aquifers to achieve accurately spatiotemporal calibration, but also can capture the peak and fluctuation of groundwater level in shallow aquifer.

Calibration of remotely sensed proportion or area estimates for misclassification error

Treesearch

Raymond L. Czaplewski; Glenn P. Catts

1992-01-01

Classifications of remotely sensed data contain misclassification errors that bias areal estimates. Monte Carlo techniques were used to compare two statistical methods that correct or calibrate remotely sensed areal estimates for misclassification bias using reference data from an error matrix. The inverse calibration estimator was consistently superior to the...

Use of geographic information systems to assess the error associated with the use of place of residence in injury research.

PubMed

Amram, Ofer; Schuurman, Nadine; Yanchar, Natalie L; Pike, Ian; Friger, Michael; Griesdale, Donald

In any spatial research, the use of accurate location data is critical to the reliability of the results. Unfortunately, however, many of the administrative data sets used in injury research do not include the location at which the injury takes place. The aim of this paper is to examine the error associated with using place of residence as opposed to place of injury when identifying injury hotspots and hospital access. Traumatic Brian Injury (TBI) data from the BC Trauma Registry (BCTR) was used to identify all TBI patients admitted to BC hospitals between January 2000 and March 2013. In order to estimate how locational error impacts the identification of injury hotspots, the data was aggregated to the level of dissemination area (DA) and census tract (CT) and a linear regression was performed using place of residence as a predictor for place of injury. In order to assess the impact of locational error in studies examining hospital access, an analysis of the driving time between place of injury and place of residence and the difference in driving time between place of residence and the treatment hospital, and place of injury and the same hospital was conducted. The driving time analysis indicated that 73.3 % of the injuries occurred within 5 min of place of residence, 11.2 % between five and ten minutes and 15.5 % over 20 min. Misclassification error occurs at both the DA and CT level. The residual map of the DA clearly shows more detailed misclassification. As expected, the driving time between place of residence and place of injury and the difference between these same two locations and the treatment hospital share a positive relationship. In fact, the larger the distance was between the two locations, the larger the error was when estimating access to hospital. Our results highlight the need for more systematic recording of place of injury as this will allow researchers to more accurately pinpoint where injuries occur. It will also allow researchers to identify the causes of these injuries and to determine how these injuries might be prevented.

Grazing Incidence Wavefront Sensing and Verification of X-Ray Optics Performance

NASA Technical Reports Server (NTRS)

Saha, Timo T.; Rohrbach, Scott; Zhang, William W.

2011-01-01

Evaluation of interferometrically measured mirror metrology data and characterization of a telescope wavefront can be powerful tools in understanding of image characteristics of an x-ray optical system. In the development of soft x-ray telescope for the International X-Ray Observatory (IXO), we have developed new approaches to support the telescope development process. Interferometrically measuring the optical components over all relevant spatial frequencies can be used to evaluate and predict the performance of an x-ray telescope. Typically, the mirrors are measured using a mount that minimizes the mount and gravity induced errors. In the assembly and mounting process the shape of the mirror segments can dramatically change. We have developed wavefront sensing techniques suitable for the x-ray optical components to aid us in the characterization and evaluation of these changes. Hartmann sensing of a telescope and its components is a simple method that can be used to evaluate low order mirror surface errors and alignment errors. Phase retrieval techniques can also be used to assess and estimate the low order axial errors of the primary and secondary mirror segments. In this paper we describe the mathematical foundation of our Hartmann and phase retrieval sensing techniques. We show how these techniques can be used in the evaluation and performance prediction process of x-ray telescopes.

Correlations Between the Contributions of Individual IVS Analysis Centers

NASA Technical Reports Server (NTRS)

Bockmann, Sarah; Artz, Thomas; Nothnagel, Axel

2010-01-01

Within almost all space-geodetic techniques, contributions of different analysis centers (ACs) are combined in order to improve the robustness of the final product. So far, the contributing series are assumed to be independent as each AC processes the observations in different ways. However, the series cannot be completely independent as each analyst uses the same set of original observations and many applied models are subject to conventions used by each AC. In this paper, it is shown that neglecting correlations between the contributing series yields too optimistic formal errors and small, but insignificant, errors in the estimated parameters derived from the adjustment of the combined solution.

Quantitative, Comparable Coherent Anti-Stokes Raman Scattering (CARS) Spectroscopy: Correcting Errors in Phase Retrieval

PubMed Central

Camp, Charles H.; Lee, Young Jong; Cicerone, Marcus T.

2017-01-01

Coherent anti-Stokes Raman scattering (CARS) microspectroscopy has demonstrated significant potential for biological and materials imaging. To date, however, the primary mechanism of disseminating CARS spectroscopic information is through pseudocolor imagery, which explicitly neglects a vast majority of the hyperspectral data. Furthermore, current paradigms in CARS spectral processing do not lend themselves to quantitative sample-to-sample comparability. The primary limitation stems from the need to accurately measure the so-called nonresonant background (NRB) that is used to extract the chemically-sensitive Raman information from the raw spectra. Measurement of the NRB on a pixel-by-pixel basis is a nontrivial task; thus, reference NRB from glass or water are typically utilized, resulting in error between the actual and estimated amplitude and phase. In this manuscript, we present a new methodology for extracting the Raman spectral features that significantly suppresses these errors through phase detrending and scaling. Classic methods of error-correction, such as baseline detrending, are demonstrated to be inaccurate and to simply mask the underlying errors. The theoretical justification is presented by re-developing the theory of phase retrieval via the Kramers-Kronig relation, and we demonstrate that these results are also applicable to maximum entropy method-based phase retrieval. This new error-correction approach is experimentally applied to glycerol spectra and tissue images, demonstrating marked consistency between spectra obtained using different NRB estimates, and between spectra obtained on different instruments. Additionally, in order to facilitate implementation of these approaches, we have made many of the tools described herein available free for download. PMID:28819335

An a-posteriori finite element error estimator for adaptive grid computation of viscous incompressible flows

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 Reynolds numbers. It is found that the velocity angle error estimator can detect most flow characteristics and produce dense grids in the regions where flow velocity directions have abrupt changes. In addition, the e theta estimator makes the derivative error dilutely distribute in the whole computational domain and also allows the refinement to be conducted at regions of high error. Through comparison of the velocity angle error across the interface with neighbouring cells, it is verified that the adaptive scheme in using etheta provides an optimum mesh which can clearly resolve local flow features in a precise way. The adaptive results justify the applicability of the etheta estimator and prove that this error estimator is a valuable adaptive indicator for the automatic refinement of unstructured grids.

Performance Assessment of a Gnss-Based Troposphere Path Delay Estimation Software

NASA Astrophysics Data System (ADS)

Mariotti, Gilles; Avanzi, Alessandro; Graziani, Alberto; Tortora, Paolo

2013-04-01

Error budgets of Deep Space Radio Science experiments are heavily affected by interplanetary and Earth transmission media, that corrupt, due to their non-unitary refraction index, the radiometric information of signals coming from the spacecraft. An effective removal of these noise sources is crucial to achieve the accuracy and signal stability levels required by radio science applications. Depending on the nature of these refractions, transmission media are divided into dispersive (that consists of ionized particles, i.e. Solar Wind and Ionosphere) and non-dispersive ones (the refraction is caused by neutral particles: Earth Troposphere). While dispersive noises are successfully removed by multifrequency combinations (as for GPS with the well-known ionofree combination), the most accurate estimation of tropospheric noise is obtained using microwave radiometers (MWR). As the use of MWRs suffers from strong operational limitations (rain and heavy clouds conditions), the GNSS-based processing is still widely adopted to provide a cost-effective, all-weather condition estimation of the troposphere path delay. This work describes the development process and reports the results of a GNSS analysis code specifically aimed to the estimation of the path delays introduced by the troposphere above deep space complexes, to be used for the calibration of Range and Doppler radiometric data. The code has been developed by the Radio Science Laboratory of the University of Bologna in Forlì, and is currently in the testing phase. To this aim, the preliminary output is compared to MWR measurements and IGS TropoSINEX products in order to assess the reliability of the estimate. The software works using ionofree carrier-phase observables and is based upon a double-difference approach, in which the GNSS receiver placed nearby the Deep Space receiver acts as the rover station. Several baselines are then created with various IGS and EUREF stations (master or reference stations) in order to perform the differentiation. The code relies on several IGS products, like SP3 precise orbits and SINEX positions available for the master stations in order to remove several error components, while the phase ambiguities (both wide and narrow lane) are resolved using the modified LAMBDA (MLAMBDA) method. The double-differenced data are then processed by a Kalman Filter that estimates the contingent positioning error of the rover station, its Zenith Wet Delay (ZWD) and the residual phase ambiguities. On the other hand, the Zenith Hydrostatic Delay (ZHD) is preliminarily computed using a mathematical model, based on surface meteorological measurements. The final product of the developed code is an output file containing the estimated ZWD and ZHD time-series in a format compatible with the major orbit determination software, e.g. the CSP card format (TRK-2-23) used by NASA JPL's Orbit Determination Program.

Statistical relations among earthquake magnitude, surface rupture length, and surface fault displacement

USGS Publications Warehouse

Bonilla, Manuel G.; Mark, Robert K.; Lienkaemper, James J.

1984-01-01

In order to refine correlations of surface-wave magnitude, fault rupture length at the ground surface, and fault displacement at the surface by including the uncertainties in these variables, the existing data were critically reviewed and a new data base was compiled. Earthquake magnitudes were redetermined as necessary to make them as consistent as possible with the Gutenberg methods and results, which make up much of the data base. Measurement errors were estimated for the three variables for 58 moderate to large shallow-focus earthquakes. Regression analyses were then made utilizing the estimated measurement errors.The regression analysis demonstrates that the relations among the variables magnitude, length, and displacement are stochastic in nature. The stochastic variance, introduced in part by incomplete surface expression of seismogenic faulting, variation in shear modulus, and regional factors, dominates the estimated measurement errors. Thus, it is appropriate to use ordinary least squares for the regression models, rather than regression models based upon an underlying deterministic relation in which the variance results primarily from measurement errors.Significant differences exist in correlations of certain combinations of length, displacement, and magnitude when events are grouped by fault type or by region, including attenuation regions delineated by Evernden and others.Estimates of the magnitude and the standard deviation of the magnitude of a prehistoric or future earthquake associated with a fault can be made by correlating Ms with the logarithms of rupture length, fault displacement, or the product of length and displacement.Fault rupture area could be reliably estimated for about 20 of the events in the data set. Regression of Ms on rupture area did not result in a marked improvement over regressions that did not involve rupture area. Because no subduction-zone earthquakes are included in this study, the reported results do not apply to such zones.

Statistical relations among earthquake magnitude, surface rupture length, and surface fault displacement

USGS Publications Warehouse

Bonilla, M.G.; Mark, R.K.; Lienkaemper, J.J.

1984-01-01

In order to refine correlations of surface-wave magnitude, fault rupture length at the ground surface, and fault displacement at the surface by including the uncertainties in these variables, the existing data were critically reviewed and a new data base was compiled. Earthquake magnitudes were redetermined as necessary to make them as consistent as possible with the Gutenberg methods and results, which necessarily make up much of the data base. Measurement errors were estimated for the three variables for 58 moderate to large shallow-focus earthquakes. Regression analyses were then made utilizing the estimated measurement errors. The regression analysis demonstrates that the relations among the variables magnitude, length, and displacement are stochastic in nature. The stochastic variance, introduced in part by incomplete surface expression of seismogenic faulting, variation in shear modulus, and regional factors, dominates the estimated measurement errors. Thus, it is appropriate to use ordinary least squares for the regression models, rather than regression models based upon an underlying deterministic relation with the variance resulting from measurement errors. Significant differences exist in correlations of certain combinations of length, displacement, and magnitude when events are qrouped by fault type or by region, including attenuation regions delineated by Evernden and others. Subdivision of the data results in too few data for some fault types and regions, and for these only regressions using all of the data as a group are reported. Estimates of the magnitude and the standard deviation of the magnitude of a prehistoric or future earthquake associated with a fault can be made by correlating M with the logarithms of rupture length, fault displacement, or the product of length and displacement. Fault rupture area could be reliably estimated for about 20 of the events in the data set. Regression of MS on rupture area did not result in a marked improvement over regressions that did not involve rupture area. Because no subduction-zone earthquakes are included in this study, the reported results do not apply to such zones.

Supervised local error estimation for nonlinear image registration using convolutional neural networks

NASA Astrophysics Data System (ADS)

Eppenhof, Koen A. J.; Pluim, Josien P. W.

2017-02-01

Error estimation in medical image registration is valuable when validating, comparing, or combining registration methods. To validate a nonlinear image registration method, ideally the registration error should be known for the entire image domain. We propose a supervised method for the estimation of a registration error map for nonlinear image registration. The method is based on a convolutional neural network that estimates the norm of the residual deformation from patches around each pixel in two registered images. This norm is interpreted as the registration error, and is defined for every pixel in the image domain. The network is trained using a set of artificially deformed images. Each training example is a pair of images: the original image, and a random deformation of that image. No manually labeled ground truth error is required. At test time, only the two registered images are required as input. We train and validate the network on registrations in a set of 2D digital subtraction angiography sequences, such that errors up to eight pixels can be estimated. We show that for this range of errors the convolutional network is able to learn the registration error in pairs of 2D registered images at subpixel precision. Finally, we present a proof of principle for the extension to 3D registration problems in chest CTs, showing that the method has the potential to estimate errors in 3D registration problems.

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.

Enhancing adaptive sparse grid approximations and improving refinement strategies using adjoint-based a posteriori error estimates

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jakeman, J.D., E-mail: jdjakem@sandia.gov; Wildey, T.

2015-01-01

In this paper we present an algorithm for adaptive sparse grid approximations of quantities of interest computed from discretized partial differential equations. We use adjoint-based a posteriori error estimates of the physical discretization error and the interpolation error in the sparse grid to enhance the sparse grid approximation and to drive adaptivity of the sparse grid. Utilizing these error estimates provides significantly more accurate functional values for random samples of the sparse grid approximation. We also demonstrate that alternative refinement strategies based upon a posteriori error estimates can lead to further increases in accuracy in the approximation over traditional hierarchicalmore » 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.« less

Regression-assisted deconvolution.

PubMed

McIntyre, Julie; Stefanski, Leonard A

2011-06-30

We present a semi-parametric deconvolution estimator for the density function of a random variable biX that is measured with error, a common challenge in many epidemiological studies. Traditional deconvolution estimators rely only on assumptions about the distribution of X and the error in its measurement, and ignore information available in auxiliary variables. Our method assumes the availability of a covariate vector statistically related to X by a mean-variance function regression model, where regression errors are normally distributed and independent of the measurement errors. Simulations suggest that the estimator achieves a much lower integrated squared error than the observed-data kernel density estimator when models are correctly specified and the assumption of normal regression errors is met. We illustrate the method using anthropometric measurements of newborns to estimate the density function of newborn length. Copyright © 2011 John Wiley & Sons, Ltd.

Post-Coronagraph Wavefront Sensor for Gemini Planet Imager

NASA Technical Reports Server (NTRS)

Wallace, J. Kent; Burruss, Rick; Pueyo, Laurent; Soummer, Remi; Shelton, Chris; Bartos, Randall; Fregoso, Felipe; Nemati, Bijan; Best, Paul; Angione, John

2009-01-01

The calibration wavefront system for the Gemini Planet Imager (GPI) will measure the complex wavefront at the apodized pupil and provide slow phase errors to the AO system to mitigate against image plane speckles that would cause a loss in contrast. This talk describes both the low-order and high-order sensors in the calibration wavefront sensor and how the information is combined to form the wavefront estimate before the coronagraph. We will show laboratory results from our calibration testbed that demonstrate the subsystem performance at levels commensurate with those required on the final instrument.

Extraction of diffuse correlation spectroscopy flow index by integration of Nth-order linear model with Monte Carlo simulation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Shang, Yu; Lin, Yu; Yu, Guoqiang, E-mail: guoqiang.yu@uky.edu

2014-05-12

Conventional semi-infinite solution for extracting blood flow index (BFI) from diffuse correlation spectroscopy (DCS) measurements may cause errors in estimation of BFI (αD{sub B}) in tissues with small volume and large curvature. We proposed an algorithm integrating Nth-order linear model of autocorrelation function with the Monte Carlo simulation of photon migrations in tissue for the extraction of αD{sub B}. The volume and geometry of the measured tissue were incorporated in the Monte Carlo simulation, which overcome the semi-infinite restrictions. The algorithm was tested using computer simulations on four tissue models with varied volumes/geometries and applied on an in vivo strokemore » model of mouse. Computer simulations shows that the high-order (N ≥ 5) linear algorithm was more accurate in extracting αD{sub B} (errors < ±2%) from the noise-free DCS data than the semi-infinite solution (errors: −5.3% to −18.0%) for different tissue models. Although adding random noises to DCS data resulted in αD{sub B} variations, the mean values of errors in extracting αD{sub B} were similar to those reconstructed from the noise-free DCS data. In addition, the errors in extracting the relative changes of αD{sub B} using both linear algorithm and semi-infinite solution were fairly small (errors < ±2.0%) and did not rely on the tissue volume/geometry. The experimental results from the in vivo stroke mice agreed with those in simulations, demonstrating the robustness of the linear algorithm. DCS with the high-order linear algorithm shows the potential for the inter-subject comparison and longitudinal monitoring of absolute BFI in a variety of tissues/organs with different volumes/geometries.« less

Sampling Errors of SSM/I and TRMM Rainfall Averages: Comparison with Error Estimates from Surface Data and a Sample Model

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.

The Sensitivity of Adverse Event Cost Estimates to Diagnostic Coding Error

PubMed Central

Wardle, Gavin; Wodchis, Walter P; Laporte, Audrey; Anderson, Geoffrey M; Baker, Ross G

2012-01-01

Objective To examine the impact of diagnostic coding error on estimates of hospital costs attributable to adverse events. Data Sources Original and reabstracted medical records of 9,670 complex medical and surgical admissions at 11 hospital corporations in Ontario from 2002 to 2004. Patient specific costs, not including physician payments, were retrieved from the Ontario Case Costing Initiative database. Study Design Adverse events were identified among the original and reabstracted records using ICD10-CA (Canadian adaptation of ICD10) codes flagged as postadmission complications. Propensity score matching and multivariate regression analysis were used to estimate the cost of the adverse events and to determine the sensitivity of cost estimates to diagnostic coding error. Principal Findings Estimates of the cost of the adverse events ranged from $16,008 (metabolic derangement) to $30,176 (upper gastrointestinal bleeding). Coding errors caused the total cost attributable to the adverse events to be underestimated by 16 percent. The impact of coding error on adverse event cost estimates was highly variable at the organizational level. Conclusions Estimates of adverse event costs are highly sensitive to coding error. Adverse event costs may be significantly underestimated if the likelihood of error is ignored. PMID:22091908

Investigating the error sources of the online state of charge estimation methods for lithium-ion batteries in electric vehicles

NASA Astrophysics Data System (ADS)

Zheng, Yuejiu; Ouyang, Minggao; Han, Xuebing; Lu, Languang; Li, Jianqiu

2018-02-01

Sate of charge (SOC) estimation is generally acknowledged as one of the most important functions in battery management system for lithium-ion batteries in new energy vehicles. Though every effort is made for various online SOC estimation methods to reliably increase the estimation accuracy as much as possible within the limited on-chip resources, little literature discusses the error sources for those SOC estimation methods. This paper firstly reviews the commonly studied SOC estimation methods from a conventional classification. A novel perspective focusing on the error analysis of the SOC estimation methods is proposed. SOC estimation methods are analyzed from the views of the measured values, models, algorithms and state parameters. Subsequently, the error flow charts are proposed to analyze the error sources from the signal measurement to the models and algorithms for the widely used online SOC estimation methods in new energy vehicles. Finally, with the consideration of the working conditions, choosing more reliable and applicable SOC estimation methods is discussed, and the future development of the promising online SOC estimation methods is suggested.

Optimization of a sensor cluster for determination of trajectories and velocities of supersonic objects

NASA Astrophysics Data System (ADS)

Cannella, Marco; Sciuto, Salvatore Andrea

2001-04-01

An evaluation of errors for a method for determination of trajectories and velocities of supersonic objects is conducted. The analytical study of a cluster, composed of three pressure transducers and generally used as an apparatus for cinematic determination of parameters of supersonic objects, is developed. Furthermore, detailed investigation into the accuracy of this cluster on determination of the slope of an incoming shock wave is carried out for optimization of the device. In particular, a specific non-dimensional parameter is proposed in order to evaluate accuracies for various values of parameters and reference graphs are provided in order to properly design the sensor cluster. Finally, on the basis of the error analysis conducted, a discussion on the best estimation of the relative distance for the sensor as a function of temporal resolution of the measuring system is presented.

Synchronisation, electronic circuit implementation, and fractional-order analysis of 5D ordinary differential equations with hidden hyperchaotic attractors

NASA Astrophysics Data System (ADS)

Wei, Zhouchao; Rajagopal, Karthikeyan; Zhang, Wei; Kingni, Sifeu Takougang; Akgül, Akif

2018-04-01

Hidden hyperchaotic attractors can be generated with three positive Lyapunov exponents in the proposed 5D hyperchaotic Burke-Shaw system with only one stable equilibrium. To the best of our knowledge, this feature has rarely been previously reported in any other higher-dimensional systems. Unidirectional linear error feedback coupling scheme is used to achieve hyperchaos synchronisation, which will be estimated by using two indicators: the normalised average root-mean squared synchronisation error and the maximum cross-correlation coefficient. The 5D hyperchaotic system has been simulated using a specially designed electronic circuit and viewed on an oscilloscope, thereby confirming the results of the numerical integration. In addition, fractional-order hidden hyperchaotic system will be considered from the following three aspects: stability, bifurcation analysis and FPGA implementation. Such implementations in real time represent hidden hyperchaotic attractors with important consequences for engineering applications.

Estimating cosmic velocity fields from density fields and tidal tensors

NASA Astrophysics Data System (ADS)

Kitaura, Francisco-Shu; Angulo, Raul E.; Hoffman, Yehuda; Gottlöber, Stefan

2012-10-01

In this work we investigate the non-linear and non-local relation between cosmological density and peculiar velocity fields. Our goal is to provide an algorithm for the reconstruction of the non-linear velocity field from the fully non-linear density. We find that including the gravitational tidal field tensor using second-order Lagrangian perturbation theory based upon an estimate of the linear component of the non-linear density field significantly improves the estimate of the cosmic flow in comparison to linear theory not only in the low density, but also and more dramatically in the high-density regions. In particular we test two estimates of the linear component: the lognormal model and the iterative Lagrangian linearization. The present approach relies on a rigorous higher order Lagrangian perturbation theory analysis which incorporates a non-local relation. It does not require additional fitting from simulations being in this sense parameter free, it is independent of statistical-geometrical optimization and it is straightforward and efficient to compute. The method is demonstrated to yield an unbiased estimator of the velocity field on scales ≳5 h-1 Mpc with closely Gaussian distributed errors. Moreover, the statistics of the divergence of the peculiar velocity field is extremely well recovered showing a good agreement with the true one from N-body simulations. The typical errors of about 10 km s-1 (1σ confidence intervals) are reduced by more than 80 per cent with respect to linear theory in the scale range between 5 and 10 h-1 Mpc in high-density regions (δ > 2). We also find that iterative Lagrangian linearization is significantly superior in the low-density regime with respect to the lognormal model.

A posteriori error estimates in voice source recovery

NASA Astrophysics Data System (ADS)

Leonov, A. S.; Sorokin, V. N.

2017-12-01

The inverse problem of voice source pulse recovery from a segment of a speech signal is under consideration. A special mathematical model is used for the solution that relates these quantities. A variational method of solving inverse problem of voice source recovery for a new parametric class of sources, that is for piecewise-linear sources (PWL-sources), is proposed. Also, a technique for a posteriori numerical error estimation for obtained solutions is presented. A computer study of the adequacy of adopted speech production model with PWL-sources is performed in solving the inverse problems for various types of voice signals, as well as corresponding study of a posteriori error estimates. Numerical experiments for speech signals show satisfactory properties of proposed a posteriori error estimates, which represent the upper bounds of possible errors in solving the inverse problem. The estimate of the most probable error in determining the source-pulse shapes is about 7-8% for the investigated speech material. It is noted that a posteriori error estimates can be used as a criterion of the quality for obtained voice source pulses in application to speaker recognition.

A Complementary Note to 'A Lag-1 Smoother Approach to System-Error Estimation': The Intrinsic Limitations of Residual Diagnostics

NASA Technical Reports Server (NTRS)

Todling, Ricardo

2015-01-01

Recently, this author studied an approach to the estimation of system error based on combining observation residuals derived from a sequential filter and fixed lag-1 smoother. While extending the methodology to a variational formulation, experimenting with simple models and making sure consistency was found between the sequential and variational formulations, the limitations of the residual-based approach came clearly to the surface. This note uses the sequential assimilation application to simple nonlinear dynamics to highlight the issue. Only when some of the underlying error statistics are assumed known is it possible to estimate the unknown component. In general, when considerable uncertainties exist in the underlying statistics as a whole, attempts to obtain separate estimates of the various error covariances are bound to lead to misrepresentation of errors. The conclusions are particularly relevant to present-day attempts to estimate observation-error correlations from observation residual statistics. A brief illustration of the issue is also provided by comparing estimates of error correlations derived from a quasi-operational assimilation system and a corresponding Observing System Simulation Experiments framework.