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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 523 records · Page 29
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Misclassification in Workers’ Telecommuting Frequency Choices Using a Generalized Extreme Value Model

Telecommuting frequency is a response variable collected in travel surveys and is, therefore, prone to errors leading to mismeasurements or misclassification. Misclassification of explanatory variables is a common risk when using statistical modeling techniques. We define “misclassification” as a response reported or recorded in the wrong category; for example, a variable is recorded as a 1 when it should be 0. Here, in this context, this study aims to develop a statistical model to analyze telecommuting data which accounts for potential misclassification errors by building on existing literature in econometrics. The empirical analysis was undertaken using the 2017 National Household Travel Survey (NHTS) and the general extreme value (GEV) models available in the literature. Specifically, the frequency of telecommuting days was analyzed using the negative binomial (NB) model recast as the multinomial logit (MNL) model. By nature—and consistent with other studies—NHTS data are prone to errors that can be classified as intentional or unintentional misinformation provided by the person being interviewed. Ignoring these errors while modeling telecommuting frequencies using standard discrete count models can result in biased parameter estimates. The misclassification parameter was calculated for both over-reporting and under-reporting scenarios. The misclassification errors can be as high as 14% over-reported and 10% under-reported, particularly for the neighboring values. Statistical fit comparison between the models shows that models that ignore misclassification have worse data fit and biased parameter estimates with significant policy implications.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS1984

Probability of error and radiometric resolution for target discrimination in radar images

A straightforward statistical analysis is presented of the problem of discriminating between two distributed targets in a radar image. The probability of making an error is calculated as a function of the target contrast, the number of samples averaged to form each observation, and the system noise. In the absence of system noise, it is observed that a target contrast of 1 dB could not be detected with less than 10 samples averaged; it is also seen that the error probability is sensitive to the number of samples averaged, as expected. Setting the target contrast at the radiometric resolution, a comparison is made of different definitions for this resolution. The comparison shows that each definition yields a different error probability, ranging from less than 0.05 to 0.35. Attention is also given to the effect of system noise, and it is shown that thermal noise does not have a significant effect on the error probability for a fixed target contrast.

Frost, V. S.↗
NASA NTRS1975

Statistical analysis of trim maneuvers in low thrust interplanetary navigation

An analytical technique for the statistical analysis of a low thrust trim maneuver in interplanetary navigation has been developed. The maneuver is treated rigorously as a nonlinear function of the trajectory errors to be removed, and the corresponding nongaussian statistics are developed. The method determines the statistical properties of the maneuver direction and duration, as well as the final state dispersion covariance. The analytical technique is applied to the terminal maneuver in a 1980 Encke slow flyby mission and is found to yield accurate statistical results with much less effort than Monte Carlo simulation. The method permits consideration of a shorter terminal maneuver than does linear analysis, with a consequent improvement in delivery accuracy.

Rinker, G. C.↗
NASA NTRS2001

Robust Combining of Disparate Classifiers Through Order Statistics

Integrating the outputs of multiple classifiers via combiners or meta-learners has led to substantial improvements in several difficult pattern recognition problems. In this article we investigate a family of combiners based on order statistics, for robust handling of situations where there are large discrepancies in performance of individual classifiers. Based on a mathematical modeling of how the decision boundaries are affected by order statistic combiners, we derive expressions for the reductions in error expected when simple output combination methods based on the the median, the maximum and in general, the ith order statistic, are used. Furthermore, we analyze the trim and spread combiners, both based on linear combinations of the ordered classifier outputs, and show that in the presence of uneven classifier performance, they often provide substantial gains over both linear and simple order statistics combiners. Experimental results on both real world data and standard public domain data sets corroborate these findings.

Tumer, Kagan↗
NASA NTRS1972

Mars gravitational field estimation error

The error covariance matrices associated with a weighted least-squares differential correction process have been analyzed for accuracy in determining the gravitational coefficients through degree and order five in the Mars gravitational potential junction. The results are presented in terms of standard deviations for the assumed estimated parameters. The covariance matrices were calculated by assuming Doppler tracking data from a Mars orbiter, a priori statistics for the estimated parameters, and model error uncertainties for tracking-station locations, the Mars ephemeris, the astronomical unit, the Mars gravitational constant (G sub M), and the gravitational coefficients of degrees six and seven. Model errors were treated by using the concept of consider parameters.

Compton, H. R.↗
NASA NTRS1994

Structure of merger remnants. 4: Isophotal shapes

This paper examines the shapes of isophotes of galaxy merger remnants. More specifically, we perform a series of numerical experiments to study galaxy mergers. The simulations explore a variety of encounter geometries, types of progenitor galaxies, and particle numbers. We 'observe' each of the remnants from 64 viewpoints to estimate how the isophotal shapes vary with the orientation of the remnant. Also, by comparing the results from the various simulations, we learn how encounter geometry and the structure of the progenitors can affect the shape of the remnants. The encounter geometry, the structure of the progenitors, and the orientation of the remnant play deciding roles in the shape of the 'observed' isophotes, so much so that these simulated merger remnants have both 'boxy' and 'disky' isophotes and ellipticities ranging from E0 to E7. Thus, the shapes seen in our merger simulation remnants span a similar range to observed isophotal shapes of real galaxies. Furthermore, to estimate the errors in this analysis, we introduce the statistical bootstrap. Bootstrapping is used rather widely in observational astronomy when dealing with small samples; however, its use is practically unheard of for tackling theoretical problems. We believe that bootstrapping can be extremely useful for dealing with the small samples found in numerical simulations; therefore, we present the rudiments and basis of the technique with emphasis on its use in N-body calculations. Also, by comparing the errors for the smaller simulations with those of the larger ones, we speculate on the number of particles required to accurately explore isophotal shapes in simulations.

Heyl, Jeremy S.↗
NASA NTRS2000

An Adaptive Buddy Check for Observational Quality Control

An adaptive buddy check algorithm is presented that adjusts tolerances for outlier observations based on the variability of surrounding data. The algorithm derives from a statistical hypothesis test combined with maximum-likelihood covariance estimation. Its stability is shown to depend on the initial identification of outliers by a simple background check. The adaptive feature ensures that the final quality control decisions are not very sensitive to prescribed statistics of first-guess and observation errors, nor on other approximations introduced into the algorithm. The implementation of the algorithm in a global atmospheric data assimilation is described. Its performance is contrasted with that of a non-adaptive buddy check, for the surface analysis of an extreme storm that took place in Europe on 27 December 1999. The adaptive algorithm allowed the inclusion of many important observations that differed greatly from the first guess and that would have been excluded on the basis of prescribed statistics. The analysis of the storm development was much improved as a result of these additional observations.

Dee, Dick P.↗
NASA NTRS1992

A monitor for the laboratory evaluation of control integrity in digital control systems operating in harsh electromagnetic environments

This paper presents a strategy for dynamically monitoring digital controllers in the laboratory for susceptibility to electromagnetic disturbances that compromise control integrity. The integrity of digital control systems operating in harsh electromagnetic environments can be compromised by upsets caused by induced transient electrical signals. Digital system upset is a functional error mode that involves no component damage, can occur simultaneously in all channels of a redundant control computer, and is software dependent. The motivation for this work is the need to develop tools and techniques that can be used in the laboratory to validate and/or certify critical aircraft controllers operating in electromagnetically adverse environments that result from lightning, high-intensity radiated fields (HIRF), and nuclear electromagnetic pulses (NEMP). The detection strategy presented in this paper provides dynamic monitoring of a given control computer for degraded functional integrity resulting from redundancy management errors, control calculation errors, and control correctness/effectiveness errors. In particular, this paper discusses the use of Kalman filtering, data fusion, and statistical decision theory in monitoring a given digital controller for control calculation errors.

Belcastro, Celeste M.↗
NASA NTRS1982

Investigation of geomagnetic field forecasting and fluid dynamics of the core

It was established that the total absolute magnetic flux crossing the core- mantle boundary has been a constant of the core motion for the last 50 years. This provides a scalar constraint that could be added to the geometric modelling procedure. The GSFC 8 8/80 model is being evaluated. The absolute magnetic flux linking the CMB to that model was plotted as a function of time during the span covered by the data, and increasing truncation level. The inclusion of the standard error of each Gauss coefficient derived from the statistics of fit in the GSFC 9/80 model is useful. The magnitude and sense (upwelling or downe welling) of vertical fluid motion adjacent to the core-mantle boundary was calculated using the model. Standard errors were found to be sufficiently small at all but one or two of the 40 or more critical points of B sub r. They do not nearly overlap the value gamma u/gamma r = 0. It is concluded that the core is upwelling and downwelling at an observationally detectable level.

Benton, E. R.↗
NASA NTRS1987

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

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.

Engelis, Theodossios↗
NASA NTRS2007

Detection of Digital Elevation Model Errors Using X-band Weather Radar

Flight in Instrument Meteorological Conditions requires pilots to manipulate flight controls while referring to a Primary Flight Display. The Primary Flight Display indicates aircraft attitude along with, in some cases, many other state variables such as altitude, speed, and guidance cues. Synthetic Vision Systems have been proposed that overlay the traditional information provided on Primary Flight Displays onto a scene depicting the location of terrain and other geo-spatial features.Terrain models used by these displays must have sufficient quality to avoid providing misleading information. This paper describes how X-band radar measurements can be used as part of a monitor, and/or maintenance system, to quantify the integrity of terrain models that are used by systems such as Synthetic Vision. Terrain shadowing effects, as seen by the radar, are compared in a statistical manner against estimated shadow feature elements extracted from the stored terrain model from the perspective of the airborne observer. A test statistic is defined that enables detection of errors as small as the range resolution of the radar. Experimental results obtained from two aircraft platforms hosting certified commercial-off-the-shelf X-band radars test the premise and illustrate its potential.

Young, Steven D.↗
DOE OSTI2024

Generalization error guaranteed auto-encoder-based nonlinear model reduction for operator learning

Many physical processes in science and engineering are naturally represented by operators between infinite-dimensional function spaces. The problem of operator learning, in this context, seeks to extract these physical processes from empirical data, which is challenging due to the infinite or high dimensionality of data. An integral component in addressing this challenge is model reduction, which reduces both the data dimensionality and problem size. In this paper, we utilize low-dimensional nonlinear structures in model reduction by investigating Auto-Encoder-based Neural Network (AENet). AENet first learns the latent variables of the input data and then learns the transformation from these latent variables to corresponding output data. Our numerical experiments validate the ability of AENet to accurately learn the solution operator of nonlinear partial differential equations. Furthermore, we establish a mathematical and statistical estimation theory that analyzes the generalization error of AENet. Finally, our theoretical framework shows that the sample complexity of training AENet is intricately tied to the intrinsic dimension of the modeled process, while also demonstrating the robustness of AENet to noise.

Auto-encoder↗
NASA NTRS1971

Cloud-free resolution element statistics program

Computer program computes number of cloud-free elements in field-of-view and percentage of total field-of-view occupied by clouds. Human error is eliminated by using visual estimation to compute cloud statistics from aerial photographs.

Liley, B.↗
NASA NTRS1998

Efficient Methods to Assimilate Satellite Retrievals Based on Information Content: Suboptimal Retrieval Assimilation - Part 2

One of the outstanding problems in data assimilation has been and continues to be how best to utilize satellite data while balancing the tradeoff between accuracy and computational cost. A number of weather prediction centers have recently achieved remarkable success in improving their forecast skill by changing the method by which satellite data are assimilated into the forecast model from the traditional approach of assimilating retrievals to the direct assimilation of radiances in a variational framework. The operational implementation of such a substantial change in methodology involves a great number of technical details, e.g., pertaining to quality control procedures, systematic error correction techniques, and tuning of the statistical parameters in the analysis algorithm. Although there are clear theoretical advantages to the direct radiance assimilation approach, it is not obvious at all to what extent the improvements that have been obtained so far can be attributed to the change in methodology, or to various technical aspects of the implementation. The issue is of interest because retrieval assimilation retains many practical and logistical advantages which may become even more significant in the near future when increasingly high-volume data sources become available. The central question we address here is: how much improvement can we expect from assimilating radiances rather than retrievals, all other things being equal? We compare the two approaches in a simplified one-dimensional theoretical framework, in which problems related to quality control and systematic error correction are conveniently absent. By assuming a perfect radiative transfer model and perfect knowledge of radiance and background error covariances, we are able to formulate a nonlinear local error analysis for each assimilation method. Direct radiance assimilation is optimal in this idealized context, while the traditional method of assimilating retrievals is suboptimal because it ignores the cross-covariances between background errors and retrieval errors. We show that interactive retrieval assimilation (where the same background used for assimilation is also used in the retrieval step) is equivalent to direct assimilation of radiances with suboptimal analysis weights. We illustrate and extend these theoretical arguments with several one-dimensional assimilation experiments, where we estimate vertical atmospheric profiles using simulated data from both the High-resolution InfraRed Sounder 2 (HIRS2) and the future Atmospheric InfraRed Sounder (AIRS).

Joiner, J.↗
NASA NTRS1973

Mariner Venus/Mercury 1973 navigation strategy

This paper presents the navigational aspects of the Mariner Venus/Mercury 1973 mission. Principal emphasis is on the development of the trajectory correction strategy, propellant costs, and delivery accuracies at Venus and Mercury. Key error sources and mission constraints are discussed. Of particular interest are the statistics of the first, post-Venus, maneuver which must correct for the magnification of errors in the Venus encounter. Finally, although not a primary objective of the mission, the analysis is extended to include a second Mercury encounter.

Mckinley, E. L.↗
NASA NTRS1996

Quantitative Measures for Software Independent Verification and Validation

As software is maintained or reused, it undergoes an evolution which tends to increase the overall complexity of the code. To understand the effects of this, we brought in statistics experts and leading researchers in software complexity, reliability, and their interrelationships. These experts' project has resulted in our ability to statistically correlate specific code complexity attributes, in orthogonal domains, to errors found over time in the HAL/S flight software which flies in the Space Shuttle. Although only a prototype-tools experiment, the result of this research appears to be extendable to all other NASA software, given appropriate data similar to that logged for the Shuttle onboard software. Our research has demonstrated that a more complete domain coverage can be mathematically demonstrated with the approach we have applied, thereby ensuring full insight into the cause-and-effects relationship between the complexity of a software system and the fault density of that system. By applying the operational profile we can characterize the dynamic effects of software path complexity under this same approach We now have the ability to measure specific attributes which have been statistically demonstrated to correlate to increased error probability, and to know which actions to take, for each complexity domain. Shuttle software verifiers can now monitor the changes in the software complexity, assess the added or decreased risk of software faults in modified code, and determine necessary corrections. The reports, tool documentation, user's guides, and new approach that have resulted from this research effort represent advances in the state of the art of software quality and reliability assurance. Details describing how to apply this technique to other NASA code are contained in this document.

Lee, Alice↗
NASA NTRS1993

The sensitivity of identified modal parameters to sensor placement errors and construction tolerances

This paper examines the sensitivity of experimentally measured modal frequencies and mode shapes to structural reassembly and sensor placement errors on a suspended three-bay truss. The statistical variations of identified mode shapes and frequencies are measured by repeated experiments. Both parameters are shown to vary measurably more with reassembly than sensor placement errors. Also, a directional stiffness in the truss joints was found to cause a parameter dependence on member orientation during reconstruction.

Hinkle, Jason↗
NASA NTRS2019

Multilevel Monte Carlo Estimation of Unbiased Expectation via Sample Reuse and the Low Variance Estimation of Asymptotic Rates

A new variant of the multilevel Monte Carlo estimator [5, 3, 9, 12] is presented for the estimation of expectation statistics that utilizes sample reuse in specified levels, explicitly removes approximation error bias associated with numerically computed output quantities of interest that have an asymptotic limit behavior, and permits a low variance estimate of the asymptotic rate of convergence to that limit. In addition, it is shown that this new multilevel Monte Carlo variant can yield a computational cost savings. A review of Monte Carlo and multilevel Monte Carlo estimators is presented that includes analysis of expected value, expected mean squared error, and the calculation of optimized multilevel sample size parameters. The multilevel Monte Carlo estimator produces estimates of expectation for numerically approximated output quantities of interest that are biased by approximation error. When the quantity of interest can be modeled as the asymptotic limit of numerically approximated output quantities of interest, it is theoretically possible to remove this approximation error bias in the multilevel Monte Carlo estimator. In actual implementations, however, this procedure is unreliable due to statistical variability and inaccuracy in estimating the needed asymptotic limit. Analysis and numerical experiment show that the proposed variant of the multilevel Monte Carlo method greatly reduces (in some cases eliminates) the statistical variability in this limit estimation.

Barth, Timothy↗