Search NASA⌕ Search

SEARCH · Search NASA

Results for “Sampling error”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 739 records · Page 41
All sourcesNASA NTRSDOE OSTI
NASA NTRS1990

Ellipsometric study of cubic SiC

Variable angle spectroscopic ellipsometry (VASE) was applied to cubic SiC. This technique gives absolute values of the refractive index (n) and the extinction coefficient (k) of a substrate and/or a thin film of unknown material. The samples were grown by chemical vapor deposition (CVD) on p-type silicon. The substrate was aligned either on the (001) axis or 1 degree of (001). Several growth temperatures and growth durations were used. The samples were divided into two groups: (1) thick films of order 10 microns grown near optimal conditions of temperature, flow, and gas ratio; and (2) thin films of order 100 A grown at various temperatures. The ellipsometric results for samples in group 1 were analyzed using a two-phase model (substrate and ambient). Results show that for wavelengths in the visible, the refractive index of these CVD samples is equal to that reported for single crystal cubic SiC, within the experimental error, which is on the order of 1 percent. However, the extinction coefficient has a relatively large value, even above the band gap. The absorption is sample dependent and has a broad peak in the visible. The results for samples in group 2 were analyzed using a three-phase model (substrate, film, and ambient). The dielectric functions of the film, deducted from the measured n and k, were further analyzed using the effective medium approximation. The results show that the films contain 30 to 40 vol. percent amorphous silicon, i.e., silicon with only short-range order.

Alterovitz, Samuel A.↗
NASA NTRS1974

A Monte Carlo investigation of experimental data requirements for fitting polynomial functions

This report examines the extent to which sample size affects the accuracy of a low order polynomial approximation of an experimentally observed quantity and establishes a trend toward improvement in the accuracy of the approximation as a function of sample size. The task is made possible through a simulated analysis carried out by the Monte Carlo method, in which data are generated by using several transcendental or algebraic functions as models. Contaminated data of varying amounts are fitted to linear quadratic or cubic polynomials, and the behavior of the mean-squared error of the residual variance is determined as a function of sample size. Results indicate that the effect of the size of the sample is significant only for relatively small sample sizes and diminishes drastically for moderate and large amounts of experimental data.

Canavos, G. C.↗
DOE OSTI2024

Binary pseudo-random array standards for calibration of 3D optical surface profilers used for metrology with significantly curved x-ray optics

High-accuracy metrology is vitally important in manufacturing ultra-high-quality free-form mirrors designed to manipulate X-ray light with nanometer-scale wavelengths. The current capabilities and possibility for improvements in X-ray mirror manufacturing are limited by inherent imperfections of the integrated metrology tools. In the case of Fizeau interferometry, metrology tools are currently calibrated with super-polished flat test-standard/reference mirrors. This is acceptable for measuring slightly curved X-ray optics. However, for even moderately curved aspherical X-ray mirrors the flat-reference calibration is not sufficiently accurate and stitching Fizeau interferometer-based surface metrology is used to mitigate the problem. But still, the retrace and aberration errors, as well as the limited spatial resolution, described with the instrument transfer function (ITF), can be transferred into the optical surface topography of X-ray mirrors obtained in stitching metrology. For ITF calibration, we have developed an original technique, based on test standards structured as two-dimensional (2D) highly-randomized (HR) binary pseudo-random arrays (BPRAs). The technique employs the unique properties of the HR BPRA patterns in the spatial frequency domain., i.e. the inherent 2D power spectral density of the HR BPRA pattern has a deterministic white-noise-like character that allows direct determination of the ITF with uniform sensitivity over the entire spatial frequency range and field-of-view of an instrument. Here, we explore technological, metrological, and analytical aspects essential for calibration of the retrace and aberration errors of Fizeau interferometers using different types of tilted test samples, including a super-polished reference mirror for the re-trace calibration and the uniformly redundant array (URA) BPRA standards for the geometrical distortion (aberration) calibration. While the first method was previously demonstrated by researchers at DIAMOND Light Source, a method based on the URA BPRA is described and demonstrated here for the first time. We outline the design and fabrication process used in fabrication of URA BPRA test standards, and present the results of application of the URA BPRA standards demonstrating the high efficacy of our approach to geometrical distortion calibration of Fizeau interferometers. We also discuss the possible sources of unexpected peculiarities of the systematic errors, including an astigmatic character of the retrace error, observed with Fizeau interferometers at the Advanced Light Source X-Ray Optics Laboratory.

Munechika, Keiko↗
NASA NTRS1974

An empirical analysis of the quantitative effect of data when fitting quadratic and cubic polynomials

A study is made of the extent to which the size of the sample affects the accuracy of a quadratic or a cubic polynomial approximation of an experimentally observed quantity, and the trend with regard to improvement in the accuracy of the approximation as a function of sample size is established. The task is made possible through a simulated analysis carried out by the Monte Carlo method in which data are simulated by using several transcendental or algebraic functions as models. Contaminated data of varying amounts are fitted to either quadratic or cubic polynomials, and the behavior of the mean-squared error of the residual variance is determined as a function of sample size. Results indicate that the effect of the size of the sample is significant only for relatively small sizes and diminishes drastically for moderate and large amounts of experimental data.

Canavos, G. C.↗
NASA NTRS1981

ECC ozonesonde performance at high altitudes: Pump efficiency

The ECC ozonesonde sampling behavior was examined at pressures ranging from 60 to 6 hPa with the objective of evaluating uncertainties in high altitude ozone data caused by variations in pumping efficiency. The averaged pump efficiency correction curve for a 43 sample set of 3A type ECC pumps showed a 2-3% bias from the curve provided by the manufacturer. In addition, random pump to pump variations (2 sigma) + or - were 5% at 6 hPa. These values probably represent minimum errors since the pumps were from the same production batch. A seven sample set of the newer 4A type ECC pumps was examined, with similar findings as for the 3A types.

Torres, A. L.↗
NASA NTRS1979

Accuracy assessment in the Large Area Crop Inventory Experiment

The Accuracy Assessment System (AAS) of the Large Area Crop Inventory Experiment (LACIE) was responsible for determining the accuracy and reliability of LACIE estimates of wheat production, area, and yield, made at regular intervals throughout the crop season, and for investigating the various LACIE error sources, quantifying these errors, and relating them to their causes. Some results of using the AAS during the three years of LACIE are reviewed. As the program culminated, AAS was able not only to meet the goal of obtaining accurate statistical estimates of sampling and classification accuracy, but also the goal of evaluating component labeling errors. Furthermore, the ground-truth data processing matured from collecting data for one crop (small grains) to collecting, quality-checking, and archiving data for all crops in a LACIE small segment.

Houston, A. G.↗
NASA NTRS1985

The implementation and use of ADA on distributed systems with high reliability requirements

The use and implementation of Ada in distributed environments in which reliability is the primary concern is investigated. Emphasis is placed on the possibility that a distributed system may be programmed entirely in Ada so that the individual tasks of the system are unconcerned with which processors they are executing on, and that failures may occur in the software or underlying hardware. A new linguistic construct, the colloquy, is introduced which solves the problems identified in an earlier proposal, the conversation. It was shown that the colloquy is at least as powerful as recovery blocks, but it is also as powerful as all the other language facilities proposed for other situations requiring backward error recovery: recovery blocks, deadlines, generalized exception handlers, traditional conversations, s-conversations, and exchanges. The major features that distinguish the colloquy are described. Sample programs that were written, but not executed, using the colloquy show that extensive backward error recovery can be included in these programs simply and elegantly. These ideas are being implemented in an experimental Ada test bed.

Knight, J. C.↗
DOE OSTI2021

Comprehensive Material Characterization and Simultaneous Model Calibration for Improved Computational Simulation Credibility

Computational simulation is increasingly relied upon for high-consequence engineering decisions, and a foundational element to solid mechanics simulations is a credible material model. Our ultimate vision is to interlace material characterization and model calibration in a real-time feedback loop, where the current model calibration results will drive the experiment to load regimes that add the most useful information to reduce parameter uncertainty. The current work investigated one key step to this Interlaced Characterization and Calibration (ICC) paradigm, using a finite load-path tree to incorporate history/path dependency of nonlinear material models into a network of surrogate models that replace computationally-expensive finite-element analyses. Our reference simulation was an elastoplastic material point subject to biaxial deformation with a Hill anisotropic yield criterion. Training data was generated using either a space-filling or adaptive sampling method, and surrogates were built using either Gaussian process or polynomial chaos expansion methods. Surrogate error was evaluated to be on the order of 10 ⁻5 and 10 ⁻3 percent for the space-filling and adaptive sampling training data, respectively. Direct Bayesian inference was performed with the surrogate network and with the reference material point simulator, and results agreed to within 3 significant figures for the mean parameter values, with a reduction in computational cost over 5 orders of magnitude. These results bought down risk regarding the surrogate network and facilitated a successful FY22-24 full LDRD proposal to research and develop the complete ICC paradigm.

36 MATERIALS SCIENCE↗
NASA NTRS1991

Error analysis of image reconstruction by a synthetic aperture interferometric radiometer

An error analysis is presented for the image reconstruction algorithm currently employed by the electronically steered thinned array radiometer aircraft instrument. This instrument samples components of the Fourier transform of the brightness temperature distribution at 1.4 GHz. Sources of error are identified, including systematic offsets and stochastic noise in the measurements, errors in the interference pattern antenna calibration, multipath scattering from the antenna support structure, mutual coupling between nearby array elements, and variations in the antenna patterns of the array elements. These errors are shown to produce artifacts in the image which appear as 'streaks' running along the instrument flight line. Possible hardware corrections are discussed. These include a reduction in the profile of the antenna support structure and a decoupling of adjacent and other nearby array elements using corrugated 'skirts'. Also discussed are several image enhancement techniques which detect and correct for some of the streaking. These techniques include a renormalization of the interference patterns based on the measured element patterns, a perturbation based interpretation of image contrast, and a covariance analysis of noise present in the image.

Ruf, Christopher S.↗
NASA NTRS1994

A statistical analysis of the broadband 0.1 to 3.5 keV spectral properties of X-ray-selected active galactic nuclei

We survey the broadband spectral properties of approximately 500 X-ray-selected active galactic nuclei (AGNs) observed with the Einstein Observatory. Included in this survey are the approximately 450 AGNs in the Extended Medium Sensitivity Survey (EMSS) of Gioia et al. (1990) and the approximately 50 AGNs in the Ultrasoft Survey of Cordova et al. (1992). We present a revised version of the latter sample, based on the post publication discovery of a software error in the Einstein Rev-1b processing. We find that the mean spectral index of the AGNs between 0.1 and 0.6 keV is softer, and the distribution of indices wider, than previous estimates based on analyses of the X-ray spectra of optically selected AGNs. A subset of these AGNs exhibit flux variabiulity, some on timescales as short as 0.05 days. A correlation between radio and hard X-ray luminosity is confirmed, but the data do not support a correlation between the radio and soft X-ray luminosities, or between radio loudness and soft X-ray spectral slope. Evidence for physically distinct soft and hard X-ray components is found, along with the possibility of a bias in previous optically selected samples toward selection of AGNs with flatter X-ray spectra.

Thompson, R. J.↗
DOE OSTI2022

Mitiq: A software package for error mitigation on noisy quantum computers

We introduce Mitiq, a Python package for error mitigation on noisy quantum computers. Error mitigation techniques can reduce the impact of noise on near-term quantum computers with minimal overhead in quantum resources by relying on a mixture of quantum sampling and classical post-processing techniques. Mitiq is an extensible toolkit of different error mitigation methods, including zero-noise extrapolation, probabilistic error cancellation, and Clifford data regression. The library is designed to be compatible with generic backends and interfaces with different quantum software frameworks. We describe Mitiq using code snippets to demonstrate usage and discuss features and contribution guidelines. We present several examples demonstrating error mitigation on IBM and Rigetti superconducting quantum processors as well as on noisy simulators.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1992

Performance Of Digital Data-Transition-Tracking Loop

Report presents theoretical study of performance of all-digital data-transition-tracking loop (DTTL). Equations derived to analyze effects of few samples per symbol period and of noncommensurate sampling and symbol rates. Noise and frequency response taken into account. Effects upon variance of phase error and on mean time to lose lock quantified through computer simulations. Because of phase ambiguity, digital DTTL performs poorly when number of samples per symbol period is small.

Hinedi, Sami M.↗
DOE OSTI2024

Combining compositional data sets introduces error in covariance network reconstruction

Microbial communities are diverse biological systems that include taxa from across multiple kingdoms of life. Notably, interactions between bacteria and fungi play a significant role in determining community structure. However, these statistical associations across kingdoms are more difficult to infer than intra-kingdom associations due to the nature of the data involved using standard network inference techniques. We quantify the challenges of cross-kingdom network inference from both theoretical and practical points of view using synthetic and real-world microbiome data. We detail the theoretical issue presented by combining compositional data sets drawn from the same environment, e.g. 16S and ITS sequencing of a single set of samples, and we survey common network inference techniques for their ability to handle this error. We then test these techniques for the accuracy and usefulness of their intra- and interkingdom associations by inferring networks from a set of simulated samples for which a ground-truth set of associations is known. We show that while the two methods mitigate the error of cross-kingdom inference, there is little difference between techniques for key practical applications including identification of strong correlations and identification of possible keystone taxa (i.e. hub nodes in the network). Furthermore, we identify a signature of the error caused by transkingdom network inference and demonstrate that it appears in networks constructed using real-world environmental microbiome data.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1987

Nonuniform sampling techniques for antenna applications

A two-dimensional sampling technique, which can employ irregularly spaced samples (amplitude and phase) in order to generate the complete far-field patterns is presented. The technique implements a matrix inversion algorithm, which depends only on the nonuniform sampled data point locations and with no dependence on the actual field values at these points. A powerful simulation algorithm is presented to allow a real-life simulation of many reflector/feed configurations and to determine the usefulness of the nonuniform sampling technique for the copolar and cross-polar patterns. Additionally, an overlapped window concept and a generalized error simulation model are discussed to identify the stability of the technique for recovering the field data among the nonuniform sampled data. Numerical results are tailored for the pattern reconstruction of a 20-m offset reflector antenna operating at L-band. This reflector is planned to be used in a proposed measurement concept of large antenna aboard the Space Shuttle, whereby it would be almost impractical to accurately control the movement of the Shuttle with respect to the RF source in prescribed directions in order to generate uniform sampled points. Also, application of the nonuniform sampling technique to patterns obtained using near-field measured data is demonstrated. Finally, results of an actual far-field measurement are presented for the construction of patterns of a reflector antenna from a set of nonuniformly distributed measured amplitude and phase data.

Rahmat-Samii, Yahya↗
DOE OSTI2024

Combining compositional data sets introduces error in covariance network reconstruction

Microbial communities are diverse biological systems that include taxa from across multiple kingdoms of life. Notably, interactions between bacteria and fungi play a significant role in determining community structure. However, these statistical associations across kingdoms are more difficult to infer than intra-kingdom associations due to the nature of the data involved using standard network inference techniques. We quantify the challenges of cross-kingdom network inference from both theoretical and practical points of view using synthetic and real-world microbiome data. We detail the theoretical issue presented by combining compositional data sets drawn from the same environment, e.g. 16S and ITS sequencing of a single set of samples, and we survey common network inference techniques for their ability to handle this error. We then test these techniques for the accuracy and usefulness of their intra- and inter-kingdom associations by inferring networks from a set of simulated samples for which a ground-truth set of associations is known. We show that while the two methods mitigate the error of cross-kingdom inference, there is little difference between techniques for key practical applications including identification of strong correlations and identification of possible keystone taxa (i.e. hub nodes in the network). Furthermore, we identify a signature of the error caused by transkingdom network inference and demonstrate that it appears in networks constructed using real-world environmental microbiome data.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Monitoring Sulfuric Acid and Temperature Using Raman Spectroscopy and Multivariate Chemometrics

Multivariate regression models were optimized for the quantification of sulfuric acid (H 2 SO 4 ) [0–8 M] and temperature (20 °C–80 °C) in the presence of ammonium sulfate ((NH 4 ) 2 SO 4 [0–0.6 M]) using Raman spectroscopy. Optical vibrational spectroscopy is a useful nondestructive technique for the in situ analysis of complex chemical systems notoriously difficult to monitor in situ and in real-time. Multivariate analysis, a chemometrics method, can be paired with these nondestructive optical methods for determining analyte concentration and speciation in complex solutions, such as dissociated species in polyprotic acids, e.g., H 2 SO 4 . The effect of temperature is often overlooked although it can have a major influence on speciation and the corresponding Raman spectra. Here, in this study, partial least squares regression models were optimized for the quantification of H 2 SO 4 and its two deprotonated forms as a function of temperature. Measuring bisulfate as a function of temperature is particularly challenging owing to changes in the second dissociation constant. A designed training set effectively minimized the sample set size and trained a robust predictive model with percent root mean square error of <3% for H 2 SO 4 . The practical strategy employed here was demonstrated to be effective for building chemometric models that directly account for dynamic temperatures with static samples and is shown to be amenable to flow cell analysis applications with a simple calibration transfer for process monitoring applications.

D-optimal design↗
DOE OSTI2022

Demonstration of 151 Sm analysis using Trinity nuclear debris samples

Here, the long-lived fission product 151 Sm (T 1/2 ≈ 90 y) present in archived dissolved Trinity nuclear test debris samples was analyzed using liquid scintillation counting and the results were compared to the expected values based upon historical analysis results. The measured 151 Sm activities were found to agree with the expected values within experimental error. This demonstration validates the use of this technique in situations where the sample is too old for traditional shorter half-life radioactive fission product signatures such as 99 Mo (T 1/2 = 2.75 d), 97 Zr (T 1/2 = 16.7 h), 147 Nd (T 1/2 = 10.98 d), and 153 Sm (T 1/2 = 46.3 h), or too dilute for determining fissions through stable isotope ratio measurements.

151Sm↗
DOE OSTI2023

Validating the Use of Gaussian Process Regression for Adaptive Mapping of Residual Stress Fields

Probing the stress state using a high density of measurement points is time intensive and presents a limitation for what is experimentally feasible. Alternatively, individual strain fields used for determining stresses can be reconstructed from a subset of points using a Gaussian process regression (GPR). Results presented in this paper evidence that determining stresses from reconstructed strain fields is a viable approach for reducing the number of measurements needed to fully sample a component’s stress state. The approach was demonstrated by reconstructing the stress fields in wire-arc additively manufactured walls fabricated using either a mild steel or low-temperature transition feedstock. Effects of errors in individual GP reconstructed strain maps and how these errors propagate to the final stress maps were assessed. Implications of the initial sampling approach and how localized strains affect convergence are explored to give guidance on how best to implement a dynamic sampling experiment.

36 MATERIALS SCIENCE↗