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Results for “biasing”
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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Development of novel on-chip, customer-design spiral biasing adaptor on for Si drift detectors and detector arrays for X-ray and nuclear physics experiments
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Model validation of tungsten erosion and redeposition properties using biased tungsten samples on DiMES
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Spectra of identified particles, geometry categorization and bias and global observables in d plus Au collisions
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Effects of exchange bias on magnetotransport in permalloy kagome artificial spin ice
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Uncovering hidden bias in neutron diffraction residual strain measurements
When calculating residual strain via neutron or X-ray diffraction, uncertainties propagated from the peak fit are often inadequate to describe the true scatter of measurements about a singular strain state, such as one that should describe a macroscopic continuum. Because diffraction is inherently a selective process, orientation-dependent scatter arises from the sub-sampling of strong microstructure and strain gradients. This paper investigates the appropriateness of propagated uncertainties with reference to their original intention, i.e. noise about a mean value. Thirty-six unique orientations of strain measurements are taken at multiple locations within an additive friction-stir deposition component with fine-scale gradients (∼200 µm) of plastic strain, texture and residual elastic strain. Multiple strain and stress calculation pathways are compared: direct substitution of three measurements into Hooke's law, direct inversion of any six unique orientations into the strain state tensor and thirty-six measurement least-squares estimation. For the last two cases, the appropriateness of the uncertainty interval is statistically evaluated on the basis of a physical constraint: common agreement under the strain transformation law. For this sample, the direct inversion of six measurements retains a conservative estimate of the uncertainty. However, propagated uncertainties in the least-squares solution greatly underestimate the true experimental scatter. A simple pathway to estimate appropriate uncertainty intervals is suggested. These results demonstrate that the interpretation of uncertainty in residual strain is strongly dependent on intrinsic sample-dependent effects, and that oversampling orientations and statistical analysis can give more accurate results with realistic uncertainties.
To have value, comparisons of high-throughput phenotyping methods need statistical tests of bias and variance
The gap between genomics and phenomics is narrowing. The rate at which it is narrowing, however, is being slowed by improper statistical comparison of methods. Quantification using Pearson’s correlation coefficient ( r ) is commonly used to assess method quality, but it is an often misleading statistic for this purpose as it is unable to provide information about the relative quality of two methods. Using r can both erroneously discount methods that are inherently more precise and validate methods that are less accurate. These errors occur because of logical flaws inherent in the use of r when comparing methods, not as a problem of limited sample size or the unavoidable possibility of a type I error. A popular alternative to using r is to measure the limits of agreement (LOA). However both r and LOA fail to identify which instrument is more or less variable than the other and can lead to incorrect conclusions about method quality. An alternative approach, comparing variances of methods, requires repeated measurements of the same subject, but avoids incorrect conclusions. Variance comparison is arguably the most important component of method validation and, thus, when repeated measurements are possible, variance comparison provides considerable value to these studies. Statistical tests to compare variances presented here are well established, easy to interpret and ubiquitously available. The widespread use of r has potentially led to numerous incorrect conclusions about method quality, hampering development, and the approach described here would be useful to advance high throughput phenotyping methods but can also extend into any branch of science. The adoption of the statistical techniques outlined in this paper will help speed the adoption of new high throughput phenotyping techniques by indicating when one should reject a new method, outright replace an old method or conditionally use a new method.
Examining the C1-P1 pseudorange bias
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Errors in GPS due to Satellite C1-P1 Code Biases
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Geophysical Model Function for the Sea State Bias in Satellite Altimetry
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Reverse Bias Characteristics of Thin BSF/BSR Silicon Solar Cells For Advanced Photovoltaic Solar Array
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Numerical Analysis of the Sea State Bias for Satellite Altimetry
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A study of the refractivity bias in occultation retrievals using end-to-end simulations
Direct Methanol Fuel Cells in portable applications are expected to provide substantially higher energy density and longer operating times over advanced batteries. The time threshold of advantage of using a fuel cell instead of a battery is almost independent of the power level, and is determined by the power density of the fuel cell and its operating efficiency.
Reducing OCO-2 regional biases through novel 3D cloud, albedo, and meteorology estimation
This ROSES project tests the addition of novel 3D cloud, albedo fine structure, temperature and water vapor profile to the OCO-2 retrieved system. All new parameters have been added to the ReFRACtor / TROPESS system for testing. Currently, we are comparing 3d-cloud retrievals from OCO-2 radiances to the MODIS-derived EAR3T cloud properties. The water and temperature profile retrievals have been implemented and result in improved XCO2 comparisons to TCCON, and now are implementing principal-component analysis (PCA) to reduce the number of parameters added. We are also studying ECOSTRESS spectral library, AVIRIS-ng, OCO-2 spectral residuals, and albedo retrievals with additional parameters to determine the appropriate characterization of albedo. We find that the current albedo parametrization (2nd order polynomial) is adequate for ocean and vegetation, but not adequate for soil and rock observations.
Biased Deposition of Nb-ON-Cu for SRF Accelerators
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Methods to Predict Bias in Criticality Safety Applications using MCNP6 and Whisper
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Vertical structure and physical processes of the Madden-Julian Oscillation: Biases and uncertainties at short range
We present an analysis of diabatic heating and moistening processes from 12 to 36 h lead time forecasts from 12 Global Circulation Models as part of the “Vertical structure and physical processes of the Madden-Julian Oscillation (MJO)” project. A lead time of 12–36 h is chosen to constrain the large-scale dynamics and thermodynamics to be close to observations while avoiding being too close to the initial spin-up of the models as they adjust to being driven from the Years of Tropical Convection (YOTC) analysis. A comparison of the vertical velocity and rainfall with the observations and YOTC analysis suggests that the phases of convection associated with the MJO are constrained in most models at this lead time although the rainfall in the suppressed phase is typically overestimated. Although the large-scale dynamics is reasonably constrained, moistening and heating profiles have large intermodel spread. In particular, there are large spreads in convective heating and moistening at midlevels during the transition to active convection. Radiative heating and cloud parameters have the largest relative spread across models at upper levels during the active phase. A detailed analysis of time step behavior shows that some models show strong intermittency in rainfall and differences in the precipitation and dynamics relationship between models. In conclusion, the wealth of model outputs archived during this project is a very valuable resource for model developers beyond the study of the MJO. Additionally, the findings of this study can inform the design of process model experiments, and inform the priorities for field experiments and future observing systems.