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At least 937 records · Page 52
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

The Impact of Below Detection Limit Samples in (NPP) Decommissioning Residual Risk Assessment - 17033

There are some technical problems with finalizing regulations and standards that address nuclear power plant (NPP) decommissioning site soil analyses for license termination. It is difficult to know how to best include detection limit observations from site soil analysis, because typical soil collection data is reported above, at, and below the detection limit. In addition, multiple detection limits are required to adequately estimate the soil radionuclide concentrations. In the literature, researchers analyzing environmental data refer to below the detection limit observations as 'left-censored data'. Left-censored data are less than the detection limit of the instrument(s) that made the measurements. Conventional approaches to managing left-censored data either ignore or simply replace the detection limits with zero, a fraction of the detection limit, or the detection limit itself. However, these approaches are statistically biased and limited in their usefulness. The Kaplan-Meier method, robust regression on order statistics method, and maximum likelihood estimation method are proposed to estimate the left-censored data more precisely. The proposed methods are applied to data from a Monazite powder manufacturing plant and the Colorado School of Mines Research Institute. Summary statistics such as the sample mean, sample standard deviation, sample percentiles are calculated from each data set and are used in risk and volume estimation calculations. Previous environmental analyses, such as the exposure assessment, recognize the uncertainty issue that is addressed in this research using the Latin hypercube sampling approach. Next, probabilistic distributions of the input parameters are developed for the uncertainty analyses. Finally, a framework methodology depicting the decision-making process in the proposed method is presented. The framework defines the critical steps, the amount of radioactivity, types of distributions, censoring percentage, sample size, and number of detection limits; it also allows the user to select the appropriate approach based on their site-specific data and analyses. The goal of this research is to develop a more precise risk assessment, estimate of the volume of soil removal, examine potential cost savings. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
NASA NTRS2001

Approach for Uncertainty Propagation and Robust Design in CFD Using Sensitivity Derivatives

This paper presents an implementation of the approximate statistical moment method for uncertainty propagation and robust optimization for a quasi 1-D Euler CFD (computational fluid dynamics) code. Given uncertainties in statistically independent, random, normally distributed input variables, a first- and second-order statistical moment matching procedure is performed to approximate the uncertainty in the CFD output. Efficient calculation of both first- and second-order sensitivity derivatives is required. In order to assess the validity of the approximations, the moments are compared with statistical moments generated through Monte Carlo simulations. The uncertainties in the CFD input variables are also incorporated into a robust optimization procedure. For this optimization, statistical moments involving first-order sensitivity derivatives appear in the objective function and system constraints. Second-order sensitivity derivatives are used in a gradient-based search to successfully execute a robust optimization. The approximate methods used throughout the analyses are found to be valid when considering robustness about input parameter mean values.

Putko, Michele M.↗
NASA NTRS2018

Improved Pyrotechnic Shock Scaling Method Based on Shock Response Spectrum and Statistical Energy Analysis

It has been more than forty years since the pyrotechnic shock scaling method was introduced. The scaling method estimates the attenuation of the Shock Response Spectrum (SRS) based on the distance from the source, structural configurations, types of structural joints and interfaces, and intervening structure [1]. The method has been successfully used in the spacecraft community countless times and still is frequently used to develop pyrotechnic shock requirements at various levels of assembly of a spacecraft. However, since the method was derived empirically from a limited set of shock test data [2], the aerospace community has been looking for an alternative approach. In this paper, a computational scaling method based on Statistical Energy Analysis (SEA) is re-visited. SEA is traditionally used as a method for investigating the diffusion of acoustic and vibratory energies of a system at steady-state [3]. Previously, an approach using the SRS as an acceleration constraint condition to the SEA model was introduced to estimate the attenuation from a shock source [4,5]. In the current investigation, additional examples are provided to further validate the approach. In addition, responses in the time domain are produced by the Local Modal Phase Reconstruction (LMPR) approach which was recently introduced to the community [6].

Fernandez, Juan P.↗
NASA NTRS1978

The CLASSY clustering algorithm: Description, evaluation, and comparison with the iterative self-organizing clustering system (ISOCLS)

A clustering method, CLASSY, was developed, which alternates maximum likelihood iteration with a procedure for splitting, combining, and eliminating the resulting statistics. The method maximizes the fit of a mixture of normal distributions to the observed first through fourth central moments of the data and produces an estimate of the proportions, means, and covariances in this mixture. The mathematical model which is the basic for CLASSY and the actual operation of the algorithm is described. Data comparing the performances of CLASSY and ISOCLS on simulated and actual LACIE data are presented.

Lennington, R. K.↗
DOE OSTI2024

Out-of-Distribution Detection and Radiological Data Monitoring Using Statistical Process Control

Abstract Machine learning (ML) models often fail with data that deviates from their training distribution. This is a significant concern for ML-enabled devices as data drift may lead to unexpected performance. This work introduces a new framework for out of distribution (OOD) detection and data drift monitoring that combines ML and geometric methods with statistical process control (SPC). We investigated different design choices, including methods for extracting feature representations and drift quantification for OOD detection in individual images and as an approach for input data monitoring. We evaluated the framework for both identifying OOD images and demonstrating the ability to detect shifts in data streams over time. We demonstrated a proof-of-concept via the following tasks: 1) differentiating axial vs. non-axial CT images, 2) differentiating CXR vs. other radiographic imaging modalities, and 3) differentiating adult CXR vs. pediatric CXR. For the identification of individual OOD images, our framework achieved high sensitivity in detecting OOD inputs: 0.980 in CT, 0.984 in CXR, and 0.854 in pediatric CXR. Our framework is also adept at monitoring data streams and identifying the time a drift occurred. In our simulations tracking drift over time, it effectively detected a shift from CXR to non-CXR instantly, a transition from axial to non-axial CT within few days, and a drift from adult to pediatric CXRs within a day—all while maintaining a low false positive rate. Through additional experiments, we demonstrate the framework is modality-agnostic and independent from the underlying model structure, making it highly customizable for specific applications and broadly applicable across different imaging modalities and deployed ML models.

Zamzmi, Ghada↗
DOE OSTI2021

Nonequilibrium information entropy approach to ternary fission of actinides

Ternary fission of actinides probes the state of the nucleus at scission. Light clusters are produced in space and time very close to the scission point. Within the nonequilibrium statistical operator method, a generalized Gibbs distribution is constructed from the information given by the observed yields of isotopes. Using this relevant statistical operator, yields are calculated taking excited states and continuum correlations into account, in accordance with the virial expansion of the equation of state. Furthermore, clusters with mass number A≤10 are well described using the nonequilibrium generalizations of temperature and chemical potentials. Improving the virial expansion, in-medium effects may become of importance in determining the contribution of weakly bound states and continuum correlations to the intrinsic partition function. Yields of larger clusters, which fail to reach this quasiequilibrium form of the relevant distribution, are described by nucleation kinetics, and a saddle-to-scission relaxation time of about 7000 fm/c is inferred. Light-charged particle emission, described by reaction kinetics and virial expansions, may therefore be regarded as a very important tool to probe the nonequilibrium time evolution of actinide nuclei during fission.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
NASA NTRS1985

Identification of dynamic systems, theory and formulation

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.

Maine, R. E.↗
DOE OSTI2022

Photometric redshift uncertainties in weak gravitational lensing shear analysis: models and marginalization

ABSTRACT Recovering credible cosmological parameter constraints in a weak lensing shear analysis requires an accurate model that can be used to marginalize over nuisance parameters describing potential sources of systematic uncertainty, such as the uncertainties on the sample redshift distribution n(z). Due to the challenge of running Markov chain Monte Carlo (MCMC) in the high-dimensional parameter spaces in which the n(z) uncertainties may be parametrized, it is common practice to simplify the n(z) parametrization or combine MCMC chains that each have a fixed n(z) resampled from the n(z) uncertainties. In this work, we propose a statistically principled Bayesian resampling approach for marginalizing over the n(z) uncertainty using multiple MCMC chains. We self-consistently compare the new method to existing ones from the literature in the context of a forecasted cosmic shear analysis for the HSC three-year shape catalogue, and find that these methods recover statistically consistent error bars for the cosmological parameter constraints for predicted HSC three-year analysis, implying that using the most computationally efficient of the approaches is appropriate. However, we find that for data sets with the constraining power of the full HSC survey data set (and, by implication, those upcoming surveys with even tighter constraints), the choice of method for marginalizing over n(z) uncertainty among the several methods from the literature may modify the 1σ uncertainties on Ωm–S8 constraints by ∼4 per cent, and a careful model selection is needed to ensure credible parameter intervals.

Zhang, Tianqing (ORCID:000000025596198X)↗
DOE OSTI2022

Optimizing observables with machine learning for better unfolding

Most measurements in particle and nuclear physics use matrix-based unfolding algorithms to correct for detector effects. In nearly all cases, the observable is defined analogously at the particle and detector level. We point out that while the particle-level observable needs to be physically motivated to link with theory, the detector-level need not be and can be optimized. We show that using deep learning to define detector-level observables has the capability to improve the measurement when combined with standard unfolding methods.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

Joint inference of multiplicative and additive systematics in galaxy density fluctuations and clustering measurements

Galaxy clustering measurements are a key probe of the matter density field in the Universe. With the era of precision cosmology upon us, surveys rely on precise measurements of the clustering signal for meaningful cosmological analysis. However, the presence of systematic contaminants can bias the observed galaxy number density, and thereby bias the galaxy two-point statistics. As the statistical uncertainties get smaller, correcting for these systematic contaminants becomes increasingly important for unbiased cosmological analysis. We present and validate a new method for understanding and mitigating both additive and multiplicative systematics in galaxy clustering measurements (two-point function) by joint inference of contaminants in the galaxy overdensity field (one-point function) using a maximum-likelihood estimator (MLE). We test this methodology with Kilo-Degree Survey-like mock galaxy catalogues and synthetic systematic template maps. We estimate the cosmological impact of such mitigation by quantifying uncertainties and possible biases in the inferred relationship between the observed and the true galaxy clustering signal. Our method robustly corrects the clustering signal to the sub-percent level and reduces numerous additive and multiplicative systematics from 1.5σ to less than 0.1σ for the scenarios we tested. In addition, we provide an empirical approach to identifying the functional form (additive, multiplicative, or other) by which specific systematics contaminate the galaxy number density. Even though this approach is tested and geared towards systematics contaminating the galaxy number density, the methods can be extended to systematics mitigation for other two-point correlation measurements.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2016

Understanding the bias introduced in quantum dot blinking using change point analysis

Quantum dots are semiconductor crystals that are less than 100 nanometers in size; that is, between 1 billionth and 100 billionths of a meter. They can be composed of one semiconductor material or a core of one semiconductor surrounded by a shell of another. The quantum dots studied in this work consisted of a cadmium selenide core surrounded by a cadmium sulfide shell. When a quantum dot is illuminated with light, some of it can be absorbed and excites the quantum dot to a higher energy state. To relax back down to its resting state, light can be emitted in the form of fluorescence. The nature of the fluorescence that is emitted from a single quantum dot differs from that which is emitted from a bulk sample (usually a solid film or a solution of quantum dots). While in bulk samples the fluorescence over time fluctuates randomly about some average, maintaining a continuous average intensity, single quantum dot fluorescence switches between two or more intensities. This phenomenon is referred to as quantum dot blinking, and its origin is of significant relevance to solar cell, light emitting diode and biological tracking applications. Quantum dot blinking can be studied by exciting an isolated quantum dot with light and recording the ensuing fluorescence using sensitive detectors. This is done over many minutes, resulting in a fluorescence intensity (in counts per second) versus time trajectory. These trajectories must be further analyzed in order to understand the nature of the blinking behavior. Conventionally, a bin and threshold analysis is used, whereby the arrival times of fluorescent photons are binned to reduce the signal noise, and a threshold is used to separate the trajectory into periods of high and low fluorescence. In this study, the effects of bin size and threshold on the blinking kinetics were investigated, and the assumption that there is only one bright and one dark state was tested. To do so, a statistically rigorous method called change point analysis was needed which does not require binning and which can determine the number of intensity levels present. Blinking in 17 quantum dots was studied using both analysis methods. The primary parameter that was compared between the two methods is called a truncation time. Increasing the bin size was found to generally lead to a larger truncation time while increasing the threshold always decreased the truncation time. Using the change point analysis method, 12 out of 17 quantum dots were found to exhibit more than two intensity levels. Lastly, the assumption that only two intensity levels exist was found to significantly affect the blinking statistics: when individual levels were combined into one bright level, the truncation time would increase. The change point analysis method was found to be more suitable for studying multilevel blinking in quantum dots than the conventional bin and threshold method, since the effects of binning and thresholding can be alleviated and the two-level assumption relaxed.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Effect of threshold parameters on infrared segmentation methods for porosity detection in electron beam powder bed fusion

In-situ process monitoring has seen significant interest in additive manufacturing to address qualification and certification goals. This is especially prevalent in metal powder bed fusion processes such as electron beam powder bed fusion (PBF-EB), with layer-wise infrared imaging being commonly used to detect defects. Here, this work compares two different segmentation methods (static thresholding and statistical thresholding) used for detecting porosity from in-situ infrared imaging data for PBF-EB. Samples were manufactured at a variety of focus offset values to induce porosity. Then, the segmented infrared images were compared to ex-situ X-ray computed tomography scans, which served as a ground-truth reference for objective evaluation. Through this analysis framework, the influential parameters, static threshold and N-value (number of standard deviations above the mean pixel value), respectively, for both image segmentation methods were analyzed and compared for their effects on porosity detection. With optimal parameter settings, the two methods had similar porosity detection performance, but the statistical method performed better under a larger variety of parameter settings.

Infrared imaging↗
DOE OSTI2015

SU-E-T-659: Quantitative Evaluation of Patient Setup Accuracy of Stereotactic Radiotherapy with the Frameless 6D-ExacTrac System Using Statistical Modeling

Purpose: To evaluate patient setup accuracy and quantify individual and cumulative positioning uncertainties associated with different hardware and software components of the stereotactic radiotherapy (SRS/SRT) with the frameless-6D-ExacTrac system. Methods: A statistical model was used to evaluate positioning uncertainties of the different components of SRS/SRT treatment with the BrainLAB 6D-ExacTrac system using the positioning shifts of 35 patients having cranial lesions (49 total lesions treated in 1, 3, 5 fractions). All these patients were immobilized with rigid head-and-neck masks, simulated with BrainLAB-localizer and planned with iPlan treatment planning system. Infrared imaging (IR) was used initially to setup patients. Then, stereoscopic x-ray images (XC) were acquired and registered to corresponding digitally-reconstructed-radiographs using bony-anatomy matching to calculate 6D-translational and rotational shifts. When the shifts were within tolerance (0.7mm and 1°), treatment was initiated. Otherwise corrections were applied and additional x-rays were acquired (XV) to verify that patient position was within tolerance. Results: The uncertainties from the mask, localizer, IR-frame, x-ray imaging, MV and kV isocentricity were quantified individually. Mask uncertainty (Translational: Lateral, Longitudinal, Vertical; Rotational: Pitch, Roll, Yaw) was the largest and varied with patients in the range (−1.05−1.50mm, −5.06–3.57mm, −5.51−3.49mm; −1.40−2.40°, −1.24−1.74°, and −2.43−1.90°) obtained from mean of XC shifts for each patient. Setup uncertainty in IR positioning (0.88,2.12,1.40mm, and 0.64,0.83,0.96°) was extracted from standard-deviation of XC. Systematic uncertainties of the localizer (−0.03,−0.01,0.03mm, and −0.03,0.00,−0.01°) and frame (0.18,0.25,−1.27mm,−0.32,0.18, and 0.47°) were extracted from means of all XV setups and mean of all XC distributions, respectively. Uncertainties in isocentricity of the MV radiotherapy machine were (0.27,0.24,0.34mm) and kV-imager (0.15,−0.4,0.21mm). Conclusion: A statistical model was developed to evaluate the individual and cumulative systematic and random uncertainties induced by the different hardware and software components of the 6D-ExacTrac-system. The immobilization mask was associated with the largest positioning uncertainty.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2019

Measurement of spleen fat on MRI-proton density fat fraction arises from reconstruction of noise

Purpose: This study compares splenic proton density fat fraction (PDFF) measured using confounder-corrected chemical shift-encoded (CSE)-MRI to magnetic resonance spectroscopy (MRS) in human patients at 3T. Methods: This was a prospectively designed ancillary study to various previously described single-center studies performed in adults and children with known or suspected nonalcoholic fatty liver disease. Patients underwent magnitude-based MRI (MRI-M), complex-based MRI (MRI-C), high signal-to-noise variants (Hi-SNR MRI-M and Hi-SNR MRI-C), and MRS at 3T for spleen PDFF estimation. PDFF from CSE-MRI methods were compared to MRS-PDFF using Wilcoxon signed-rank tests. Demographics were summarized descriptively. Spearman’s rank correlations were computed pairwise between CSE-MRI methods. Individual patient measurements were plotted for qualitative assessment. A significance level of 0.05 was used. Results: Forty-seven patients (20 female, 27 male) including 12 adults (median 55 years old) and 35 children (median 12 years old). Median PDFF estimated by MRS, MRI-M, Hi-SNR MRI-M, MRI-C, and Hi-SNR MRI-C was 1.0, 2.3, 1.9, 2.2, and 2.0%. The four CSE-MRI methods estimated statistically significant higher spleen PDFF values compared to MRS (p < 0.0001 for all). Pairwise associations in spleen PDFF values measured by different CSE-MRI methods were weak, with the highest Spearman’s rank correlations being 0.295 between MRI-M and Hi-SNR MRI-M; none were significant after correction for multiple comparisons. No qualitative relationship was observed between PDFF measurements among the various methods. Conclusion: Overestimation of PDFF by CSE-MRI compared to MRS and poor agreement between related CSE-MRI methods suggest that non-zero PDFF values in human spleen are artifactual.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2015

WE-AB-204-11: Development of a Nuclear Medicine Dosimetry Module for the GPU-Based Monte Carlo Code ARCHER

Purpose: To develop a nuclear medicine dosimetry module for the GPU-based Monte Carlo code ARCHER. Methods: We have developed a nuclear medicine dosimetry module for the fast Monte Carlo code ARCHER. The coupled electron-photon Monte Carlo transport kernel included in ARCHER is built upon the Dose Planning Method code (DPM). The developed module manages the radioactive decay simulation by consecutively tracking several types of radiation on a per disintegration basis using the statistical sampling method. Optimization techniques such as persistent threads and prefetching are studied and implemented. The developed module is verified against the VIDA code, which is based on Geant4 toolkit and has previously been verified against OLINDA/EXM. A voxelized geometry is used in the preliminary test: a sphere made of ICRP soft tissue is surrounded by a box filled with water. Uniform activity distribution of I-131 is assumed in the sphere. Results: The self-absorption dose factors (mGy/MBqs) of the sphere with varying diameters are calculated by ARCHER and VIDA respectively. ARCHER’s result is in agreement with VIDA’s that are obtained from a previous publication. VIDA takes hours of CPU time to finish the computation, while it takes ARCHER 4.31 seconds for the 12.4-cm uniform activity sphere case. For a fairer CPU-GPU comparison, more effort will be made to eliminate the algorithmic differences. Conclusion: The coupled electron-photon Monte Carlo code ARCHER has been extended to radioactive decay simulation for nuclear medicine dosimetry. The developed code exhibits good performance in our preliminary test. The GPU-based Monte Carlo code is developed with grant support from the National Institute of Biomedical Imaging and Bioengineering through an R01 grant (R01EB015478)

60 APPLIED LIFE SCIENCES↗