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At least 1,081 records · Page 60
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

MO-FG-202-06: Improving the Performance of Gamma Analysis QA with Radiomics- Based Image Analysis

Purpose: The use of gamma analysis for IMRT quality assurance has well-known limitations. Traditionally, a simple thresholding technique is used to evaluated passing criteria. However, like any image the gamma distribution is rich in information which thresholding mostly discards. We therefore propose a novel method of analyzing gamma images that uses quantitative image features borrowed from radiomics, with the goal of improving error detection. Methods: 368 gamma images were generated from 184 clinical IMRT beams. For each beam the dose to a phantom was measured with EPID dosimetry and compared to the TPS dose calculated with and without normally distributed (2mm sigma) errors in MLC positions. The magnitude of 17 intensity histogram and size-zone radiomic features were derived from each image. The features that differed most significantly between image sets were determined with ROC analysis. A linear machine-learning model was trained on these features to classify images as with or without errors on 180 gamma images.The model was then applied to an independent validation set of 188 additional gamma distributions, half with and half without errors. Results: The most significant features for detecting errors were histogram kurtosis (p=0.007) and three size-zone metrics (p<1e-6 for each). The sizezone metrics detected clusters of high gamma-value pixels under mispositioned MLCs. The model applied to the validation set had an AUC of 0.8, compared to 0.56 for traditional gamma analysis with the decision threshold restricted to 98% or less. Conclusion: A radiomics-based image analysis method was developed that is more effective in detecting error than traditional gamma analysis. Though the pilot study here considers only MLC position errors, radiomics-based methods for other error types are being developed, which may provide better error detection and useful information on the source of detected errors. This work was partially supported by a grant from the Agency for Healthcare Research and Quality, grant number R18 HS022244-01.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

Robust analysis of trends in noisy tokamak confinement data using geodesic least squares regression

Regression analysis is a very common activity in fusion science for unveiling trends and parametric dependencies, but it can be a difficult matter. We have recently developed the method of geodesic least squares (GLS) regression that is able to handle errors in all variables, is robust against data outliers and uncertainty in the regression model, and can be used with arbitrary distribution models and regression functions. We here report on first results of application of GLS to estimation of the multi-machine scaling law for the energy confinement time in tokamaks, demonstrating improved consistency of the GLS results compared to standard least squares.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Should We Conserve Entropy or Energy when Computing CAPE with Mixed-Phase Precipitation Physics?

Abstract The rapidly increasing resolution of global atmospheric reanalysis and climate model datasets necessitates finding methods for computing convective available potential energy (CAPE) both efficiently and accurately. To this end, this article compares two common methods for computing CAPE which conserve either energy or entropy. Inaccuracies in these computations arise from both physical and numerical errors. For instance, computing CAPE with entropy conserved results in physical errors from nonequilibrium phase transitions but minimizes numerical errors because solutions are analytic at each height. In contrast, computing CAPE with energy conserved avoids these physical errors, but accumulates numerical errors that are grid-resolution-dependent because the numerical integration of a differential equation is required. Analysis of CAPE computed with large databases of soundings from the tropical Amazon and midlatitude storm environments shows that physical errors from the entropy method are typically 1%–3% as large as CAPE, which is comparable to the numerical errors from conserving energy with grid spacing of 25 and 250 m using explicit first-order and second-order integration schemes, respectively. Errors in entropy-based CAPE calculations are also insensitive to vertical grid spacing, in contrast to energy-based calculations whose error strongly scales with the grid spacing. It is shown that entropy-based methods are advantageous when intercomparing datasets with differing vertical resolution because they produce accurate and reasonably fast results that are insensitive to grid resolution, whereas a second-order energy-based method is advantageous when analyzing data with a consistent vertical resolution because of its superior computational efficiency. Significance Statement Convective available potential energy (CAPE) is a measure of instability in the atmosphere that helps forecasters and researchers understand when and where thunderstorms will form. The purpose of this article is to identify the most efficient and accurate methods for computing CAPE. Two methods are considered here, one that relates to the entropy (a measure of thermodynamic disorder) of an air parcel and one that relates to the energy of an air parcel. Results indicate that the entropy method is most accurate and insensitive to the resolution of the data used for the calculation (which can vary considerably), whereas the energy method uses the least computation time.

Peters, John M.↗
DOE OSTI2020

Reliability Analysis for Unreliable FSM Computations

Finite State Machines (FSMs) are fundamental in both hardware design and software development. However, the reliability of FSM computations remains poorly understood. This becomes increasingly critical as the technology scaling becomes more aggressive. Existing reliability analyses are mainly designed for generic computations and are unaware of the special error tolerance characteristics in FSM computations. In this work, we introduce RelyFSM – a state-level reliability analysis framework for FSM computations. By qualitatively reasoning about the transition structures in FSM computations, RelyFSM can precisely capture their inherent error tolerance. Basically, RelyFSM takes an FSM specification as input and analyzes its reliability for a specific execution. At the core of the analysis is a set of mathematical models that accurately define the behaviors of unreliable FSM executions. Our evaluation with a spectrum of FSM benchmarks confirms the accuracy and efficiency of RelyFSM in analyzing the reliability of real-world FSM computations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Examining the Impact of Local Constraint Violations on Energy Computations in DFT

ABSTRACT This work examines the impact of locally imposed constraints in Density Functional Theory (DFT). Using a metric referred to as the extent of violation index (EVI), we quantify how well exchange‐correlation functionals adhere to local constraints. Applying EVIs to a diverse set of molecules for GGA functionals reveals constraint violations, particularly for semi‐empirical functionals. We leverage EVIs to explore potential connections between these violations and errors in chemical properties. While no correlation is observed for atomization energies, a significant statistical correlation emerges between EVIs and total energies. Similarly, the analysis of reaction energies suggests weak positive correlations for specific constraints. However, definitive conclusions about error cancellation mechanisms cannot be made at this time. These observations revealed by EVIs may be useful for consideration when designing future generations of semilocal functionals.

Khanna, Vaibhav [Department of Chemistry Universit↗
DOE OSTI2021

Coordination Sphere of Lanthanide Aqua Ions Resolved with Ab Initio Molecular Dynamics and X-ray Absorption Spectroscopy

To resolve the fleeting structures of lanthanide Ln 3+ aqua ions in solution, we (i) performed the first ab initio molecular dynamics (AIMD) simulations of the entire series of Ln 3+ aqua ions in explicit water solvent using pseudopotentials and basis sets recently optimized for lanthanides and (ii) measured the symmetry of the hydrating waters about Ln 3+ ions (Nd 3+ , Dy 3+ , Er 3+ , Lu 3+ ) for the first time with extended X-ray absorption fine structure (EXAFS). EXAFS spectra were measured experimentally and generated from AIMD trajectories to directly compare simulation, which concurrently considers the electronic structure and the atomic dynamics in solution, with experiment. In this study, we performed a comprehensive evaluation of EXAFS multiple-scattering analysis (up to 6.5 Å) to measure Ln–O distances and angular correlations (i.e., symmetry) and elucidate the molecular geometry of the first hydration shell. This evaluation, in combination with symmetry-dependent L 3 - and L 1 -edge spectral analysis, shows that the AIMD simulations remarkably reproduces the experimental EXAFS data. The error in the predicted Ln–O distances is less than 0.07 Å for the later lanthanides, while we observed excellent agreement with predicted distances within experimental uncertainty for the early lanthanides. Our analysis revealed a dynamic, symmetrically disordered first coordination shell, which does not conform to a single molecular geometry for most lanthanides. This work sheds critical light on the highly elusive coordination geometry of the Ln 3+ aqua ions.

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

Identifying Nuclear Data Correlated Through Predicting Bias in Integral Experiments via Applying Principal Component Analysis to Random Forest

ABSTRACT Nuclear data (ND) are the input data for neutron‐transport simulations to answer questions related to nuclear technologies. Subsets of ND, here > 20,000 data points, are validated with respect to thousands of criticality experiments that represent various applications on a small scale. The aim of validation with these experiments is to find errors in ND or methods. The key challenge here is that several hundreds of ND are used to simulate one integral value. Hence, one cannot clearly identify what ND are leading to bias in criticality measurements. In fact, a mistake in one nuclear‐data observable can be compensated with an error in another, and the predicted criticality value would still be predicted in agreement with experimental data. Random forest (RF) was previously employed to predict bias in criticality measurements using sensitivities of simulated criticality experiments to ND. The SHapley Additive exPlanations (SHAP) metric was then applied to attribute the importance of each ND experiment and observable to bias prediction. This, however, did not highlight what ND were jointly related to predicting bias. This is important as it could inform us about where compensating errors in ND could hide. We tackle this shortcoming here by first decomposing the ND sensitivities to integral‐experiment simulations into principal components. Then we use principal component projections to predict bias via the RF and SHAP. The SHAP values and principal components are employed to reconstruct detailed SHAP values for each ND observable. We demonstrate that these extended SHAP bias predictions are more robust, less noisy, and more efficient. In addition, we show that this approach accounts for covariance in ND sensitivities and automates the identification of where compensating errors could hide in ND.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

Threefold reduction of modeled uncertainty in direct radiative effects over biomass burning regions by constraining absorbing aerosols

Absorbing aerosols emitted from biomass burning (BB) greatly affect the radiation balance, cloudiness, and circulation over tropical regions. Assessments of these impacts rely heavily on the modeled aerosol absorption from poorly constrained global models and thus exhibit large uncertainties. By combining the AeroCom model ensemble with satellite and in situ observations, we provide constraints on the aerosol absorption optical depth (AAOD) over the Amazon and Africa. Our approach enables identification of error contributions from emission, lifetime, and MAC (mass absorption coefficient) per model, with MAC and emission dominating the AAOD errors over Amazon and Africa, respectively. In addition to primary emissions, our analysis suggests substantial formation of secondary organic aerosols over the Amazon but not over Africa. Furthermore, we find that differences in direct aerosol radiative effects between models decrease by threefold over the BB source and outflow regions after correcting the identified errors. This highlights the potential to greatly reduce the uncertainty in the most uncertain radiative forcing agent.

09 BIOMASS FUELS↗
DOE OSTI2022

The HUNTER Dynamic Human Reliability Analysis Tool: Development of a Module for Performance Shaping Factors

The Human Unimodel for Nuclear Technology to Enhance Reliability (HUNTER) is a framework to support dynamic human reliability analysis (HRA) in communication with a variety of methods and tools. This paper explores how we developed one of the HUNTER modules: namely, the Individual module for evaluating performance shaping factors (PSFs). A PSF is any factor that influences human performance (e.g., workload or complexity). In the existing HRA, they are used to highlight human errors and adjust error probabilities. We consider the eight PSFs suggested in the Standardized Plant Analysis Risk-HRA (SPAR-H) method, a representative HRA method widely used in the nuclear field. To support our dynamic modeling using the eight SPAR-H PSFs, we reviewed the human performance literature and developed data-based mathematical models to rate and quantify PSFs in the context of dynamic HRA. We also designed the Individual module to consist of two functions: (1) the PSF qualification function for automatically or manually evaluating PSF levels, and (2) the PSF quantification function for dynamically or statically determining PSF multiplier values and integrating them to adjust human error probabilities (HEPs). How each function works in regard to the SPAR-H PSFs, and how the PSFs serve to adjust the HEPs, were investigated via literature review and are discussed in this paper.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2015

SU-E-T-584: Optical Tracking Guided Patient-Specific VMAT QA with ArcCHECK

Purpose: To investigate the novel use of an in-house optical tracking system (OTS) to improve the efficacy of VMAT QA with a cylindrical dosimeter (ArcCHECK™). Methods: The translational and rotational setup errors of ArcCHECK are convoluted which makes it challenging to position the device efficiently and accurately. We first aligned the ArcCHECK to the machine cross-hair at three cardinal angles (0°, 90°, and 270°) to establish a reference position. Four infrared reflective markers were attached to the back of the device. An OTS with 0.2mm/0.2° accuracy was used to control its setup uncertainty. Translational uncertainties of 1 mm and 2 mm in three directions (in, right, and up) were applied on the device. Four open beams of various field sizes and six clinical VMAT arcs were delivered and measured for all simulated setup errors. The measurements were compared with Pinnacle™ calculations using Gamma analysis to evaluate the impact of setup uncertainty. This study also evaluated the improvement in setup reproducibility and efficiency with the aid of the OTS. Results: For open beams, with 3%/3mm, the mean passing rates dropped by less than 5% for all shifts; with 2%/2mm, two significant drops(>5%) were observed: 15.38±6.75% for 2 mm lateral shift and 9.35±4.88% for 2 mm longitudinal shift. For VMAT arcs, the mean passing rates using 2%/2mm dropped by 10.47±7.46% and 22.02±11.39% for 1 and 2 mm shift, respectively. With 3%/3mm, significant drop only occurred with 2 mm longitudinal shift (13.73±8.30%). Setup time could be reduced by >15 min with the aid of the OTS. Conclusion: OTS is an effective tool for separating translational and rotational uncertainties. The current VMAT QA solution was not strongly sensitive to translation errors of 2mm with widely accepted criterion (3%/3mm). This finding raises concerns regarding the efficacy of such QA system in detecting errors in the dynamic VMAT delivery.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2015

Analytical Modeling of a Novel Transverse Flux Machine for Direct Drive Wind Turbine Applications

This paper presents a nonlinear analytical model of a novel double sided flux concentrating Transverse Flux Machine (TFM) based on the Magnetic Equivalent Circuit (MEC) model. The analytical model uses a series-parallel combination of flux tubes to predict the flux paths through different parts of the machine including air gaps, permanent magnets (PM), stator, and rotor. The two-dimensional MEC model approximates the complex three-dimensional flux paths of the TFM and includes the effects of magnetic saturation. The model is capable of adapting to any geometry which makes it a good alternative for evaluating prospective designs of TFM as compared to finite element solvers which are numerically intensive and require more computation time. A single phase, 1 kW, 400 rpm machine is analytically modeled and its resulting flux distribution, no-load EMF and torque, verified with Finite Element Analysis (FEA). The results are found to be in agreement with less than 5% error, while reducing the computation time by 25 times.

17 WIND ENERGY↗
DOE OSTI2021

Multi defect detection and analysis of electron microscopy images with deep learning

Electron microscopy is widely used to explore defects in crystal structures, but human detecting of defects is often time-consuming, error-prone, and unreliable, and is not scalable to large numbers of images or real-time analysis. In this work, we discuss the application of machine learning approaches to find the location and geometry of different defect clusters in irradiated steels. We show that a deep learning based Faster R-CNN analysis system has a performance comparable to human analysis with relatively small training data sets. Furthermore, this study proves the promising ability to apply deep learning to assist the development of automated microscopy data analysis even when multiple features are present and paves the way for fast, scalable, and reliable analysis systems for massive amounts of modern electron microscopy data.

36 MATERIALS SCIENCE↗
DOE OSTI2022

The Simulation and Design of an On-Chip Superconducting Millimetre Filter-Bank Spectrometer

Abstract Superconducting on-chip filter banks provide a scalable, space saving solution to create imaging spectrometers at millimetre and submillimetre wavelengths. We present an easy to realise, lithographed superconducting filter design with a high tolerance to fabrication error. Using a capacitively coupled $$\lambda /2$$ λ / 2 microstrip resonator to define a narrow ( $$\lambda /\Delta \lambda = 300$$ λ / Δ λ = 300 ) spectral pass band, the filtered output of a given spectrometer channel directly connects to a lumped-element kinetic inductance detector. We show the tolerance analysis of our design, demonstrating $$<11\%$$ < 11 % change in filter quality factor to any one realistic fabrication error and a full filter-bank efficiency forecast to be 50% after accounting for fabrication errors and dielectric loss tangent.

Robson, G. (ORCID:0000000315279326)↗
DOE OSTI2015

Magnetic Resonance Imaging–Guided versus Surrogate-Based Motion Tracking in Liver Radiation Therapy: A Prospective Comparative Study

Purpose: This study applied automatic feature detection on cine–magnetic resonance imaging (MRI) liver images in order to provide a prospective comparison between MRI-guided and surrogate-based tracking methods for motion-compensated liver radiation therapy. Methods and Materials: In a population of 30 subjects (5 volunteers plus 25 patients), 2 oblique sagittal slices were acquired across the liver at high temporal resolution. An algorithm based on scale invariant feature transform (SIFT) was used to extract and track multiple features throughout the image sequence. The position of abdominal markers was also measured directly from the image series, and the internal motion of each feature was quantified through multiparametric analysis. Surrogate-based tumor tracking with a state-of-the-art external/internal correlation model was simulated. The geometrical tracking error was measured, and its correlation with external motion parameters was also investigated. Finally, the potential gain in tracking accuracy relying on MRI guidance was quantified as a function of the maximum allowed tracking error. Results: An average of 45 features was extracted for each subject across the whole liver. The multi-parametric motion analysis reported relevant inter- and intrasubject variability, highlighting the value of patient-specific and spatially-distributed measurements. Surrogate-based tracking errors (relative to the motion amplitude) were were in the range 7% to 23% (1.02-3.57mm) and were significantly influenced by external motion parameters. The gain of MRI guidance compared to surrogate-based motion tracking was larger than 30% in 50% of the subjects when considering a 1.5-mm tracking error tolerance. Conclusions: Automatic feature detection applied to cine-MRI allows detailed liver motion description to be obtained. Such information was used to quantify the performance of surrogate-based tracking methods and to provide a prospective comparison with respect to MRI-guided radiation therapy, which could support the definition of patient-specific optimal treatment strategies.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2026

PDE-constrained high-order mesh optimization

Here, we present a novel framework for PDE-constrained r-adaptivity of high-order meshes. The proposed method formulates mesh movement as an optimization problem, with an objective function defined as a convex combination of a mesh quality metric and a measure of the accuracy of the PDE solution obtained via finite element discretization. The proposed formulation achieves optimized, well-defined high-order meshes by integrating mesh quality control, PDE solution accuracy, and robust gradient regularization. We adopt the Target-Matrix Optimization Paradigm to control geometric properties across the mesh, independent of the PDE of interest. To incorporate the accuracy of the PDE solution, we introduce error measures that control the finite element discretization error. The implicit dependence of these error measures on the mesh nodal positions is accurately captured by adjoint sensitivity analysis. Additionally, a convolution-based gradient regularization strategy is used to ensure stable and effective adaptation of high-order meshes. We demonstrate that the proposed framework can improve mesh quality and reduce the error by up to 10 times for the solution of Poisson and linear elasto-static problems. The approach is general with respect to the dimensionality, the order of the mesh, the types of mesh elements, and can be applied to any PDE that admits well-defined adjoint operators.

Computer science↗
DOE OSTI2018

Clinical analysis of the approximate, 3-dimensional, biological effective dose equation in multiphase treatment plans

A multiphase, approximate biological effective dose (BED{sub A}) equation was introduced because most treatment planning systems (TPS) are incapable of calculating the true BED (BED{sub T}). This work investigates the accuracy and precision of the multiphase BED{sub A} relative to the BED{sub T} in clinical cases. Ten patients with head and neck cancer and 10 patients with prostate cancer were studied using their treatment plans from Pinnacle{sup 3} 9.2 (Philips Medical, Fitchburg, WI). The organs at risk (OARs) that were studied are the normal brain, left and right optic nerves, optic chiasm, spinal cord, brainstem, bladder, and rectum. BED{sub A} and BED{sub T} distributions were calculated using MATLAB 2010b (MathWorks, Natick, MA) and analyzed on a voxel basis for percent error, percent error volume histograms (PEVHs), Pearson correlation coefficient, and Bland-Altman analysis. The maximum BED values that were calculated using the BED{sub A} and BED{sub T} methods were also analyzed. BED{sub A} was found to always underestimate BED{sub T}. The accuracy and precision of BED{sub A} distributions varied between the organs: for optic chiasm and brainstem, 50% of the patients had an overall BED{sub A} percent error of A percent error of A percent error of A distributions had maximum errors ranging from 2% to 11%, with the 11% error occurring for bladder. BED{sub A} produced much more accurate maximum BED values with adjacent organs such as normal brain, bladder, and rectum. This study has shown that BED{sub A} can calculate BED distributions with acceptable accuracy under certain circumstances. However, its consistency and accuracy strongly depend on the dose distributions of the different treatment phases. One should be cautious when using BED{sub A}.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2025

LTAU-FF: Loss Trajectory Analysis for Uncertainty in atomistic Force Fields

Model ensembles are effective tools for estimating prediction uncertainty in deep learning atomistic force fields. However, their widespread adoption is hindered by high computational costs and overconfident error estimates. In this work, we address these challenges by leveraging distributions of per-sample errors obtained during training and employing a distance-based similarity search in the model latent space. Our method, which we call LTAU (Loss Trajectory Analysis for Uncertainty), efficiently estimates the full probability distribution function of errors for any test point using the logged training errors, achieving speeds that are 2–3 orders of magnitudes faster than typical ensemble methods and allowing it to be used for tasks where training or evaluating multiple models would be infeasible. We apply LTAU towards estimating parametric uncertainty in atomistic force fields (LTAU-FF), demonstrating that it produces well-calibrated confidence intervals and predicts errors that correlate strongly with the true errors for data near the training domain. Furthermore, we show that the errors predicted by LTAU-FF can be used in practical applications for detecting out-of-domain data, tuning model performance, and predicting failure during simulations. We believe that LTAU will be a valuable tool for uncertainty quantification in atomistic force fields and is a promising method that should be further explored in other domains of machine learning.

97 MATHEMATICS AND COMPUTING↗