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At least 1,747 records · Page 97
All sourcesNASA NTRSDOE OSTI
DOE OSTI2013

A unified statistical framework for material decomposition using multienergy photon counting x-ray detectors

Purpose: Material decomposition using multienergy photon counting x-ray detectors (PCXD) has been an active research area over the past few years. Even with some success, the problem of optimal energy selection and three material decomposition including malignant tissue is still on going research topic, and more systematic studies are required. This paper aims to address this in a unified statistical framework in a mammographic environment.Methods: A unified statistical framework for energy level optimization and decomposition of three materials is proposed. In particular, an energy level optimization algorithm is derived using the theory of the minimum variance unbiased estimator, and an iterative algorithm is proposed for material composition as well as system parameter estimation under the unified statistical estimation framework. To verify the performance of the proposed algorithm, the authors performed simulation studies as well as real experiments using physical breast phantom and ex vivo breast specimen. Quantitative comparisons using various performance measures were conducted, and qualitative performance evaluations for ex vivo breast specimen were also performed by comparing the ground-truth malignant tissue areas identified by radiologists.Results: Both simulation and real experiments confirmed that the optimized energy bins by the proposed method allow better material decomposition quality. Moreover, for the specimen thickness estimation errors up to 2 mm, the proposed method provides good reconstruction results in both simulation and real ex vivo breast phantom experiments compared to existing methods.Conclusions: The proposed statistical framework of PCXD has been successfully applied for the energy optimization and decomposition of three material in a mammographic environment. Experimental results using the physical breast phantom and ex vivo specimen support the practicality of the proposed algorithm.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2014

MO-A-16A-01: QA Procedures and Metrics: In Search of QA Usability

Radiation therapy has undergone considerable changes in the past two decades with a surge of new technology and treatment delivery methods. The complexity of radiation therapy treatments has increased and there has been increased awareness and publicity about the associated risks. In response, there has been proliferation of guidelines for medical physicists to adopt to ensure that treatments are delivered safely. Task Group recommendations are copious, and clinical physicists' hours are longer, stretched to various degrees between site planning and management, IT support, physics QA, and treatment planning responsibilities.Radiation oncology has many quality control practices in place to ensure the delivery of high-quality, safe treatments. Incident reporting systems have been developed to collect statistics about near miss events at many radiation oncology centers. However, tools are lacking to assess the impact of these various control measures. A recent effort to address this shortcoming is the work of Ford et al (2012) who recently published a methodology enumerating quality control quantification for measuring the effectiveness of safety barriers. Over 4000 near-miss incidents reported from 2 academic radiation oncology clinics were analyzed using quality control quantification, and a profile of the most effective quality control measures (metrics) was identified.There is a critical need to identify a QA metric to help the busy clinical physicists to focus their limited time and resources most effectively in order to minimize or eliminate errors in the radiation treatment delivery processes. In this symposium the usefulness of workflows and QA metrics to assure safe and high quality patient care will be explored.Two presentations will be given:Quality Metrics and Risk Management with High Risk Radiation Oncology ProceduresStrategies and metrics for quality management in the TG-100 Era Learning Objectives: Provide an overview and the need for QA usability metrics: Different cultures/practices affecting the effectiveness of methods and metrics. Show examples of quality assurance workflows, Statistical process control, that monitor the treatment planning and delivery process to identify errors. To learn to identify and prioritize risks and QA procedures in radiation oncology. Try to answer the question: Can a quality assurance program aided by quality assurance metrics help minimize errors and ensure safe treatment delivery. Should such metrics be institution specific.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2019

Statistical change detection of building energy consumption: Applications to savings estimation

The surge in interval meter data availability and associated activity in energy data analytics has inspired new interest in advanced methods for building efficiency savings estimation. Statistical and machine learning approaches are being explored to improve the energy baseline models used to measure and verify savings. One outstanding challenge is the ability to identify and account for operational changes that may confound savings estimates. In the measurement and verification (M&V) context, ‘non-routine events’ (NREs) cause changes in building energy use that are not attributable to installed efficiency measures, and not accounted for in the baseline model’s independent variables. In the M&V process NREs must be accounted for as ‘adjustments’ to appropriately attribute the estimated energy savings to the specific efficiency interventions that were implemented. Currently this is a manual and custom process, conducted using professional judgment and engineering expertise. As such it remains a barrier in scaling and standardizing meter-based savings estimation. Here in this work, a data driven methodology was developed to (partially) automate, and therefore streamline the process of detecting NREs in the post-retrofit period and making associated savings adjustments. The proposed NRE detection algorithm is based on a statistical change point detection method and a dissimilarity metric. The dissimilarity metric measures the proximity between the actual time series of the post-retrofit energy consumption and the projected baseline, which is generated using a statistical baseline model. The suggested approach for NRE adjustment involves the NRE detection algorithm, the M&V practitioner, and a regression modeling algorithm. The performance of the detection and adjustment algorithm was evaluated using a simulation-generated test data set, and two benchmark algorithms. Results show a high true positive detection rate (75%-100% across the test cases), higher than ideal false positive detection rates (20%-70%), and low errors in energy adjustment (<0.7%). These results indicate that the algorithm holds for helping M&V practitioners to streamline the process of handling NREs. Moreover, the change point algorithm and underlying statistical principles could prove valuable for other building analytics applications such as anomaly detection and fault diagnostics.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2016

Validating the simulation of large-scale parallel applications using statistical characteristics

Simulation is a widely adopted method to analyze and predict the performance of large-scale parallel applications. Validating the hardware model is highly important for complex simulations with a large number of parameters. Common practice involves calculating the percent error between the projected and the real execution time of a benchmark program. However, in a high-dimensional parameter space, this coarse-grained approach often suffers from parameter insensitivity, which may not be known a priori. Moreover, the traditional approach cannot be applied to the validation of software models, such as application skeletons used in online simulations. In this work, we present a methodology and a toolset for validating both hardware and software models by quantitatively comparing fine-grained statistical characteristics obtained from execution traces. Although statistical information has been used in tasks like performance optimization, this is the first attempt to apply it to simulation validation. Lastly, our experimental results show that the proposed evaluation approach offers significant improvement in fidelity when compared to evaluation using total execution time, and the proposed metrics serve as reliable criteria that progress toward automating the simulation tuning process.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Robust Statistical Approach for Determination of Graphite Nitridation Using Bayesian Model Comparison

A better estimation of surface reaction efficiency of semiconductor-grade graphite with atomic nitrogen, as well as the calibration error are calculated using Bayesian updating based on experimental data. Compared with a conventional deterministic model, the stochastic model approach is a powerful tool in the sense that the model is capable of taking into account underlying error correlations among the data quantities. In this paper, we investigate four different stochastic models (called “stochastic system model classes” herein) corresponding to different descriptions of modeling and measurement error structures, given one deterministic physical model. These stochastic system model classes differ in the covariance matrix structure that is used in the uncertainty model to represent uncertainties associated with the physical model and experimental measurements. For each model class, Bayesian inference is used to estimate the posterior probabilities of the physical model parameters as well as of the stochastic model parameters. Model comparison and selection are then applied based on two measures including Bayesian evidence and Bayesian information criterion, as well as the deviance information criterion. Both measures suggest the stochastic model class, which considers that a correlation between errors in two data quantities among different data points is the most plausible. With the stochastic model class, the range of uncertainty in surface reaction efficiency is estimated to be about two orders of magnitude at [Formula: see text].

Engineering↗
DOE OSTI2014

SU-E-T-51: Bayesian Network Models for Radiotherapy Error Detection

Purpose: To develop a probabilistic model of radiotherapy plans using Bayesian networks that will detect potential errors in radiation delivery. Methods: Semi-structured interviews with medical physicists and other domain experts were employed to generate a set of layered nodes and arcs forming a Bayesian Network (BN) which encapsulates relevant radiotherapy concepts and their associated interdependencies. Concepts in the final network were limited to those whose parameters are represented in the institutional database at a level significant enough to develop mathematical distributions. The concept-relation knowledge base was constructed using the Web Ontology Language (OWL) and translated into Hugin Expert Bayes Network files via the the RHugin package in the R statistical programming language. A subset of de-identified data derived from a Mosaiq relational database representing 1937 unique prescription cases was processed and pre-screened for errors and then used by the Hugin implementation of the Estimation-Maximization (EM) algorithm for machine learning all parameter distributions. Individual networks were generated for each of several commonly treated anatomic regions identified by ICD-9 neoplasm categories including lung, brain, lymphoma, and female breast. Results: The resulting Bayesian networks represent a large part of the probabilistic knowledge inherent in treatment planning. By populating the networks entirely with data captured from a clinical oncology information management system over the course of several years of normal practice, we were able to create accurate probability tables with no additional time spent by experts or clinicians. These probabilistic descriptions of the treatment planning allow one to check if a treatment plan is within the normal scope of practice, given some initial set of clinical evidence and thereby detect for potential outliers to be flagged for further investigation. Conclusion: The networks developed here support the use of probabilistic models into clinical chart checking for improved detection of potential errors in RT plans.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

zPerf: A Statistical Gray-Box Approach to Performance Modeling and Extrapolation for Scientific Lossy Compression

With the scaling up of simulation-based scientific discovery on high-performance computing systems, the disparity between compute and I/O has increased, forcing domain scientists to save only a small amount of simulation data to persistent storage. This can result in the loss of essential physics fields that are needed for data analysis. While error-bounded lossy compression has made tremendous progress in bridging the gap between compute and I/O, the lack of understanding of compression performance remains a key hurdle to its wide adoption. Here, in this work, we present zPerf, a statistical gray-box performance modeling approach for scientific lossy compression. Our contributions are threefold: 1) We develop zPerf to estimate the performance of lossy compression techniques, based on in-depth understanding and statistical modeling for data features and core compression metrics; 2) We demonstrate the in-detailed implementation of zPerf using two case studies, where we derive the performance modeling for SZ and ZFP, two leading lossy compressors; 3) We evaluate the effectiveness of zPerf on real-world datasets across various domains. Based on the evaluation, we demonstrate the efficacy of the zPerf performance model; 4) We further discuss three case studies where zPerf is applied to extrapolate the compression ratio of SZ and ZFP with alternative encoding schemes as well as ZFP with an alternative transform scheme. Through the case studies, we demonstrate the potential of zPerf for exploring the design space of lossy compression, which has hardly been studied in the literature.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2003

Impact of Flow-Dependent Error Correlations and Tropospheric Chemistry on Assimilated Ozone

The presentation compares different versions of a global three-dimensional ozone data assimilation system developed at NASA's Data Assimilation Office. The Solar Backscatter Ultraviolet/2 (SBUV/2) total and partial ozone column retrievals are the sole data assimilated in all of the experiments presented. We study the impact of changing the forecast error covariance model from a version assuming static correlations with a one that captures a short-term Lagrangian evolution of those correlations. This is further combined with a study of the impact of neglecting the tropospheric ozone production, loss and dry deposition rates, which are obtained from the Harvard GEOS-CHEM model. We compare statistical characteristics of the assimilated data and the results of validation against independent observations, obtained from WMO balloon-borne sondes and the Polar Ozone and Aerosol Measurement (POAM) III instrument. Experiments show that allowing forecast error correlations to evolve with the flow results in positive impact on assimilated ozone within the regions where data were not assimilated, particularly at high latitudes in both hemispheres. On the other hand, the main sensitivity to tropospheric chemistry is in the Tropics and sub-Tropics. The best agreement between the assimilated ozone and the in-situ sonde data is in the experiment using both flow-dependent error covariances and tropospheric chemistry.

Wargan, K.↗
DOE OSTI2017

Model-based optimization of near-field binary-pixelated beam shapers

The optimization of components that rely on spatially dithered distributions of transparent or opaque pixels and an imaging system with far-field filtering for transmission control is demonstrated. The binary-pixel distribution can be iteratively optimized to lower an error function that takes into account the design transmission and the characteristics of the required far-field filter. Simulations using a design transmission chosen in the context of high-energy lasers show that the beam-fluence modulation at an image plane can be reduced by a factor of 2, leading to performance similar to using a non-optimized spatial-dithering algorithm with pixels of size reduced by a factor of 2 without the additional fabrication complexity or cost. The optimization process preserves the pixel distribution statistical properties. Analysis shows that the optimized pixel distribution starting from a high-noise distribution defined by a random-draw algorithm should be more resilient to fabrication errors than the optimized pixel distributions starting from a low-noise, error-diffusion algorithm, while leading to similar beamshaping performance. Furthermore, this is confirmed by experimental results obtained with various pixel distributions and induced fabrication errors.

43 PARTICLE ACCELERATORS↗
DOE OSTI2017

Information criteria for quantifying loss of reversibility in parallelized KMC

Parallel Kinetic Monte Carlo (KMC) is a potent tool to simulate stochastic particle systems efficiently. However, despite literature on quantifying domain decomposition errors of the particle system for this class of algorithms in the short and in the long time regime, no study yet explores and quantifies the loss of time-reversibility in Parallel KMC. Inspired by concepts from non-equilibrium statistical mechanics, we propose the entropy production per unit time, or entropy production rate, given in terms of an observable and a corresponding estimator, as a metric that quantifies the loss of reversibility. Typically, this is a quantity that cannot be computed explicitly for Parallel KMC, which is why we develop a posteriori estimators that have good scaling properties with respect to the size of the system. Through these estimators, we can connect the different parameters of the scheme, such as the communication time step of the parallelization, the choice of the domain decomposition, and the computational schedule, with its performance in controlling the loss of reversibility. From this point of view, the entropy production rate can be seen both as an information criterion to compare the reversibility of different parallel schemes and as a tool to diagnose reversibility issues with a particular scheme. As a demonstration, we use Sandia Lab's SPPARKS software to compare different parallelization schemes and different domain (lattice) decompositions.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Two-Step Estimation in a Heteroscedastic Linear Regression Model

We study the problem of estimating a parameter in some heteroscedastic linear regression model in the case where the regressors consist of all order statistics based on the sample of identically distributed not necessarily independent observations with finite second moment. It is assumed that the random errors depend on the parameter and distributions of the corresponding regressors. We propose a two-step procedure for finding explicit asymptotically normal estimators.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Informed total-error-minimizing priors: Interpretable cosmological parameter constraints despite complex nuisance effects

While Bayesian inference techniques are standard in cosmological analyses, it is common to interpret resulting parameter constraints with a frequentist intuition. This intuition can fail, for example, when marginalizing high-dimensional parameter spaces onto subsets of parameters, because of what has come to be known as projection effects or prior volume effects. We present the method of informed total-error-minimizing (ITEM) priors to address this problem. An ITEM prior is a prior distribution on a set of nuisance parameters, such as those describing astrophysical or calibration systematics, intended to enforce the validity of a frequentist interpretation of the posterior constraints derived for a set of target parameters (e.g., cosmological parameters). Our method works as follows. For a set of plausible nuisance realizations, we generate target parameter posteriors using several different candidate priors for the nuisance parameters. We reject candidate priors that do not accomplish the minimum requirements of bias (of point estimates) and coverage (of confidence regions among a set of noisy realizations of the data) for the target parameters on one or more of the plausible nuisance realizations. Of the priors that survive this cut, we select the ITEM prior as the one that minimizes the total error of the marginalized posteriors of the target parameters. As a proof of concept, we applied our method to the density split statistics measured in Dark Energy Survey Year 1 data. We demonstrate that the ITEM priors substantially reduce prior volume effects that otherwise arise and that they allow for sharpened yet robust constraints on the parameters of interest.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2018

Spatially Dependent Multiple Testing Under Model Misspecification, With Application to Detection of Anthropogenic Influence on Extreme Climate Events

The Weather Risk Attribution Forecast (WRAF) is a forecasting tool that uses output from global climate models to make simultaneous attribution statements about whether and how greenhouse gas emissions have contributed to extreme weather across the globe. However, in conducting a large number of simultaneous hypothesis tests, the WRAF is prone to identifying false “discoveries.” A common technique for addressing this multiple testing problem is to adjust the procedure in a way that controls the proportion of true null hypotheses that are incorrectly rejected, or the false discovery rate (FDR). Unfortunately, generic FDR procedures suffer from low power when the hypotheses are dependent, and techniques designed to account for dependence are sensitive to misspecification of the underlying statistical model. In this paper, we develop a Bayesian decision-theoretical approach for dependent multiple testing and a nonparametric hierarchical statistical model that flexibly controls false discovery and is robust to model misspecification. We illustrate the robustness of our procedure to model error with a simulation study, using a framework that accounts for generic spatial dependence and allows the practitioner to flexibly specify the decision criteria. Finally, we apply our procedure to several seasonal forecasts and discuss implementation for the WRAF workflow. Lastly, supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

STRONG LENS TIME DELAY CHALLENGE. II. RESULTS OF TDC1

We present the results of the first strong lens time delay challenge. The motivation, experimental design, and entry level challenge are described in a companion paper. This paper presents the main challenge, TDC1, which consisted of analyzing thousands of simulated light curves blindly. The observational properties of the light curves cover the range in quality obtained for current targeted efforts (e.g., COSMOGRAIL) and expected from future synoptic surveys (e.g., LSST), and include simulated systematic errors. Seven teams participated in TDC1, submitting results from 78 different method variants. After describing each method, we compute and analyze basic statistics measuring accuracy (or bias) A, goodness of fit χ{sup 2}, precision P, and success rate f. For some methods we identify outliers as an important issue. Other methods show that outliers can be controlled via visual inspection or conservative quality control. Several methods are competitive, i.e., give |A| < 0.03, P < 0.03, and χ{sup 2} < 1.5, with some of the methods already reaching sub-percent accuracy. The fraction of light curves yielding a time delay measurement is typically in the range f = 20%-40%. It depends strongly on the quality of the data: COSMOGRAIL-quality cadence and light curve lengths yield significantly higher f than does sparser sampling. Taking the results of TDC1 at face value, we estimate that LSST should provide around 400 robust time-delay measurements, each with P < 0.03 and |A| < 0.01, comparable to current lens modeling uncertainties. In terms of observing strategies, we find that A and f depend mostly on season length, while P depends mostly on cadence and campaign duration.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Certified quantum measurement of Majorana fermions

Here, we propose a quantum self-testing protocol to certify measurements of fermion parity involving Majorana fermion modes. We further show that observing a set of ideal measurement statistics implies anticommutativity of the implemented Majorana fermion parity operators, a necessary prerequisite for Majorana detection. Our protocol is robust to experimental errors. We obtain lower bounds on the fidelities of the state and measurement operators that are linear in the errors. We propose to analyze experimental outcomes in terms of a contextuality witness W , which satisfies 〈 W 〉 ≤ 3 for any classical probabilistic model of the data. A violation of the inequality witnesses quantum contextuality, and the closeness to the maximum ideal value 〈 W 〉 = 5 indicates the degree of confidence in the detection of Majorana fermions.

36 MATERIALS SCIENCE↗
NASA NTRS1979

Magnetic field directional discontinuities. 1: Minimum variance errors

Errors associated with the minimum variance analysis of directional discontinuity normal components were investigated using both computer simulation of discontinuities with controlled properties and the examination of current sheets observed by the Mariner 10 spacecraft. The simulated discontinuities were created by adding fluctuations, represented by isotropic noise, to exactly known but varying (in a plane) magnetic field components. An empirical expression for the magnitude of the error in an estimated discontinuity normal component, relative to the total field across the discontinuity, was derived, as well as other relevant statistical properties. Use of the empirical relation in the analysis of 644 discontinuities observed by Mariner 10 provides a more precise, but probably conservative, estimate of an upper bound on the relative normal component value for tangential discontinuities that can be used to separate rotational from tangential discontinuities in studies using only magnetic field data from a single spacecraft, at least for the interplanetary region of space considered.

Lepping, R. P.↗
NASA NTRS1977

Feature discrimination and detection probability in synthetic aperture radar imaging system

Images obtained using synthetic aperture radar (SAR) systems can only represent the intensities of resolution cells in the scene of interest probabilistically since radar receiver noise and Rayleigh scattering of the transmitted radiation are always present. Consequently, when features to be identified differ only by their contribution to the mean power of the radar return, discrimination can be treated by detection theory. In this paper, we develop a 'sufficient statistic' for discriminating between competing features and compare it with some suboptimal methods frequently used. Discrimination is measured by probability of detection error and depends on number of samples or 'looks', signal-to-noise ratio (SNR), and ratio of mean power returns from the competing features. Our results show discrimination and image quality rapidly saturate with SNR (very small improvement for SNR not less than 10 dB) but continue to improve with increasing number of looks.

Lipes, R. G.↗
NASA NTRS1980

Achievable flatness in a large microwave power transmitting antenna

A dual reference SPS system with pseudoisotropic graphite composite as a representative dimensionally stable composite was studied. The loads, accelerations, thermal environments, temperatures and distortions were calculated for a variety of operational SPS conditions along with statistical considerations of material properties, manufacturing tolerances, measurement accuracy and the resulting loss of sight (LOS) and local slope distributions. A LOS error and a subarray rms slope error of two arc minutes can be achieved with a passive system. Results show that existing materials measurement, manufacturing, assembly and alignment techniques can be used to build the microwave power transmission system antenna structure. Manufacturing tolerance can be critical to rms slope error. The slope error budget can be met with a passive system. Structural joints without free play are essential in the assembly of the large truss structure. Variations in material properties, particularly for coefficient of thermal expansion from part to part, is more significant than actual value.

Ried, R. C.↗