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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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At least 775 records · Page 43
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

Selecting a Classification Ensemble and Detecting Process Drift in an Evolving Data Stream

We characterize the commercial behavior of a group of companies in a common line of business using a small ensemble of classifiers on a stream of records containing commercial activity information. This approach is able to effectively find a subset of classifiers that can be used to predict company labels with reasonable accuracy. Performance of the ensemble, its error rate under stable conditions, can be characterized using an exponentially weighted moving average (EWMA) statistic. The behavior of the EWMA statistic can be used to monitor a record stream from the commercial network and determine when significant changes have occurred. Results indicate that larger classification ensembles may not necessarily be optimal, pointing to the need to search the combinatorial classifier space in a systematic way. Results also show that current and past performance of an ensemble can be used to detect when statistically significant changes in the activity of the network have occurred. The dataset used in this work contains tens of thousands of high level commercial activity records with continuous and categorical variables and hundreds of labels, making classification challenging.

Ensemble classifiers↗
DOE OSTI2022

A Comparison of Infectious Disease Forecasting Methods across Locations, Diseases, and Time

Accurate infectious disease forecasting can inform efforts to prevent outbreaks and mitigate adverse impacts. This study compares the performance of statistical, machine learning (ML), and deep learning (DL) approaches in forecasting infectious disease incidences across different countries and time intervals. We forecasted three diverse diseases: campylobacteriosis, typhoid, and Q-fever, using a wide variety of features (n = 46) from public datasets, e.g., landscape, climate, and socioeconomic factors. We compared autoregressive statistical models to two tree-based ML models (extreme gradient boosted trees [XGB] and random forest [RF]) and two DL models (multi-layer perceptron and encoder–decoder model). The disease models were trained on data from seven different countries at the region-level between 2009–2017. Forecasting performance of all models was assessed using mean absolute error, root mean square error, and Poisson deviance across Australia, Israel, and the United States for the months of January through August of 2018. The overall model results were compared across diseases as well as various data splits, including country, regions with highest and lowest cases, and the forecasted months out (i.e., nowcasting, short-term, and long-term forecasting). Overall, the XGB models performed the best for all diseases and, in general, tree-based ML models performed the best when looking at data splits. There were a few instances where the statistical or DL models had minutely smaller error metrics for specific subsets of typhoid, which is a disease with very low case counts. Feature importance per disease was measured by using four tree-based ML models (i.e., XGB and RF with and without region name as a feature). The most important feature groups included previous case counts, region name, population counts and density, mortality causes of neonatal to under 5 years of age, sanitation factors, and elevation. This study demonstrates the power of ML approaches to incorporate a wide range of factors to forecast various diseases, regardless of location, more accurately than traditional statistical approaches.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2018

Statistical Interpolation for Surface Reconstruction of PDV and BLR Data

Data collection is becoming more and more dense in dynamic experiments, requiring a variety of diagnostics. Data are detected through such means as holography, Mie scattering / extinction, assay foils, surface imaging, and high-speed photography. The data are measured by radiography, velocimetry, ranging, Fiber Bragg, and pyrometry. The question is how can we unify information from multiple diagnostics and incorporate uncertainties to tell a coherent story about an experiment in a meaningful way? This presentation provides an overview of statistical interpolation by means of Kriging. Kriging involves three basic steps: (1) compute and model covariance spatial structure; (2) compute weights by minimizing mean squared prediction error; and (3) predict at the desired prediction sites.

dynamic experiments, holography, Mie scattering/ex↗
DOE OSTI2020

Detecting anomalous packets in network transfers: investigations using PCA, autoencoder and isolation forest in TCP

Large-scale scientific workflows rely heavily on high-performance file transfers. These transfers require strict quality parameters such as guaranteed bandwidth, no packet loss or data duplication. To have successful file transfers, methods such as predetermined thresholds and statistical analysis need to be done to determine abnormal patterns. Network administrators routinely monitor and analyze network data for diagnosing and alleviating these, making decisions based on their experience. However, as networks grow and become complex, monitoring large data files and quickly processing them, makes it improbable to identify errors and rectify these. Abnormal file transfers have been classified by simply setting alert thresholds, via tools such as PerfSonar and TCP statistics (Tstat). This paper investigates the feasibility of unsupervised feature extraction methods for identifying network anomaly patterns with three unsupervised classification methods—principal component analysis, autoencoder and isolation forest. Here, we collect file transfer statistics from two experiment sets—synthetic iPerf generated traffic and 1000 Genome workflow runs, with synthetically introduced anomalies. Our results show that while PCA and a simple autoencoder finds it difficult to detect clusters, the tree-variant isolation forest is able to identify anomalous packets by breaking down TCP traces into tree classes early.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

A practical method to test the validity of the standard Gumbel distribution in logit-based multinomial choice models of travel behavior

Most multinomial choice models (e.g., the multinomial logit model) adopted in practice assume an extreme-value Gumbel distribution for the random components (error terms) of utility functions. This distributional assumption offers a closed-form likelihood expression when the utility maximization principle is applied to model choice behaviors. As a result, model coefficients can be easily estimated using the standard maximum likelihood estimation method. However, maximum likelihood estimators are consistent and efficient only if distributional assumptions on the random error terms are valid. It is therefore critical to test the validity of underlying distributional assumptions on the error terms that form the basis of parameter estimation and policy evaluation. In this paper, a practical yet statistically rigorous method is proposed to test the validity of the distributional assumption on the random components of utility functions in both the multinomial logit (MNL) model and multiple discrete-continuous extreme value (MDCEV) model. Based on a semi-nonparametric approach, a closed-form likelihood function that nests the MNL or MDCEV model being tested is derived. The proposed method allows traditional likelihood ratio tests to be used to test violations of the standard Gumbel distribution assumption. Simulation experiments are conducted to demonstrate that the proposed test yields acceptable Type-I and Type-II error probabilities at commonly available sample sizes. The test is then applied to three real-world discrete and discrete-continuous choice models. For all three models, the proposed test rejects the validity of the standard Gumbel distribution in most utility functions, calling for the development of robust choice models that overcome adverse effects of violations of distributional assumptions on the error terms in random utility functions.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2013

THE GAMMA-RAY BURST HUBBLE DIAGRAM AND ITS IMPLICATIONS FOR COSMOLOGY

In this paper, we continue to build support for the proposal to use gamma-ray bursts (GRBs) as standard candles in constructing the Hubble diagram at redshifts beyond the current reach of Type Ia supernova observations. We confirm that correlations among certain spectral and light-curve features can indeed be used as luminosity indicators, and demonstrate from the most up-to-date GRB sample appropriate for this work that the {Lambda}CDM model optimized with these data is characterized by parameter values consistent with those in the concordance model. Specifically, we find that ({Omega}{sub m},{Omega}{sub {Lambda}}){approx}(0.25{sub -0.06}{sup +0.05}, 0.75{sub -0.05}{sup +0.06}), which are consistent, to within 1{sigma}, with (0.29, 0.71) obtained from the 9 yr Wilkinson Microwave Anisotropy Probe data. We also carry out a comparative analysis between {Lambda}CDM and the R{sub h} = ct universe and find that the optimal {Lambda}CDM model fits the GRB Hubble diagram with a reduced {chi}{sup 2}{sub dof}{approx}2.26, whereas the fit using R{sub h} = ct results in a {chi}{sup 2}{sub dof}{approx}2.14. In both cases, about 20% of the events lie at least 2{sigma} away from the best-fit curves, suggesting that either some contamination by non-standard GRB luminosities is unavoidable or that the errors and intrinsic scatter associated with the data are being underestimated. With these optimized fits, we use three statistical tools-the Akaike information criterion, the Kullback information criterion, and the Bayes information criterion-to show that, based on the GRB Hubble diagram, the likelihood of R{sub h} = ct being closer to the correct model is {approx}85%-96%, compared to {approx}4%-15% for {Lambda}CDM.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2013

A Bayesian Measurment Error Model for Misaligned Radiographic Data

An understanding of the inherent variability in micro-computed tomography (micro-CT) data is essential to tasks such as statistical process control and the validation of radiographic simulation tools. The data present unique challenges to variability analysis due to the relatively low resolution of radiographs, and also due to minor variations from run to run which can result in misalignment or magnification changes between repeated measurements of a sample. Positioning changes artificially inflate the variability of the data in ways that mask true physical phenomena. We present a novel Bayesian nonparametric regression model that incorporates both additive and multiplicative measurement error in addition to heteroscedasticity to address this problem. We also use this model to assess the effects of sample thickness and sample position on measurement variability for an aluminum specimen. Supplementary materials for this article are available online.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Bayesian model averaging for analysis of lattice field theory results

Statistical modeling is a key component in the extraction of physical results from lattice field theory calculations. Although the general models used are often strongly motivated by physics, many model variations can frequently be considered for the same lattice data. Model averaging, which amounts to a probability-weighted average over all model variations, can incorporate systematic errors associated with model choice without being overly conservative. We discuss the framework of model averaging from the perspective of Bayesian statistics, and give useful formulae and approximations for the particular case of least-squares fitting, commonly used in modeling lattice results. In addition, we frame the common problem of data subset selection (e.g. choice of minimum and maximum time separation for fitting a two-point correlation function) as a model selection problem and study model averaging as a straightforward alternative to manual selection of fit ranges. Numerical examples involving both mock and real lattice data are given.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2016

Confronting Weather and Climate Models with Observational Data from Soil Moisture Networks over the United States

Four land surface models in uncoupled and coupled configurations are compared to observations of daily soil moisture from 19 networks in the conterminous United States to determine the viability of such comparisons and explore the characteristics of model and observational data. First, observations are analyzed for error characteristics and representation of spatial and temporal variability. Some networks have multiple stations within an area comparable to model grid boxes; for those it is found that aggregation of stations before calculation of statistics has little effect on estimates of variance, but soil moisture memory is sensitive to aggregation. Statistics for some networks stand out as unlike those of their neighbors, likely because of differences in instrumentation, calibration, and maintenance. Buried sensors appear to have less random error than near-field remote sensing techniques, and heat-dissipation sensors show less temporal variability than other types. Model soil moistures are evaluated using three metrics: standard deviation in time, temporal correlation (memory), and spatial correlation (length scale). Models do relatively well in capturing large-scale variability of metrics across climate regimes, but they poorly reproduce observed patterns at scales of hundreds of kilometers and smaller. Uncoupled land models do no better than coupled model configurations, nor do reanalyses outperform free-running models. Spatial decorrelation scales are found to be difficult to diagnose. Using data for model validation, calibration, or data assimilation from multiple soil moisture networks with different types of sensors and measurement techniques requires great caution. Finally, data from models and observations should be put on the same spatial and temporal scales before comparison.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2013

REANALYSIS OF F-STATISTIC GRAVITATIONAL-WAVE SEARCHES WITH THE HIGHER CRITICISM STATISTIC

We propose a new method of gravitational-wave detection using a modified form of higher criticism, a statistical technique introduced by Donoho and Jin. Higher criticism is designed to detect a group of sparse, weak sources, none of which are strong enough to be reliably estimated or detected individually. We apply higher criticism as a second-pass method to synthetic F-statistic and C-statistic data for a monochromatic periodic source in a binary system and quantify the improvement relative to the first-pass methods. We find that higher criticism on C-statistic data is more sensitive by {approx}6% than the C-statistic alone under optimal conditions (i.e., binary orbit known exactly) and the relative advantage increases as the error in the orbital parameters increases. Higher criticism is robust even when the source is not monochromatic (e.g., phase-wandering in an accreting system). Applying higher criticism to a phase-wandering source over multiple time intervals gives a {approx}> 30% increase in detectability with few assumptions about the frequency evolution. By contrast, in all-sky searches for unknown periodic sources, which are dominated by the brightest source, second-pass higher criticism does not provide any benefits over a first-pass search.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Cluster cosmology without cluster finding

ABSTRACT We propose that observations of supermassive galaxies contain cosmological statistical constraining power similar to conventional cluster cosmology, and we provide promising indications that the associated systematic errors are comparably easier to control. We consider a fiducial spectroscopic and stellar mass complete sample of galaxies drawn from the Dark Energy Spectroscopic Instrument (DESI) and forecast how constraints on Ωm–σ8 from this sample will compare with those from number counts of clusters based on richness λ. At fixed number density, we find that massive galaxies offer similar constraints to galaxy clusters. However, a mass-complete galaxy sample from DESI has the potential to probe lower halo masses than standard optical cluster samples (which are typically limited to λ ≳ 20 and Mhalo ≳ 1013.5 M⊙ h−1); additionally, it is straightforward to cleanly measure projected galaxy clustering wp for such a DESI sample, which we show can substantially improve the constraining power on Ωm. We also compare the constraining power of M*-limited samples to those from larger but mass-incomplete samples [e.g. the DESI Bright Galaxy Survey (BGS) sample]; relative to a lower number density M*-limited samples, we find that a BGS-like sample improves statistical constraints by 60 per cent for Ωm and 40 per cent for σ8, but this uses small-scale information that will be harder to model for BGS. Our initial assessment of the systematics associated with supermassive galaxy cosmology yields promising results. The proposed samples have a ∼10 per cent satellite fraction, but we show that cosmological constraints may be robust to the impact of satellites. These findings motivate future work to realize the potential of supermassive galaxies to probe lower halo masses than richness-based clusters and to potentially avoid persistent systematics associated with optical cluster finding.

(cosmology): large-scale structure of Universe↗
DOE OSTI2014

Ensemble Kalman filters for dynamical systems with unresolved turbulence

Ensemble Kalman filters are developed for turbulent dynamical systems where the forecast model does not resolve all the active scales of motion. Coarse-resolution models are intended to predict the large-scale part of the true dynamics, but observations invariably include contributions from both the resolved large scales and the unresolved small scales. The error due to the contribution of unresolved scales to the observations, called ‘representation’ or ‘representativeness’ error, is often included as part of the observation error, in addition to the raw measurement error, when estimating the large-scale part of the system. It is here shown how stochastic superparameterization (a multiscale method for subgridscale parameterization) can be used to provide estimates of the statistics of the unresolved scales. In addition, a new framework is developed wherein small-scale statistics can be used to estimate both the resolved and unresolved components of the solution. The one-dimensional test problem from dispersive wave turbulence used here is computationally tractable yet is particularly difficult for filtering because of the non-Gaussian extreme event statistics and substantial small scale turbulence: a shallow energy spectrum proportional to k{sup −5/6} (where k is the wavenumber) results in two-thirds of the climatological variance being carried by the unresolved small scales. Because the unresolved scales contain so much energy, filters that ignore the representation error fail utterly to provide meaningful estimates of the system state. Inclusion of a time-independent climatological estimate of the representation error in a standard framework leads to inaccurate estimates of the large-scale part of the signal; accurate estimates of the large scales are only achieved by using stochastic superparameterization to provide evolving, large-scale dependent predictions of the small-scale statistics. Again, because the unresolved scales contain so much energy, even an accurate estimate of the large-scale part of the system does not provide an accurate estimate of the true state. By providing simultaneous estimates of both the large- and small-scale parts of the solution, the new framework is able to provide accurate estimates of the true system state.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2015

Partial least square method for modelling ergonomic risks factors on express bus accidents in the east coast of peninsular west Malaysia

Public, stake holders and authorities in Malaysian government show great concern towards high numbers of passenger’s injuries and passengers fatalities in express bus accident. This paper studies the underlying factors involved in determining ergonomics risk factors towards human error as the reasons in express bus accidents in order to develop an integrated analytical framework. Reliable information about drivers towards bus accident should lead to the design of strategies intended to make the public feel safe in public transport services. In addition there is an analysis of ergonomics risk factors to determine highly ergonomic risk factors which led to accidents. The research was performed in east coast of peninsular Malaysia using variance-based structural equation modeling namely the Partial Least Squares (PLS) regression techniques. A questionnaire survey was carried out at random among 65 express bus drivers operating from the city of Kuantan in Pahang and among 49 express bus drivers operating from the city of Kuala Terengganu in Terengganu to all towns in the east coast of peninsular west Malaysia. The ergonomic risks factors questionnaire is based on demographic information, occupational information, organizational safety climate, ergonomic workplace, physiological factors, stress at workplace, physical fatigue and near miss accidents. The correlation and significant values between latent constructs (near miss accident) were analyzed using SEM SmartPLS, 3M. The finding shows that the correlated ergonomic risks factors (occupational information, t=2.04, stress at workplace, t = 2.81, physiological factor, t=2.08) are significant to physical fatigue and as the mediator to near miss accident at t = 2.14 at p<0.05and T-statistics, t>1.96. The results shows that the effects of physical fatigue due to ergonomic risks factors influence the human error as the reasons in express bus accidents.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Measuring the muon precession frequency in the E989 Fermilab g-2 experiment

Since more than 50 years the electron and muon anomalies, $a_e$ and $a_\mu$, defined in terms of the gyromagnetic factor $g_i$ for particle $i$ as $a_i=(g_i-2)/2$, have provided a deep insight into the quantum structure of elementary particles.They have been, and continue to be, a milestone for the development of the Standard Model of Particle Physics against which all new theories have to be compared.For almost 20 years, the experimental value of $a_\mu$ has shown a tantalizing discrepancy of more than $3 \sigma$ from the theoretical prediction making it mandatory for experimentalists to improve the current result, dominated by the E821 experiment at BNL\cite{BNL}. The Muon $g-2$ E989 experiment at Fermilab will use the same storage ring technique used at BNL, and previously in the CERN-III experiment, with the goal of decreasing by a factor of 4 the current error on $a_\mu$, which will allow for a finer comparison with the theoretical prediction. E989 started collecting data in winter 2018 accumulating, in the period April-July 2018 (Run1) almost twice the statistics of the previous experiment (before application of data quality cuts). In this document, the experiment will be briefly described, underlying the improvements which will allow to reduce the systematic error, and some preliminary result will be shown.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2017

Spatio-temporal distribution of Oklahoma earthquakes: Exploring relationships using a nearest-neighbor approach: Nearest-neighbor analysis of Oklahoma

Determining the spatiotemporal characteristics of natural and induced seismic events holds the opportunity to gain new insights into why these events occur. Linking the seismicity characteristics with other geologic, geographic, natural, or anthropogenic factors could help to identify the causes and suggest mitigation strategies that reduce the risk associated with such events. The nearest-neighbor approach utilized in this work represents a practical first step toward identifying statistically correlated clusters of recorded earthquake events. Detailed study of the Oklahoma earthquake catalog’s inherent errors, empirical model parameters, and model assumptions is presented. We found that the cluster analysis results are stable with respect to empirical parameters (e.g., fractal dimension) but were sensitive to epicenter location errors and seismicity rates. Most critically, we show that the patterns in the distribution of earthquake clusters in Oklahoma are primarily defined by spatial relationships between events. This observation is a stark contrast to California (also known for induced seismicity) where a comparable cluster distribution is defined by both spatial and temporal interactions between events. These results highlight the difficulty in understanding the mechanisms and behavior of induced seismicity but provide insights for future work.

58 GEOSCIENCES↗
DOE OSTI2025

Automated vehicle microscopic energy consumption study (AV-Micro): Data collection and model development

While the Adaptive Cruise Control (ACC) system in automated vehicles (AVs) is expected to impact transportation energy significantly, existing AV energy consumption models only directly adopt those developed with Human-driven Vehicle (HV) data without even slight adaptation or calibration to accommodate unique AV energy consumption features. This study will investigate how accurately HV data-based models can predict the energy consumption of AVs. Empirical trajectory data and corresponding instantaneous energy consumption rates from both AVs and HVs were collected. We adopted two classical HV data-based models to fit these data. The calibration results indicated that these models yield around 20 30% prediction errors for AVs. To further improve the prediction accuracy, this study designed an AV-Micro model by incorporating components of multiple classic energy consumption models that better capture ACC energy consumption features, including piecewise driving behavior. With this, the AV-Micro model achieves lower than 10% prediction errors. The AV-Micro model’s high consistency across different test runs was verified with statistical significance tests, demonstrating its adaptability in different driving profiles. To confirm the discrepancies between the energy consumption features of AVs and HVs, more statistical significance tests were conducted to show that the AV-Micro model cannot be directly applied to HV data. The findings by calibrated AV-Micro models revealed that AVs consume approximately 80.5–146.4 J more energy than HVs for each meter traveled. Furthermore, the frequency analysis of energy consumption indicates that there is still some room for AVs to improve energy efficiency, particularly given their larger amplitude high-frequency fluctuations.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2016

Iterative Monte Carlo analysis of spin-dependent parton distributions

We present a comprehensive new global QCD analysis of polarized inclusive deep-inelastic scattering, including the latest high-precision data on longitudinal and transverse polarization asymmetries from Jefferson Lab and elsewhere. The analysis is performed using a new iterative Monte Carlo fitting technique which generates stable fits to polarized parton distribution functions (PDFs) with statistically rigorous uncertainties. Inclusion of the Jefferson Lab data leads to a reduction in the PDF errors for the valence and sea quarks, as well as in the gluon polarization uncertainty at x ≳ 0.1. Furthermore, the study also provides the first determination of the flavor-separated twist-3 PDFs and the d 2 moment of the nucleon within a global PDF analysis.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2014

An analysis of the uncertainty and bias in DCE-MRI measurements using the spoiled gradient-recalled echo pulse sequence

Purpose: The pharmacokinetic parameters derived from dynamic contrast-enhanced (DCE) MRI have been used in more than 100 phase I trials and investigator led studies. A comparison of the absolute values of these quantities requires an estimation of their respective probability distribution function (PDF). The statistical variation of the DCE-MRI measurement is analyzed by considering the fundamental sources of error in the MR signal intensity acquired with the spoiled gradient-echo (SPGR) pulse sequence. Methods: The variance in the SPGR signal intensity arises from quadrature detection and excitation flip angle inconsistency. The noise power was measured in 11 phantoms of contrast agent concentration in the range [0–1] mM (in steps of 0.1 mM) and in onein vivo acquisition of a tumor-bearing mouse. The distribution of the flip angle was determined in a uniform 10 mM CuSO{sub 4} phantom using the spin echo double angle method. The PDF of a wide range of T1 values measured with the varying flip angle (VFA) technique was estimated through numerical simulations of the SPGR equation. The resultant uncertainty in contrast agent concentration was incorporated in the most common model of tracer exchange kinetics and the PDF of the derived pharmacokinetic parameters was studied numerically. Results: The VFA method is an unbiased technique for measuringT1 only in the absence of bias in excitation flip angle. The time-dependent concentration of the contrast agent measured in vivo is within the theoretically predicted uncertainty. The uncertainty in measuring K{sup trans} with SPGR pulse sequences is of the same order, but always higher than, the uncertainty in measuring the pre-injection longitudinal relaxation time (T1{sub 0}). The lowest achievable bias/uncertainty in estimating this parameter is approximately 20%–70% higher than the bias/uncertainty in the measurement of the pre-injection T1 map. The fractional volume parameters derived from the extended Tofts model were found to be extremely sensitive to the variance in signal intensity. The SNR of the pre-injection T1 map indicates the limiting precision with which K{sup trans} can be calculated. Conclusions: Current small-animal imaging systems and pulse sequences robust to motion artifacts have the capacity for reproducible quantitative acquisitions with DCE-MRI. In these circumstances, it is feasible to achieve a level of precision limited only by physiologic variability.

62 RADIOLOGY AND NUCLEAR MEDICINE↗