Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “SAMPLED DATA”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 721 records · Page 40
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Uniform Beam Simulation Technique for Beam Scans and Machine Learning Studies at Fermilab

Fermilab's neutrino facilities, including NuMI and the upcoming LBNF, use proton beams to produce positively and negatively charged pions and kaons. Detailed simulations are necessary to study particle interactions and beam propagation. To efficiently analyze beam scan effects, we propose a technique to generate multiple simulation samples with high statistics. These samples can be used to develop beamline simulation based machine learning applications. In this technique, we generate a uniformly distributed single simulation data sample. We calculate Gaussian weights for each beam configurations and apply them to post-processing measurements. In this poster, we demonstrate the proposed simulation technique. This technique reduces simulation time and computing resources significantly.

Wickremasinghe, Athula↗
DOE OSTI2025

Lost and Found: Rediscovering Microbiome-Associated Phenotypes that Reshape Agricultural Sustainability

Overview Code and data repository for NIL Manuscript. Documentation includes sequence processing examples and data analysis. Supplemental sequence processing and R statistical analysis for publication, which compares the microbiome of teosinte-B73 Near Isogenic Lines. Sample Data Amplicon sequence data for 16S rRNA genes, the fungal ITS2 region, and nitrogen-cycling functional genes are available through the NCBI Sequence Read Archive (SRA) under accession number PRJNA1042643(https://www.ncbi.nlm.nih.gov/bioproject/PRJNA1042643). Raw metabolomic data are available on Metabolomics Workbench, Project ID: PR002654. This study is available at the NIH Common Fund's National Metabolomics Data Repository (NMDR) website, the Metabolomics Workbench, https://www.metabolomicsworkbench.org where it has been assigned Study ID ST004211. The data can be accessed directly via its Project DOI: http://dx.doi.org/10.21228/M8KV8T.

Near Isogeneic Lines↗
NASA NTRS1984

Spectral analysis of groove spacing on Ganymede

The technique used to analyze groove spacing on Ganymede is presented. Data from Voyager images are used determine the surface topography and position of the grooves. Power spectal estimates are statistically analyzed and sample data is included.

Grimm, R. E.↗
NASA NTRS1985

Study of muons near shower cores at sea level using the E594 neutrino detector

The E594 neutrino detector has been used to study the lateral distribution of muons of energy 3 GeV near shower cores. The detector consists of a 340 ton fine grain calorimeter with 400,000 cells of flash chamber and dimensions of 3.7 m x 20 m x 3.7 m (height). The average density in the calorimeter is 1.4 gm/sq cm, and the average Z is 21. The detector was triggered by four 0.6 sq m scintillators placed immediately on the top of the calorimeter. The trigger required at least two of these four counters. The accompanying extensive air showers (EAS) was sampled by 14 scintillation counters located up to 15 m from the calorimeter. Several off line cuts have been applied to the data. Demanding five particles in at least two of the trigger detectors, a total of 20 particles in all of them together, and an arrival angle for the shower 450 deg reduced the data sample to 11053 events. Of these in 4869 cases, a computer algorithm found at least three muons in the calorimeter.

Goodman, J. A.↗
NASA NTRS1988

Detection techniques using multispectral data to index soil erosional status

Indexing techniques that can be used to detect soil erosion utilizing the known band widths of the Landsat MSS and TM sensors are identified. The indexing techniques focus on iron oxides, clays, and organic matter as properties revealing soil erosional status. For data aquisition, a Collins visible and infrared intelligent spectrometer was used to collect data from 0.4-24 microns. Pressed polytetrafluorethylene was used as the reflectance standard and was aquired at the same time that the sample data were aquired.

Pelletier, R. E.↗
NASA NTRS1992

An expert system shell for inferring vegetation characteristics: Implementation of additional techniques (task E)

The NASA VEGetation Workbench (VEG) is a knowledge based system that infers vegetation characteristics from reflectance data. The VEG subgoal PROPORTION.GROUND.COVER has been completed and a number of additional techniques that infer the proportion ground cover of a sample have been implemented. Some techniques operate on sample data at a single wavelength. The techniques previously incorporated in VEG for other subgoals operated on data at a single wavelength so implementing the additional single wavelength techniques required no changes to the structure of VEG. Two techniques which use data at multiple wavelengths to infer proportion ground cover were also implemented. This work involved modifying the structure of VEG so that multiple wavelength techniques could be incorporated. All the new techniques were tested using both the VEG 'Research Mode' and the 'Automatic Mode.'

Harrison, P. Ann↗
NASA NTRS2005

Evaluation of Decision Trees for Cloud Detection from AVHRR Data

Automated cloud detection and tracking is an important step in assessing changes in radiation budgets associated with global climate change via remote sensing. Data products based on satellite imagery are available to the scientific community for studying trends in the Earth's atmosphere. The data products include pixel-based cloud masks that assign cloud-cover classifications to pixels. Many cloud-mask algorithms have the form of decision trees. The decision trees employ sequential tests that scientists designed based on empirical astrophysics studies and simulations. Limitations of existing cloud masks restrict our ability to accurately track changes in cloud patterns over time. In a previous study we compared automatically learned decision trees to cloud masks included in Advanced Very High Resolution Radiometer (AVHRR) data products from the year 2000. In this paper we report the replication of the study for five-year data, and for a gold standard based on surface observations performed by scientists at weather stations in the British Islands. For our sample data, the accuracy of automatically learned decision trees was greater than the accuracy of the cloud masks p < 0.001.

AVHRR (ADVANCED VERY HIGH RESOLUTION RADIOMETER)↗
NASA NTRS2000

Spectral analysis of gappy images with application to Sea WiFS data

For various reasons, such as the presence of clouds, satellite orbit configuration, data sampling pattern, remote sensing instruments yield irregularly spaced or gappy/patchy measurements. To obviate these problems, we use original, non-gridded satellite data and construct a spatio-temporal (or just spatial) autocorrelation function of the field's variation.

image analysis spectral analysis Sea WiFS↗
DOE OSTI2021

Omics-Lethal Human Viruses, MERS-CoV Experiment MCL001

The purpose of this experiment was to evaluate the human host response to wild-type Middle Eastern Respiratory Syndrome coronavirus (icMERS) and icMERS-CoV mutants icMERS-CoV-RFP, icMERS-CoV-dNSP16, icMERS-CoV-d4B, and icMERS-CoV-d3 virus infection. Sample data was obtained from human bronchial epithelial cells (Calu-3 clone 2B4) for mRNA and miRNA transcriptome expression analysis. Secondary host-associated viral dataset downloads contain one or more statistically processed (normalization data transformation) quantitative dataset collections resulting in qualitative expression analyses of primary host-pathogen experimental study designs. Transcriptomics dataset downloads each have a direct relationship to a primary sample submission corresponding to a specific MERS-CoV virus infection.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2013

THE BOSS Ly{alpha} FOREST SAMPLE FROM SDSS DATA RELEASE 9

We present the BOSS Lyman-{alpha} (Ly{alpha}) Forest Sample from SDSS Data Release 9, comprising 54,468 quasar spectra with z{sub qso} > 2.15 suitable for Ly{alpha} forest analysis. This data set probes the intergalactic medium with absorption redshifts 2.0 < z{sub {alpha}} < 5.7 over an area of 3275 deg{sup 2}, and encompasses an approximate comoving volume of 20 h {sup -3} Gpc{sup 3}. With each spectrum, we have included several products designed to aid in Ly{alpha} forest analysis: improved sky masks that flag pixels where data may be unreliable, corrections for known biases in the pipeline estimated noise, masks for the cores of damped Ly{alpha} systems and corrections for their wings, and estimates of the unabsorbed continua so that the observed flux can be converted to a fractional transmission. The continua are derived using a principal component fit to the quasar spectrum redward of rest-frame Ly{alpha} ({lambda} > 1216 A), extrapolated into the forest region and normalized by a linear function to fit the expected evolution of the Ly{alpha} forest mean flux. The estimated continuum errors are {approx}< 5% rms. We also discuss possible systematics arising from uncertain spectrophotometry and artifacts in the flux calibration; global corrections for the latter are provided. Our sample provides a convenient starting point for users to analyze clustering in BOSS Ly{alpha} forest data, and it provides a fiducial data set that can be used to compare results from different analyses of baryon acoustic oscillations in the Ly{alpha} forest. The full data set is available from the SDSS-III DR9 Web site.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Measurement of the CP-violating phase ϕs in the B s 0 → J / ψ ϕ ( 1020 ) → μ + μ − K + K − channel in proton-proton collisions at s = 13 TeV

The CP -violating weak phase ϕ s and the decay width difference Δ Γ s between the light and heavy B s 0 mass eigenstates are measured with the CMS detector at the LHC in a sample of 48 500 reconstructed B s 0 → J / ψ ϕ ( 1020 ) → μ + μ − K + K − events. The measurement is based on a data sample corresponding to an integrated luminosity of 96.4 fb − 1 , collected in proton-proton collisions at s = 13 TeV in 2017–2018. To extract the values of ϕ s and Δ Γ s , a time-dependent and flavor-tagged angular analysis of the μ + μ − K + K − final state is performed. The analysis employs a dedicated tagging trigger and a novel opposite-side muon flavor tagger based on machine learning techniques. The measurement yields ϕ s = − 11 ± 50 (stat) ± 10 (syst) mrad and Δ Γ s = 0.114 ± 0.014 (stat) ± 0.007 (syst) ps − 1 , in agreement with the standard model predictions. When combined with the previous CMS measurement at s = 8 TeV , the following values are obtained: ϕ s = − 21 ± 44 (stat) ± 10 (syst) mrad , Δ Γ s = 0.1032 ± 0.0095 (stat) ± 0.0048 (syst) ps − 1 , a significant improvement over the 8 TeV result.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2017

Scaling Up Data-Parallel Analytics Platforms: Linear Algebraic Operation Cases

Linear algebraic operations such as matrix manipulations form the kernel of many machine learning and other crucial algorithms. Scaling up as well as scaling out such algorithms are key to supporting large scale data analysis that require efficient processing over millions of data samples. To this end, we present, ARION, a hardware acceleration based approach for scaling-up individual tasks of Spark, a popular data-parallel analytics platform. We support both linear algebraic operations of between two dense matrices, and between sparse and dense matrices in distributed environments. ARION provides a flexible control of acceleration according to matrix density, along with efficient scheduling based on runtime resource utilization. We demonstrate the benefit of our approach for general matrix multiplication operations over large matrices with up to four billion elements by using Gramian matrix computation that is commonly used in machine learning. Experiments show that our approach achieves more than 2× and 1.5× end-to-end performance speedups for dense and sparse matrices, respectively, and up to 57.04× faster computation compared to MLlib, a state of the art Spark-based implementation. This work is sponsored in part by the NSF under the grants: CNS-1565314, CNS-1405697, and CNS-1615411. The manuscript has been authored by UT-Battelle, LLC under Contract No. DE-AC05-00OR22725 with the U.S. Department of Energy. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan). This research used resources of the Oak Ridge Leadership Computing Facility at the Oak Ridge National Laboratory, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC05-00OR22725.

Xu, Luna↗
DOE OSTI2021

Data-driven Event Identification in the U.S. Power Systems Based on 2D-OLPP and RUSBoosting Trees

Accurate event identification is an essential part of situation awareness ability for power system operators. Therefore, this work proposes an integrated event identification algorithm for power systems. First, to obtain and filter suitable inputs for event identification, an event detection trigger based on the rate of change of frequency (RoCoF) is presented. Then, the wave arrival time difference-based triangulation method considering the anisotropy of wave propagation speed is utilized to estimate the location of the detected event. Next, the two-dimensional orthogonal locality preserving projection (2D-OLPP)-based method, which is suitable for multiple types of measured data, is employed to achieve higher effectiveness in extracting the event features compared with traditional one-dimensional projection and principle component analysis (PCA). Finally, the random undersampling boosted (RUSBoosted) trees-based classifier, which can mitigate the data sample imbalance issue, is utilized to identify the type of the detected event. Furthermore, the proposed approach is demonstrated using the actual measurement data of U.S. power systems from FNET/GridEye. Comparison results show that the proposed event identification algorithm can achieve better performance than existing approaches.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2012

Measurement of charged-particle event shape variables in s = 7 TeV proton-proton interactions with the ATLAS detector

CERN-LHC. Measurements of the event shape variables Transverse Thrust, Transverse Thrust Minor and Transverse Sphericity in proton-proton collisions at a centre-of-mass energy of 7 TeV. Events with at least six final state charged particles are selected from a data sample collected in April 2010 with a minimum prescale factor for the minimum-bias trigger. In addition to the event shape distributions, data on the evolution of their mean values with charged particle multiplicity and scalar PT sum are presented.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
NASA NTRS1991

Advanced techniques and technology for efficient data storage, access, and transfer

Advanced techniques for efficiently representing most forms of data are being implemented in practical hardware and software form through the joint efforts of three NASA centers. These techniques adapt to local statistical variations to continually provide near optimum code efficiency when representing data without error. Demonstrated in several earlier space applications, these techniques are the basis of initial NASA data compression standards specifications. Since the techniques clearly apply to most NASA science data, NASA invested in the development of both hardware and software implementations for general use. This investment includes high-speed single-chip very large scale integration (VLSI) coding and decoding modules as well as machine-transferrable software routines. The hardware chips were tested in the laboratory at data rates as high as 700 Mbits/s. A coding module's definition includes a predictive preprocessing stage and a powerful adaptive coding stage. The function of the preprocessor is to optimally process incoming data into a standard form data source that the second stage can handle.The built-in preprocessor of the VLSI coder chips is ideal for high-speed sampled data applications such as imaging and high-quality audio, but additionally, the second stage adaptive coder can be used separately with any source that can be externally preprocessed into the 'standard form'. This generic functionality assures that the applicability of these techniques and their recent high-speed implementations should be equally broad outside of NASA.

Rice, Robert F.↗
NASA NTRS1984

Data base management: MSTRAC (keynote paper), part 8

Technical Aspects of MST radars were discussed. The name of the group is MST Radar Coordination (MSTRAC). The geophysical parameters to be included in the sample data tapes were determined. It was suggested that MSTRAC compile a catalog of existing data at each of the facilities. It is found that: (1) there is a community of interested users for MST data; (2) the initial responses from observatories indicates that interest in MSTRAC is not overwhelming, probably because of time involved; and (3) the program needs reassessment.

Avery, S. K.↗
DOE OSTI2024

Search for long-lived heavy neutrinos in the decays of B mesons produced in proton-proton collisions at $ \sqrt{s} $ = 13 TeV

A search for long-lived heavy neutrinos (N) in the decays of B mesons produced in proton-proton collisions at $ \sqrt{s} $ = 13 TeV is presented. The data sample corresponds to an integrated luminosity of 41.6 fb$^{−1}$ collected in 2018 by the CMS experiment at the CERN LHC, using a dedicated data stream that enhances the number of recorded events containing B mesons. The search probes heavy neutrinos with masses in the range 1 < m$_{N}$ < 3 GeV and decay lengths in the range 10$^{−2}$ < cτ$_{N}$ < 10$^{4}$ mm, where τ$_{N}$ is the N proper mean lifetime. Signal events are defined by the signature B → ℓ$_{B}$NX; N → ℓ$^{±}$π$^{∓}$, where the leptons ℓ$_{B}$ and ℓ can be either a muon or an electron, provided that at least one of them is a muon. The hadronic recoil system, X, is treated inclusively and is not reconstructed. No significant excess of events over the standard model background is observed in any of the ℓ$^{±}$π$^{∓}$ invariant mass distributions. Limits at 95% confidence level on the sum of the squares of the mixing amplitudes between heavy and light neutrinos, |V$_{N}$|$^{2}$, and on cτ$_{N}$ are obtained in different mixing scenarios for both Majorana and Dirac-like N particles. The most stringent upper limit |V$_{N}$|$^{2}$ < 2.0 × 10$^{−5}$ is obtained at m$_{N}$ = 1.95 GeV for the Majorana case where N mixes exclusively with muon neutrinos. The limits on |V$_{N}$|$^{2}$ for masses 1 < m$_{N}$ < 1.7 GeV are the most stringent from a collider experiment to date.[graphic not available: see fulltext]

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2017

Scaling up to address data science challenges

Statistics and Data Science provide a variety of perspectives and technical approaches for exploring and understanding Big Data. Partnerships between scientists from different fields such as statistics, machine learning, computer science, and applied mathematics can lead to innovative approaches for addressing problems involving increasingly large amounts of data in a rigorous and effective manner that takes advantage of advances in computing. Here, this article will explore various challenges in Data Science and will highlight statistical approaches that can facilitate analysis of large-scale data including sampling and data reduction methods, techniques for effective analysis and visualization of large-scale simulations, and algorithms and procedures for efficient processing.

97 MATHEMATICS AND COMPUTING↗