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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 415 records · Page 23
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Image-driven discriminative and generative machine learning algorithms for establishing microstructure–processing relationships

We investigate methods of microstructure representation for the purpose of predicting processing condition from microstructure image data. A binary alloy that is currently under development as a nuclear fuel was studied for the purpose of developing an improved machine learning approach to image recognition, characterization, and building predictive capabilities linking microstructure to processing conditions. Here, we test different microstructure representations and evaluate model performance based on classification accuracy. A classification accuracy of 95.8% was achieved fordistinguishing between micrographs corresponding to ten different thermo-mechanical material processing conditions.We find that our newly developed microstructure representation describes image data well, and the traditional approachof utilizing area fractions of different phases is insufficient for distinguishing between multiple classes using a relativelysmall, imbalanced original data set of 272 images. To explore the applicability of generative methods for supplementing such limited data sets, generative adversarial networks were trained to generate artificial microstructure images. Two different generative networks were trained and tested to assess performance. Challenges and best practices associated with applying machine learning to limited microstructure image data sets is also discussed. Our work has implications for quantitative microstructure analysis, and development of microstructure-processing relationships in limited data sets typical of metallurgical process design studies.

36 MATERIALS SCIENCE↗
DOE OSTI2026

metagRoot: a comprehensive database of protein families associated with plant root microbiomes

The plant root microbiome is vital in plant health, nutrient uptake, and environmental resilience. To explore and harness this diversity, we present metagRoot, a specialized and enriched database focused on the protein families of the plant root microbiome. MetagRoot integrates metagenomic, metatranscriptomic, and reference genome-derived protein data to characterize 71 091 enriched protein families, each containing at least 100 sequences. These families are annotated with multiple sequence alignments, CRISPR elements, hidden Markov models, taxonomic and functional classifications, ecosystem and geolocation metadata, and predicted 3D structures using AlphaFold2. MetagRoot is a powerful tool for decoding the molecular landscape of root-associated microbial communities and advancing microbiome-informed agricultural practices by enriching protein family information with ecological and structural context. The database is available at https://pavlopoulos-lab.org/metagroot/ or https://www.metagroot.org.

Chasapi, Maria N↗
DOE OSTI2024

A Framework for Integrating Quantum Simulation and High Performance Computing

Scientific applications are starting to explore the viability of quantum computing. This exploration typically begins with quantum simulations that can run on existing classical platforms, albeit without the performance advantages of real quantum resources. In the context of high-performance computing (HPC), the incorporation of simulation software can often take advantage of the powerful resources to help scale-up the simulation size. The configuration, installation and operation of these quantum simulation packages on HPC resources can often be rather daunting and increases friction for experimentation by scientific application developers. We describe a framework to help streamline access to quantum simulation software running on HPC resources. This includes an interface for circuit-based quantum computing tasks, as well as the necessary resource management infrastructure to make effective use of the underlying HPC resources. The primary contributions of this work include a classification of different usage models for quantum simulation in an HPC context, a review of the software architecture for our approach and a detailed description of the prototype implementation to experiment with these ideas using two different simulators (TNQVM & NWQ-Sim). We include initial experimental results running on the Frontier supercomputer at the Oak Ridge Leadership Computing Facility (OLCF) using a synthetic workload generated via the SupermarQ quantum benchmarking framework.

Shehata, Amir [ORNL] (ORCID:0000000224531426)↗
DOE OSTI2024

ℤ2 × ℤ2 Equivariant Quantum Neural Networks: Benchmarking against Classical Neural Networks

This paper presents a comparative analysis of the performance of Equivariant Quantum Neural Networks (EQNNs) and Quantum Neural Networks (QNNs), juxtaposed against their classical counterparts: Equivariant Neural Networks (ENNs) and Deep Neural Networks (DNNs). We evaluate the performance of each network with three two-dimensional toy examples for a binary classification task, focusing on model complexity (measured by the number of parameters) and the size of the training dataset. Our results show that the Z2×Z2 EQNN and the QNN provide superior performance for smaller parameter sets and modest training data samples.

Dong, Zhongtian (ORCID:0000000210003454)↗
DOE OSTI2022

Modeling the Spectral Diversity of Quasars in the Sixteenth Data Release from the Sloan Digital Sky Survey

Abstract We present a new approach to capturing the broad diversity of emission-line and continuum properties in quasar spectra. We identify populations of spectrally similar quasars through pixel-level clustering on 12,968 high signal-to-noise ratio (S/N) spectra from the Sloan Digital Sky Survey (SDSS) in the redshift range of 1.57 < z < 2.4. Our clustering analysis finds 396 quasar spectra that are not assigned to any population, 15 misclassified spectra, and 6 quasars with incorrect redshifts. We compress the quasar populations into a library of 684 high-S/N composite spectra, anchored in redshift space by the Mg ii emission line. Principal component analysis on the library results in an eigenspectrum basis spanning 1067–4007 Å. We model independent samples of SDSS quasar spectra with the eigenbasis, allowing for a free redshift parameter. Our models achieve a median reduced χ 2 on non–broad absorption line quasar spectra that is reduced by 8.5% relative to models using the eigenspectra from the SDSS spectroscopic pipeline. A significant contribution to the relative improvement is from the ability to reconstruct the range of emission-line variation. The redshift estimates from our model are consistent with the Mg ii emission-line redshift with an average offset that displays 51.4% less redshift-dependent variation relative to the SDSS eigenspectra. Our method for developing quasar spectra models can improve automated classification and predict the intrinsic spectrum in regions affected by intervening absorbers such as Ly α , C iv , and Mg ii , thus benefiting studies of large-scale structure.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

One Million Open-source Cislunar Orbits

Cislunar space, encompassing the region from geosynchronous orbit to beyond the Moon, is poised to become a cornerstone for future exploration, scientific discovery, and national security. Missions in this region, spanning durations from weeks to decades, require robust infrastructure and reliable transit capabilities. The complex gravitational influences of the Moon, Sun, and planets, along with thermal radiation from Earth and the Sun, lead to significant trajectory deviations, resulting in kilometer-scale errors within days. Leveraging the high-performance computing resources at Lawrence Livermore National Laboratory (LLNL), we have simulated one million high-fidelity cislunar trajectories, now publicly available via LLNL’s Green Data Oasis and the Unified Data Library. Generated using the open-source Space Situational Awareness Python package, these trajectories match the precision of commercial tools such as AGI’s Systems Tool Kit and NASA’s General Mission Analysis Tool. This data set is a valuable resource for reference, statistical analysis of cislunar orbit populations, and training machine learning models for rapid orbit classification with minimal observational input. Preliminary analysis reveals stable bands in Keplerian element space, particularly around five geosynchronous radii across a range of inclinations and eccentricities. Beyond this threshold, the Moon’s influence disrupts most unassisted orbits, though co-orbiting L4/L5 Lunar Trojans persist throughout the six-year simulation.

Astronomy and AstroPhysics↗
NASA NTRS1983

A simulation study of scene confusion factors in sensing soil moisture from orbital radar

Simulated C-band radar imagery for a 124-km by 108-km test site in eastern Kansas is used to classify soil moisture. Simulated radar resolutions are 100 m by 100 m, 1 km by 1km, and 3 km by 3 km. Distributions of actual near-surface soil moisture are established daily for a 23-day accounting period using a water budget model. Within the 23-day period, three orbital radar overpasses are simulated roughly corresponding to generally moist, wet, and dry soil moisture conditions. The radar simulations are performed by a target/sensor interaction model dependent upon a terrain model, land-use classification, and near-surface soil moisture distribution. The accuracy of soil-moisture classification is evaluated for each single-date radar observation and also for multi-date detection of relative soil moisture change. In general, the results for single-date moisture detection show that 70% to 90% of cropland can be correctly classified to within +/- 20% of the true percent of field capacity. For a given radar resolution, the expected classification accuracy is shown to be dependent upon both the general soil moisture condition and also the geographical distribution of land-use and topographic relief. An analysis of cropland, urban, pasture/rangeland, and woodland subregions within the test site indicates that multi-temporal detection of relative soil moisture change is least sensitive to classification error resulting from scene complexity and topographic effects.

Ulaby, F. T.↗
NASA NTRS1984

Applications of array processors in the analysis of remote sensing images

The architectures, programming characteristics, and ranges of application of past, present, and planned array processors for the digital processing of remote-sensing images are compared. Such functions as radiometric and geometric corrections, principal-components analysis, cluster coding, histogram generation, grey-level mapping, convolution, classification, and mensuration and modeling operations are considered, and both pipeline-type and single-instruction/multiple-data-stream (SIMD) arrays are evaluated. Numerical results are presented in a table, and it is found that the pipeline-type arrays normally used with minicomputers increase their speed significantly at low cost, while even further gains are provided by the more expensive SIMD arrays. Most image-processing operations become I/O-limited when SIMD arrays are used with current I/O devices.

Ramapriyan, H. K.↗
NASA NTRS1987

Use of topographic and climatological models in a geographical data base to improve Landsat MSS classification for Olympic National Park

An unsupervised computer classification of vegetation/landcover of Olympic National Park and surrounding environs was initially carried out using four bands of Landsat MSS data. The primary objective of the project was to derive a level of landcover classifications useful for park management applications while maintaining an acceptably high level of classification accuracy. Initially, nine generalized vegetation/landcover classes were derived. Overall classification accuracy was 91.7 percent. In an attempt to refine the level of classification, a geographic information system (GIS) approach was employed. Topographic data and watershed boundaries (inferred precipitation/temperature) data were registered with the Landsat MSS data. The resultant boolean operations yielded 21 vegetation/landcover classes while maintaining the same level of classification accuracy. The final classification provided much better identification and location of the major forest types within the park at the same high level of accuracy, and these met the project objective. This classification could now become inputs into a GIS system to help provide answers to park management coupled with other ancillary data programs such as fire management.

Cibula, William G.↗
NASA NTRS1987

Intelligent tutoring systems as tools for investigating individual differences in learning

The ultimate goal of this research is to build an improved model-based selection and classification system for the United States Air Force. Researchers are developing innovative approaches to ability testing. The Learning Abilities Measurement Program (LAMP) examines individual differences in learning abilities, seeking answers to the questions of why some people learn more and better than others and whether there are basic cognitive processes applicable across tasks and domains that are predictive of successful performance (or whether there are more complex problem solving behaviors involved).

Shute, Valerie J.↗
NASA NTRS1992

Remote sensing of Earth terrain

Research findings are summarized for projects dealing with the following: application of theoretical models to active and passive remote sensing of saline ice; radiative transfer theory for polarimetric remote sensing of pine forest; scattering of electromagnetic waves from a dense medium consisting of correlated Mie scatterers with size distribution and applications to dry snow; variance of phase fluctuations of waves propagating through a random medium; theoretical modeling for passive microwave remote sensing of earth terrain; polarimetric signatures of a canopy of dielectric cylinders based on first and second order vector radiative transfer theory; branching model for vegetation; polarimetric passive remote sensing of periodic surfaces; composite volume and surface scattering model; and radar image classification.

Kong, J. A.↗
NASA NTRS1993

Remote sensing of Earth terrain

Progress report on remote sensing of Earth terrain covering the period from Jan. to June 1993 is presented. Areas of research include: radiative transfer model for active and passive remote sensing of vegetation canopy; polarimetric thermal emission from rough ocean surfaces; polarimetric passive remote sensing of ocean wind vectors; polarimetric thermal emission from periodic water surfaces; layer model with tandom spheriodal scatterers for remote sensing of vegetation canopy; application of theoretical models to active and passive remote sensing of saline ice; radiative transfer theory for polarimetric remote sensing of pine forest; scattering of electromagnetic waves from a dense medium consisting of correlated mie scatterers with size distributions and applications to dry snow; variance of phase fluctuations of waves propagating through a random medium; polarimetric signatures of a canopy of dielectric cylinders based on first and second order vector radiative transfer theory; branching model for vegetation; polarimetric passive remote sensing of periodic surfaces; composite volume and surface scattering model; and radar image classification.

Kong, J. A.↗
NASA NTRS1994

Retrieval of Surface Directional Reflectance Properties Using Ground Level Multiangle Measurements

Knowledge of the directional reflectance properties of natural surfaces such as soils and vegetation canopies is essential for classification studies and canopy model inversion. Atmospheric correction schemes, using various levels of approximation, are described to retrieve surface bidirectional reflectance factors (BRFs) and directional-hemispherical reftectances (albedos)from multiangle radiance measurements taken at ground level. The retrieval schemes are tested on simulated data incorporating realistic surface BRFs and atmospheric models containing aerosols. Sensitivity of the atmospherically corrected BRFs and associated directional-hemispherical reflectances to various aerosol properties and the sun-view geometry is illustrated. A measurement strategy for obtaining highly accurate surface reflectance properties also is examined in the context of instrument radiametric calibration, knowledge of the atmospheric properties, and sun-view angular coverage.

Martonchik, John V.↗
NASA NTRS1999

"Interactive Classification Technology"

The investigators are upgrading a knowledge representation language called SL (Symbolic Language) and an automated reasoning system called SMS (Symbolic Manipulation System) to enable the technologies to be used in automated reasoning and interactive classification systems. The overall goals of the project are: a) the enhancement of the representation language SL to accommodate multiple perspectives and a wider range of meaning; b) the development of a sufficient set of operators to enable the interpreter of SL to handle representations of basic cognitive acts; and c) the development of a default inference scheme to operate over SL notation as it is encoded. As to particular goals the first-year work plan focused on inferencing and.representation issues, including: 1) the development of higher level cognitive/ classification functions and conceptual models for use in inferencing and decision making; 2) the specification of a more detailed scheme of defaults and the enrichment of SL notation to accommodate the scheme; and 3) the adoption of additional perspectives for inferencing.

deBessonet, Cary↗
NASA NTRS1993

Retrieval of Surface Directional Reflectance and Hemispherical Albedo Using Multi-angle Measurements, I. Ground Level Observations

Knowledge of the directional reflectance properties of natural surfaces such as soils and vegetation canopies is essential for classification studies and canopy model inversions. Atmospheric correction schemes, using various levels of approximation, are described to retrieve surface bidirectional reflectance factors (BRFs) and directional-hemispherical reflectances (albedos) from multi-angle radiance measurements taken at ground level.

factors (BRFs) radiometric calibration sun-view an↗
NASA NTRS2007

Hierarchically Structured Non-Intrusive Sign Language Recognition

This work presents a hierarchically structured approach at the nonintrusive recognition of sign language from a monocular frontal view. Robustness is achieved through sophisticated localization and tracking methods, including a combined EM/CAMSHIFT overlap resolution procedure and the parallel pursuit of multiple hypotheses about hands position and movement. This allows handling of ambiguities and automatically corrects tracking errors. A biomechanical skeleton model and dynamic motion prediction using Kalman filters represents high level knowledge. Classification is performed by Hidden Markov Models. 152 signs from German sign language were recognized with an accuracy of 97.6%.

Zieren, Jorg↗