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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 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS1983

Estimating Location Parameters in a Mixture Model

Mixture models of the form h = sum to M terms, one for each positive integer from 1 to N lambda sub j f (j) sub theta sub j where theta sub j is a translation parameter are considered. An approach is discussed which makes use of a Caratheodory theorem on the trigonometric moment problem to determine M and theta sub j j = 1,2,...,M. This theorem is also applied to show that translates of many common distributions lead to identifiable mixtures.

Heydorn, R. P.↗
NASA NTRS1983

Estimating proportions of materials using mixture models

An approach to proportion estimation based on the notion of a mixture model, appropriate parametric forms for a mixture model that appears to fit observed remotely sensed data, methods for estimating the parameters in these models, methods for labelling proportion determination from the mixture model, and methods which use the mixture model estimates as auxiliary variable values in some proportion estimation schemes are addressed.

Heydorn, R. P.↗
NASA NTRS1997

Nonlinear Spectral Mixture Modeling of Lunar Multispectral: Implications for Lateral Transport

Linear and nonlinear spectral mixture models applied to Clementine multispectral images of the Moon result in roughly similar spatial distributions of endmember abundances. However, there are important differences in the absolute values of the predicted abundances. The magnitude of these differences and the implications for understanding geological processes are investigated across a geologic contact between mare and highland in the Grimaldi Basin on the western nearside of the Moon. Vertical and lateral mass transport due to impact cratering has redistributed mare and highland materials across the contact, creating a gradient in composition. Solutions to linear and nonlinear spectral mixture models for identical spectral endmembers of mare, highland, and fresh crater materials are compared across this simple geologic contact in the Grimaldi Basin. Profiles of mare abundance across the contact are extracted and compared quantitatively. Profiles from the linear mixture models indicate that the geologic contact has an average mare abundance of 60%, and the compositional boundary is asymmetric with more mare transported onto the highland side of the contact than highland onto the mare side of the contact. In contrast the nonlinear abundance profiles indicate that the geologic contact has an average mare abundance of 50%, and the compositional boundary is remarkably symmetric. Given the expectation that materials will be intimately mixed on the surface of the Moon, and that the asymmetries implied by the linear model are not consistent with our understanding of lunar surface processes, the nonlinear spectral mixture model is preferred and should be applied whenever quantitative abundance information is required. The remarkable symmetry in the compositional gradients across this contact indicate that lateral mass transport dominates over vertical transport at this boundary.

Mustard, John F.↗
NASA NTRS1986

Spectral mixture modeling - A new analysis of rock and soil types at the Viking Lander 1 site

A Viking Lander 1 image was modeled as mixtures of reflectance spectra of palagonite dust, gray andesitelike rock, and a coarse rocklike soil. The rocks are covered to varying degrees by dust but otherwise appear unweathered. Rocklike soil occurs as lag deposits in deflation zones around stones and on top of a drift and as a layer in a trench dug by the lander. This soil probably is derived from the rocks by wind abrasion and/or spallation. Dust is the major component of the soil and covers most of the surface. The dust is unrelated spectrally to the rock but is equivalent to the global-scale dust observed telescopically. A new method was developed to model a multispectral image as mixtures of end-member spectra and to compare image spectra directly with laboratory reference spectra. The method for the first time uses shade and secondary illumination effects as spectral end-members; thus the effects of topography and illumination on all scales can be isolated or removed. The image was calibrated absolutely from the laboratory spectra, in close agreement with direct calibrations. The method has broad applications to interpreting multispectral images, including satellite images.

Adams, J. B.↗
NASA NTRS1983

Mixture Models for Dependent Observations

Parametric mixture models appropriate for data presented in homogeneous blocks of varying sizes from several unidentified source populations are considered. For most applications, the data elements within each block are dependent. Models are proposed for multivariate normal data incorporating two types of dependence, exchangeability of elements within blocks, and a Markov structure for blocks. The consequences of assuming exchangeability, when in fact the Markov structure holds, are explored. Computational problems for each model are considered, and results of a simple test of the exchangeability hypothesis for LANDSAT data are presented.

Peters, C.↗
NASA NTRS1999

Monte Carlo mixture model of lifetime cancer incidence risk from radiation exposure on shuttle and international space station

Estimating uncertainty in lifetime cancer risk for human exposure to space radiation is a unique challenge. Conventional risk assessment with low-linear-energy-transfer (LET)-based risk from Japanese atomic bomb survivor studies may be inappropriate for relativistic protons and nuclei in space due to track structure effects. This paper develops a Monte Carlo mixture model (MCMM) for transferring additive, National Institutes of Health multiplicative, and multiplicative excess cancer incidence risks based on Japanese atomic bomb survivor data to determine excess incidence risk for various US astronaut exposure profiles. The MCMM serves as an anchor point for future risk projection methods involving biophysical models of DNA damage from space radiation. Lifetime incidence risks of radiation-induced cancer for the MCMM based on low-LET Japanese data for nonleukemia (all cancers except leukemia) were 2.77 (90% confidence limit, 0.75-11.34) for males exposed to 1 Sv at age 45 and 2.20 (90% confidence limit, 0.59-10.12) for males exposed at age 55. For females, mixture model risks for nonleukemia exposed separately to 1 Sv at ages of 45 and 55 were 2.98 (90% confidence limit, 0.90-11.70) and 2.44 (90% confidence limit, 0.70-10.30), respectively. Risks for high-LET 200 MeV protons (LET=0.45 keV/micrometer), 1 MeV alpha-particles (LET=100 keV/micrometer), and 600 MeV iron particles (LET=180 keV/micrometer) were scored on a per particle basis by determining the particle fluence required for an average of one particle per cell nucleus of area 100 micrometer(2). Lifetime risk per proton was 2.68x10(-2)% (90% confidence limit, 0.79x10(-3)%-0. 514x10(-2)%). For alpha-particles, lifetime risk was 14.2% (90% confidence limit, 2.5%-31.2%). Conversely, lifetime risk per iron particle was 23.7% (90% confidence limit, 4.5%-53.0%). Uncertainty in the DDREF for high-LET particles may be less than that for low-LET radiation because typically there is very little dose-rate dependence. Probability density functions for high-LET radiation quality and dose-rate may be preferable to conventional risk assessment approaches. Nuclear reactions and track structure effects in tissue may not be properly estimated by existing data using in vitro models for estimating RBEs. The method used here is being extended to estimate uncertainty in spacecraft shielding effectiveness in various space radiation environments.

NASA Center LaRC↗
NASA NTRS2017

Early Season Large-Area Winter Crop Mapping Using MODIS NDVI Data, Growing Degree Days Information and a Gaussian Mixture Model

Knowledge on geographical location and distribution of crops at global, national and regional scales is an extremely valuable source of information applications. Traditional approaches to crop mapping using remote sensing data rely heavily on reference or ground truth data in order to train/calibrate classification models. As a rule, such models are only applicable to a single vegetation season and should be recalibrated to be applicable for other seasons. This paper addresses the problem of early season large-area winter crop mapping using Moderate Resolution Imaging Spectroradiometer (MODIS) derived Normalized Difference Vegetation Index (NDVI) time-series and growing degree days (GDD) information derived from the Modern-Era Retrospective analysis for Research and Applications (MERRA-2) product. The model is based on the assumption that winter crops have developed biomass during early spring while other crops (spring and summer) have no biomass. As winter crop development is temporally and spatially non-uniform due to the presence of different agro-climatic zones, we use GDD to account for such discrepancies. A Gaussian mixture model (GMM) is applied to discriminate winter crops from other crops (spring and summer). The proposed method has the following advantages: low input data requirements, robustness, applicability to global scale application and can provide winter crop maps 1.5-2 months before harvest. The model is applied to two study regions, the State of Kansas in the US and Ukraine, and for multiple seasons (2001-2014). Validation using the US Department of Agriculture (USDA) Crop Data Layer (CDL) for Kansas and ground measurements for Ukraine shows that accuracies of greater than 90% can be achieved in mapping winter crops 1.5-2 months before harvest. Results also show good correspondence to official statistics with average coefficients of determination R(exp. 2) greater than 0.85.

mixture model↗
NASA NTRS2017

Gaussian Mixture Models for Temporal Depth Fusion

Sensing the 3D environment of a moving robot is essential for collision avoidance. Most 3D sensors produce dense depth maps, which are subject to imperfections due to various environmental factors. Temporal fusion of depth maps is crucial to overcome those. Temporal fusion is traditionally done in 3D space with voxel data structures, but it can be approached by temporal fusion in image space, with potential benefits in reduced memory and computational cost for applications like reactive collision avoidance for micro air vehicles. In this paper, we present an efficient Gaussian Mixture Models based depth map fusion approach, introducing an online update scheme for dense representations. The environment is modeled from an ego-centric point of view, where each pixel is represented by a mixture of Gaussian inverse-depth models. Consecutive frames are related to each other by transformations obtained from visual odometry. This approach achieves better accuracy than alternative image space depth map fusion techniques at lower computational cost.

Matthies, Larry↗
NASA NTRS1997

Analysis of Forest Foliage Using a Multivariate Mixture Model

Data with wet chemical measurements and near infrared spectra of ground leaf samples were analyzed to test a multivariate regression technique for estimating component spectra which is based on a linear mixture model for absorbance. The resulting unmixed spectra for carbohydrates, lignin, and protein resemble the spectra of extracted plant starches, cellulose, lignin, and protein. The unmixed protein spectrum has prominent absorption spectra at wavelengths which have been associated with nitrogen bonds.

Hlavka, C. A.↗
NASA NTRS2015

Determining the Water Ice Content of Martian Regolith by Nonlinear Spectral Mixture Modeling

In the search for evidence of life, Icebreaker will drill in to the Martian ice-rich regolith to collect samples, which will then be analyzed by an array of instruments designed to identify biomarkers. In addition, drilling into the subsurface will provide the opportunity to assess the vertical distribution of ice to a depth of 1 meter. The purpose of this particular project was to understand the uncertainties involved in the use of the imaging system to constrain the water ice content in regolith samples.

Subtractive Kramers-Kronig analysis↗
NASA NTRS2005

Multi-scale Rule-of-Mixtures Model of Carbon Nanotube/Carbon Fiber/Epoxy Lamina

A unidirectional carbon fiber/epoxy lamina in which the carbon fibers are coated with single-walled carbon nanotubes is modeled with a multi-scale method, the atomistically informed rule-of-mixtures. This multi-scale model is designed to include the effect of the carbon nanotubes on the constitutive properties of the lamina. It included concepts from the molecular dynamics/equivalent continuum methods, micromechanics, and the strength of materials. Within the model both the nanotube volume fraction and nanotube distribution were varied. It was found that for a lamina with 60% carbon fiber volume fraction, the Young's modulus in the fiber direction varied with changes in the nanotube distribution, from 138.8 to 140 GPa with nanotube volume fractions ranging from 0.0001 to 0.0125. The presence of nanotube near the surface of the carbon fiber is therefore expected to have a small, but positive, effect on the constitutive properties of the lamina.

Frankland, Sarah-Jane V.↗
NASA NTRS1999

Porous Media and Mixture Models for Hygrothermal Behavior of Phenolic Composites

Theoretical models are proposed to describe the interaction of water with phenolic polymer. The theoretical models involve the study of the flow of a viscous fluid through a porous media and the thermodynamic theory of mixtures. From the theory, a set of mathematical relations are developed to simulate the effect of water on the thermostructural response of phenolic composites. The expressions are applied to simulate the measured effect of water in a series of experiments conducted on carbon phenolic composites.

Sullivan, Roy M.↗
NASA NTRS1996

Mapping Chaparral in the Santa Monica Mountains Using Multiple Spectral Mixture Models

California chaparral is one of the most important natural vegetation communities in Southern California, representing a significant source of species diversity and, through a high susceptibility to fire, playing a major role in ecosystem dynamics. Due to steep topographic gradients, harsh edaphic conditions and variable fire histories, chaparral typically forms a complex mosaic of different species dominants and age classes, each with unique successional responses to fire and canopy characteristics (e.g. moisture content, biomass, fuel load) that modify fire susceptibility. The high human cost of fire and intimate mixing along the urban interface combine to modify the natural fire regime as well as provide additional impetus for a better understanding of how to predict fire and its management. Management problems have been further magnified by nearly seventy years of fire suppression and drought related die-back over the last few years resulting in a large accumulation of highly combustible fuels. Chaparral communities in the Santa Monica Mountains exemplify many of the management challenges associated with fire and biodiversity. A study was initiated in the Santa Monica Mountains to investigate the use of the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) for providing improved maps of chaparral coupled with direct estimates of canopy attributes (e.g. biomass, leaf area, fuel load). The Santa Monica Mountains are an east-west trending range located approximately 75 kilometers north of Los Angeles extending westward into Ventura County. Within the Santa Monica Mountains a diverse number of ecosystems are located, including four distinct types of chaparral, wetlands, riparian habitats, woodlands, and coastal sage scrub. In this study we focus on mapping three types of chaparral, oak woodlands and grasslands. Chaparral mapped included coastal sage scrub, chamise chaparral and mixed chaparral that consisted predominantly of two species of Ceanothus.

Green Robert O.↗