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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 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
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↗
NASA NTRS2012

NASA Ames DEVELOP Interns Collaborate with the South Bay Salt Pond Restoration Project to Monitor and Study Restoration Efforts using NASA's Satellites

In the past, natural tidal marshes in the south bay were segmented by levees and converted into ponds for use in salt production. In an effort to provide habitat for migratory birds and other native plants and animals, as well as to rebuild natural capital, the South Bay Salt Pond Restoration Project (SBSPRP) is focused on restoring a portion of the over 15,000 acres of wetlands in California's South San Francisco Bay. The process of restoration begins when a levee is breached; the bay water and sediment flow into the ponds and eventually restore natural tidal marshes. Since the spring of 2010 the NASA Ames Research Center (ARC) DEVELOP student internship program has collaborated with the South Bay Salt Pond Restoration Project (SBSPRP) to study the effects of these restoration efforts and to provide valuable information to assist in habitat management and ecological forecasting. All of the studies were based on remote sensing techniques -- NASA's area of expertise in the field of Earth Science, and used various analytical techniques such as predictive modeling, flora and fauna classification, and spectral detection, to name a few. Each study was conducted by a team of aspiring scientists as a part of the DEVELOP program at Ames.

Newcomer, Michelle E.↗
NASA NTRS2018

Particle Size Distributions in Chondritic Meteorites: Evidence for Pre-Planetesimal Histories

Magnesium-rich silicate chondrules and calcium-, aluminum-rich refractory inclusions (CAIs) are fundamental components of primitive chondritic meteorites. It has been suggested that concentration of these early-formed particles by nebular sorting processes may lead to accretion of planetesimals, the planetary bodies that represent the building blocks of the terrestrial planets. In this case, the size distributions of the particles may constrain the accretion process. Here we present new particle size distribution data for Northwest Africa 5717, a primitive ordinary chondrite (ungrouped 3.05) and the well-known carbonaceous chondrite Allende (CV3). Instead of the relatively narrow size distributions obtained in previous studies (Ebel et al., 2016; Friedrich et al., 2015; Paque and Cuzzi, 1997, and references therein), we observed broad size distributions for all particle types in both meteorites. Detailed microscopic image analysis of Allende shows differences in the size distributions of chondrule subtypes, but collectively these subpopulations comprise a composite "chondrule" size distribution that is similar to the broad size distribution found for CAIs. Also, we find accretionary 'dust' rims on only a subset (approximately 15-20 percent) of the chondrules contained in Allende, which indicates that subpopulations of chondrules experienced distinct histories prior to planetary accretion. For the rimmed subset, we find positive correlation between rim thickness and chondrule size. The remarkable similarity between the size distributions of various subgroups of particles, both with and without fine grained rims, implies a common size sorting process. Chondrite classification schemes, astrophysical disk models that predict a narrow chondrule size population and/or a common localized formation event, and conventional particle analysis methods must all be critically reevaluated. We support the idea that distinct "lithologies" in NWA 5717 are nebular aggregates of chondrules. If greater than or equal to centimeter-sized aggregates of chondrules can form it will have implications for planet formation and suggests the sticking stage is where the preferential size physics is operating.

chondritic meteorites↗
NASA NTRS2024

Automatic Detection of Large-scale Flux Ropes and Their Geoeffectiveness with a Machine-learning Approach

Detecting large-scale flux ropes (FRs) embedded in interplanetary coronal mass ejections (ICMEs) and assessing their geoeffectiveness are essential, since they can drive severe space weather. At 1 au, these FRs have an average duration of 1 day. Their most common magnetic features are large, smoothly rotating magnetic fields. Their manual detection has become a relatively common practice over decades, although visual detection can be time-consuming and subject to observer bias. Our study proposes a pipeline that utilizes two supervised binary classification machine-learning models trained with solar wind magnetic properties to automatically detect large-scale FRs and additionally determine their geoeffectiveness. The first model is used to generate a list of autodetected FRs. Using the properties of the southward magnetic field, the second model determines the geoeffectiveness of FRs. Our method identifies 88.6% and 80% of large-scale ICMEs (duration 1day) observed at 1au by the Wind and the Solar TErrestrial RElations Observatory missions, respectively. While testing with continuous solar wind data obtained from Wind, our pipeline detected 56 of the 64 large-scale ICMEs during the 2008–2014 period (recall = 0.875), but also many false positives (precision = 0.56), as we do not take into account any additional solar wind properties other than the magnetic properties. We find an accuracy of 0.88 when estimating the geoeffectiveness of the autodetected FRs using our method. Thus, in space-weather nowcasting and forecasting at L1 or any planetary missions, our pipeline can be utilized to offer a first-order detection of large-scale FRs and their geoeffectiveness.

Sanchita Pal↗
NASA NTRS2001

Models of an In-Situ Propellant Production Plant for Mars Exploration

An in-situ propellant production system (ISPP) is designed to make rocket fuel from chemicals in the Martian atmosphere in order to reduce the amount of materials that would need to be brought from Earth to support Mars missions. We have developed a description of a hypothetical ISPP system that we would like to make available to researchers who are interested in the problem of automatically diagnosing failures in complex NASA systems. This problem description will help researchers to investigate problems of interest to NASA. We would like to make the following material publicly available: (1) a 'common sense' model of an ISPP system; (2) low- and medium-fidelity simulations of the ISPP system written in Microsoft Excel and HCC; and (3) previously published data and diagrams concerning ISPP components. We do not believe there are any export considerations on these materials for the following reasons: (1) These models are not useful for guidance and real time control of vehicles, encrpytion, or any other software purpose categorized under the Export Control Classification Numbers; and (2) The models are very high level and would not by themselves enable real-time control of a real hardware system. The models are at the level of common sense. They capture, for example, that if a heater is turned on an increase in temperature should result(see the attached excerpt). We do not believe there is any commercial value to this material, given the low commercial demand for propellant plants on mars. We have spoken to acting Code IC Division Chief Dan Clancy, and he concurs with our desire to make these materials publicly available via a technical report.

Goodrich, Charlie↗
NASA NTRS2000

BOREAS TE-18 Landsat TM Maximum Likelihood Classification Image of the NSA

The BOREAS TE-18 team focused its efforts on using remotely sensed data to characterize the successional and disturbance dynamics of the boreal forest for use in carbon modeling. The objective of this classification is to provide the BOREAS investigators with a data product that characterizes the land cover of the NSA. A Landsat-5 TM image from 20-Aug-1988 was used to derive this classification. A standard supervised maximum likelihood classification approach was used to produce this classification. The data are provided in a binary image format file. The data files are available on a CD-ROM (see document number 20010000884), or from the Oak Ridge National Laboratory (ORNL) Distributed Activity Archive Center (DAAC).

Hall, Forrest G.↗
NASA NTRS1998

Characteristics of Forests in Western Sayani Mountains, Siberia from SAR Data

This paper investigated the possibility of using spaceborne radar data to map forest types and logging in the mountainous Western Sayani area in Siberia. L and C band HH, HV, and VV polarized images from the Shuttle Imaging Radar-C instrument were used in the study. Techniques to reduce topographic effects in the radar images were investigated. These included radiometric correction using illumination angle inferred from a digital elevation model, and reducing apparent effects of topography through band ratios. Forest classification was performed after terrain correction utilizing typical supervised techniques and principal component analyses. An ancillary data set of local elevations was also used to improve the forest classification. Map accuracy for each technique was estimated for training sites based on Russian forestry maps, satellite imagery and field measurements. The results indicate that it is necessary to correct for topography when attempting to classify forests in mountainous terrain. Radiometric correction based on a DEM (Digital Elevation Model) improved classification results but required reducing the SAR (Synthetic Aperture Radar) resolution to match the DEM. Using ratios of SAR channels that include cross-polarization improved classification and

Ranson, K. Jon↗
NASA NTRS1981

Modeling the effects of various radiant transfers in mountainous terrain on sensor response

A model is developed for the study of remote sensing spectral data problems encountered in mountainous terrain, where slope orientation with respect to sources of incident radiation must be considered for accurate classification of multispectral data. The model simulates the effects of solar, sky and adjacent slope irradiance fluxes and the bidirectional reflectance properties of the target surface on sensor response for various slope orientations. The errors associated with two practical techniques used to account for topographic effects on sensor response were evaluated in light of the model, and it was found that the simple cosine correction of the direct solar source showed reflectance errors greater than 11% while the technique considering both direct solar and diffuse sky sources was 50% more accurate.

Kimes, D. S.↗
NASA NTRSDate not supplied

Multi-Class Anomaly Detection in Flight Data using Semi-Supervised Explainable Deep Learning Model

Identifying precursor for safety incidents in aviation data is a crucial task, yet extremely challenging. The main approach, in practice, leverages domain expertise to define expected tolerances in system’s behavior and alarm exceedance from such safety margins. However, this approach is incapable of identifying unknown risk and vulnerabilities. Machine learning has been long studied and deployed to identify precursors for such anomalies, with the great challenge of the need for sufficient labelled set of data to achieve a reliable and accurate performance. In this article, we develop an explainable deep semi-supervised model for anomaly detection in aviation, building upon recent advancements in the machine learning literature. The proposed model combines feature engineering and classification in the feature space, while leveraging all available data (labelled and unlabeled). Validating on two case studies of anomaly detection in take-off and landing phases of commercial aircraft, we show that our model is able to outperform state-of-the-art supervised anomaly detection model and reach significantly high accuracy and low false alarm with minimum amount of available labelled data.

Anomaly Detection↗
NASA NTRS1976

Development of a methodology for classifying software errors

A mathematical formalization of the intuition behind classification of software errors is devised and then extended to a classification discipline: Every classification scheme should have an easily discernible mathematical structure and certain properties of the scheme should be decidable (although whether or not these properties hold is relative to the intended use of the scheme). Classification of errors then becomes an iterative process of generalization from actual errors to terms defining the errors together with adjustment of definitions according to the classification discipline. Alternatively, whenever possible, small scale models may be built to give more substance to the definitions. The classification discipline and the difficulties of definition are illustrated by examples of classification schemes from the literature and a new study of observed errors in published papers of programming methodologies.

Gerhart, S. L.↗
NASA NTRS1990

An AI-based approach to structural damage identification by modal analysis

Flexible-structure damage is presently addressed by a combined model- and parameter-identification approach which employs the AI methodologies of classification, heuristic search, and object-oriented model knowledge representation. The conditions for model-space search convergence to the best model are discussed in terms of search-tree organization and initial model parameter error. In the illustrative example of a truss structure presented, the use of both model and parameter identification is shown to lead to smaller parameter corrections than would be required by parameter identification alone.

Glass, B. J.↗
NASA NTRS2022

Thermal Imaging of Dust Hiding the Black Hole in NGC 1068

In the widely accepted ‘unified model’1 solution of the classification puzzle of active galactic nuclei, the orientation of a dusty accretion torus around the central black hole dominates their appearance. In ‘type-1’ systems, the bright nucleus is visible at the centre of a face-on torus. In ‘type-2’ systems the thick, nearly edge-on torus hides the central engine. Later studies suggested evolutionary effects2 and added dusty clumps and polar winds3 but left the basic picture intact. However, recent high-resolution images4 of the archetypal type-2 galaxy NGC 10685,6, suggested a more radical revision. The images displayed a ring-like emission feature that was proposed to be hot dust surrounding the black hole at the radius where the radiation from the central engine evaporates the dust. That ring is too thin and too far tilted from edge-on to hide the central engine, and ad hoc foreground extinction is needed to explain the type-2 classification. These images quickly generated reinterpretations of the dichotomy between types 1 and 27,8. Here we present new multi-band mid-infrared images of NGC 1068 that detail the dust temperature distribution and reaffirm the original model. Combined with radio data ( J.F.G. and C.M.V.I., manuscript in preparation), our maps locate the central engine that is below the previously reported ring and obscured by a thick, nearly edge-on disk, as predicted by the unified model. We also identify emission from polar flows and absorbing dust that is mineralogically distinct from that towards the Milky Way centre.

Violeta Gamez Rosas↗
NASA NTRS1983

Queueing analysis of a canonical model of real-time multiprocessors

A logical classification of multiprocessor structures from the point of view of control applications is presented. A computation of the response time distribution for a canonical model of a real time multiprocessor is presented. The multiprocessor is approximated by a blocking model. Two separate models are derived: one created from the system's point of view, and the other from the point of view of an incoming task.

Krishna, C. M.↗
NASA NTRS2012

AGN Winds and Blazar Phenomenology

The launch of {\em Fermi} produced a significant number of AGN detections to allow statistical treatment of their properties. One of the first such systematics was the "Blazar Divide" in FSRQs and BL Lacs according to their gamma-ray spectral index and luminosity. Further data accumulation indicated this separation to be less clear than thought before. An MHD wind model which can model successfully the Seyfert X-ray absorber properties provides the vestiges of an account of the observed blazar classification. We propose to employ this model to model in detail the broad band blazar spectra and their statistical properties in terms of the physical parameters of these MHD winds.

Kazanas, Demos↗
NASA NTRS1984

Texture classification using autoregressive filtering

A general theory of image texture models is proposed and its applicability to the problem of scene segmentation using texture classification is discussed. An algorithm, based on half-plane autoregressive filtering, which optimally utilizes second order statistics to discriminate between texture classes represented by arbitrary wide sense stationary random fields is described. Empirical results of applying this algorithm to natural and sysnthesized scenes are presented and future research is outlined.

Lawton, W. M.↗
NASA NTRS2008

NASA Tech Briefs, December 2008

Topics covered include: Crew Activity Analyzer; Distributing Data to Hand-Held Devices in a Wireless Network; Reducing Surface Clutter in Cloud Profiling Radar Data; MODIS Atmospheric Data Handler; Multibeam Altimeter Navigation Update Using Faceted Shape Model; Spaceborne Hybrid-FPGA System for Processing FTIR Data; FPGA Coprocessor for Accelerated Classification of Images; SiC JFET Transistor Circuit Model for Extreme Temperature Range; TDR Using Autocorrelation and Varying-Duration Pulses; Update on Development of SiC Multi-Chip Power Modules; Radio Ranging System for Guidance of Approaching Spacecraft; Electromagnetically Clean Solar Arrays; Improved Short-Circuit Protection for Power Cells in Series; Electromagnetically Clean Solar Arrays; Logic Gates Made of N-Channel JFETs and Epitaxial Resistors; Improved Short-Circuit Protection for Power Cells in Series; Communication Limits Due to Photon-Detector Jitter; System for Removing Pollutants from Incinerator Exhaust; Sealing and External Sterilization of a Sample Container; Converting EOS Data from HDF-EOS to netCDF; HDF-EOS 2 and HDF-EOS 5 Compatibility Library; HDF-EOS Web Server; HDF-EOS 5 Validator; XML DTD and Schemas for HDF-EOS; Converting from XML to HDF-EOS; Simulating Attitudes and Trajectories of Multiple Spacecraft; Specialized Color Function for Display of Signed Data; Delivering Alert Messages to Members of a Work Force; Delivering Images for Mars Rover Science Planning; Oxide Fiber Cathode Materials for Rechargeable Lithium Cells; Electrocatalytic Reduction of Carbon Dioxide to Methane; Heterogeneous Superconducting Low-Noise Sensing Coils; Progress toward Making Epoxy/Carbon-Nanotube Composites; Predicting Properties of Unidirectional-Nanofiber Composites; Deployable Crew Quarters; Nonventing, Regenerable, Lightweight Heat Absorber; Miniature High-Force, Long-Stroke SMA Linear Actuators; "Bootstrap" Configuration for Multistage Pulse-Tube Coolers; Reducing Liquid Loss during Ullage Venting in Microgravity; Ka-Band Transponder for Deep-Space Radio Science; Replication of Space-Shuttle Computers in FPGAs and ASICs; Demisable Reaction-Wheel Assembly; Spatial and Temporal Low-Dimensional Models for Fluid Flow; Advanced Land Imager Assessment System; Range Imaging without Moving Parts.

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