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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 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
NASA NTRS2019

Characterization and Application of Artificial Light Sources for Nighttime Aerosol Optical Depth Retrievals using the Visible Infrared Imager Radiometer Suite Day/Night Band

Using nighttime observations from Visible Infrared Imager Radiometer Suite (VIIRS) Day/Night band (DNB), the characteristics of artificial light sources are evaluated as functions of observation conditions, and incremental improvements are documented on nighttime aerosol retrievals using VIIRS DNB data on a regional scale. We find that the standard deviation of instantaneous radiance for a given artificial light source is strongly dependent upon the satellite viewing angle but is weakly dependent on lunar fraction and lunar angle. Retrieval of nighttime aerosol optical thickness (AOT) based on the novel use of these artificial light sources is demonstrated for three selected regions (United States, Middle East and India) during 2015. Reason-able agreement is found between nighttime AOTs from the VIIRS DNB and temporally adjacent daytime AOTs from the AErosol RObotic NETwork (AERONET) as well as from coincident nighttime AOT retrievals from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), indicating the potential of this method to begin filling critical gaps in diurnal AOT information at both regional and global scales. Issues related to cloud, snow and ice contamination during the winter season, as well as data loss due to the misclassification of thick aerosol plumes as clouds, must be addressed to make the algorithm operationally robust.

Jianglong Zhang↗
NASA NTRS2002

MIMIC II: a massive temporal ICU patient database to support research in intelligent patient monitoring

Development and evaluation of Intensive Care Unit (ICU) decision-support systems would be greatly facilitated by the availability of a large-scale ICU patient database. Following our previous efforts with the MIMIC (Multi-parameter Intelligent Monitoring for Intensive Care) Database, we have leveraged advances in networking and storage technologies to develop a far more massive temporal database, MIMIC II. MIMIC II is an ongoing effort: data is continuously and prospectively archived from all ICU patients in our hospital. MIMIC II now consists of over 800 ICU patient records including over 120 gigabytes of data and is growing. A customized archiving system was used to store continuously up to four waveforms and 30 different parameters from ICU patient monitors. An integrated user-friendly relational database was developed for browsing of patients' clinical information (lab results, fluid balance, medications, nurses' progress notes). Based upon its unprecedented size and scope, MIMIC II will prove to be an important resource for intelligent patient monitoring research, and will support efforts in medical data mining and knowledge-discovery.

NASA Discipline Cardiopulmonary↗
NASA NTRS2005

A Comparison of Geographic Information Systems, Complex Networks, and Other Models for Analyzing Transportation Network Topologies

This report reviews six classes of models that are used for studying transportation network topologies. The report is motivated by two main questions. First, what can the "new science" of complex networks (scale-free, small-world networks) contribute to our understanding of transport network structure, compared to more traditional methods? Second, how can geographic information systems (GIS) contribute to studying transport networks? The report defines terms that can be used to classify different kinds of models by their function, composition, mechanism, spatial and temporal dimensions, certainty, linearity, and resolution. Six broad classes of models for analyzing transport network topologies are then explored: GIS; static graph theory; complex networks; mathematical programming; simulation; and agent-based modeling. Each class of models is defined and classified according to the attributes introduced earlier. The paper identifies some typical types of research questions about network structure that have been addressed by each class of model in the literature.

Alexandrov, Natalia↗
NASA NTRS1996

Turbulent Compressible Convection with Rotation: Flow Structure and Evolution - Part 1

The effects of Coriolis forces on compressible convection are studied using three-dimensional numerical simulations carried out within a local modified f-plane model. The physics is simplified by considering a perfect gas occupying a rectilinear domain placed tangentially to a rotating sphere at various latitudes, through which a destabilizing heat flux is driven. The resulting convection is considered for a range of Rayleigh, Taylor, and Prandtl (and thus Rossby) numbers, evaluating conditions where the influence of rotation is both weak and strong. Given the computational demands of these high-resolution simulations, the parameter space is explored sparsely to ascertain the differences between laminar and turbulent rotating convection. The first paper in this series examines the effects of rotation on the flow structure within the convection, its evolution, and some consequences for mixing. Subsequent papers consider the large-scale mean shear flows that are generated by the convection, and the effects of rotation on the convective energetics and transport properties. It is found here that the structure of rotating turbulent convection is similar to earlier nonrotating studies, with a laminar, cellular surface network disguising a fully turbulent interior punctuated by vertically coherent structures. However, the temporal signature of the surface flows is modified by inertial motions to yield new cellular evolution patterns and an overall increase in the mobility of the network. The turbulent convection contains vortex tubes of many scales, including large-scale coherent structures spanning the full vertical extent of the domain involving multiple density scale heights. Remarkably, such structures align with the rotation vector via the influence of Coriolis forces on turbulent motions, in contrast with the zonal tilting of streamlines found in laminar flows. Such novel turbulent mechanisms alter the correlations which drive mean shearing flows and affect the convective transport properties. In contrast to this large-scale anisotropy, small-scale vortex tubes at greater depths are randomly orientated by the rotational mixing of momentum, leading to an increased degree of isotropy on the medium to small scales of motion there. Rotation also influences the thermodynamic mixing properties of the convection. In particular, interaction of the larger coherent vortices causes a loss of correlation between the vertical velocity and the temperature leaving a mean stratification which is not isentropic.

Brummell, Nicholas H.↗
NASA NTRS2012

Particle Filtering for Model-Based Anomaly Detection in Sensor Networks

A novel technique has been developed for anomaly detection of rocket engine test stand (RETS) data. The objective was to develop a system that postprocesses a csv file containing the sensor readings and activities (time-series) from a rocket engine test, and detects any anomalies that might have occurred during the test. The output consists of the names of the sensors that show anomalous behavior, and the start and end time of each anomaly. In order to reduce the involvement of domain experts significantly, several data-driven approaches have been proposed where models are automatically acquired from the data, thus bypassing the cost and effort of building system models. Many supervised learning methods can efficiently learn operational and fault models, given large amounts of both nominal and fault data. However, for domains such as RETS data, the amount of anomalous data that is actually available is relatively small, making most supervised learning methods rather ineffective, and in general met with limited success in anomaly detection. The fundamental problem with existing approaches is that they assume that the data are iid, i.e., independent and identically distributed, which is violated in typical RETS data. None of these techniques naturally exploit the temporal information inherent in time series data from the sensor networks. There are correlations among the sensor readings, not only at the same time, but also across time. However, these approaches have not explicitly identified and exploited such correlations. Given these limitations of model-free methods, there has been renewed interest in model-based methods, specifically graphical methods that explicitly reason temporally. The Gaussian Mixture Model (GMM) in a Linear Dynamic System approach assumes that the multi-dimensional test data is a mixture of multi-variate Gaussians, and fits a given number of Gaussian clusters with the help of the wellknown Expectation Maximization (EM) algorithm. The parameters thus learned are used for calculating the joint distribution of the observations. However, this GMM assumption is essentially an approximation and signals the potential viability of non-parametric density estimators. This is the key idea underlying the new approach.

Solano, Wanda↗
NASA NTRS1973

Monitoring the vernal advancement and retrogradation (green wave effect) of natural vegetation

The author has identified the following significant results. Preliminary evaluation of autumnal phase ground truth data suggests that the sampling procedures at the Great Plains Corridor network test sites are adequate to show relatively small temporal changes in above-ground vegetation biomass and vegetation condition. Vegetation changes measured August through December, reflect grazing intensity and environmental conditions at the test sites. Preliminary analysis of black and white imagery suggests that detail in vegetation patterns is much greater than originally anticipated. A first look analysis of single band imagery and digital data at two locations shows that woodland, grassland, and cropland areas are easily delineated. Computer derived grey-scale maps from MSS digital data were shown to be useful in identifying the location of small fields and features of the natural and cultivated lands. Single band imagery and digital data are believed to have important application for synoptic land use mapping and inventory. Initial ratio analysis, using band 5 and 7 data, suggests the applicability in the greenness of a vegetative scene.

Rouse, J. W., Jr.↗
NASA NTRS1983

The structure of a microburst - As observed by ground-based and airborne Doppler radar

Attention is given to the microburst observed near Denver, CO, on June 29, 1982, in the course of the Joint Airport Weather Study (JAWS). The JAWS ground radar network was specifically established to furnish high spatial and temporal resolution multiple Doppler data for microburst observations. The data, which were collected from directly above the microburst, permitted direct measurements of vertical velocities to be made. P-3 surveillance aircraft Doppler data was also available for this microburst, whose considerable complexity is noted.

Mueller, C. K.↗
NASA NTRS1987

Distinct meteoroid families identified on the lunar seismograms

The meteoroid impact-seismic activity data recorded by the Apollo lunar seismic network is examined. The study investigates the difference in temporal distribution between large and small impacts, clustering of impacts in a two-dimensional space of the time of the year and the time of the month, and the relationship of these observations with terrestrial observations. Several distinct families of meteoroids impacting the moon are identified. Most meteoroids producing small impact-seismic events, including ones associated with cometary showers, appear to approach from retrograde heliocentric orbits. In contrast, most meteoroids associated with large impact-seismic events appear to approach from prograde orbits; the observation is consistent with a hypothesis that many of them represent stony asteroidal material. It is suggested that the previously reported discrepancy between lunar and terrestrial meteoroid-flux estimates may be due to the differences in lunar and terrestrial detection efficiency among various families of meteoroids.

Oberst, Jurgen↗
NASA NTRS1990

Application of adjoint operators to neural learning

A technique for the efficient analytical computation of such parameters of the neural architecture as synaptic weights and neural gain is presented as a single solution of a set of adjoint equations. The learning model discussed concentrates on the adiabatic approximation only. A problem of interest is represented by a system of N coupled equations, and then adjoint operators are introduced. A neural network is formalized as an adaptive dynamical system whose temporal evolution is governed by a set of coupled nonlinear differential equations. An approach based on the minimization of a constrained neuromorphic energylike function is applied, and the complete learning dynamics are obtained as a result of the calculations.

Barhen, J.↗
NASA NTRS2005

Spatial Predictive Modeling and Remote Sensing of Land Use Change in the Chesapeake Bay Watershed

This project was focused on modeling the processes by which increasing demand for developed land uses, brought about by changes in the regional economy and the socio-demographics of the region, are translated into a changing spatial pattern of land use. Our study focused on a portion of the Chesapeake Bay Watershed where the spatial patterns of sprawl represent a set of conditions generally prevalent in much of the U.S. Working in the region permitted us access to (i) a time-series of multi-scale and multi-temporal (including historical) satellite imagery and (ii) an established network of collaborating partners and agencies willing to share resources and to utilize developed techniques and model results. In addition, a unique parcel-level tax assessment database and linked parcel boundary maps exists for two counties in the Maryland portion of this region that made it possible to establish a historical cross-section time-series database of parcel level development decisions. Scenario analyses of future land use dynamics provided critical quantitative insight into the impact of alternative land management and policy decisions. These also have been specifically aimed at addressing growth control policies aimed at curbing exurban (sprawl) development. Our initial technical approach included three components: (i) spatial econometric modeling of the development decision, (ii) remote sensing of suburban change and residential land use density, including comparisons of past change from Landsat analyses and more traditional sources, and (iii) linkages between the two through variable initialization and supplementation of parcel level data. To these we added a fourth component, (iv) cellular automata modeling of urbanization, which proved to be a valuable addition to the project. This project has generated both remote sensing and spatially explicit socio-economic data to estimate and calibrate the parameters for two different types of land use change models and has undertaken analyses of these models. One (the CA model) is driven largely by observations on past patterns of land use change, while the other (the EC model) is driven by mechanisms of the land use change decision at the parcel level. Our project may be the first serious attempt at developing both types of models for the same area, using as much common data as possible. We have identified the strengths and weaknesses of the two approaches and plan to continue to revise each model in the light of new data and new lessons learned through continued collaboration. Questions, approaches, findings, publication and presentation lists concerning the research are also presented.

Goetz, Scott J.↗
NASA NTRS2017

R2U2: Tool Overview

R2U2 (Realizable, Responsive, Unobtrusive Unit) is an extensible framework for runtime System HealthManagement (SHM) of cyber-physical systems. R2U2 can be run in hardware (e.g., FPGAs), or software; can monitorhardware, software, or a combination of the two; and can analyze a range of different types of system requirementsduring runtime. An R2U2 requirement is specified utilizing a hierarchical combination of building blocks: temporal formula runtime observers (in LTL or MTL), Bayesian networks, sensor filters, and Boolean testers. Importantly, the framework is extensible; it is designed to enable definitions of new building blocks in combination with the core structure. Originally deployed on Unmanned Aerial Systems (UAS), R2U2 is designed to run on a wide range of embedded platforms, from autonomous systems like rovers, satellites, and robots, to human-assistive ground systems and cockpits. R2U2 is named after the requirements it satisfies; while the exact requirements vary by platform and mission, the ability to formally reason about realizability, responsiveness, and unobtrusiveness is necessary for flight certifiability, safety-critical system assurance, and achievement of technology readiness levels for target systems. Realizability ensures that R2U2 is suficiently expressive to encapsulate meaningful runtime requirements while maintaining adaptability to run on different platforms, transition between different mission stages, and update quickly between missions. Responsiveness entails continuously monitoring the system under test, real-time reasoning, reporting intermediate status, and as-early-as-possible requirements evaluations. Unobtrusiveness ensures compliance with the crucial properties of the target architecture: functionality, certifiability, timing, tolerances, cost, or other constraints.

Rozier, Kristin Y.↗
NASA NTRS1999

The Unit of Lightning

For the past century, scientists have made quantitative measurements of lightning discharges. In the process, they refined the definition of a lightning unit, or basic quantum of lightning, in order to base it on observable parameters. Although many components of a lightning discharge have been identified, lightning usually occur in groups of discharges or pulses that, although complex, can be organized into units of flashes. This unit definition is based mainly on measurements of lightning from electric field, video, and ground flash lightning locating networks. More recent instrumentation with various combinations of high sensitivity, high temporal, or high spatial resolution often measure signals produced by lightning that do not cleanly divide into flashes. The data from these systems indicate the need or a more fundamental unit for lightning. Such a unit would be of benefit for both basic understanding of lightning and comparing lightning information between instruments. Without a common lightning unit definition, intercomparisons are difficult. For an example, the Lightning Detection And Ranging system (LDAR) at Kennedy Space Center (KSC) have detected ,flash' rates as high as 600 per minute while analysis based on the Advanced Ground Based Field Mill network (AGBFM) detect only 33 "flashes" per minute in the same area and time periods. The satellite based Optical Transient Detector (OTD) and Lightning Imaging Sensor (LIS) sometimes see single "flashes" that last longer than 10 seconds. Such long duration flashes are not found in electric field records or ground strike location system data sets. The unit of lightning should be based on the fundamental components of the lightning discharge. This should make the unit as generally applicable as possible. For example, studies of NO(x) production by lightning depend on parameters of the individual lightning channels and not the summary flash characteristics. For such studies, the best unit of lightning may be closer to the classical stroke in the electric field data sets, flash in the LDAR data sets, and group in the OTD/LIS data sets. For this type of study, however, more than just the stroke count information is needed. In this paper, we will outline the current definitions of the unit of lightning and propose new basic definitions for the unit of lightning. Since different instruments detect lightning in different ways and different studies may need almost mutually exclusive aspects of the lightning discharge, there may not be a single definition of the unit of lightning that can be applied to all systems and all studies. However, common components can be found since all instrumentation are detecting aspects of the same phenomenon.

Mach, Douglas M.↗
NASA NTRS2016

A New Operational Snow Retrieval Algorithm Applied to Historical AMSR-E Brightness Temperatures

Snow is a key element of the water and energy cycles and the knowledge of spatio-temporal distribution of snow depth and snow water equivalent (SWE) is fundamental for hydrological and climatological applications. SWE and snow depth estimates can be obtained from spaceborne microwave brightness temperatures at global scale and high temporal resolution (daily). In this regard, the data recorded by the Advanced Microwave Scanning Radiometer-Earth Orbiting System (EOS) (AMSR-E) onboard the National Aeronautics and Space Administration's (NASA) AQUA spacecraft have been used to generate operational estimates of SWE and snow depth, complementing estimates generated with other microwave sensors flying on other platforms. In this study, we report the results concerning the development and assessment of a new operational algorithm applied to historical AMSR-E data. The new algorithm here proposed makes use of climatological data, electromagnetic modeling and artificial neural networks for estimating snow depth as well as a spatio-temporal dynamic density scheme to convert snow depth to SWE. The outputs of the new algorithm are compared with those of the current AMSR-E operational algorithm as well as in-situ measurements and other operational snow products, specifically the Canadian Meteorological Center (CMC) and GlobSnow datasets. Our results show that the AMSR-E algorithm here proposed generally performs better than the operational one and addresses some major issues identified in the spatial distribution of snow depth fields associated with the evolution of effective grain size.

passive microwave↗
NASA NTRS2018

NeMO-Net - The Neural Multi-Modal Observation & Training Network for Global Coral Reef Assessment

In the past decade, coral reefs worldwide have experienced unprecedented stresses due to climate change, ocean acidification, and anthropomorphic pressures, instigating massive bleaching and die-off of these fragile and diverse ecosystems. Furthermore, remote sensing of these shallow marine habitats is hindered by ocean wave distortion, refraction and optical attenuation, leading invariably to data products that are often of low resolution and signal-to-noise (SNR) ratio. However, recent advances in UAV and Fluid Lensing technology have allowed us to capture multispectral 3D imagery of these systems at sub-cm scales from above the water surface, giving us an unprecedented view of their growth and decay. By combining spatial and spectral information from varying resolutions, we seek to augment and improve the classification accuracy of previously low-resolution datasets at large temporal scales.NeMO-Net, the first open-source deep convolutional neural network (CNN) and interactive learning and training software, currently being developed at NASA Ames, is aimed at assessing the present and past dynamics of coral reef ecosystems through determination of percent living cover and morphology. The latest iteration uses fully convolutional networks to segment and identify coral imagery taken by UAVs and satellites, including WorldView-2 and Sentinel. We present results taken from the Indian Ocean where classification accuracy has exceeded 91% for 24 geomorphological classes given ample training data. In addition, we utilize deep Laplacian Pyramid Super-Resolution Networks (LapSRN) to reconstruct high resolution information from low resolution imagery, trained from various UAV and satellite datasets. Finally, in the case of insufficient training data, we have developed an interactive online platform that allows users to easily segment and submit their classifications, which has been integrated with the current NeMO-Net workflow. Specifically, we present results from the Fiji islands in which preliminary user data has allowed for the accurate identification of 9 separate classes, despite issues such as cloud shadowing and spectral variation. The project is being supported by NASA's Earth Science Technology Office (ESTO) Advanced Information Systems Technology (AIST-16) Program.

Neural↗
NASA NTRS1986

Meso-beta scale perturbations of the wind field by thunderstorm cells

Data from the high density storm-scale rawinsonde network of the Severe Environmental Storms and Mesoscale Experiment revealed temporal and spatial changes in the divergence fields of the troposphere in response to severe storm evolution on May 2, 1979; these changes were detectable on the meso-beta scale. This unique set of data was subsequently used to study the evolution of the wind, divergence and vertical motion fields in the presence of intense convection. Mid- and upper-tropospheric divergence was superimposed over low-level convergence. The divergence, which has a maximum value of .0004/s, occurred 75 to 100 km upwind as well as over the tornadic cells. To the south of the storm cells, the kinematic pattern was in reverse, upper level convergence was superimposed over low-level divergence. A vertical motion doublet was found to ascend over the squall line and descend about 70 km south of the squall line. It is suggested that the following effects are accountable for the nature of the kinematic fields: (1) blocking of tropospheric environmental flow by the storm cells, (2) anvil outflows, particularly from the tornadic cells, and (3) divergence from the exit region of the jet stream.

Ulanski, S. L.↗
NASA NTRS1987

Heuristic control of the Utah/MIT dextrous robot hand

Basic hand grips and sensor interactions that a dextrous robot hand will need as part of the operation of an EVA Retriever are analyzed. What is to be done with a dextrous robot hand is examined along with how such a complex machine might be controlled. It was assumed throughout that an anthropomorphic robot hand should perform tasks just as a human would; i.e., the most efficient approach to developing control strategies for the hand would be to model actual hand actions and do the same tasks in the same ways. Therefore, basic hand grips that human hands perform, as well as hand grip action were analyzed. It was also important to examine what is termed sensor fusion. This is the integration of various disparate sensor feedback paths. These feedback paths can be spatially and temporally separated, as well as, of different sensor types. Neural networks are seen as a means of integrating these varied sensor inputs and types. Basic heuristics of hand actions and grips were developed. These heuristics offer promise of control dextrous robot hands in a more natural and efficient way.

Bass, Andrew H., Jr.↗
NASA NTRS1991

A space-time neural network

Introduced here is a novel technique which adds the dimension of time to the well known back propagation neural network algorithm. Cited here are several reasons why the inclusion of automated spatial and temporal associations are crucial to effective systems modeling. An overview of other works which also model spatiotemporal dynamics is furnished. A detailed description is given of the processes necessary to implement the space-time network algorithm. Several demonstrations that illustrate the capabilities and performance of this new architecture are given.

Villarreal, James A.↗
NASA NTRS1991

Network for the detection of stratospheric change (NDSC)

The notion of a ground-based long-term measuring network specifically designed to provide the earliest possible detection of changes in the composition and structure of the stratosphere and to understand the causes of those changes is examined. The network's short-term goals are: to study the temporal and spatial variability of atmospheric composition and structure; to provide the basis for ground truth and complementary measurements for satellite systems such as the NASA Upper Atmosphere Research Satellite; and to critically test multidimensional stratospheric models and provide the broad data base required for improved model development. Priorities, instrumentation, station considerations, and site requirements are also discussed.

Kurylo, Michael J.↗