Search NASA⌕ Search

SEARCH · Search NASA

Results for “emulator classification”

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.

8 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS2016

Statistical Emulator for Expensive Classification Simulators

Expensive simulators prevent any kind of meaningful analysis to be performed on the phenomena they model. To get around this problem the concept of using a statistical emulator as a surrogate representation of the simulator was introduced in the 1980's. Presently, simulators have become more and more complex and as a result running a single example on these simulators is very expensive and can take days to weeks or even months. Many new techniques have been introduced, termed criteria, which sequentially select the next best (most informative to the emulator) point that should be run on the simulator. These criteria methods allow for the creation of an emulator with only a small number of simulator runs. We follow and extend this framework to expensive classification simulators.

Ross, Jerret↗
NASA NTRS1984

Using spatial logic in classification of Landsat TM data

A strategy for spatial/spectral classification of Landsat TM data is presented. The strategy is founded upon 'spatial logic', a logic that seeks to emulate important aspects of visual image interpretation. The carefully structured classification process begins with spectral stratification of the data into water, vegetated and non-vegetated pixels. A region growing algorithm is then used to define 'fields' of similar land cover composition. Fields are characterized by cover composition, size and neighborhood characteristics. A supervised iterative contextual classification algorithm is developed to assign final land use/land cover labels. Maps are generalized using a spatial post-processing technique. Positive, though preliminary, results are presented.

Merchant, J. W.↗
NASA NTRS2022

The Earth Model Column Collaboratory (EMC2) v1.1: An Open-Source Ground-Based Lidar and Radar Instrument Simulator and Subcolumn Generator for Large-Scale Models

Climate models are essential for our comprehensive understanding of Earth's atmosphere and can provide critical insights on future changes decades ahead. Because of these critical roles, today's climate models are continuously being developed and evaluated using constraining observations and measurements obtained by satellites, airborne, and ground-based instruments. Instrument simulators can provide a bridge between the measured or retrieved quantities and their sampling in models and field observations while considering instrument sensitivity limitations. Here we present the Earth Model Column Collaboratory (EMC2), an open-source ground-based lidar and radar instrument simulator and subcolumn generator, specifically designed for large-scale models, in particular climate models, but also applicable to high-resolution model output. EMC2 provides a flexible framework enabling direct comparison of model output with ground-based observations, including generation of subcolumns that may statistically represent finer model spatial resolutions. In addition, EMC2 emulates ground-based (and air- or space-borne) measurements while remaining faithful to large-scale models' physical assumptions implemented in their cloud or radiation schemes. The simulator uses either single particle or bulk particle size distribution lookup tables, depending on the selected scheme approach, to perform the forward calculations. To facilitate model evaluation, EMC2 also includes three hydrometeor classification methods, namely, radar- and sounding-based cloud and precipitation detection and classification, lidar-based phase classification, and a Cloud Feedback Model Intercomparison Project Observational Simulator Package (COSP) lidar simulator emulator. The software is written in Python, is easy to use, and can be straightforwardly customized for different models, radars, and lidars. Following the description of the logic, functionality, features, and software structure of EMC2, we present a case study of highly supercooled mixed-phase cloud based on measurements from the U.S. Department of Energy Atmospheric Radiation Measurement (ARM) West Antarctic Radiation Experiment (AWARE). We compare observations with the application of EMC2 to outputs from four configurations of the NASA Goddard Institute for Space Studies (GISS) climate model (ModelE3) in single-column model (SCM) mode and from a large-eddy simulation (LES) model. We show that two of the four ModelE3 configurations can form and maintain highly supercooled precipitating cloud for several hours, consistent with observations and LES. While our focus is on one of these ModelE3 configurations, which performed slightly better in this case study, both of these configurations and the LES results post-processed with EMC2 generally provide reasonable agreement with observed lidar and radar variables. As briefly demonstrated here, EMC2 can provide a lightweight and flexible framework for comparing the results of both large-scale and high-resolution models directly with observations, with relatively little overhead and multiple options for achieving consistency with model microphysical or radiation scheme physics.

Earth Model Column Collaboratory↗
NASA NTRS2018

Delay Tolerant Network Routing as a Machine Learning Classification Problem

This paper discusses a machine learning-based approach to routing for delay tolerant networks (DTNs) [1]. DTNs are networks which experience frequent disconnections between nodes, uncertainty of an end-to-end path, long one-way trip times, and may have high error rates and asymmetric links. Such networks exist in deep space satellite networks, very rural environments, disaster areas and underwater environments. In this work, we use machine learning classifiers to predict a set of neighboring nodes which are the most likely to deliver a message to a desired location based on message history delivery information.We use the Common Open Research Emulator (CORE) [2] to emulate the DTN environment based on real-world location traces and collect network traffic statistics from the Bundle Protocol implementation IBR-DTN [3]. The software architecture for classification-based routing, analysis and preparation of the network history data and prediction results are discussed.

Delay Tolerant Networks↗
NASA NTRS2019

Application of Machine Learning Techniques to Delay Tolerant Network Routing

This dissertation discusses several machine learning techniques to improve routing in delay tolerant networks (DTNs). These are networks in which there may be long one-way trip times, asymmetric links, high error rates, and deterministic as well as non-deterministic loss of contact between network nodes, such as interplanetary satellite networks, mobile ad hoc networks and wireless sensor networks. This work uses historical network statistics to train a multi-label classifier to predict reliable paths through the network. In addition, a clustering technique is used to predict future mobile node locations. Both of these techniques are used to reduce the consumption of resources such as network bandwidth, memory and data storage that is required by replication routing methods often used in opportunistic DTN environments. Thesis contributions include: an emulation tool chain developed to create a DTN test bed for machine learning, the network and software architecture for a machine learning based routing method, the development and implementation of classification and clustering techniques and performance evaluation in terms of machine learning and routing metrics.

Dudukovich, Rachel M.↗
NASA NTRS2019

State Predictor of Classification Cognitive Engine Applied to Channel Fading

This study presents the application of machine learning (ML) to a space-to-ground communication link, showing how ML can be used to detect the presence of detrimental channel fading. Using this channel state information, the communication link can be used more efficiently by reducing the amount of lost data during fading. The motivation for this work is based on channel fading observed during on-orbit operations with NASA's Space Communication and Navigation (SCaN) testbed on the International Space Station (ISS). This paper presents the process to extract a target concept (fading and not-fading) from the raw data. The pre-processing and data exploration effort is explained in detail, with a list of assumptions made for parsing and labelling the dataset. The model selection process is explained, specifically emphasizing the benefits of using an ensemble of algorithms with majority voting for binary classification of the channel state. Experimental results are shown, highlighting how an end-to-end communication system can utilize knowledge of the channel fading status to identity fading and take appropriate action. With a laboratory testbed to emulate channel fading, the overall performance is compared to standard adaptive methods without fading knowledge, such as adaptive coding and modulation.

Fading↗
NASA NTRS2010

Characterization of Orbital Debris Photometric Properties Derived from Laboratory-Based Measurements

To better characterize and model optical data acquired from ground-based telescopes, the Optical Measurements Center (OMC) at NASA/JSC attempts to emulate illumination conditions seen in space using equipment and techniques that parallel telescopic observations and source-target-sensor orientations. The OMC uses a 75 Watt Xenon arc lamp as a solar simulator, an SBIG CCD camera with standard Johnson/Bessel filters, and a robotic arm to simulate an object's position and rotation. The laboratory uses known shapes, materials suspected to be consistent with the orbital debris population, and three phase angles to best match the lighting conditions of the telescope based data. The fourteen objects studied in the laboratory are fragments or materials acquired through ground-tests of scaled-model satellites/rocket bodies as well as material samples in more/less "flight-ready" condition. All fragments were measured at 10 increments in a full 360 rotation at 6 , 36 , and 60 phase angles. This paper will investigate published color photometric data for a series of orbital debris targets and compare it to the empirical photometric measurements generated in the OMC. Using the data acquired over specific rotational angles through different filters (B, V, R, I), a color index is acquired (B-R, R-I). Using these values and their associated lightcurves, this laboratory data is compared to observational data obtained on the 1 m telescope of the Astronomical Institute of the University of Bern (AUIB), the 0.9 m operated by the Small- and Medium-Aperture Research Telescope System (SMARTS) Consortium and the Curtis-Schmidt 0.6 m Michigan Orbital Debris Space Debris Telescope both located at Cerro Tololo Inter-American Observatory (CTIO). An empirical based optical characterization model will be presented to provide preliminary correlations between laboratory based and telescope-based data in the context of classification of GEO debris objects.

Cowardin, H.↗
NASA NTRS2009

An Assessment of GEO Orbital Debris Photometric Properties Derived from Laboratory-Based Measurements

Optical observations of orbital debris offer insights that differ from radar measurements (specifically the size parameter and wavelength regime). For example, time-dependent photometric data yield lightcurves in multiple bandpasses that aid in material identification and possible periodic orientations. This data can also be used to help identify shapes and optical properties at multiple phase angles. Capitalizing on optical data products and applying them to generate a more complete understanding of orbital space objects, is a key objective of NASA s Optical Measurement Program, and a primary driver for creation of the Optical Measurements Center (OMC). The OMC attempts to emulate space-based illumination conditions using equipment and techniques that parallel telescopic observations and source-target-sensor orientations. The OMC uses a 300 Watt Xenon arc lamp as a solar simulator, a CCD camera with Johnson/Bessel colored filters, and a robotic arm to orientate/rotate objects to simulate an object's orbit/rotational period. A high-resolution, high bandwidth (350nm-2500nm) Analytical Spectral Devices (ASD) spectrometer is also employed to baseline various material types. Since observation of GEO targets are generally restricted to the optical regime (due to radar range limitations), analysis of their properties is tailored to those revealed by optical data products. In this connection, much attention has been directed towards understanding the lightcurves of orbital debris with high area-to-mass (A/m) ratios (greater than 0.9 square meters per kilogram). A small population of GEO debris was recently identified, which exhibits the properties of high A/m objects, such as variable eccentricities and inclinations a dynamical characteristic generally resulting from varying solar radiation pressure on high A/m objects. Materials such as multi-layered insulation (MLI) and solar panels are two examples of materials with high area-to mass ratios. Lightcurves for such objects can vary greatly (even for the same object under different illumination conditions). For example, specular reflections from multiple facets of the target surface (e.g. Mylar or Aluminized Kapton) can lead to erratic, orientation-dependent light curves. This paper will investigate published color photometric data for a series of orbital debris targets and compare it to the empirical photometric measurements generated in the OMC. The specific materials investigated (known to exist in GEO) are: an intact piece of MLI, separated layers of MLI, and multiple solar cells materials. Using the data acquired over specific rotational angles through different filters (B, V, R, I), a color index is acquired (B-R, R-I). As a secondary check, the spectrometer is used to define color indexes for the same material. Using these values and their associated lightcurves, this laboratory data is compared to observational data obtained on the 1m telescope of the Astronomical Institute of the University of Bern (AUIB) and the 0.9 m Small and Moderate Aperture Research Telescope System (SMARTS) telescope at Cerro Tololo Inter-American Observatory (CTIO). We will present laboratory generated lightcurves with color indexes of the high A/m materials alongside telescopic data of targets with high A/m values. We will discuss the relationship of laboratory to telescope data in the context of classification of GEO debris objects.

Rodriquez-Cowardin, H.↗