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At least 955 records · Page 53
All sourcesNASA NTRSDOE OSTI
NASA NTRS2002

Investigation of Error Patterns in Geographical Databases

The objective of the research conducted in this project is to develop a methodology to investigate the accuracy of Airport Safety Modeling Data (ASMD) using statistical, visualization, and Artificial Neural Network (ANN) techniques. Such a methodology can contribute to answering the following research questions: Over a representative sampling of ASMD databases, can statistical error analysis techniques be accurately learned and replicated by ANN modeling techniques? This representative ASMD sample should include numerous airports and a variety of terrain characterizations. Is it possible to identify and automate the recognition of patterns of error related to geographical features? Do such patterns of error relate to specific geographical features, such as elevation or terrain slope? Is it possible to combine the errors in small regions into an error prediction for a larger region? What are the data density reduction implications of this work? ASMD may be used as the source of terrain data for a synthetic visual system to be used in the cockpit of aircraft when visual reference to ground features is not possible during conditions of marginal weather or reduced visibility. In this research, United States Geologic Survey (USGS) digital elevation model (DEM) data has been selected as the benchmark. Artificial Neural Networks (ANNS) have been used and tested as alternate methods in place of the statistical methods in similar problems. They often perform better in pattern recognition, prediction and classification and categorization problems. Many studies show that when the data is complex and noisy, the accuracy of ANN models is generally higher than those of comparable traditional methods.

Dryer, David↗
NASA NTRS2014

Concepts to Support HRP Integration Using Publications and Modeling

Initial efforts are underway to enhance the Human Research Program (HRP)'s identification and support of potential cross-disciplinary scientific collaborations. To increase the emphasis on integration in HRP's science portfolio management, concepts are being explored through the development of a set of tools. These tools are intended to enable modeling, analysis, and visualization of the state of the human system in the spaceflight environment; HRP's current understanding of that state with an indication of uncertainties; and how that state changes due to HRP programmatic progress and design reference mission definitions. In this talk, we will discuss proof-of-concept work performed using a subset of publications captured in the HRP publications database. The publications were tagged in the database with words representing factors influencing health and performance in spaceflight, as well as with words representing the risks HRP research is reducing. Analysis was performed on the publication tag data to identify relationships between factors and between risks. Network representations were then created as one type of visualization of these relationships. This enables future analyses of the structure of the networks based on results from network theory. Such analyses can provide insights into HRP's current human system knowledge state as informed by the publication data. The network structure analyses can also elucidate potential improvements by identifying network connections to establish or strengthen for maximized information flow. The relationships identified in the publication data were subsequently used as inputs to a model captured in the Systems Modeling Language (SysML), which functions as a repository for relationship information to be gleaned from multiple sources. Example network visualization outputs from a simple SysML model were then also created to compare to the visualizations based on the publication data only. We will also discuss ideas for building upon this proof-of-concept work to further support an integrated approach to human spaceflight risk reduction.

Mindock, J.↗
NASA NTRS1984

Propfan experimental data analysis

A data reduction method, which is consistent with the performance prediction methods used for analysis of new aircraft designs, is defined and compared to the method currently used by NASA using data obtained from an Ames Res. Center 11 foot transonic wind tunnel test. Pressure and flow visualization data from the Ames test for both the powered straight underwing nacelle, and an unpowered contoured overwing nacelle installation is used to determine the flow phenomena present for a wind mounted turboprop installation. The test data is compared to analytic methods, showing the analytic methods to be suitable for design and analysis of new configurations. The data analysis indicated that designs with zero interference drag levels are achieveable with proper wind and nacelle tailoring. A new overwing contoured nacelle design and a modification to the wing leading edge extension for the current wind tunnel model design are evaluated. Hardware constraints of the current model parts prevent obtaining any significant performance improvement due to a modified nacelle contouring. A new aspect ratio wing design for an up outboard rotation turboprop installation is defined, and an advanced contoured nacelle is provided.

Vernon, David F.↗
NASA NTRS1999

Accounting for Laminar Run & Trip Drag in Supersonic Cruise Performance Testing

An improved laminar run and trip drag correction methodology for supersonic cruise performance testing was derived. This method required more careful analysis of the flow visualization images which revealed delayed transition particularly on the inboard upper surface, even for the largest trip disks. In addition, a new code was developed to estimate the laminar run correction. Once the data were corrected for laminar run, the correct approach to the analysis of the trip drag became evident. Although the data originally appeared confusing, the corrected data are consistent with previous results. Furthermore, the modified approach, which was described in this presentation, extends prior historical work by taking into account the delayed transition caused by the blunt leading edges.

Goodsell, Aga M.↗
NASA NTRS1980

Evaluation of SLAR and thematic mapper MSS data for forest cover mapping using computer-aided analysis techniques

Separate holograms of horizontally (HH) and vertically (HV) polarized responses obtained by the APQ-102 side-looking radar were processed through an optical correlator and the resulting image was recorded on positive film from which black and white negative and positive prints were made. Visual comparison of the HH and HV images reveals a distinct dark band in the imagery which covers about 30% of the radar strip. Preliminary evaluaton of the flight line 1 date indicates that various features on the HH and HV images seem to have different response levels. The amount of sidelap due to the look angle between flight lines 1 and 2 is negligible. NASA mission #425 to obtain flightlines of NS-001 MSS data and supporting aerial photography was successfully flown. Flight line 3 data are of very good quality and virtually cloud-free. Results of data analysis for selection of test fields and for evaluation of waveband combination and spatial resolution are presented.

Hoffer, R. M.↗
NASA NTRS1995

Automated Verification Of Small Parts In Turbine Wheels

Automated visual-inspection system determines whether turbine wheel assembled in correct positions. Includes two subsystems; part manipulator and control-and-analysis subsystem. Video camera having fixed focal length held stationary while part manipulator rotates turbine wheel to discrete angles so camera views specified positions on wheel. Control-and-analysis subsystem controls rotary table, processes image data acquired by camera, and makes record of inspection. Automated inspection of turbine wheel containing many small parts eliminates tedium and occasional error in repetitive visual and manual inspection by human technicians, enabling technicians to concentrate on nonroutine inspection problems.

Gutow, David A.↗
NASA NTRSDate not supplied

Exploring Anomalous PM 2.5 from Wildfires and Dust Storms using Data and Services at NASA GES DISC

The presence of fine particles in the atmosphere with a diameter of less than 2.5 µm, called particulate matter 2.5 (PM 2.5 ), poses a significant threat to human health as a criteria air pollutant. Fortunately, NASA's Goddard Earth Sciences Data and Information Services Center (GES DISC) provides easy access to several PM 2.5 concentration products. These datasets include the reanalysis of global hourly and monthly aerosol components including PM 2.5 data from the Modern-Era Retrospective analysis for Research and Applications, version 2 (MERRA-2), as well as 3-hourly real-time ensemble forecasts of PM 2.5 from the Hazardous Air Quality Ensemble System (HAQES). The HAQES products are developed by the George Mason University Air Quality Laboratory as part of NASA's Health Air Quality Applied Science Team (HAQAST). The GES DISC is actively collaborating with scientists in the HAQAST program to further expand air quality data collections. Two new datasets are currently being archived: one is the machine learning-based global hourly PM 2.5 derived from MERRA-2; the other is the localized data (NO 2 , O 3 , and PM 2.5 ) time series derived from NASA's GEOS Composition Forecasting (GEOS-CF) system. In this presentation, we will explore the spatial patterns and long-distance transport characteristics of elevated PM 2.5 during extreme pollution events, such as the June 2023 Canadian wildfires, which are still active at the time of writing; and severe spring dust storms in 2023 over Asia. To gain comprehensive insights, we will utilize various PM 2.5 data in conjunction with satellite-observed aerosol data from TROPOspheric Monitoring Instrument (TROPOMI) on Sentinel-5P. The primary focus of this presentation will be to demonstrate effective use of data tools and services to visualize and explore extreme air pollution phenomena. Additionally, we will provide guidance on how users can download specific data of interest, facilitating further analysis and research in this critical area.

air quality↗
NASA NTRS1986

Statistical dependency in visual scanning

A method to identify statistical dependencies in the positions of eye fixations is developed and applied to eye movement data from subjects who viewed dynamic displays of air traffic and judged future relative position of aircraft. Analysis of approximately 23,000 fixations on points of interest on the display identified statistical dependencies in scanning that were independent of the physical placement of the points of interest. Identification of these dependencies is inconsistent with random-sampling-based theories used to model visual search and information seeking.

Ellis, Stephen R.↗
NASA NTRS1977

Automated electroencephalography system and electroencephalographic correlates of space motion sickness, part 3

Computer quantification methods were used to analyze the Skylab electroencephalographic data obtained during the course of the M133 series of experiments. This undertaking was prompted by initial observations made during visual analysis of the tape-recorded sleep records where there appeared to be an increase of the alpha-rhythm frequency during some inflight recording sessions, as compared to preflight baseline observations. A number of potential etiological factors are identified and their various possible influences discussed. The presence of the zero-g state is thought to be an important factor, possibly influencing EEG through alteration of vestibular function and/or by producing fluid shifts secondary to loss of hydrostatic pressure.

Frost, J. D., Jr.↗
NASA NTRS1981

Assessment of the first radiances received from the VSSR Atmospheric Sounder (VAS) instrument

The first orderly, calibrated radiances from the VAS-D instrument on the GOES-4 satellite are examined for: image quality, radiometric precision, radiation transfer verification at clear air radiosonde sites, regression retrieval accuracy, and mesoscale analysis features. Postlaunch problems involving calibration and data processing irregularities of scientific or operational significance are included. The radiances provide good visual and relative radiometric data for empirically conditioned retrievals of mesoscale temperature and moisture fields in clear air.

Chesters, D.↗
NASA NTRS1985

A redetermination of the luminosity, distance, and reddening of Beta Lyrae

The most likely distance to the Beta Lyrae system is redetermined to lie at 370 pc, corresponding to a distance modulus of 7.8 mag, while the correct modulus value probably lies in the 7.7-8.0 mag range, corresponding to a distance range between 345 and 400 pc. On the basis of Wilson's (1974) model, the phase-averaged absolute visual magnitude of the primary component is -4.1 mag; this star is therefore a very luminous giant, classifiable as B8.5 or B9, with a luminosity class of II-Ib. The present data are based on optical and IUE scans of the brightest visual companion to Beta Lyrae, HD 174664, together with an analysis of the H line profiles in its spectrum.

Dobias, J. J.↗
NASA NTRS1993

Design and test of an object-oriented GIS to map plant species in the Southern Rockies

Elevational and latitudinal shifts occur in the flora of the Rocky Mountains due to long term climate change. In order to specify which species are successfully migrating with these changes, and which are not, an object-oriented, image-based geographic information system (GIS) is being created to animate evolving ecological regimes of temperature and precipitation. Research at the Earth Data Analysis Center (EDAC) is developing a landscape model that includes the spatial, spectral and temporal domains. It is designed to visualize migratory changes in the Rocky Mountain flora, and to specify future community compositions. The object-oriented database will eventually tag each of the nearly 6000 species with a unique hue, intensity, and saturation value, so their movements can be individually traced. An associated GIS includes environmental parameters that control the distribution of each species in the landscape, and satellite imagery is used to help visualize the terrain. Polygons for the GIS are delineated as landform facets that are static in ecological time. The model manages these facets as a triangular irregular net (TIN), and their analysis assesses the gradual progression of species as they migrate through the TIN. Using an appropriate climate change model, the goal will be to stop the modeling process to assess both the rate and direction of species' change and to specify the changing community composition of each landscape facet.

Morain, Stanley A.↗
NASA NTRS2003

Software for Allocating Resources in the Deep Space Network

TIGRAS 2.0 is a computer program designed to satisfy a need for improved means for analyzing the tracking demands of interplanetary space-flight missions upon the set of ground antenna resources of the Deep Space Network (DSN) and for allocating those resources. Written in Microsoft Visual C++, TIGRAS 2.0 provides a single rich graphical analysis environment for use by diverse DSN personnel, by connecting to various data sources (relational databases or files) based on the stages of the analyses being performed. Notable among the algorithms implemented by TIGRAS 2.0 are a DSN antenna-load-forecasting algorithm and a conflict-aware DSN schedule-generating algorithm. Computers running TIGRAS 2.0 can also be connected using SOAP/XML to a Web services server that provides analysis services via the World Wide Web. TIGRAS 2.0 supports multiple windows and multiple panes in each window for users to view and use information, all in the same environment, to eliminate repeated switching among various application programs and Web pages. TIGRAS 2.0 enables the use of multiple windows for various requirements, trajectory-based time intervals during which spacecraft are viewable, ground resources, forecasts, and schedules. Each window includes a time navigation pane, a selection pane, a graphical display pane, a list pane, and a statistics pane.

Wang, Yeou-Fang↗
NASA NTRS2018

GMI-IPS: Python Processing Software for Aircraft Campaigns

NASA's Atmospheric Tomography Mission (ATom) seeks to understand the impact of anthropogenic air pollution on gases in the Earth's atmosphere. Four flight campaigns are being deployed on a seasonal basis to establish a continuous global-scale data set intended to improve the representation of chemically reactive gases in global atmospheric chemistry models. The Global Modeling Initiative (GMI), is creating chemical transport simulations on a global scale for each of the ATom flight campaigns. To meet the computational demands required to translate the GMI simulation data to grids associated with the flights from the ATom campaigns, the GMI ICARTT Processing Software (GMI-IPS) has been developed and is providing key functionality for data processing and analysis in this ongoing effort. The GMI-IPS is written in Python and provides computational kernels for data interpolation and visualization tasks on GMI simulation data. A key feature of the GMI-IPS, is its ability to read ICARTT files, a text-based file format for airborne instrument data, and extract the required flight information that defines regional and temporal grid parameters associated with an ATom flight. Perhaps most importantly, the GMI-IPS creates ICARTT files containing GMI simulated data, which are used in collaboration with ATom instrument teams and other modeling groups. The initial main task of the GMI-IPS is to interpolate GMI model data to the finer temporal resolution (1-10 seconds) of a given flight. The model data includes basic fields such as temperature and pressure, but the main focus of this effort is to provide species concentrations of chemical gases for ATom flights. The software, which uses parallel computation techniques for data intensive tasks, linearly interpolates each of the model fields to the time resolution of the flight. The temporally interpolated data is then saved to disk, and is used to create additional derived quantities. In order to translate the GMI model data to the spatial grid of the flight path as defined by the pressure, latitude, and longitude points at each flight time record, a weighted average is then calculated from the nearest neighbors in two dimensions (latitude, longitude). Using SciPya's Regular Grid Interpolator, interpolation functions are generated for the GMI model grid and the calculated weighted averages. The flight path points are then extracted from the ATom ICARTT instrument file, and are sent to the multi-dimensional interpolating functions to generate GMI field quantities along the spatial path of the flight. The interpolated field quantities are then written to a ICARTT data file, which is stored for further manipulation. The GMI-IPS is aware of a generic ATom ICARTT header format, containing basic information for all flight campaigns. The GMI-IPS includes logic to edit metadata for the derived field quantities, as well as modify the generic header data such as processing dates and associated instrument files. The ICARTT interpolated data is then appended to the modified header data, and the ICARTT processing is complete for the given flight and ready for collaboration. The output ICARTT data adheres to the ICARTT file format standards V1.1. The visualization component of the GMI-IPS uses Matplotlib extensively and has several functions ranging in complexity. First, it creates a model background curtain for the flight (time versus model eta levels) with the interpolated flight data superimposed on the curtain. Secondly, it creates a time-series plot of the interpolated flight data. Lastly, the visualization component creates averaged 2D model slices (longitude versus latitude) with overlaid flight track circles at key pressure levels. The GMI-IPS consists of a handful of classes and supporting functionality that have been generalized to be compatible with any ICARTT file that adheres to the base class definition. The base class represents a generic ICARTT entry, only defining a single time entry and 3D spatial positioning parameters. Other classes inherit from this base class; several classes for input ICARTT instrument files, which contain the necessary flight positioning information as a basis for data processing, as well as other classes for output ICARTT files, which contain the interpolated model data. Utility classes provide functionality for routine procedures such as: comparing field names among ICARTT files, reading ICARTT entries from a data file and storing them in data structures, and returning a reduced spatial grid based on a collection of ICARTT entries. Although the GMI-IPS is compatible with GMI model data, it can be adapted with reasonable effort for any simulation that creates Hierarchical Data Format (HDF) files. The same can be said of its adaptability to ICARTT files outside of the context of the ATom mission. The GMI-IPS contains just under 30,000 lines of code, eight classes, and a dozen drivers and utility programs. It is maintained with GIT source code management and has been used to deliver processed GMI model data for the ATom campaigns that have taken place to date.

Damon, M. R.↗
NASA NTRS1990

Proceedings of the LDEF Materials Data Analysis Workshop

The 5-year, 10-month flight of the Long Duration Exposure Facility (LDEF) greatly enhanced the potential value of most LDEF materials, compared to the original 1-year flight plan. NASA recognized this potential by forming the LDEF Space Environmental Effects on Materials Special Investigation Group in early 1989 to address the expanded opportunities available in the LDEF structure and on experimental trays, so that the value of all LDEF materials to current and future space missions would be assessed and documented. The LDEF Materials Data Analysis Workshop served as one step toward the realization of that responsibility and ran concurrently with activities surrounding the successful return of the spacecraft to the NASA Kennedy Space Center. A compilation of visual aids utilized by speakers at the workshop is presented. Session 1 summarized current information on analysis responsibilities and plans and was aimed at updating the workshop attendees: the LDEF Advisory Committee, Principle Investigators, Special Investigation Group Members, and others involved in LDEF analyses or management. Sessions 2 and 3 addressed materials data analysis methodology, specimen preparation, shipment and archival, and initial plans for the LDEF Materials Data Base. A complementary objective of the workshop was to stimulate interest and awareness of opportunities to vastly expand the overall data base by considering the entire spacecraft as a materials experiment.

Stein, Bland A.↗
NASA NTRS1983

Investigation of radiometric properties of LANDSAT-4 MSS

Two full frames of radiometrically corrected (Type A) LANDSAT-4 MSS data were analyzed digitally and visually for evidence of residual calibration differences between detectors, quantization effects, and other sensor-related artifacts. Both standard statistical and Fourier analysis techniques were employed. Opportunities for coincident coverage by LANDSATS 3 and 4 were identified in the contiguous 48 states. Paired acquisitions for two scenes were obtained and analyzed to establish relationships between signal values from common areas imaged by the two MSS Systems. Initial findings on detector-to-detector differences, coherent noise, and LANDSAT 4 to LANDSAT 3 calibration are summarized. Results show that LANDSAT 4 MSS produces data of generally good quality with dynamic ranges and target responses similar to those of previous sensors.

Rice, D. P.↗
NASA NTRSDate not supplied

Open Science for Life in Space: Data Sharing and Tools for Knowledge Discovery

The fast-growing array of space biological data, which in the past was simply archived after minimal analysis, holds great potential if it can be reorganized and formatted for Open Science. Organizing the data for such analysis is a challenge because of its diverse nature (molecular, cellular, tissue, whole organism, behavior; tabular, imagery). Open Science is the concept that the more people have access to scientifically curated data, the more knowledge will be gained. This led NASA to start the development of GeneLab in 2015. GeneLab houses spaceflight and space-analog multi-omics datasets from plant, rodent, small animal, and microbial experiments. The success and knowledge gained from GeneLab led to a new alliance of NASA “Open Science Data Repositories” (OSDR), which include the Ames Life Sciences Data Archive (ALSDA) and the NASA Biological Institutional Scientific Collection (NBISC). Both are adopting the GeneLab data system, so data are more findable, accessible, interoperable, and reusable (FAIR). OSDR systems provide users the ability to upload, download, search, share, analyze, and visualize. Open Science also needs strong confidence in the data, which is gained through building science communities. With ~400 current members, GeneLab and ALSDA formed Analysis Working Groups (AWGs) to provide feedback on processing pipelines, metadata curation standards (for ‘omics and phenotypic-physiological-behavioral assays), and to collaborate in effectively reusing data. The AWG also led to the development of the Radiation Biology Ontology (RBO), ensuring radiation metadata are efficiently captured, connected, and interoperable. Feedback from the AWG provided design input toward the new single point-of-entry data submission portal for all investigators to submit, curate, and share their research data. Space biological data is now maximally open access, collected-curated with rich metadata, and formatted for interoperability to enable systems biology, meta-analysis, knowledge graphs, machine learning, modeling, and other reuse approaches. With potential for further federation of OSDR for data mining with traditional biological and medical databases (NIH, NCI, EBI, etc.), a new era for space biology has begun to support the knowledge discovery necessary for Lunar and Martian missions.

Ryan T Scott↗
NASA NTRS1974

Analysis of cross beam runs taken at Haswell Field site, October 1969

A series of cross beam experiments was conducted at the ESSA field site at Haswell, Colorado. Both the optical visual and infrared units were operated on a comparison basis. A data report of the initial auto- and cross-correlation evaluation of these tests is presented. The original test data from the Haswell tests were recorded on F M magnetic tape. A copy of the original tapes was made and employed for the present analysis. The tapes were played back at 60 inches per second for analysis. Some of the infrared units runs (Run Nos. 8,25,40,41,42,43,44,45) were played back at 30 inches per second in order to gain sufficient averaging time. The averaging time was nominally 3200 seconds real time at 60 inches per second and 1600 seconds real time at 30 inches per second. These averaging times correspond to 100 seconds of averaging time of the correlator.

Huff, L. L.↗