Results for “Data segmentation” — Page 62

NASA NTRS2016

Improving Operational Acceptability of Dynamic Weather Routes (DWR) Through Analysis of Commonly Used Routings

The Dynamic Weather Routes (DWR) tool is a ground-based trajectory automation system that continuously and automatically analyzes active in-flight aircraft in en route airspace to find simple modifications to flight plan routes that can save significant flying time, while avoiding weather and considering traffic conflicts, airspace sector congestion, special use airspace, and FAA routing restrictions. Trials of the DWR system have shown that significant delay savings are possible. However, some DWR advised routes are also rejected by dispatchers or modified before being accepted. Similarly, of those sent by dispatchers to flight crews as proposed route change requests, many are not accepted by air traffic control, or are modified before implementation as Center route amendments. Such actions suggest that the operational acceptability of DWR advised route corrections could be improved, which may reduce workload and increase delay savings. This paper analyzes the historical usage of different flight routings, varying from simple waypoint pairs to lengthy strings of waypoints incorporating jet routes, in order to improve DWR route acceptability. An approach is developed that can be incorporated into DWR, advising routings with high historical usage and savings potential similar to that of the nominal DWR advisory. It is hypothesized that modifying a nominal DWR routing to one that is commonly used, and nearby, will result in more actual savings since common routings are generally familiar and operationally acceptable to air traffic control. The approach allows routing segments with high historical usage to be concatenated to form routes that meet all DWR constraints. The relevance of a routes historical usage to its acceptance by dispatchers and air traffic control is quantified by analyzing historical DWR data. Results indicate that while historical usage may be less of a concern to flight dispatchers accepting or rejecting DWR advised route corrections, it may be important to air traffic control acceptance of DWR routes.

Operational Acceptability↗

NASA NTRSDate not supplied

ArcjetCV: a new machine learning application for extracting time-resolved recession measurements from arc jet test videos

Arc jet Computer Vision (ArcjetCV) is a software application built to automate analysis of arc jet ground test video footage. This includes tracking material recession, sting arm motion, and the shock-material standoff distance. This provides a new capability to resolve and validate new physics associated with non-linear processes. This is an essential step to reduce testing, modeling, and validation uncertainties for heatshield material performance. ArcjetCV uses several types of machine learning (convolutional neural net: CNN, decision tree: DT, k-means unsupervised clustering: KM) to automate the video processing pipeline. These include inferring the start/stop of time segments of interest (1D CNN), measuring the time-dependent 2D recession of the material samples (2D CNN, DT), measuring the time-dependent shock standoff distance (2D CNN, DT), and post-processing cleaning of the recession data (KM). The software also provides a graphical user interface for ease of use. The results of using this tool on arc jet videos show non-linear time-dependent effects can be important for certain materials.

machine learning↗

NASA NTRSDate not supplied

ArcjetCV: A New Machine Learning Application for Extracting Time-Resolved Recession Measurements From Arc Jet Test Videos

Arc jet Computer Vision (ArcjetCV) is a software application built to automate analysis of arc jet ground test video footage. This includes tracking material recession, sting arm motion, and the shock-material standoff distance. This provides a new capability to resolve and validate new physics associated with non-linear processes. This is an essential step to reduce testing, modeling, and validation uncertainties for heatshield material performance. ArcjetCV uses several types of machine learning (convolutional neural net: CNN, decision tree: DT, k-means unsupervised clustering: KM) to automate the video processing pipeline. These include inferring the start/stop of time segments of interest (1D CNN), measuring the time-dependent 2D recession of the material samples (2D CNN, DT), measuring the time-dependent shock standoff distance (2D CNN, DT), and post-processing cleaning of the recession data (KM). The software also provides a graphical user interface for ease of use. The results of using this tool on arc jet videos show non-linear time-dependent effects can be important for certain materials.

machine learning↗

NASA NTRSDate not supplied

ArcjetCV: A New Machine Learning Application for Extracting Time-Resolved Recession Measurements From Arc Jet Test Videos

Arc jet Computer Vision (ArcjetCV) is a software application built to automate analysis of arc jet ground test video footage. This includes tracking material recession and the shock-material standoff distance. This provides a new capability to resolve and validate new physics associated with non-linear processes. This is an essential step to reduce testing, modeling, and validation uncertainties for heatshield material performance. ArcjetCV uses several types of machine learning (convolutional neural net: CNN, decision tree: DT, k-means unsupervised clustering: KM) to automate the video processing pipeline. These include inferring the start/stop of time segments of interest (1D CNN), measuring the time-dependent 2D recession of the material samples (2D CNN, DT), measuring the time-dependent shock standoff distance (2D CNN, DT), and post-processing cleaning of the recession data (KM). The software also provides a graphical user interface for ease of use. The results of using this tool on arc jet videos show non-linear time-dependent effects can be important for certain materials and characterizing certain failure modes.

machine learning↗

NASA NTRSDate not supplied

arcjetCV: A New Machine Learning Application for Extracting Time-Resolved Recession Measurements From Arc Jet Test Videos

Arc jet Computer Vision (ArcjetCV) is a software application built to automate analysis of arc jet ground test video footage. This includes tracking material recession and the shock-material standoff distance. This provides a new capability to resolve and validate new physics associated with non-linear processes. This is an essential step to reduce testing, modeling, and validation uncertainties for heatshield material performance. ArcjetCV uses several types of machine learning (convolutional neural net: CNN, decision tree: DT, k-means unsupervised clustering: KM) to automate the video processing pipeline. These include inferring the start/stop of time segments of interest (1D CNN), measuring the time-dependent 2D recession of the material samples (2D CNN, DT), measuring the time-dependent shock standoff distance (2D CNN, DT), and post-processing cleaning of the recession data (KM). The software also provides a graphical user interface for ease of use. The results of using this tool on arc jet videos show non-linear time-dependent effects can be important for certain materials and characterizing certain failure modes.

machine learning↗

NASA NTRSDate not supplied

arcjetCV: automating recession extraction from video

Arc jet Computer Vision (arcjetCV)[1][2] is a software application built to automate time-resolved recession tracking of edges in test videos, specifically for tracking material recession and the shock-material standoff. This provides a new capability to resolve and validate new physics associated with non-linear processes and an essential step to reduce testing, modeling, and validation uncertainties for heatshield material performance. ArcticCV uses several types of machine learning (convolutional neural net: CNN, decision tree: DT, k-means unsupervised clustering: KM) to automate the video processing pipeline. These include inferring the start/stop of time segments of interest (1D CNN), measuring the time-dependent 2D recession of the material samples (2D CNN, DT), measuring the time-dependent shock standoff distance (2D CNN, DT), and post-processing cleaning of the recession data (KM). The software also provides a graphical user interface for ease of use. The results of using this tool on arc jet videos show non-linear time-dependent effects can be important for certain materials and characterizing certain failure modes.

Recession tracking↗

NASA NTRSDate not supplied

ArcjetCV: Automating Recession Tracking

Arc jet Computer Vision (arcjetCV) is a software application built to automate time-resolved recession tracking of edges in test videos, specifically for tracking material recession and the shock-material standoff. This provides a new capability to resolve and validate new physics associated with non-linear processes and an essential step to reduce testing, modeling, and validation uncertainties for heatshield material performance. ArcticCV uses several types of machine learning (convolutional neural net: CNN, decision tree: DT, k-means unsupervised clustering: KM) to automate the video processing pipeline. These include inferring the start/stop of time segments of interest (1D CNN), measuring the time-dependent 2D recession of the material samples (2D CNN, DT), measuring the time-dependent shock standoff distance (2D CNN, DT), and post-processing cleaning of the recession data (KM). The software also provides a graphical user interface for ease of use. The results of using this tool on arc jet videos show non-linear time-dependent effects can be important for certain materials and characterizing certain failure modes.

Recession tracking↗

NASA NTRSDate not supplied

ArcjetCV: Automating Arc Jet Analysis

Arc jet Computer Vision (arcjetCV) is a software application built to automate time-resolved recession tracking of edges in test videos, specifically for tracking material recession and the shock-material standoff. This provides a new capability to resolve and validate new physics associated with non-linear processes and an essential step to reduce testing, modeling, and validation uncertainties for heatshield material performance. ArcticCV uses several types of machine learning (convolutional neural net: CNN, decision tree: DT, k-means unsupervised clustering: KM) to automate the video processing pipeline. These include inferring the start/stop of time segments of interest (1D CNN), measuring the time-dependent 2D recession of the material samples (2D CNN, DT), measuring the time-dependent shock standoff distance (2D CNN, DT), and post-processing cleaning of the recession data (KM). The software also provides a graphical user interface for ease of use. The results of using this tool on arc jet videos show non-linear time-dependent effects can be important for certain materials and characterizing certain failure modes.

Recession tracking↗

NASA NTRS2004

Mapping and Monitoring Delmarva Fox Squirrel Habitat Using an Airborne LiDAR Profiler

Twenty five hundred thirty nine kilometers of airborne laser profiling and videography data were acquired over the state of Delaware during the summer of 2000. The laser ranging measurements and video from approximately one-half of that data set (1304 km) were analyzed to identify and locate forested sites that might potentially support populations of Delmarva fox squirrel (DFS, Sciurus niger cinereus). The DFS is an endangered species previously endemic to tall, dense, mature forests with open understories on the Eastern Shore of the Chesapeake Bay. The airborne LiDAR employed in this study can measure forest canopy height and canopy closure, but cannot measure or infer understory canopy conditions. Hence the LiDAR must be viewed as a tool to map potential, not actual, habitat. Fifty-three potentially suitable DFS sites were identified in the 1304 km of flight transect data. Each of the 53 sites met the following criteria according to the LiDAR and video record: (1 ) at least 120m of contiguous forest; (2) an average canopy height greater than 20m; (3) an average canopy closure of >80%; and (4) no roofs, impervious surface (e.g., asphalt, concrete), and/or open water anywhere along the 120m length of the laser segment. Thirty-two of the 53 sites were visited on the ground and measurements taken for a DFS habitat suitability model. Seventy eight percent of the sites (25 of 32) were judged by the model to be suited to supporting a DFS population. Twenty-eight of the 32 sites visited in the field were in forest cover types (hardwood, mixed wood, conifer, wetlands) according to a land cover GIS map. Of these, 23 (82%) were suited to support DFS. The remaining 4 sites were located in nonforest cover types - agricultural or residential areas. Two of the four, or 50% were suited to the DFS. All of the LiDAR flight data, 2539 km, were analyzed to

Nelson, Ross↗

NASA NTRS1976

Fault tectonics and earthquake hazards in parts of southern California

The author has identified the following significant results. Four previously unknown faults were discovered in basement terrane of the Peninsular Ranges. These have been named the San Ysidro Creek fault, Thing Valley fault, Canyon City fault, and Warren Canyon fault. In addition fault gouge and breccia were recognized along the San Diego River fault. Study of features on Skylab imagery and review of geologic and seismic data suggest that the risk of a damaging earthquake is greater along the northwestern portion of the Elsinore fault than along the southeastern portion. Physiographic indicators of active faulting along the Garlock fault identifiable in Skylab imagery include scarps, linear ridges, shutter ridges, faceted ridges, linear valleys, undrained depressions and offset drainage. The following previously unrecognized fault segments are postulated for the Salton Trough Area: (1) An extension of a previously known fault in the San Andreas fault set located southeast of the Salton Sea; (2) An extension of the active San Jacinto fault zone along a tonal change in cultivated fields across Mexicali Valley ( the tonal change may represent different soil conditions along opposite sides of a fault). For the Skylab and LANDSAT images studied, pseudocolor transformations offer no advantages over the original images in the recognition of faults in Skylab and LANDSAT images. Alluvial deposits of different ages, a marble unit and iron oxide gossans of the Mojave Mining District are more readily differentiated on images prepared from ratios of individual bands of the S-192 multispectral scanner data. The San Andreas fault was also made more distinct in the 8/2 and 9/2 band ratios by enhancement of vegetation differences on opposite sides of the fault. Preliminary analysis indicates a significant earth resources potential for the discrimination of soil and rock types, including mineral alteration zones. This application should be actively pursued.

Merifield, P. M.↗

NASA NTRS1972

Integration of safety engineering into a cost optimized development program.

A six-segment management model is presented, each segment of which represents a major area in a new product development program. The first segment of the model covers integration of specialist engineers into 'systems requirement definition' or the system engineering documentation process. The second covers preparation of five basic types of 'development program plans.' The third segment covers integration of system requirements, scheduling, and funding of specialist engineering activities into 'work breakdown structures,' 'cost accounts,' and 'work packages.' The fourth covers 'requirement communication' by line organizations. The fifth covers 'performance measurement' based on work package data. The sixth covers 'baseline requirements achievement tracking.'

Ball, L. W.↗

NASA NTRS1973

Seismicity and recent faulting in eastern California and western and central Nevada: A preliminary report

The author has identified the following significant results. ERTS-1 imagery covering the eastern California-Nevada seismic belt were utilized to study the fault pattern in relation to the distribution of earthquake epicenters and Quaternary volcanic rocks. Many suspected faults not previously mapped were identified. These include several suspected shear zones in Nevada, faults showing evidence of recent breakage, and major lineaments. Highly seismic areas are generally characterized by Holocene faulting and Quaternary volcanic activity. However, several major fault segments showing evidence of recent breakage are associated with little or no seismicity. The tectonic pattern strongly suggests that the eastern California-Nevada seismic belt coincides with a major crustal rift associated with zones of lateral shear. New data on potentially active fault zones have direct practical applications in national and local earthquake hazard reduction programs. Positive contacts have been made with Kern and Ventura Counties to make results of this investigation available for application to their earthquake hazards definition projects.

Abdel-Gawad, M.↗

NASA NTRS1977

Performance tests of signature extension algorithms

Comparative tests were performed on seven signature extension algorithms to evaluate their effectiveness in correcting for changes in atmospheric haze and sun angle in a LANDSAT scene. Four of the algorithms were cluster matching, and two were maximum likelihood algorithms. The seventh algorithm determined the haze level in both training and recognition segments and used a set of tables calculated from an atmospheric model to determine the affine transformation that corrects the training signatures for changes in sun angle and haze level. Three of the algorithms were tested on a simulated data set, and all of the algorithms were tested on consecutive-day data.

Abotteen, R. A.↗

NASA NTRS1977

Performance tests of signature extension algorithms

Comparative tests were performed on seven signature extension algorithms to evaluate their effectiveness in correcting for changes in atmospheric haze and sun angle in a Landsat scene. Four of the algorithms were cluster matching, and two were maximum likelihood algorithms. The seventh algorithm determined the haze level in both training and recognition segments and used a set of tables calculated from an atmospheric model to determine the affine transformation that corrects the training signatures for changes in sun angle and haze level. Three of the algorithms were tested on a simulated data set, and all of the algorithms were tested on consecutive-day data. The classification performance on the data sets using the algorithms is presented, along with results of statistical tests on the accuracy and proportion estimates. The three algorithms tested on the simulated data produced significant improvements over the results obtained using untransformed signatures. For the consecutive-day data, the tested algorithms produced improvements in most but not all cases. The tests indicated also that no statistically significant differences were noted among the algorithms.

Abotteen, R.↗

NASA NTRS1979

An evaluation of procedure 1

LACIE Procedure 1 has undergone continuous testing and evaluation, starting with analytical and experimental studies even before it was implemented in ERIPS software and continuing to the present with performance evaluations using blind-site data. The strengths and weaknesses of the procedure are indicated and some areas for possible improvement are identified. Results from three of the experiments performed and an evaluation of LACIE Procedure 1 proportion estimates for some blind-site segments are discussed.

Wheeler, S. G.↗

NASA NTRS1981

Electric fields and electrostatic potentials in the high latitude ionosphere

Recent interpretive studies of electric field-driven ionospheric plasma convection data from the AE-C satellite are described, where the instruments employed include an ion drift meter and an ion-retarding potential analyzer. Electrostatic potential curves are derived from ion drift velocity measurements for high-latitude segments of the satellite's orbit. The potential curves are shown to be useful in determining the character of the global electrostatic potential pattern, with emphasis on the separation of convective cells. Results are given for six orbits, with attention to the mid-day auroral region.

Banks, P. M.↗

NASA NTRS1989

Biooptical variability in the Greenland Sea observed with the Multispectral Airborne Radiometer System (MARS)

A site-specific ocean color remote sensing algorithm was developed and used to convert Multispectral Airborne Radiometer System (MARS) spectral radiance measurements to chlorophyll-a concentration profiles along aircraft tracklines in the Greenland Sea. The analysis is described and the results given in graphical or tabular form. Section 2 describes the salient characteristics and history of development of the MARS instrument. Section 3 describes the analyses of MARS flight segments over consolidated sea ice, resulting in a set of altitude dependent ratios used (over water) to estimate radiance reflected by the surface and atmosphere from total radiance measured. Section 4 presents optically weighted pigment concentrations calculated from profile data, and spectral reflectances measured in situ from the top meter of the water column; this data was analyzed to develop an algorithm relating chlorophyll-a concentrations to the ratio of radiance reflectances at 441 and 550 nm (with a selection of coefficients dependent upon whether significant gelvin presence is implied by a low ratio of reflectances at 410 and 550 nm). Section 5 describes the scaling adjustments which were derived to reconcile the MARS upwelled radiance ratios at 410:550 nm and 441:550 nm to in situ reflectance ratios measured simultaneously on the surface. Section 6 graphically presents the locations of MARS data tracklines and positions of the surface monitoring R/V. Section 7 presents stick-plots of MARS tracklines selected to illustrate two-dimensional spatial variability within the box covered by each day's flight. Section 8 presents curves of chlorophyll-a concentration profiles derived from MARS data along survey tracklines. Significant results are summarized in Section 1.

Mueller, James L.↗

NASA NTRS1988

Fine structure in RF spectra of lightning return stroke wave forms

The power spectra of the wide-band (10 Hz to 100 kHz) magnetic-field signals for a number of lightning return strokes measured during a thunderstorm which occurred in Lindau in August, 1984 have been calculated. The RF magnetic field data are obtained with the engineering unit of the Galileo Jupiter Probe lightning experiment. Each return stroke data stream is passed through an adaptive filter designed to whiten its spectrum. The spectra of the magnetic field data definitely show fine structure, with two or three distinct peaks in the spectra of many of the waveforms. A peak at f of about 60-70 kHz is often seen in the power spectra of the waveform time segments preceding and following the rise-to-peak amplitude of the return stroke.

Lanzerotti, L. J.↗