Search NASA⌕ Search

SEARCH · Search NASA

Results for “pattern detection”

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.

At least 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
NASA NTRS2011

Local Leak Detection and Health Monitoring of Pressurized Tanks

An optical gas-detection sensor safely monitors pressurized systems (such as cryogenic tanks) and distribution systems for leaks. This sensor system is a fiber-coupled, solid optical body interferometer that allows for the miniaturized sensing element of the device to be placed in the smallest of recesses, and measures a wide range of gas species and densities (leaks). The deflection of the fringe pattern is detected and recorded to yield the time-varying gas density in the gap. This technology can be used by manufacturers or storage facilities with toxic, hazardous, or explosive gases. The approach is to monitor the change in the index of refraction associated with low-level gas leaks into a vacuum environment. The completion of this work will provide NASA with an enabling capability to detect gas system leaks in space, and to verify that pressurized systems are in a safe (i.e. non-leaking) condition during manned docking and transit operations. By recording the output of the sensor, a time-history of the leak can be constructed to indicate its severity. Project risk is mitigated by having several interferometric geometries and detection techniques available, each potentially leveraging hardware and lessons learned to enhance detectability.

Polzin, Kurt↗
NASA NTRS2001

Automatic Fringe Detection for Oil Film Interferometry Measurement of Skin Friction

This report summarizes two years of work on investigating algorithms for automatically detecting fringe patterns in images acquired using oil-drop interferometry for the determination of skin friction. Several different analysis methods were tested, and a combination of a windowed Fourier transform followed by a correlation was found to be most effective. The implementation of this method is discussed and details of the process are described. The results indicate that this method shows promise for automating the fringe detection process, but further testing is required.

Naughton, Jonathan W.↗
NASA NTRS2020

Characterizing spatiotemporal patterns of crop phenology across North America during 2000–2016 using satellite imagery and agricultural survey data

Crop phenology represents an integrative indicator of climate change and plays a vital role in terrestrial carbon dynamics and sustainable agricultural development. However, spatiotemporal variations of crop phenology remain unclear at large scales. This knowledge gap has hindered our ability to realistically quantify the biogeochemical dynamics in agroecosystems, predict future climate, and make informed decisions for climate change mitigation and adaptation. In this study, we improved an EVI-curve-based approach and used it to detect spatiotemporal patterns in cropping intensity and five major phenological stages over North America during 2000–2016 using vegetation index in combination with agricultural survey data and other ancillary maps. Our predicted crop phenological stages showed strong linear relationships with the survey-based datasets, with R(exp 2), RMSEs, and MAEs in the ranges of 0.35 –0.99, three to ten days, and two to eight days, respectively. During the study period, the planting dates were advanced by 0.60 days/year (p < 0.01), and harvesting dates were delayed by 0.78 days/year (p < 0.01) over North America. A minimum temperature increase by 1 °C caused a 4.26-day planting advance (r = −0.50, p < 0. 01) or a 0.66-day harvest delay (r = 0.10, p < 0.01). While, a higher maximum temperature resulted in a planting advance by 4.48 days/°C (r = −0.62, p < 0.01) or a harvest advance by 2.22 days/°C (r = −0.40, p < 0.01). Our analysis illustrated evident spatiotemporal variations in crop phenology in response to climate change and management practices. The derived crop phenological datasets and cropping intensity maps can be used in regional climate assessments and in developing adaptation strategies.

North America↗
NASA NTRS2018

Robotic External Leak Locator (RELL) Leak Plume Field Detection on the International Space Station (ISS)

The Robotic External Leak Locator (RELL) was deployed to the International Space Station (ISS) with the objective of demonstrating the ability to detect and locate small leaks. On-orbit operations began in late November 2016 and following scanning activities to characterize the natural and induced environment of the ISS, RELL focused on the United States External Active Thermal Control System (EATCS). RELL successfully detected ammonia related to a known small ammonia leak in the port-side EATCS, with the highest pressure values around the inboard Radiator Beam Valve Module 1 (RBVM 1). An additional day of scanning was subsequently performed in December 2017 to focus on RBVM 1. RELL was approved for additional external operations in February 2017 with the goal of fine tuning the location of the leak. Using grid scanning patterns, RELL detected ammonia around RBVM 1 and located the approximate source of the leak. The potential leak site was inspected by a crew member during an Extravehicular Activity (EVA) in March 2017, and the suspected radiator-side lines were isolated from the port-side EATCS coolant loop in April 2017. Subsequent monitoring of the system pressures showed that the leak has stopped, indicating RELL accurately located the source of the EATCS leak. These activities verify that RELL enhances the ISS Program's ability to not only locate small leaks, but isolate the source with minimal impact to the entire ISS system.

External Active Thermal Control System↗
NASA NTRS1972

Public health applications of remote sensing of the environment, an evaluation

The available techniques were examined in the field of remote sensing (including aerial photography, infrared detection, radar, etc.) and applications to a number of problems in the wide field of public health determined. The specific areas of public health examined included: air pollution, water pollution, communicable disease, and the combined problems of urban growth and the effect of disasters on human communities. The assessment of the possible applications of remote sensing to these problems was made primarily by examination of the available literature in each field, and by interviews with health authorities, physicists, biologists, and other interested workers. Three types of programs employing remote sensors were outlined in the air pollution field: (1) proving ability of sensors to monitor pollutants at three levels of interest - point source, ambient levels in cities, and global patterns; (2) detection of effects of pollutants on the environment at local and global levels; and (3) routine monitoring.

Source record↗
NASA NTRS2022

Deriving Severe Hail Likelihood from Satellite Observations and Model Reanalysis Parameters using a Deep Neural Network

Geostationary satellite imagers, such as those of the Geostationary Operational Environmental Satellite (GOES) series, have been observing severe convection at 15–60-minute intervals for over 40 years. When properly assessed, such a data record can be valuable in efforts of estimating severe storm risk throughout the diurnal cycle based on automated detection of patterns consistently found atop severe storms. Furthermore, environmental conditions favorable for severe weather are well-known and are thought to be represented well by modern reanalysis products. Promoting resilience against such hazards on local and global scales is a chief goal the NASA Disasters program, which seeks to encourage use of satellite observations to mitigate risk. For instance, hail is the costliest severe weather hazard across the globe in terms of insured loss, but reporting inconsistencies for hail events globally make it difficult to develop models that can quantify the risk. Satellite observation and model reanalysis taken together have the potential to, with reasonable skill and specificity, characterize environmental conditions that are favorable for hazardous weather, and thereby enable creation of hazard climatologie. Such climatologies are particularly useful over regions without extensive radar networks or storm reporting. By mapping the multivariate combination of observed cloud features and reanalysis environmental parameters/indices to United States Next Generation Weather Radar (NEXRAD) radar-estimated Maximum Expected Size of Hail (MESH) by way of a deep neural network (DNN), estimates of likelihood for potentially severe hail can be produced. Such estimates are of greater complexity and efficiency than could be performed with previous multivariate or logistic regression analyses for observed points within convective systems. Statistical distributions of convective parameters from satellite and reanalysis are shown to highlight non-severe/severe class separation for well-known hailstorm predictors, e.g., overshooting cloud top characteristics, deep-layer wind shear, mid-level stability, helicity, and convective inhibition. These complex, multivariate predictor relationships are exploited within a DNN, which can efficiently produce a quantitative hail risk metric with better than 70% detection rate and under 30% false alarms. These hail classifications can then be aggregated across the satellite record to yield a hazard climatology for hail frequency and severity – knowledge of which is of particular interest to those who manage risk (e.g., insurers) and are seeking opportunities to identify hail-prone regions, particularly in developing nations. This NASA study uses satellite observations and model parameters in a DNN to perform climatological hailstorm analysis in support of catastrophe model development, with the hope of promoting risk resilience particularly in regions without adequate weather radar coverage.

Passive Remote Sensing↗
NASA NTRS1973

California coast nearshore processes study

The author has identified the following significant results. Large scale sediment plumes from intermittent streams and rivers form detectable seasonal patterns on ERTS-1 imagery. The ocean current systems, as plotted from three California coast ERTS mosaics, were identified. Offshore patterns of sediment in areas such as the Santa Barbara Channel are traceable. These patterns extend offshore to heretofore unanticipated ranges as shown on the ERTS-1 imagery. Flying spot scanner enhancements of NASA tapes resulted in details of subtle and often invisible (to the eye) nearshore features. The suspended sediments off San Francisco and in Monterey Bay are emphasized in detail. These are areas of extremely changeable offshore sediment transport patterns. Computer generated contouring of radiance levels resulted in maps that can be used in determining surface and nearsurface suspended sediment distribution. Tentative calibrations of ERTS-1 spectral brightness against sediment load have been made using shipboard measurements. Information from the combined enhancement and interpretation techniques is applicable to operational coastal engineering programs.

Pirie, D. M.↗
NASA NTRS1975

ERTS applications in state land use planning

The progress made and limitations encountered in using ERTS-1 data for resource management in Ohio is surveyed. Photo-opto-electronic techniques were used with special facility equipment and resolution to 10-30 meters was required to determine strip mine features. Lake Erie's sediment patterns were detected along with flooding conditions, large scale vegetative damage caused by toxic air pollutants was identified, Ohio land use categories were tabulated and thematic map containing forested areas was derived. The experimental findings regarding utility/relevance assessment were ranked in 4 classes for all the applications involved. Preliminary recommendations for operational satellite earth resources survey data requirements are presented and data analysis and product dissemination are proposed to be centralized in conjunction with thermal IR data and an increased resolution.

Pincura, P. G.↗
NASA NTRS1979

Apparatus for providing a servo drive signal in a high-speed stepping interferometer

An analog voltage approximately linearly proportional to a desired offset from the present null position of a moving mirror in an interferometer is applied to the mirror moving means. As the mirror moves to the next null position, as determined by the analog voltage, the fringes of a laser reference interference pattern are detected. At the occurrence of each fringe the analog voltage is reduced proportionally so that when the next null position is reached, this driving analog is effectively zero. A binary up/down counter, by its internal count, causes a digital/analog converter to supply the analog voltage to the mirror moving means. Fringe detection and direction of movement logic cause the binary up/down counter to be decremented from its offset count as the mirror is moved to the new null position. Undesirable movement of the mirror due to vibration or other sources causes a correcting drive signal to be applied to the mirror moving means that is proportional to the distance of movement.

Schindler, R. A.↗
NASA NTRS1982

Incipient failure detection (IFD) of SSME ball bearings

Because of the immense noise background during the operation of a large engine such as the SSME, the relatively low level unique ball bearing signatures were often buried by the overall machine signal. As a result, the most commonly used bearing failure detection technique, pattern recognition using power spectral density (PSD) constructed from the extracted bearing signals, is rendered useless. Data enhancement techniques were carried out by using a HP5451C Fourier Analyzer. The signal was preprocessed by a Digital Audio Crop. DAC-1024I noise cancelling filter in order to estimate the desired signal corrupted by the backgound noise. Reference levels of good bearings were established. Any deviation of bearing signals from these reference levels indicate the incipient bearing failures.

Source record↗
NASA NTRS1985

A software simulation study of a (255,223) Reed-Solomon encoder-decoder

A set of software programs which simulates a (255,223) Reed-Solomon encoder/decoder pair is described. The transform decoder algorithm uses a modified Euclid algorithm, and closely follows the pipeline architecture proposed for the hardware decoder. Uncorrectable error patterns are detected by a simple test, and the inverse transform is computed by a finite field FFT. Numerical examples of the decoder operation are given for some test codewords, with and without errors. The use of the software package is briefly described.

Pollara, F.↗
NASA NTRS1985

Signal processing in SETI

It is believed that the Galaxy might contain ten billion potential life sites. In view of the physical inaccessibility of extraterrestrial life on account of the vast distances involved, a logical first step in a search for extraterrestrial intelligence (SETI) appears to be an attempt to detect signals already being radiated. The characteristics of the signals to be expected are discussed together with the search strategy of a NASA program. It is pointed out that all presently planned searches will use existing radio-astronomy antennas. If no extraterrestrial intelligence signals are discovered, society will have to decide whether SETI justifies a dedicated facility of much greater collecting area. Attention is given to a multichannel spectrum analyzer, CW signal detection, pulse detection, the pattern detector, and details of SETI system operation.

Cullers, D. K.↗
NASA NTRS1993

Thin Hot-Film Sensors On Polyimide Film

Array of closely spaced hot-film sensors with thickness well below critical reference height with regard to air-flow pattern nonintrusively detect laminar boundary-layer transitions with very high resolution. Method developed at NASA Langley Research Center to fabricate such sensors on polyimide films to detect boundary-layer transitions with resolution as high as 0.050 in. These films formed by combination of vacuum deposition and photolithography.

Hopson, Purnell↗
NASA NTRS1993

Geography of end-Cretaceous marine bivalve extinctions

Analysis of the end-Cretaceous mass extinction, based on 3514 occurrences of 340 genera of marine bivalves (Mollusca), suggests that extinction intensities were uniformly global; no latitudinal gradients or other geographic patterns are detected. Elevated extinction intensities in some tropical areas are entirely a result of the distribution of one extinct group of highly specialized bivalves, the rudists. When rudists are omitted, intensities at those localities are statistically indistinguishable from those of both the rudist-free tropics and extratropical localities.

Raup, David M.↗
NASA NTRS1993

Prelaunch optical characterization of the Laser Geodynamic Satellite (LAGEOS 2)

The optical range correction (the distance between the apparent retroreflective skin of the satellite and the center of mass) of the LAGEOS 2 was determined using computer analysis of theoretical and experimentally measured far field diffraction patterns, and with short pulse lasers using both streak camera-based range receivers and more conventional PMT-based range receivers. The three measurement techniques yielded range correction values from 248 to 253 millimeters dependent on laser wavelength, pulsewidth, and polarization, location of the receiver in the far field diffraction pattern and detection technique (peak, half maximum, centroid, or constant fraction). The Lidar cross section of LAGEOS 2 was measured at 4 to 10 million square meters, comparable to the LAGEOS 1.

Minott, Peter O.↗
NASA NTRS1995

Geophysical phenomena classification by artificial neural networks

Space science information systems involve accessing vast data bases. There is a need for an automatic process by which properties of the whole data set can be assimilated and presented to the user. Where data are in the form of spectrograms, phenomena can be detected by pattern recognition techniques. Presented are the first results obtained by applying unsupervised Artificial Neural Networks (ANN's) to the classification of magnetospheric wave spectra. The networks used here were a simple unsupervised Hamming network run on a PC and a more sophisticated CALM network run on a Sparc workstation. The ANN's were compared in their geophysical data recognition performance. CALM networks offer such qualities as fast learning, superiority in generalizing, the ability to continuously adapt to changes in the pattern set, and the possibility to modularize the network to allow the inter-relation between phenomena and data sets. This work is the first step toward an information system interface being developed at Sussex, the Whole Information System Expert (WISE). Phenomena in the data are automatically identified and provided to the user in the form of a data occurrence morphology, the Whole Information System Data Occurrence Morphology (WISDOM), along with relationships to other parameters and phenomena.

Gough, M. P.↗