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At least 1,297 records · Page 72
All sourcesNASA NTRSDOE OSTI
NASA NTRS2004

An Artificial Intelligence Classification Tool and Its Application to Gamma-Ray Bursts

Despite being the most energetic phenomenon in the known universe, the astrophysics of gamma-ray bursts (GRBs) has still proven difficult to understand. It has only been within the past five years that the GRB distance scale has been firmly established, on the basis of a few dozen bursts with x-ray, optical, and radio afterglows. The afterglows indicate source redshifts of z=1 to z=5, total energy outputs of roughly 10(exp 52) ergs, and energy confined to the far x-ray to near gamma-ray regime of the electromagnetic spectrum. The multi-wavelength afterglow observations have thus far provided more insight on the nature of the GRB mechanism than the GRB observations; far more papers have been written about the few observed gamma-ray burst afterglows in the past few years than about the thousands of detected gamma-ray bursts. One reason the GRB central engine is still so poorly understood is that GRBs have complex, overlapping characteristics that do not appear to be produced by one homogeneous process. At least two subclasses have been found on the basis of duration, spectral hardness, and fluence (time integrated flux); Class 1 bursts are softer, longer, and brighter than Class 2 bursts (with two second durations indicating a rough division). A third GRB subclass, overlapping the other two, has been identified using statistical clustering techniques; Class 3 bursts are intermediate between Class 1 and Class 2 bursts in brightness and duration, but are softer than Class 1 bursts. We are developing a tool to aid scientists in the study of GRB properties. In the process of developing this tool, we are building a large gamma-ray burst classification database. We are also scientifically analyzing some GRB data as we develop the tool. Tool development thus proceeds in tandem with the dataset for which it is being designed. The tool invokes a modified KDD (Knowledge Discovery in Databases) process, which is described as follows.

Hakkila, Jon↗
NASA NTRS2004

Classification of Particle Shapes from Lidar Depolarization Ratios in Convective Ice Clouds Compared to in situ Observations During CRYSTAL-FACE

This manuscript describes a method to class@ cirrus cloud ice particle shape using lidar depolarization measurements as a basis for segregating different particle shape regimes. Measurements from the ER-2 Cloud Physics Lidar (CPL) system during CRYSTAL-FACE provide the basis for this work. While the CPL onboard the ER-2 aircraft was providing remote sensing measurements of cirrus clouds, the Cloud Particle Imager (CPI) onboard the WB-57 aircraft was flying inside those same clouds to sample particle sizes. The results of classifying particle shapes using the CPL data are compared to the in situ measurements made using the CPI , and there is found to be good agreement between the particle shape inferred from the CPL data and that actually measured by the CPI. If proven practical, application of this technique to spaceborne observations could lead to large-scale classification of cirrus cloud particle shapes.

Noel, Vincent↗
NASA NTRS2004

Classification of Tropical Oceanic Precipitation using High Altitude Aircraft: Microwave and Electric Field Measurements

During the 1998 and 2001 hurricane seasons of the western Atlantic Ocean and Gulf of Mexico, the Advanced Microwave Precipitation Radiometer (AMPR), the ER-2 Doppler (EDOP) radar, and the Lightning Instrument Package (LIP) were flown aboard the National Aeronautics and Space Administration ER-2 high altitude aircraft as part of the Third Convection and Moisture Experiment (CAMEX-3) and the Fourth Convection and Moisture Experiment (CAMEX-4). Several hurricanes, tropical storms, and other precipitation systems were sampled during these experiments. An oceanic rainfall screening technique has been developed using AMPR passive microwave observations of these systems collected at frequencies of 10.7, 19.35,37.1, and 85.5 GHz. This technique combines the information content of the four AMPR frequencies regarding the gross vertical structure of hydrometeors into an intuitive and easily executable precipitation mapping format. The results have been verified using vertical profiles of EDOP reflectivity and lower altitude horizontal reflectivity scans collected by the National Oceanic and Atmospheric Administration WP-3D Orion radar. Matching the rainfall classification results with coincident electric field information collected by the LIP readily identifies convective rain regions within the precipitation fields. This technique shows promise as a real-time research and analysis tool for monitoring vertical updraft strength and convective intensity from airborne platforms such as remotely operated or uninhabited aerial vehicles. The technique is analyzed and discussed for a wide variety of precipitation types using the 26 August 1998 observations of Hurricane Bonnie near landfall.

Hood, Robbie E.↗
NASA NTRS2002

Classification image weights and internal noise level estimation

For the linear discrimination of two stimuli in white Gaussian noise in the presence of internal noise, a method is described for estimating linear classification weights from the sum of noise images segregated by stimulus and response. The recommended method for combining the two response images for the same stimulus is to difference the average images. Weights are derived for combining images over stimuli and observers. Methods for estimating the level of internal noise are described with emphasis on the case of repeated presentations of the same noise sample. Simple tests for particular hypotheses about the weights are shown based on observer agreement with a noiseless version of the hypothesis.

Pattern Recognition, Visual/physiology↗
NASA NTRS1996

Archaeal-eubacterial mergers in the origin of Eukarya: phylogenetic classification of life

A symbiosis-based phylogeny leads to a consistent, useful classification system for all life. "Kingdoms" and "Domains" are replaced by biological names for the most inclusive taxa: Prokarya (bacteria) and Eukarya (symbiosis-derived nucleated organisms). The earliest Eukarya, anaerobic mastigotes, hypothetically originated from permanent whole-cell fusion between members of Archaea (e.g., Thermoplasma-like organisms) and of Eubacteria (e.g., Spirochaeta-like organisms). Molecular biology, life-history, and fossil record evidence support the reunification of bacteria as Prokarya while subdividing Eukarya into uniquely defined subtaxa: Protoctista, Animalia, Fungi, and Plantae.

Non-NASA Center↗
NASA NTRS2005

Aircraft Fault Detection and Classification Using Multi-Level Immune Learning Detection

This work is an extension of a recently developed software tool called MILD (Multi-level Immune Learning Detection), which implements a negative selection algorithm for anomaly and fault detection that is inspired by the human immune system. The immunity-based approach can detect a broad spectrum of known and unforeseen faults. We extend MILD by applying a neural network classifier to identify the pattern of fault detectors that are activated during fault detection. Consequently, MILD now performs fault detection and identification of the system under investigation. This paper describes the application of MILD to detect and classify faults of a generic transport aircraft augmented with an intelligent flight controller. The intelligent control architecture is designed to accommodate faults without the need to explicitly identify them. Adding knowledge about the existence and type of a fault will improve the handling qualities of a degraded aircraft and impact tactical and strategic maneuvering decisions. In addition, providing fault information to the pilot is important for maintaining situational awareness so that he can avoid performing an action that might lead to unexpected behavior - e.g., an action that exceeds the remaining control authority of the damaged aircraft. We discuss the detection and classification results of simulated failures of the aircraft's control system and show that MILD is effective at determining the problem with low false alarm and misclassification rates.

Wong, Derek↗
NASA NTRS1996

Land Covering Classifications of Boreas Modeling Grid Using AIRSAR Images

Mapping forest types in the boreal ecosystem in an integrated part of any modeling excercise of biogeophysical processes characterizing the interaction of forest with the atmosphere. In this paper, we report the results of the land cover classification of the SAR data acquired during the BOREAS (BOReal Ecosystem Atmospheric Study) intensive field campaigns over the modeling sub-grid of the southern study area in Saskatchewan , Canada. A Bayesian-maximum-a-posteriori classifier has been applied on the NASA/JPL AIRSAR images covering the region during the peak of the growing season in July, 1994.

Atmospheric Study↗
NASA NTRS2006

An Automated Classification Technique for Detecting Defects in Battery Cells

Battery cell defect classification is primarily done manually by a human conducting a visual inspection to determine if the battery cell is acceptable for a particular use or device. Human visual inspection is a time consuming task when compared to an inspection process conducted by a machine vision system. Human inspection is also subject to human error and fatigue over time. We present a machine vision technique that can be used to automatically identify defective sections of battery cells via a morphological feature-based classifier using an adaptive two-dimensional fast Fourier transformation technique. The initial area of interest is automatically classified as either an anode or cathode cell view as well as classified as an acceptable or a defective battery cell. Each battery cell is labeled and cataloged for comparison and analysis. The result is the implementation of an automated machine vision technique that provides a highly repeatable and reproducible method of identifying and quantifying defects in battery cells.

McDowell, Mark↗
NASA NTRS2004

Classification of Tropical Oceanic Precipitation using High-Altitude Aircraft Microwave and Electric Field Measurements

During the 1998 and 2001 hurricane seasons of the western Atlantic Ocean and Gulf of Mexico, the Advanced Microwave Precipitation Radiometer (AMPR), the ER-2 Doppler (EDOP) radar, and the Lightning Instrument Package (LIP) were flown aboard the NASA ER-2 high-altitude aircraft as part of the Third Convection and Moisture Experiment (CAMEX-3) and the Fourth Convection and Moisture Experiment (CAMEX-4). Several hurricanes, tropical storms, and other precipitation systems were sampled during these experiments. An oceanic rainfall screening technique has been developed using AMPR passive microwave observations of these systems collected at frequencies of 10.7, 19.35, 37.1, and 85.5 GHz. This technique combines the information content of the four AMPR frequencies regarding the gross vertical structure of hydrometeors into an intuitive and easily executable precipitation mapping format. The results have been verified using vertical profiles of EDOP reflectivity and lower-altitude horizontal reflectivity scans collected by the NOAA WP3D Orion radar. Matching the rainfall classification results with coincident electric field information collected by the LIP readily identifies convective rain regions within the precipitation fields. This technique shows promise as a real-time research and analysis tool for monitoring vertical updraft strength and convective intensity from airborne platforms such as remotely operated or uninhabited aerial vehicles. The technique is analyzed and discussed for a wide variety of precipitation types using the 26 August 1998 observations of Hurricane Bonnie near landfall.

Hood, Robbie E.↗
NASA NTRS2007

SWIFT Detects a remarkable Gamma-ray Burst, GRB 060514, that introduces a New Classification Scheme

Gamma ray bursts (GFU3s) are known to come in two duration classes, separated at approx.2 s. Long bursts originate from star forming regions in galaxies, have accompanying supernovae (SNe) when near enough to observe and are likely caused by massive-star collapsars. Recent observations show that short bursts originate in regions within their host galaxies with lower star formation rates, consistent with binary neutron star (NS) or NS - black hole (BH) mergers. Moreover, although their hosts are predominantly nearby galaxies, no SNe have been so far associated with short GRBs. We report here on the bright, nearby GRB 060614 that does not fit in either class. Its approx.102 s duration groups it with long GRBs, while its temporal lag and peak luminosity fall entirely within the short GRB subclass. Moreover, very deep optical observations exclude an accompanying supernova, similar to short GRBs. This combination of a long duration event without accompanying SN poses a challenge to both a collapsar and merging NS interpretation and opens the door on a new GRB classification scheme that straddles both long and short bursts.

Gehrels, N.↗
NASA NTRS2004

Toward a Global Map of Raindrop Size Distributions: Rain-Type Classification and Its Implications for Validating Global Rainfall Products - Part 1

Variability in the global distribution of precipitation is recognized as a key element in assessing the impact of climate change for life on earth. The response of precipitation to climate forcings is, however, poorly understood because of discrepancies in the magnitude and sign of climatic trends in satellite-based rainfall estimates. Quantifying and ultimately removing these biases is critical for studying the response of the hydrologic cycle to climate change. In addition, estimates of random errors owing to variability in algorithm assumptions on local spatial and temporal scales are critical for establishing how strongly their products should be weighted in data assimilation or model validation applications and for assigning a level of confidence to climate trends diagnosed from the data. This paper explores the potential for refining assumed drop size distributions (DSDs) in global radar rainfall algorithms by establishing a link between satellite observables and information gleaned from regional validation experiments where polarimetric radar, Doppler radar, and disdrometer measurements can be used to infer raindrop size distributions. By virtue of the limited information available in the satellite retrieval framework, the current method deviates from approaches adopted in the ground-based radar community that attempt to relate microphysical processes and resultant DSDs to local meteorological conditions. Instead, the technique exploits the fact that different microphysical pathways for rainfall production are likely to lead to differences in both the DSD of the resulting raindrops and the three-dimensional structure of associated radar reflectivity profiles. Objective rain-type classification based on the complete three-dimensional structure of observed reflectivity profiles is found to partially mitigate random and systematic errors in DSDs implied by differential reflectivity measurements. In particular, it is shown that vertical and horizontal reflectivity structure obtained from spaceborne radar can be used to reproduce significant differences in Z(sub dr) between the easterly and westerly climate regimes observed in the Tropical Rainfall Measuring Mission Large-scale Biosphere-Atmosphere (TRMM-LBA) field experiment as well as the even larger differences between Amazonian rainfall and that observed in eastern Colorado. As such, the technique offers a potential methodology for placing locally observed DSD information into a global framework.

L'Ecuyer, Tristan S.↗
NASA NTRS2006

The Future of Asset Management for Human Space Exploration: Supply Classification and an Integrated Database

One of the major logistical challenges in human space exploration is asset management. This paper presents observations on the practice of asset management in support of human space flight to date and discusses a functional-based supply classification and a framework for an integrated database that could be used to improve asset management and logistics for human missions to the Moon, Mars and beyond.

interplanetary supply chain management↗
NASA NTRS2007

Preplanetary Nebulae: A Hubble Space Telescope Imaging Survey and a New Morphological Classification System

Using the Hubble Space Telescope (HST ), we have carried out a survey of candidate preplanetary nebulae (PPNs). We report here our discoveries of objects having well-resolved geometric structures, and we use the large sample of PPNs now imaged with HST (including previously studied objects in this class) to devise a comprehensive morphological classification system for this category of objects. The wide variety of aspherical morphologies which we have found for PPNs are qualitatively similar to those found for young planetary nebulae (PNs) in previous surveys. We also find prominent halos surrounding the central aspherical shapes in many of our objects; these are direct signatures of the undisturbed circumstellar envelopes of the progenitor AGB stars. Although the majority of these have surface brightness distributions consistent with a constant mass-loss rate with a constant expansion velocity, there are also examples of objects with varying mass-loss rates. As in our surveys of young PNs, we find no round PPNs. The similarities in morphologies between our survey objects and young PNs supports the view that the former are the progenitors of aspherical PNs. This suggests that the primary shaping of a PN does not occur during the PN phase via the fast radiative wind of the hot central star, but significantly earlier in its evolution.

AGB and post-AGB↗
NASA NTRS2010

Development of an Autofluorescence Spectral Database for the Identification and Classification of Microbial Extremophiles

Extremophiles are microorganisms that have adapted to severe conditions that were once considered devoid of life. The extreme settings in which these organisms flourish on earth resemble many extraterrestrial environments. Identification and classification of extremophiles in situ (without the requirement for excessive handling and processing) can provide a basis for designing remotely operated instruments for extraterrestrial life exploration. An important consideration when designing such experiments is to prevent contamination of the environments. We are developing a reference spectral database of autofluorescence from microbial extremophiles using long-UV excitation (405 nm). Aromatic compounds are essential components of living systems, and biological molecules such as aromatic amino acids, nucleotides, porphyrins and vitamins can also exhibit fluorescence under long-UV excitation conditions. Autofluorescence spectra were obtained from a confocal microscope that additionally allowed observations of microbial geometry and motility. It was observed that all extremophiles studied displayed an autofluorescence peak at around 470 nm, followed by a long decay that was species specific. The autofluorescence database can potentially be used as a reference to identify and classify past or present microbial life in our solar system.

Sabanayagam, Chandran↗
NASA NTRS2011

Antarctic Meteorite Classification and Petrographic Database

The Antarctic Meteorite collection, which is comprised of over 18,700 meteorites, is one of the largest collections of meteorites in the world. These meteorites have been collected since the late 1970's as part of a three-agency agreement between NASA, the National Science Foundation, and the Smithsonian Institution [1]. Samples collected each season are analyzed at NASA s Meteorite Lab and the Smithsonian Institution and results are published twice a year in the Antarctic Meteorite Newsletter, which has been in publication since 1978. Each newsletter lists the samples collected and processed and provides more in-depth details on selected samples of importance to the scientific community. Data about these meteorites is also published on the NASA Curation website [2] and made available through the Meteorite Classification Database allowing scientists to search by a variety of parameters

Todd, Nancy S.↗