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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.

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At least 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS2016

Psychophysiological Sensing and State Classification for Attention Management in Commercial Aviation

Attention-related human performance limiting states (AHPLS) can cause pilots to lose airplane state awareness (ASA), and their detection is important to improving commercial aviation safety. The Commercial Aviation Safety Team found that the majority of recent international commercial aviation accidents attributable to loss of control inflight involved flight crew loss of airplane state awareness, and that distraction of various forms was involved in all of them. Research on AHPLS, including channelized attention, diverted attention, startle / surprise, and confirmation bias, has been recommended in a Safety Enhancement (SE) entitled "Training for Attention Management." To accomplish the detection of such cognitive and psychophysiological states, a broad suite of sensors has been implemented to simultaneously measure their physiological markers during high fidelity flight simulation human subject studies. Pilot participants were asked to perform benchmark tasks and experimental flight scenarios designed to induce AHPLS. Pattern classification was employed to distinguish the AHPLS induced by the benchmark tasks. Unimodal classification using pre-processed electroencephalography (EEG) signals as input features to extreme gradient boosting, random forest and deep neural network multiclass classifiers was implemented. Multi-modal classification using galvanic skin response (GSR) in addition to the same EEG signals and using the same types of classifiers produced increased accuracy with respect to the unimodal case (90 percent vs. 86 percent), although only via the deep neural network classifier. These initial results are a first step toward the goal of demonstrating simultaneous real time classification of multiple states using multiple sensing modalities in high-fidelity flight simulators. This detection is intended to support and inform training methods under development to mitigate the loss of ASA and thus reduce accidents and incidents.

Harrivel, Angela R.↗
NASA NTRS2015

Data Processing And Machine Learning Methods For Multi-Modal Operator State Classification Systems

This document is intended as an introduction to a set of common signal processing learning methods that may be used in the software portion of a functional crew state monitoring system. This includes overviews of both the theory of the methods involved, as well as examples of implementation. Practical considerations are discussed for implementing modular, flexible, and scalable processing and classification software for a multi-modal, multi-channel monitoring system. Example source code is also given for all of the discussed processing and classification methods.

Machine learning↗
NASA NTRS1986

Ragland - An LL3.4 chondrite find from New Mexico

The Ragland, New Mexico, chondrite was found in 1978. It consists of a single stone of 12.16 kg that broke into three pieces. The stone is moderately weathered and has a pronounced chondritic texture. Bulk composition favors an LL classification, and modal analysis and oxygen isotopic composition are consistent with this. The thermoluminescence sensitivity of 0.056 + or - 0.020 normalized to Dhajala, compositional variability of olivine (mean Fa 18.3, sigma = 10.1) and low-Ca pyroxene (mean Fs 14.6, sigma = 6.7), and Ca concentrations in olivine indicate metamorphic subtype 3.4 + or - 0.1. The isotopically heavy oxygen composition, which is characteristic of subtypes 3.0-3.1, may be a primary characteristic and not a result of weathering. Low concentrations of radiogenic Ar-40 and planetary Ar-36 suggest noble gas loss.

Recca, S. I.↗
NASA NTRS1979

Satellite pattern classification using charge transfer devices

The potential uses of Charge Transfer Devices (CTDs) in pattern classification operations are explored. The needs for a hardware-based pattern classifier are established, and a matrix multiplication subsystem based upon a sum-of-products CTD is presented. Applications of the subsystem to the classification of multi-modal Gaussian distributions in general and to LANDSAT data processing in particular are discussed. Finally, the potential impact of this technology on satellite data processing methodologies is discussed.

Snyder, W. E.↗
NASA NTRS1976

The Apollo 16 drill core - Modal petrology and characterization of the mineral and lithic component

A string of 59 polished thin sections covering the length of the Apollo 16 deep drill core has been examined. A modal analysis, involving the optical classification of 116,000 points was made, and over 500 mineral and lithic fragments from the core were chemically analyzed using an electron microprobe. These data were used to identify and characterize the source areas of the core material and to reconstruct the accumulation history of the core.

Vaniman, D. T.↗
NASA NTRS2019

Image Processing Methods for Coronal Hole Segmentation, Matching, and Map Classification

The paper presents the results from a multi-year effort to develop and validate image processing methods forselecting the best physical models based on solar image observations. The approach consists of selecting the physical models based on their agreement with coronal holes extracted from the images. Ultimately, the goal is to use physical models to predict geomagnetic storms. We decompose the problem into three subproblems: (i) coronal hole segmentation based on physical constraints, (ii) matching clusters of coronal holes between different maps, and (iii) physical map classification. For segmenting coronal holes, we develop a multi-modal method that uses segmentation maps from three different methods to initialize a level-set method that evolves the initial coronal hole segmentation to the magnetic boundary. Then, we introducea new method based on Linear Programming for matchingclusters of coronal holes. The final matching is then performedusing Random Forests. The methods were carefully validatedusing consensus maps derived from multiple readers, manualclustering, manual map classification, and method validation for50 maps. The proposed multi-modal segmentation method significantly outperformed SegNet, U-net, Henney-Harvey, and FCNby providing accurate boundary detection. Overall, the methodgave a 95.5% map classification accuracy.

Jatla, Venkatesh↗
NASA NTRS1988

Classification of six ordinary chondrites from Texas

Based on optical microscopy, modal and electron microprobe analyses, six ordinary chondrites from Texas were classified in compositional groups, petrologic types, and shock facies. These meteorites are Comanche (stone), L5c; Haskell, L5c; Deport (a), H4b; Naruna (a), H4b; Naruna (b), H4b; and Clarendon (b), H5d.

Ehlmann, Arthur J.↗
NASA NTRS1985

A review of time-domain and frequency-domain component mode synthesis method

Hurty (1965) has conducted a dynamic analysis of structural systems using component modes. The component mode synthesis (CMS) procedure considered by him represents a form of substructure coupling analysis which is often utilized in structural dynamics. Time-domain CMS methods employing real modes are discussed, taking into account real component modes, normal modes, redundant constraint modes, rigid-body modes, attachment modes, inertia-relief modes, statically-complete interface mode sets, dynamic component mode supersets, component modal models, the coupling of components, and the classification of methods. Attention is also given to the experimental determination of component mode synthesis parameters, time-domain CMS methods for damped systems, and frequency-domain CMS methods for damped systems.

Craig, R. R., Jr.↗
NASA NTRS2017

Reassessing the Formation of CK7 Northwest Africa (NWA) 8186

The classification of meteorites is commonly determined using isotopes, modal mineralogy, and bulk compositions [1]. Bulk rare earth elements (REEs) in meteorites are additionally utilized to understand parent body processes. Numerous authors have shown that chondritic groups exhibit REE patterns that may be attributable to their parent bodies [e.g. 2-4], and variations in abundances and concentrations of REEs may reflect early nebular processes, thermal metamorphism, and aqueous alteration on the parent body [5-6].

Srinivasan, P.↗
NASA NTRS2017

Prediction of Cognitive States During Flight Simulation Using Multimodal Psychophysiological Sensing

The Commercial Aviation Safety Team found the majority of recent international commercial aviation accidents attributable to loss of control inflight involved flight crew loss of airplane state awareness (ASA), and distraction was involved in all of them. Research on attention-related human performance limiting states (AHPLS) such as channelized attention, diverted attention, startle/surprise, and confirmation bias, has been recommended in a Safety Enhancement (SE) entitled "Training for Attention Management." To accomplish the detection of such cognitive and psychophysiological states, a broad suite of sensors was implemented to simultaneously measure their physiological markers during a high fidelity flight simulation human subject study. Twenty-four pilot participants were asked to wear the sensors while they performed benchmark tasks and motion-based flight scenarios designed to induce AHPLS. Pattern classification was employed to predict the occurrence of AHPLS during flight simulation also designed to induce those states. Classifier training data were collected during performance of the benchmark tasks. Multimodal classification was performed, using pre-processed electroencephalography, galvanic skin response, electrocardiogram, and respiration signals as input features. A combination of one, some or all modalities were used. Extreme gradient boosting, random forest and two support vector machine classifiers were implemented. The best accuracy for each modality-classifier combination is reported. Results using a select set of features and using the full set of available features are presented. Further, results are presented for training one classifier with the combined features and for training multiple classifiers with features from each modality separately. Using the select set of features and combined training, multistate prediction accuracy averaged 0.64 +/- 0.14 across thirteen participants and was significantly higher than that for the separate training case. These results support the goal of demonstrating simultaneous real-time classification of multiple states using multiple sensing modalities in high fidelity flight simulators. This detection is intended to support and inform training methods under development to mitigate the loss of ASA and thus reduce accidents and incidents.

Harrivel, Angela R.↗
NASA NTRSDate not supplied

Satellite-Derived Imagery and Transfer Learning: A Novel Technique for Land Cover Classification

Land cover classification is a continuing research topic due to its relevance to land use and land cover changes from impacts such as climate change, agriculture, urbanization, and hazardous weather. Simple access to frequently changing land cover classifications could provide knowledge and decision support to various researchers and agencies across the globe for each of above-mentioned and related influences. This research aims to provide a novel technique for land cover classification of remote sensing imagery; harnessing Artificial Neural Networks and transfer learning (TL). Knowledge sharing techniques within machine learning are typically utilized when training datasets are sparse or transitions between data modalities is required. In this case, two data modalities, multi and hyperspectral data, are considered for knowledge transfer. The large number of continuous spectral bands available from hyperspectral sensors typically provide increased sophisticated land classification capability compared to more traditional multispectral imagery with limited discrete spectral bands. However, large-scale, frequent access to hyperspectral imagery is relatively limited. The proposed classification technique would therefore prove useful in regions in which hyperspectral data are not readily available for classification but multispectral data are. Image segmentation models are trained on each multi and hyperspectral datasets. Knowledge sharing techniques are then applied to each model to understand what knowledge, if any, is gained when moving between the data modalities. The datasets utilized for training and testing a U-Net model include the European Space Agency’s multispectral imager Sentinel-2 and the hyperspectral German Aerospace Center’s Earth Sensing Imaging Spectrometer (DESIS). Initially, only two classes, land and water, are classified for simplicity. However, more complex classes can be added if knowledge sharing is successful. The workflow for image classification through supervised image segmentation with a U-Net model will be discussed along with metrics calculated before and after TL for both Sentinel-2 and DESIS data are applied. Additionally, future steps to advance the sophistication of this technique as well as other applicable methodologies will be explored.

Emily Foshee↗
NASA NTRS2018

Next-Generation Sensing Technologies for Exploring Ocean Worlds

Dr. Ved Chirayath's plenary presentation will highlight two instrument technologies he invented at NASA including Fluid Lensing, the first remote sensing technology capable of imaging through ocean waves in 3D at sub-cm resolutions, and MiDAR (Multispectral Imaging, Detection and Active Reflectance), a next-generation active hyperspectral remote sensing and optical communications instrument. Fluid Lensing has been used to provide the first 3D multispectral imagery of shallow marine systems from unmanned aerial vehicles (UAVs, or drones), including coral reefs in American Samoa and stromatolite reefs in Hamelin Pool, Western Australia. MiDAR is being deployed on aircraft, and underwater remotely operated vehicles (ROVs) as a new method to remotely sense living and nonliving structures in extreme environments. MiDAR images targets with high-intensity narrowband structured optical radiation to measure an object's non-linear spectral reflectance, image through fluid interfaces such as ocean waves with active fluid lensing, and simultaneously transmit high-bandwidth data. As an active instrument, MiDAR is capable of remotely sensing reflectance at the centimeter (cm) spatial scale with a signal-to-noise ratio (SNR) multiple orders of magnitude higher than passive airborne and spaceborne remote sensing systems with significantly reduced integration time. This allows for rapid video-frame-rate hyperspectral sensing into the far ultraviolet and VNIR wavelengths. Finally, Chirayath will present preliminary results from NASA NeMO-Net (Neural Multi-Modal Observation and Training Network), the first neural network for global coral reef classification using fluid lensing and MiDAR.

Technologies↗
NASA NTRS1990

An AI-based approach to structural damage identification by modal analysis

Flexible-structure damage is presently addressed by a combined model- and parameter-identification approach which employs the AI methodologies of classification, heuristic search, and object-oriented model knowledge representation. The conditions for model-space search convergence to the best model are discussed in terms of search-tree organization and initial model parameter error. In the illustrative example of a truss structure presented, the use of both model and parameter identification is shown to lead to smaller parameter corrections than would be required by parameter identification alone.

Glass, B. J.↗
NASA NTRS2008

New Insights into the Composition and Texture of Lunar Regolith Using Ultrafast Automated Electron-Beam Analysis

Sieved grain mounts of Apollo 16 drive tube samples have been examined using QEMSCAN - an innovative electron beam technology. By combining multiple energy-dispersive X-ray detectors, fully automated control, and off-line image processing, to produce digital mineral maps of particles exposed on polished surfaces, the result is an unprecedented quantity of mineralogical and petrographic data, on a particle-by-particle basis. Experimental analysis of four size fractions (500-250 microns, 150-90 microns, 75-45 microns and < 20 microns), prepared from two samples (64002,374 and 64002,262), has produced a robust and uniform dataset which allows for the quantification of mineralogy; texture; particle shape, size and density; and the digital classification of distinct particle types in each measured sample. These preliminary data show that there is a decrease in plagioclase modal content and an opposing increase in glass modal content, with decreasing particle size. These findings, together with data on trace phases (metals, sulphides, phosphates, and oxides), provide not only new insights into the make-up of lunar regolith at the Apollo 16 landing site, but also key physical parameters which can be used to design lunar simulants, and compute Figures of Merit for each material produced.

Rickman, Doug↗
NASA NTRS2010

A Cabonaceous Chondrite Dominated Lithology from the HED Parent; PRA 04401

The paired howardite breccias Mt. Pratt (PRA) 04401 and PRA 04402 are notable for their high proportion of carbonaceous chondrite clasts [1]. They consist predominantly of coarse (0.1-7 mm) diogenite (orthopyroxene), eucrite (plagioclase + pyroxene), and carbonaceous chondrite clasts set in a finer grained matrix of these same materials. Coarse C-chondrite clasts up to 7 mm are composed mainly of fine-grained phyllosilicates with lesser sulfides and high-mg# anhydrous magnesian silicates. Most of these clasts appear to be texturally consistent with CM2 classification [1] and some contain relict chondrules. The clasts are angular and reaction or alteration textures are not apparent in the surrounding matrix. PRA 04401 contains about 70 modal% C-chondrite clasts while PRA 04402 contains about 7%. Although many howardites are known to contain abundant C-chondrite clasts [2,3,4], PRA 04401 is, to our knowledge, the most chondrite-rich howardite lithology identified to date. Low EPMA totals from CM2-type clasts in other howardites suggest that they frequently contain 10 wt% or more water [2], a figure consistent with their mineralogy. PRA 04401, therefore, demonstrates the potential for hydrous lithologies with greater than 5 wt% water to occur locally within the nominally anhydrous HED parent body. Since the origin of this water is xenogenic, it might therefore be concentrated in portions of the asteroid surface where it would be more readily observable by remote sensing techniques. We plan to further examine C-chondrite clasts in PRA 04401/2 with the intent of establishing firm chemical classification, estimating water content, and evaluating their relationship with the host breccia. To help place them in context of the HED parent, we will also compare these breccias with other howardites to evaluate which lithologies are likely to be more prevalent on the asteroid surface.

Herrin, Jason S.↗
NASA NTRS2007

A New Modal Analysis Method to put Constraints on the Aqueous Alteration of CR Chondrites and Estimate the Unaltered CR Composition

CR carbonaceous chondrites are of the major interest since they contain one of the most primitive organic matters. However, aqueous alteration has more or less overprinted their original features in a way that needed to be assessed. That was done in the present study by comparing the mineralogy of the most altered CR1 chondrite, GRO 95577, to a less altered CR2, Renazzo. Their modal analyses were achieved thanks to a new method, based on X-ray elemental maps acquired on electron microprobe, and on IDL image treatment. It allowed the collection of new data on the composition of Renazzo and confirmed the classification of GRO 95577 as a CR1. New alteration products for CRs, vermiculite and clinochlore, were observed. The homogeneity of the Fe-poor clays in the CR1 and the distinctive matrix composition in the two chondrites suggest a wide-range of aqueous alteration on CRs. The preservation of the outlines of the chondrules in GRO 95577 and the elemental transfers of Al, Fe and Ca throughout the chondrule and of Fe and S from the matrix to the chondrule favor the idea of an asteroidal location of the aqueous alteration. From their mineralogical descriptions and modal abundances, the element repartitions in Renazzo and GRO 95577 were computed. It indicates a possible relationship between these two chondrites via an isochemical alteration process. Knowing the chemical reactions that occurred during the alteration, it was thus possible to decipher the mineralogical modal abundances in the unaltered CR body.

Perronnet, M.↗
NASA NTRS2007

A New Modal Analysis Method to put Constraints on the Aqueous Alteration of CR Chondrites and Estimate the Unaltered CR Composition

carbonaceous chondrites are of the major interest since they contain one of the most primitive organic matters. However, aqueous alteration has more or less overprinted their original features in a way that needed to be assessed. That was done in the present study by comparing the mineralogy of the most altered CR1 chondrite, GRO 95577, to a less altered CR2, Renazzo. Their modal analyses were achieved thanks to a new method, based on X-ray elemental maps acquired on electron microprobe, and on IDL image treatment. It allowed the collection of new data on the composition of Renazzo and confirmed the classification of GRO 95577 as a CR1. New alteration products for CRs, vermiculite and clinochlore, were observed. The homogeneity of the Fe-poor clays in the CR1 and the distinctive matrix composition in the two chondrites suggest a wide-range of aqueous alteration on CRs. The preservation of the outlines of the chondrules in GRO 95577 and the elemental transfers of Al, Fe and Ca throughout the chondrule and of Fe and S from the matrix to the chondrule favor the idea of an asteroidal location of the aqueous alteration. From their mineralogical descriptions and modal abundances, the element repartitions in Renazzo and GRO 95577 were computed. It indicates a possible relationship between these two chondrites via an isochemical alteration process. Knowing the chemical reactions that occurred during the alteration, it was thus possible to decipher the mineralogical modal abundances in the unaltered CR body.

Perronnet, M.↗
NASA NTRS1992

Classification of four ordinary chondrites from the Monnig Meteorite Collection

We classified four ordinary chondrites from the Monnig Meteorite Collection into compositional groups, petrologic types and shock stages, based on optical microscopy in transmitted and reflected light, and electron microprobe and modal analyses. These meteorites are Allen, Texas, H4(S2): May Day, Kansas, H4(S2); Pony Creek, Texas, H4(S3); and Springer, Oklahoma, H5(S3).

Ehlmann, Arthur J.↗