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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
NASA NTRS1992

High speed network issues in a distributed visualization application

The author examines the issues in creating an UltraNet TCP/IP socket-based distributed application. The visualization computation is performed on a Connection Machine, and the results are rendered using a Silicon Graphics 4D/320 VGX workstation. The UltraNet network facilitates high-bandwidth communication between the computers. Ideally, taking advantage of the UltraNet is no more complex than developing TCP/IP and UNIX BSD socket-type applications on a single machine. In practice, there are several problems in developing an application using the UltraNet. The author identifies potential problems and discusses techniques for overcoming them. The performance of UltraNet communication is measured and found to be 10 MB/s for SGI 4D/320 workstations.

Krystynak, John↗
DOE OSTI2024

“Understanding Robustness Lottery”: A Geometric Visual Comparative Analysis of Neural Network Pruning Approaches

Deep learning approaches have provided state-of-the-art performance in many applications by relying on large and overparameterized neural networks. However, such networks are very brittle and are difficult to deploy on resource-limited platforms. Model pruning, i.e., reducing the size of the network, is a widely adopted strategy that can lead to a more robust and compact model. Many heuristics exist for model pruning, but our understanding of the pruning process remains limited due to the black-box nature of a neural network model. Empirical studies show that some heuristics improve performance whereas others can make models more brittle. Here, this work aims to shed light on how different pruning methods alter the network’s internal feature representation and the corresponding impact on model performance. To facilitate a comprehensive comparison and characterization of the high-dimensional model feature space, we introduce a visual geometric analysis of feature representations. We evaluated a set of critical geometric concepts decomposed from the commonly adopted classification loss and used them to design a visualization system to compare and highlight the impact of pruning on model performance and feature representation. The proposed tool provides an environment for an in-depth comparison of pruning methods and a comprehensive understanding of how the model responds to common data corruption. By leveraging the proposed visualization, machine learning researchers can reveal the similarities between pruning methods and redundancy in robustness evaluation benchmarks, obtain geometric insights about the differences between pruned models that achieve superior robustness performance, and identify samples that are robust or fragile to model pruning and common data corruption.

Li, Zhimin [Univ. of Utah, Salt Lake City, UT (Uni↗
DOE OSTI2022

Driver Distraction Behavior Detection using a Vision Transformer Model based on Transfer Learning Strategy

Driver distraction behavior is one of the critical factors in traffic accidents. Thus, advanced driver state detection system has become the focus in the field of intelligent vehicle. However, in practical applications, insufficient samples of driving distraction behaviors bring great challenges to training a personalized behavior distraction detection model for a specific driver. To this end, a novel transformer model based on a transfer learning strategy is proposed in this paper to accurately recognize driver distraction behavior. Inspired by the effect of the transformer network in visual recognition, we firstly present a transformer behavior distraction detection system to identify the behavior categories that cause driver distraction. Then, for the specific driving dataset in practical application scenarios, the transfer learning strategy is introduced into the driver distraction detection model to further train the general transformer network. The effectiveness of the transformer based on the transfer learning strategy is validated compared with other traditional deep learning methods. The results show that the proposed detection method has better generalization ability and higher accuracy.

Fang, Zhenwu↗
NASA NTRS1992

Distributed Visualization for Computational Fluid Dynamics

Distributed concurrent visualization and computation in computational fluid dynamics (CFD) is not a new concept. Specialized applications such as Realtime Interactive Particle-tracer (RIP) and vendor specific tools like Distributed Graphics Language (DGL) have been in use for some time. This paper describes a current project underway at NASA Lewis Research Center to provide the CFD researcher with an easy method for incorporating distributed processing concepts into program development. Details on the FORTRAN capable interface to a set of network and visualization functions are presented along with some results from initial CFD case studies that employ these techniques.

Don J Sosoka↗
DOE OSTI2020

X-ray Nanocomputed Tomography in Zernike Phase Contrast for Studying 3D Morphology of Li–O 2 Battery Electrode

In this work, we investigated the lithium peroxide (Li 2 O 2 ) and pore size distribution in lithium-O 2 battery electrodes at different states of charge using transmission X-ray microscopy coupled with Zernike phase contrast to carry out nano-Computed Tomography. We report that such technique enables, at the nanoscale, to distinguish light elements such as carbon and Li 2 O 2 in Li-O 2 battery cathode electrodes. We verified by wave-propagation simulation that this approach efficiently improves the contrast of images in comparison with the pure absorption. The Li 2 O 2 distribution and thickness, interphases, and pore network are visualized and quantified, which give a valuable insight into our cathode architecture. From this 3D analysis, we highlight modifications of the air-cathode morphology and the Li 2 O 2 spatial organization as well as their potential implication in terms of carbon surface passivation and pore-clogging. After the full recharge process, this technique can also reveal the spatial distribution of the residual Li 2 O 2 and other byproducts.

25 ENERGY STORAGE↗
DOE OSTI2021

Xylan Is Critical for Proper Bundling and Alignment of Cellulose Microfibrils in Plant Secondary Cell Walls

Plant biomass represents an abundant and increasingly important natural resource and it mainly consists of a number of cell types that have undergone extensive secondary cell wall (SCW) formation. These cell types are abundant in the stems of Arabidopsis, a well-studied model system for hardwood, the wood of eudicot plants. The main constituents of hardwood include cellulose, lignin, and xylan, the latter in the form of glucuronoxylan (GX). The binding of GX to cellulose in the eudicot SCW represents one of the best-understood molecular interactions within plant cell walls. The evenly spaced acetylation and 4-O-methyl glucuronic acid (MeGlcA) substitutions of the xylan polymer backbone facilitates binding in a linear two-fold screw conformation to the hydrophilic side of cellulose and signifies a high level of molecular specificity. However, the wider implications of GX–cellulose interactions for cellulose network formation and SCW architecture have remained less explored. In this study, we seek to expand our knowledge on this by characterizing the cellulose microfibril organization in three well-characterized GX mutants. The selected mutants display a range of GX deficiency from mild to severe, with findings indicating even the weakest mutant having significant perturbations of the cellulose network, as visualized by both scanning electron microscopy (SEM) and atomic force microscopy (AFM). We show by image analysis that microfibril width is increased by as much as three times in the severe mutants compared to the wild type and that the degree of directional dispersion of the fibrils is approximately doubled in all the three mutants. Further, we find that these changes correlate with both altered nanomechanical properties of the SCW, as observed by AFM, and with increases in enzymatic hydrolysis. Results from this study indicate the critical role that normal GX composition has on cellulose bundle formation and cellulose organization as a whole within the SCWs.

09 BIOMASS FUELS↗
NASA NTRS1992

Satcom systems and technologies into the 21st century

The role of communications satellites in the domestic U.S. telecommunications market for the next 25 years is examined, the focus being on the new satellite communications systems concepts and their enabling technologies. A simple procedure for the identification of critical satellite technologies in the years 2000-2015 is described. Study results indicate that by the year 2015, satellites will find commercial applications in the distribution of space-gathered data, supercomputer network, scientific visualization to remote areas, mobile services, VSAT, broadband ISDN, and data services.

Kwan, Robert K.↗
NASA NTRS1991

The use of communication technology in medicine

NYNEX Science and Technology is engineering a multi-layered approach to multimedia communications by combining high-resolution images, video, voice, and text into a new fiber-optic service. The service, Media Broadband Service (MBS), is a network-based visual communications capability. It permits real time sharing of images in support of collaborative work among geographically dispersed locations. The health care industry was identified as a primary target market due to their need for high resolution images, the need to transport these images over great distances, and the need to achieve the transport in a short amount of time. The NYNEX Corporation, the current state of the MBS project, including the market needs driving the development of MBS, the overall design of the service, its current implementation and development status, and the progress of MBS projects underway for various customers participating in the initial service offering are described.

Reis, Howard P.↗
NASA NTRS1993

Techniques and resources for storm-scale numerical weather prediction

The topics discussed include the following: multiscale application of the 5th-generation PSU/NCAR mesoscale model, the coupling of nonhydrostatic atmospheric and hydrostatic ocean models for air-sea interaction studies; a numerical simulation of cloud formation over complex topography; adaptive grid simulations of convection; an unstructured grid, nonhydrostatic meso/cloud scale model; efficient mesoscale modeling for multiple scales using variable resolution; initialization of cloud-scale models with Doppler radar data; and making effective use of future computing architectures, networks, and visualization software.

Droegemeier, Kelvin↗
NASA NTRS1999

Research and Educational Opportunities with NASA GSFC

The research carried out in the Earth sciences at NASA's Goddard Space Flight Center will be the focus of the presentation. These research interests span the full breath of disciplines in Earth System Science. Branches and research groups focus on areas as diverse as planetary geomagnetics and atmospheric chemistry. There are 4 major laboratories in the directorate and 2 organizations that focus on scientific information technology. These laboratories focus on atmospheric sciences (atmospheric chemistry, climate and radiation, regional processes, atmospheric modeling), hydrological sciences (snow, ice, oceans, and seasonal-to-interannual prediction), terrestrial physics (geology, terrestrial biology, land-atmosphere interactions, geophysics), climate modeling (global warming, greenhouse gases, climate change). Two of the labs also have branches that focus on sensor development especially using lidar and microwave technologies. In the information technologies, areas of scientific and technical research, and expertise include massively parallel computers, technologies for high speed electronic networks, scientific visualization, information systems, archive and distribution systems, and information processing. Examples will be presented from each of these research disciplines.

Meeson, Blanche W.↗
NASA NTRS2004

Supporting Weather Data

Since its founding in 1992, Global Science & Technology, Inc. (GST), of Greenbelt, Maryland, has been developing technologies and providing services in support of NASA scientific research. GST specialties include scientific analysis, science data and information systems, data visualization, communications, networking and Web technologies, computer science, and software system engineering. As a longtime contractor to Goddard Space Flight Center s Earth Science Directorate, GST scientific, engineering, and information technology staff have extensive qualifications with the synthesis of satellite, in situ, and Earth science data for weather- and climate-related projects. GST s experience in this arena is end-to-end, from building satellite ground receiving systems and science data systems, to product generation and research and analysis.

Source record↗
NASA NTRS2024

Interpretable Machine Learning for Molecular Biosignatures: a Novel Single-Sample Feature Importance Method That Is Sensitive To Statistical Interactions

Isotope ratio mass spectrometry (IRMS) of volatiles (e.g., CO 2 ) promises to be a powerful tool for potential biosignature detection for future missions to ocean worlds (OW) such as Europa and Enceladus. Machine learning (ML) methods for IRMS data could enable science autonomy by onboard prediction of seawater chemistry and biosignature presence. However, ML models are likely to be complex and involve statistical interactions between features (variables), which can make predictions seem opaque and enigmatic. For ML predictions as significant as extraterrestrial biosignatures, we must place extraordinary confidence in models. It is therefore essential that these models make interpretable predictions (i.e., human-understandable) and include false-prediction diagnostics. We achieve high accuracy and interpretability in ML biosignature and seawater chemistry models for OW through a nearest-neighbors feature selection tool that detects statistical interactions between predictors, constructs interaction networks for visualization of selected features working together to make a prediction, and reports single-sample feature importance scores for false-detection diagnostics. Here we develop a novel single-sample nearest-neighbors projected distance regression(ssNPDR) feature selection method that improves upon existing single-sample algorithms through the inclusion of statistical interactions while providing false-prediction diagnostics for ML models.

geochemistry↗
DOE OSTI2022

Ambient Aerosol Is Physically Larger on Cloudy Days in Bondville, Illinois

Particle chemical composition affects aerosol optical and physical properties in ways important for the fate, transport, and impact of atmospheric particulate matter. For example, hygroscopic constituents take up water to increase the physical size of a particle, which can alter the extinction properties and atmospheric lifetime. At the collocated AERosol RObotic NETwork (AERONET) and Interagency Monitoring of PROtected Visual Environments (IMPROVE) network monitoring stations in rural Bondville, Illinois, we employ a novel cloudiness determination method to compare measured aerosol physicochemical properties on predominantly cloudy and clear sky days from 2010 to 2019. On cloudy days, aerosol optical depth (AOD) is significantly higher than on clear sky days in all seasons. Measured angstrom ngstro''m exponents are significantly smaller on cloudy days, indicating physically larger average particle size for the sampled populations in all seasons except winter. Mass concentrations of fine particulate matter that include estimates of aerosol liquid water (ALW) are higher on cloudy days in all seasons but winter. More ALW on cloudy days is consistent with larger particle sizes inferred from angstrom ngstro''m exponent measurements. Aerosol chemical composition that affects hygroscopicity plays a determining impact on cloudy versus clear sky differences in AOD, angstrom ngstro''m exponents, and ALW. This work highlights the need for simultaneous collocated, high-time-resolution measurements of both aerosol chemical and physical properties, in particular at cloudy times when quantitative understanding of tropospheric composition is most uncertain.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Merope: Unsupervised Learning for Network Operations (Merope) v1

The ability to analyze network flow characteristics is critical to understand network patterns that can help us understand how our networks are behaving. Merope is a collection of many unsupervised learning approaches and visualizers to allow networks to be more visual and analytical.

Kiran, Mariam↗