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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 1,117 records · Page 62
All sourcesNASA NTRSDOE OSTI
NASA NTRS2008

Accessing and Utilizing Remote Sensing Data for Vectorborne Infectious Diseases Surveillance and Modeling

Background: The transmission of vectorborne infectious diseases is often influenced by environmental, meteorological and climatic parameters, because the vector life cycle depends on these factors. For example, the geophysical parameters relevant to malaria transmission include precipitation, surface temperature, humidity, elevation, and vegetation type. Because these parameters are routinely measured by satellites, remote sensing is an important technological tool for predicting, preventing, and containing a number of vectorborne infectious diseases, such as malaria, dengue, West Nile virus, etc. Methods: A variety of NASA remote sensing data can be used for modeling vectorborne infectious disease transmission. We will discuss both the well known and less known remote sensing data, including Landsat, AVHRR (Advanced Very High Resolution Radiometer), MODIS (Moderate Resolution Imaging Spectroradiometer), TRMM (Tropical Rainfall Measuring Mission), ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer), EO-1 (Earth Observing One) ALI (Advanced Land Imager), and SIESIP (Seasonal to Interannual Earth Science Information Partner) dataset. Giovanni is a Web-based application developed by the NASA Goddard Earth Sciences Data and Information Services Center. It provides a simple and intuitive way to visualize, analyze, and access vast amounts of Earth science remote sensing data. After remote sensing data is obtained, a variety of techniques, including generalized linear models and artificial intelligence oriented methods, t 3 can be used to model the dependency of disease transmission on these parameters. Results: The processes of accessing, visualizing and utilizing precipitation data using Giovanni, and acquiring other data at additional websites are illustrated. Malaria incidence time series for some parts of Thailand and Indonesia are used to demonstrate that malaria incidences are reasonably well modeled with generalized linear models and artificial intelligence based techniques. Conclusions: Remote sensing data relevant to the transmission of vectorborne infectious diseases can be conveniently accessed at NASA and some other websites. These data are useful for vectorborne infectious disease surveillance and modeling.

Kiang, Richard↗
DOE OSTI2019

State-based network similarity visualization

We introduce an approach for the interactive visual analysis of weighted, dynamic networks. These networks arise in areas such as computational neuroscience, sociology, and biology. Network analysis remains challenging due to complex time-varying network behavior. For example, edges disappear/reappear, communities grow/vanish, or overall network topology changes. Our technique, TimeSum, detects the important topological changes in graph data to abstract the dynamic network and visualize one summary representation for each temporal phase, a state. We define a network state as a graph with similar topology over a specific time interval. To enable a holistic comparison of networks, we use a difference network to depict edge and community changes. Here, we present case studies to demonstrate that our methods are effective and useful for extracting and exploring complex dynamic behavior of networks.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
NASA NTRS1992

Infrared flow visualization of V/STOL aircraft

During low altitude operations of vertical/short takeoff and landing (V/STOVL) aircraft complex aeropropulsive flowfields are produced by the strong interactions between the engine exhaust jet or propeller wash and the runway and aircraft surfaces. Measurement of these unsteady 3 dimensional flowfields has not proven practical with conventional measurement techniques. It has been demonstrated that modern infrared imaging systems provide a practical means of obtaining flow visualization images of these complex aeropropulsive flowfields. Infrared images obtained with a dual wavelength scanning system are presented which provide detailed information on the flowfields about the NASA Ames XV-15 tiltrotor aircraft, the E-7 STOVL research model and the NASA Ames YAV-8B Harrier aircraft. It was found that the use of a dual wavelength infrared system provides valuable information about both the engine exhaust flowfield and the resulting heating of aircraft and runway surfaces.

Birckelbaw, Larry D.↗
DOE OSTI2019

Galaxy morphological classification in deep-wide surveys via unsupervised machine learning

ABSTRACT Galaxy morphology is a fundamental quantity, which is essential not only for the full spectrum of galaxy-evolution studies, but also for a plethora of science in observational cosmology (e.g. as a prior for photometric-redshift measurements and as contextual data for transient light-curve classifications). While a rich literature exists on morphological-classification techniques, the unprecedented data volumes, coupled, in some cases, with the short cadences of forthcoming ‘Big-Data’ surveys (e.g. from the LSST), present novel challenges for this field. Large data volumes make such data sets intractable for visual inspection (even via massively distributed platforms like Galaxy Zoo), while short cadences make it difficult to employ techniques like supervised machine learning, since it may be impractical to repeatedly produce training sets on short time-scales. Unsupervised machine learning, which does not require training sets, is ideally suited to the morphological analysis of new and forthcoming surveys. Here, we employ an algorithm that performs clustering of graph representations, in order to group image patches with similar visual properties and objects constructed from those patches, like galaxies. We implement the algorithm on the Hyper-Suprime-Cam Subaru-Strategic-Program Ultra-Deep survey, to autonomously reduce the galaxy population to a small number (160) of ‘morphological clusters’, populated by galaxies with similar morphologies, which are then benchmarked using visual inspection. The morphological classifications (which we release publicly) exhibit a high level of purity, and reproduce known trends in key galaxy properties as a function of morphological type at z < 1 (e.g. stellar-mass functions, rest-frame colours, and the position of galaxies on the star-formation main sequence). Our study demonstrates the power of unsupervised machine learning in performing accurate morphological analysis, which will become indispensable in this new era of deep-wide surveys.

Martin, G.↗
DOE OSTI2024

Rapid detection of rare events from in situ X-ray diffraction data using machine learning

High-energy X-ray diffraction methods can non-destructively map the 3D microstructure and associated attributes of metallic polycrystalline engineering materials in their bulk form. These methods are often combined with external stimuli such as thermo-mechanical loading to take snapshots of the evolving microstructure and attributes over time. However, the extreme data volumes and the high costs of traditional data acquisition and reduction approaches pose a barrier to quickly extracting actionable insights and improving the temporal resolution of these snapshots. This article presents a fully automated technique capable of rapidly detecting the onset of plasticity in high-energy X-ray microscopy data. The technique is computationally faster by at least 50 times than the traditional approaches and works for data sets that are up to nine times sparser than a full data set. This new technique leverages self-supervised image representation learning and clustering to transform massive data sets into compact, semantic-rich representations of visually salient characteristics ( e.g. peak shapes). These characteristics can rapidly indicate anomalous events, such as changes in diffraction peak shapes. It is anticipated that this technique will provide just-in-time actionable information to drive smarter experiments that effectively deploy multi-modal X-ray diffraction methods spanning many decades of length scales.

Zheng, Weijian↗
NASA NTRS1995

Improved reduced-resolution satellite imagery

The resolution of satellite imagery is often traded-off to satisfy transmission time and bandwidth, memory, and display limitations. Although there are many ways to achieve the same reduction in resolution, algorithms vary in their ability to preserve the visual quality of the original imagery. These issues are investigated in the context of the Landsat browse system, which permits the user to preview a reduced resolution version of a Landsat image. Wavelets-based techniques for resolution reduction are proposed as alternatives to subsampling used in the current system. Experts judged imagery generated by the wavelets-based methods visually superior, confirming initial quantitative results. In particular, compared to subsampling, the wavelets-based techniques were much less likely to obscure roads, transmission lines, and other linear features present in the original image, introduce artifacts and noise, and otherwise reduce the usefulness of the image. The wavelets-based techniques afford multiple levels of resolution reduction and computational speed. This study is applicable to a wide range of reduced resolution applications in satellite imaging systems, including low resolution display, spaceborne browse, emergency image transmission, and real-time video downlinking.

Ellison, James↗
DOE OSTI2024

Multiparametric AFM Insights into Electron Transport Mechanisms in Biomemristors

Biomemristors have attracted significant attention due to their applications in biodegradable and flexible electronic devices, and their prospective functions in information storage, neural synapses, and neuromorphic computation. Hence, understanding biomaterials' resistive switching (RS) mechanism is essential to design novel biomemristors with enhanced performance. In the last decade, various atomic force microscopy (AFM) modes, such as Kelvin probe force microscopy (KPFM) and conductive atomic force microscopy (cAFM), in situ/operando visualize the electron transport behaviors within biomemristors at the nanoscale. This review provides a comprehensive discussion of multiparametric AFM techniques to characterize biomemristors and inspire new ideas in developing next-generation consumer electronics. We also summarize the progress of revealing the internal mechanism of biomemristors with functional AFM to visualize and understand the switching behavior. Finally, the opportunities and challenges of further utilizing multiparametric AFM to explore the electron transport mechanisms in biomemristors are presented.

Shi, Chenyang↗
DOE OSTI2021

Detecting operons in bacterial genomes via visual representation learning

Contiguous genes in prokaryotes are often arranged into operons. Detecting operons plays a critical role in inferring gene functionality and regulatory networks. Human experts annotate operons by visually inspecting gene neighborhoods across pileups of related genomes. These visual representations capture the inter-genic distance, strand direction, gene size, functional relatedness, and gene neighborhood conservation, which are the most prominent operon features mentioned in the literature. By studying these features, an expert can then decide whether a genomic region is part of an operon. We propose a deep learning based method named Operon Hunter that uses visual representations of genomic fragments to make operon predictions. Using transfer learning and data augmentation techniques facilitates leveraging the powerful neural networks trained on image datasets by re-training them on a more limited dataset of extensively validated operons. Our method outperforms the previously reported state-of-the-art tools, especially when it comes to predicting full operons and their boundaries accurately. Furthermore, our approach makes it possible to visually identify the features influencing the network’s decisions to be subsequently cross-checked by human experts.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1995

Volumetric imaging of supersonic boundary layers using filtered Rayleigh scattering background suppression

We demonstrate the use of Filtererd Rayleigh Scattering and a 3D reconstruction technique to interrogate the highly three dimensional flow field inside of a supersonic inlet model. A 3 inch by 3 inch by 2.5 inch volume is reconstructed yielding 3D visualizations of the crossing shock waves and of the boundary layer. In this paper we discuss the details of the techniques used, and present the reconstructured 3D images.

Forkey, Joseph N.↗
NASA NTRS1969

Technique for improving solid state mosaic images

Method identifies and corrects mosaic image faults in solid state visual displays and opto-electronic presentation systems. Composite video signals containing faults due to defective sensing elements are corrected by a memory unit that contains the stored fault pattern and supplies the appropriate fault word to the blanking circuit.

Saboe, J. M.↗
NASA NTRS2005

Real-Time Aerodynamic Flow and Data Visualization in an Interactive Virtual Environment

Significant advances have been made to non-intrusive flow field diagnostics in the past decade. Camera based techniques are now capable of determining physical qualities such as surface deformation, surface pressure and temperature, flow velocities, and molecular species concentration. In each case, extracting the pertinent information from the large volume of acquired data requires powerful and efficient data visualization tools. The additional requirement for real time visualization is fueled by an increased emphasis on minimizing test time in expensive facilities. This paper will address a capability titled LiveView3D, which is the first step in the development phase of an in depth, real time data visualization and analysis tool for use in aerospace testing facilities.

Schwartz, Richard J.↗
NASA NTRS2013

Measuring Temperature-Dependent Propagating Disturbances in Coronal Fan Loops Using Multiple SDO-AIA Channels and Surfing Transform Technique

A set of co-aligned high resolution images from the Atmospheric Imaging Assembly (AIA) on board the Solar Dynamics Observatory (SDO) is used to investigate propagating disturbances (PDs) in warm fan loops at the periphery of a non-flaring active region NOAA AR 11082. To measure PD speeds at multiple coronal temperatures, a new data analysis methodology is proposed enabling quantitative description of sub visual coronal motions with low signal-to-noise ratios of the order of 0.1. The technique operates with a set of one-dimensional surfing signals extracted from position-timeplots of several AIA channels through a modified version of Radon transform. The signals are used to evaluate a two-dimensional power spectral density distribution in the frequency - velocity space which exhibits a resonance in the presence of quasi-periodic PDs. By applying this analysis to the same fan loop structures observed in several AIA channels, we found that the traveling velocity of PDs increases with the temperature of the coronal plasma following the square root dependence predicted for the slow mode magneto-acoustic wave which seems to be the dominating wave mode in the studied loop structures. This result extends recent observations by Kiddie et al. (2012) to a more general class of fan loop systems not associated with sunspots and demonstrating consistent slow mode activity in up to four AIA channels.

Solar Corona↗
DOE OSTI2019

Diffusion-weighted imaging as a part of PET/MR for small lesion detection in patients with primary abdominal and pelvic cancer, with or without TOF reconstruction technique

Objectives: To investigate the value of diffusion-weighted imaging (DWI) in detection of small lesions (≤ 10 mm) in patients with primary abdominal and pelvic cancer in hybrid PET/MR with or without time-of-flight (TOF) technique. Materials and methods: Twenty patients (11 females and 9 males, mean age 67.23 ± 12.90 years) with histologically confirmed primary abdominal and pelvic cancer underwent hybrid PET/MR examination. A total of 64 small lesions were included in this study, which were divided into two groups (≤ 10 mm and 10–30 mm). Visual scores of small lesion detection ability were rated by five-point ordinal scale. The visual scores and detectability of small lesions on TOF PET image, noTOF PET image, and DWI sequences of hybrid PET/MR examination with or without TOF technique were analyzed. Logistic regression model was established for analysis in the value of DWI in hybrid PET/MR examination with or without TOF technique in detection of the small lesions between two groups. Results: The visual evaluation revealed the small lesion (≤ 10 mm) visual scores of DWI (mean ± SD: 4.23 ± 1.41), TOF PET image (mean ± SD: 4.14 ± 0.89), and noTOF PET image (mean ± SD: 2.68 ± 1.13);.and the visual scores of small lesions (10–30 mm) on DWI (mean ± SD: 4.98 ± 0.15), TOF PET image (mean ± SD: 4.57 ± 0.59), and noTOF PET image (mean ± SD: 3.98 ± 1.05). The visual scores of all small lesions on DWI were higher than that on TOF PET data and noTOF PET data in both two groups (**P < 0.01). The missed diagnosis rates of small FDG avid lesions (≤ 10 mm) of DWI and noTOF PET image were 9.1% and 9.1%, respectively. However, the TOF PET-based clinical diagnosis detected all small lesions (≤ 30 mm). DWI was of great importance in detection of small lesions (≤ 10 mm) in the absence of TOF technique in PET/MR examination (**P < 0.01). DWI’s effect on detection of small lesions(10-30 mm) has shown no difference between PET/MR examinations with TOF and without TOF techniques (P > 0.05). Conclusion: DWI has significant value in the detection of small lesions (≤ 10 mm) in hybrid PET/MR examination without TOF technique for patients with primary abdominal and pelvic cancer. However, it had less detection benefits in the small lesions (≤ 10 mm) in hybrid PET/MR examination with TOF PET image.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
NASA NTRS2014

The Use of NASA near Real-time and Archived Satellite Data to Support Disaster Assessment

With support from a NASA's Applied Sciences Program, The Short‐term Prediction Research and Transition (SPoRT) Center has explored a variety of techniques for utilizing archived and near real‐time NASA satellite data to support disaster assessment activities. MODIS data from the NASA Land Atmosphere Near Real‐time Capability for EOS currently provides true color and other imagery for assessment and potential applications including, but not limited to, flooding, fires, and tornadoes. In May 2013, the SPoRT Center developed unique power outage composites using the VIIRS Day/Night Band to represent the first clear sky view of damage inflicted upon Moore and Oklahoma City, Oklahoma following the devastating EF‐5 tornado that occurred on May 20. Pre‐event imagery provided by the NASA funded Web‐Enabled Landsat Data project offer a basis of comparison for monitoring post-disaster recovery efforts. Techniques have also been developed to generate products from higher resolution imagery from the recently available International Space Station SERVIR Environmental Research and Visualization System instrument. Of paramount importance is to deliver these products to end users expeditiously and in formats compatible with Decision Support Systems (DSS). Delivery techniques include a Tile Map Service (TMS) and a Web Mapping Service (WMS). These mechanisms allow easy integration of satellite products into DSS's, including the National Weather Service's Damage Assessment Toolkit for use by personnel conducting damage surveys. This poster will present an overview of the developed techniques and products and compare the strengths and weaknesses of the TMS and WMS.

McGrath, Kevin M.↗
DOE OSTI2019

Multivariate Pointwise Information-Driven Data Sampling and Visualization

With increasing computing capabilities of modern supercomputers, the size of the data generated from the scientific simulations is growing rapidly. As a result, application scientists need effective data summarization techniques that can reduce large-scale multivariate spatiotemporal data sets while preserving the important data properties so that the reduced data can answer domain-specific queries involving multiple variables with sufficient accuracy. While analyzing complex scientific events, domain experts often analyze and visualize two or more variables together to obtain a better understanding of the characteristics of the data features. Therefore, data summarization techniques are required to analyze multi-variable relationships in detail and then perform data reduction such that the important features involving multiple variables are preserved in the reduced data. To achieve this, in this work, we propose a data sub-sampling algorithm for performing statistical data summarization that leverages pointwise information theoretic measures to quantify the statistical association of data points considering multiple variables and generates a sub-sampled data that preserves the statistical association among multi-variables. Using such reduced sampled data, we show that multivariate feature query and analysis can be done effectively. The efficacy of the proposed multivariate association driven sampling algorithm is presented by applying it on several scientific data sets.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Automated Fire Detection for Industrial Settings with Pretrained Convolutional Networks

Early fire detection in industrial environments is critical to preventing equipment damage, personal injury, and operational disruptions. Traditional smoke detectors, while effective, often experience delays due to the time required for smoke to reach sensors, allowing fires to spread. Manual fire watch operations and human surveillance of camera feeds are resource-intensive and prone to human error. To address these challenges, this paper explores the application of convolutional neural networks for automated fire detection, specifically in industrial settings. By leveraging 11 different pre-trained machine vision models from TensorFlow and enhancing them with transfer learning on a custom-built industrial fire dataset, we optimized fire detection performance. Here, we analyzed each machine vision model architecture in terms of its depth, width, and input image resolution, considering both resource requirements and detection accuracy. We further explored the option of combining multiple models into an ensemble classifier to evaluate whether the performance improvements could justify the much greater computational complexity and other practical impacts. A cost-benefit analysis is presented to evaluate the trade-offs between performance and computational expense. Our findings identify that EfficientNetV2L, specifically tailored for industrial applications, provides the optimal balance between costs involved in training and using the model versus the overall fire detection performance. Additionally, we present a qualitative analysis of model performance using the technique of gradient-based class activation mapping to provide explainability by visualizing model decisions.

artificial intelligence↗
DOE OSTI2020

Deep learning for in situ data compression of large turbulent flow simulations

As the size of turbulent flow simulations continues to grow, in situ data compression is becoming increasingly important for visualization, analysis, and restart checkpointing. For these applications, single-pass compression techniques with low computational and communication overhead are crucial. In this paper we present a deep-learning approach to in situ compression using an autoencoder architecture that is customized for three-dimensional turbulent flows and is well suited for contemporary heterogeneous computing resources. The autoencoder is compared against a recently introduced randomized single-pass singular value decomposition (SVD) for three different canonical turbulent flows: decaying homogeneous isotropic turbulence, a Taylor-Green vortex, and turbulent channel flow. Our proposed fully convolutional autoencoder architecture compresses turbulent flow snapshots by a factor of 64 with a single pass, allows for arbitrarily sized input fields, is cheaper to compute than the randomized single-pass SVD for typical simulation sizes, performs well on unseen flow configurations, and has been made publicly available. The results reported here show that the autoencoder dramatically outperforms a randomized single-pass SVD with similar compression ratio and yields comparable performance to a higher-rank decomposition with an order of magnitude less compression in regard to preserving a number of important statistical quantities such as turbulent kinetic energy, enstrophy, and Reynolds stresses.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

A Comprehensive Methodology to Evaluate Losses and Process Variations in Silicon Solar Cell Manufacturing

Novel, high-throughput metrology methods are used in this paper for detailed performance loss analysis of approximately 400 industrial crystalline silicon solar cells, all coming from the same production line. The characterization sequence combines traditional global cell measurements (e.g., current-voltage, Suns-VOC) with camera-based cell imaging of voltage distribution and power dissipation from photoluminescence and lock-in thermography, respectively. Spatial variations in current collection are visualized using a high-speed external quantum efficiency and reflectance measurement technique. A nondestructive transfer length method (TLM) measurement technique is also implemented, featuring circular TLM structures hidden within the busbars of the cells. The variance of individual loss parameters and their impact on cell performance are quantified for this large group of cells. Based on correlations performed across parameters, recombination losses in the bulk and rear surface of the cell are shown to be the primary limiting factor for the cell efficiency. The nature of these distributions and correlations provide important insights about loss mechanisms in industrial solar cells, helping to prioritize efforts for optimizing the performance of the production line. Additionally, many of the parameters extracted from these techniques can be tied back to incoming material quality issues (e.g., poor bulk carrier lifetime, nonuniform wafer doping) and to individual unit processes (e.g., texturing, phosphorus diffusion, silicon nitride deposition, metallization), allowing the data to be used directly for process control in manufacturing. All of the datasets are made available for download.

14 SOLAR ENERGY↗