Search NASA⌕ Search

SEARCH · Search NASA

Results for “potential data”

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 685 records · Page 38
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Clinical Natural Language Processing for Radiation Oncology: A Review and Practical Primer

Natural language processing (NLP), which aims to convert human language into expressions that can be analyzed by computers, is one of the most rapidly developing and widely used technologies in the field of artificial intelligence. Natural language processing algorithms convert unstructured free text data into structured data that can be extracted and analyzed at scale. In medicine, this unlocking of the rich, expressive data within clinical free text in electronic medical records will help untap the full potential of big data for research and clinical purposes. Recent major NLP algorithmic advances have significantly improved the performance of these algorithms, leading to a surge in academic and industry interest in developing tools to automate information extraction and phenotyping from clinical texts. Thus, these technologies are poised to transform medical research and alter clinical practices in the future. Radiation oncology stands to benefit from NLP algorithms if they are appropriately developed and deployed, as they may enable advances such as automated inclusion of radiation therapy details into cancer registries, discovery of novel insights about cancer care, and improved patient data curation and presentation at the point of care. However, challenges remain before the full value of NLP is realized, such as the plethora of jargon specific to radiation oncology, nonstandard nomenclature, a lack of publicly available labeled data for model development, and interoperability limitations between radiation oncology data silos. Successful development and implementation of high quality and high value NLP models for radiation oncology will require close collaboration between computer scientists and the radiation oncology community. Here, we present a primer on artificial intelligence algorithms in general and NLP algorithms in particular; provide guidance on how to assess the performance of such algorithms; review prior research on NLP algorithms for oncology; and describe future avenues for NLP in radiation oncology research and clinics.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2022

Helium interaction with solutes and impurities in neutron-irradiated nanostructured ferritic alloys: A first principles study

Density functional theory calculations are performed to explore the binding between He and alloying solutes, impurities, and transmutation products expected in neutron irradiated nanostructured ferritic alloys (NFAs), here 14YWT is taken as an example. Elements that exhibit significant binding (attraction) with an interstitial He are Y (binding energy = 0.46 eV), Mg (0.32), O (0.33), Ti (0.16), and C (0.15). Those that provide significant binding to a substitutional He are O (1.44), Y (1.24), N (0.73), H (0.56), Mg (0.52), Ti (0.34), Si (0.34), C (0.33), Al (0.32), Ni (0.26), Ta (0.23), and Mn (0.16). The presence of these elements in Fe matrix could reduce the transport of He towards oxide particles, dislocations, and internal boundaries, and could promote He bubble nucleation in the matrix. For convenience, we compile existing binding energy data of He with He n and He n V (He-vacancy) clusters. Dissociation pathway analysis reveals that, in general, the most likely dissociation of a He n V cluster is by a sequential emission of individual He atoms. Furthermore, larger bubbles are more prone to dissociation than smaller ones. In addition, we estimate the binding energy (segregation energy) of He in bulk Y 2 Ti 2 O 7 (YTO) single crystal, YTO/Fe interface, and YTO particle embedded in Fe, with respect to interstitial He in Fe, from existing formation energies of He in these structures. We also compile available data of He binding with Fe self-interstitial atom (SIA), SIA clusters, and edge and screw dislocations. Note that given the absence of DFT data, the binding with SIA clusters and dislocations are gathered from simulations with empirical potentials. Finally, the data presented in this paper is important to inform multiscale simulations of He bubble accumulation.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Development of machine learning framework for interface force closures based on bubble tracking data

Interfacial force closures in the two-fluid model play a critical role for the predictive capabilities of void fraction distribution. However, the practices of interfacial force modeling have long been challenged by the inherent physical complexity of the two-phase flows. The rapidly expanding computational capabilities in the recent years have made high-fidelity data from the interface-captured direct numerical simulation become more available, and hence potential for data-driven interfacial force modeling has prevailed. In this work, we established a data-driven modeling framework integrated to the HZDR multiphase Eulerian-Eulerian framework for computational fluid dynamics simulations. The data-driven framework is verified in a benchmark problem, where a feedforward neural network managed to capture the non-linear mapping between bubble Reynolds number and drag coefficient and reproduce the void distribution resulting from the baseline model in the test case. The second focus is on utilizing the bubble tracking data set to form a closure for the bubble drag in the turbulent bubbly flow, in which the drag coefficient is set to be correlated with the bubble Reynolds number and the Eötvös number. Pseudo-steady state filtering in the Frenet Frame was carried out to obtain the drag coefficient from the turbulent bubbly flow data. The performance of the data-driven drag model is also examined through a case study, where improvement of model’s prediction near-wall is regarded necessary. In conclusion, discussion and further plans of investigation are provided.

42 ENGINEERING↗
DOE OSTI2018

A data mining approach to estimating rooftop photovoltaic potential in the US

This paper aims to quantify the amount of suitable rooftop area for photovoltaic (PV) energy generation in the continental United States (US). The approach is data-driven, combining Geographic Information Systems analysis of an extensive dataset of Light Detection and Ranging (LiDAR) measurements collected by the Department of Homeland Security with a statistical model trained on these same data. The model developed herein can predict the quantity of suitable roof area where LiDAR data is not available. This analysis focuses on small buildings (1000 to 5000 square feet) which account for more than half of the total available rooftop space in these data (58%) and demonstrate a greater variability in suitability compared to larger buildings which are nearly all suitable for PV installations. This paper presents new results characterizing the size, shape and suitability of US rooftops with respect to PV installations. Overall 28% of small building roofs appear suitable in the continental United States for rooftop solar. Nationally, small building rooftops could accommodate an expected 731 GW of PV capacity and generate 926 TWh/year of PV energy on 4920 km2 of suitable rooftop space which equates to 25% the current US electricity sales.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
NASA NTRS1980

Satellite Earth resources data, module U-3

Satellite data image products potentially useful in solving Earth resource and environmental problems are described. Sources for satellite data and user information are given. Recommendations for suitability of use of data from each satellite are presented. Satellite sources of Earth resources data are summarized for satellites launched since 1965 and those projected for launch through the late 1980s. The sensors of interest on each satellite, the wavelength or frequency of operation, and the resolution are given. Color ranges are illustrated and compared. The form and utility of aircraft and LANDSAT images are compared. Data from the Gemini-Apollo photography, Skylab, meteorological satellites, the Heat Capacity Mapping Mission, the Coastal Zone Color Scanner, Seasat, LANDSAT, and projected future satellites are briefly described.

Source record↗
NASA NTRS1980

Analysis of urban area land cover using SEASAT Synthetic Aperture Radar data

Digitally processed SEASAT synthetic aperture raar (SAR) imagery of the Denver, Colorado urban area was examined to explore the potential of SAR data for mapping urban land cover and the compatability of SAR derived land cover classes with the United States Geological Survey classification system. The imagery is examined at three different scales to determine the effect of image enlargement on accuracy and level of detail extractable. At each scale the value of employing a simplistic preprocessing smoothing algorithm to improve image interpretation is addressed. A visual interpretation approach and an automated machine/visual approach are employed to evaluate the feasibility of producing a semiautomated land cover classification from SAR data. Confusion matrices of omission and commission errors are employed to define classification accuracies for each interpretation approach and image scale.

Henderson, F. M.↗
NASA NTRS1985

The Landsat thematic mapper World Data Base

A World Data Base of potential thematic mapper (TM) scenes was developed to aid in acquisition planning. The World Data Base contains geopolitical, geographic and economic regions along with a format that enables users to find the satellite day, sun angle and cloud cover probability for any month of the year. Scenes that have been acquired by TM and have an average cloud cover of 30 percent of less from July 1982 when TM was launched until the Landsat system was taken over by NOAA in September 1984 are also in the World Data Base. Processed data are referenced in maps and data bases at EROS Data Center; however, a large number of acquistions have never been processed and therefore are not accessible. The World Data Base enables the rapid location of scenes and areas with the least effort making it invaluable in TM scheduling. Users of TM data can use the World Data Base to determine if scenes of interest have been acquired, the acquisition date, and if scenes have been processed to computer-compatible tape (CCT). These uses of the World Data Base make it a valuable tool in the acquisition and location of TM scenes.

Ludwig, R. W.↗
NASA NTRS1999

Uniform Data Access Using GXD

This paper gives an overview of GXD, a framework facilitating publication and use of data from diverse data sources. GXD defines an object-oriented data model designed to represent a wide range of things including data, its metadata, resources and query results. GXD also defines a data transport language. a dialect of XML, for representing instances of the data model. This language allows for a wide range of data source implementations by supporting both the direct incorporation of data and the specification of data by various rules. The GXD software library, proto-typed in Java, includes client and server runtimes. The server runtime facilitates the generation of entities containing data encoded in the GXD transport language. The GXD client runtime interprets these entities (potentially from many data sources) to create an illusion of a globally interconnected data space, one that is independent of data source location and implementation.

Vanderbilt, Peter↗
NASA NTRS2007

Evaluation of Integrating the Invasive Species Forecasting System to Support National Park Service Decisions on Fire Management Activities and Invasive Plant Species Control

The USGS and NASA, in conjunction with Colorado State University, George Mason University and other partners, have developed the Invasive Species Forecasting System (ISFS), a flexible tool that capitalizes on NASA's remote sensing resource to produce dynamic habitat maps of invasive terrestrial plant species across the United States. In 2006 ISFS was adopted to generate predictive invasive habitat maps to benefit noxious plant and fire management teams in three major National Park systems: The Greater Yellowstone Area (Yellowstone / Grand Tetons National Parks), Sequoia and Kings Canyon National Park, and interior Alaskan (between Denali, Gates of The Arctic and Yukon-Charley). One of the objectives of this study is to explore how the ISFS enhances decision support apparatus in use by National Park management teams. The first step with each park system was to work closely with park managers to select top-priority invasive species. Specific species were chosen for each study area based on management priorities, availability of observational data, and their potential for invasion after fire disturbances. Once focal species were selected, sources of presence/absence data were collected from previous surveys for each species in and around the Parks. Using logistic regression to couple presence/absence points with environmental data layers, the first round of ISFS habitat suitability maps were generated for each National Park system and presented during park visits over the summer of 2006. This first engagement provided a demonstration of what the park service can expect from ISFS and initiated the ongoing dialog on how the parks can best utilized the system to enhance their decisions related to invasive species control. During the park visits it was discovered that separate "expert opinion" maps would provide a valuable baseline to compare against the ISFS model output. Opinion maps are a means of spatially representing qualitative knowledge into a quantitative two-dimensional map. Furthermore, our approach combines the qualitative expert opinion habitat maps -- with the quantitative ISFS habitat maps in a difference map that shows where the two maps agree and disagree. The objective of the difference map is to help focus future field sampling and improve model results. This paper presents a demonstration of the habitat, expert opinion, and difference map for Yellowstone National Park.

Ma, Peter↗
NASA NTRSDate not supplied

Managing Multi-Instrument Data Streams in Secure Environments

The capture and curation of all primary instrument data is a potentially valuable source of added insight into experiments or diagnostics in laboratory experiments. The data can, when properly curated, enable analysis beyond the current practice that uses just a subset of the as-measured data. Complete curated data can also be input for machine learning and other data exploration tools. Conveniently storing and accessing instrument data requires that the instruments are connected to databases and users through a networking infrastructure. This infrastructure needs to accommodate a wide array of instruments which can range from single laboratory mounted probes for environment monitoring to computers managing multiple instruments. These resources may also include mobile devices on which researchers record instrument and experiment state related notes. These varied data sources bring with them the challenges of different communications capabilities and protocols as well as the primary data typically being produced in proprietary formats. These challenges are further compounded when the instruments need to operate in secure environments such as required in national laboratories. We will discuss the SmartLab, an ongoing effort to set up a system for instrument and simulation data curation at NASA Langley Research Center. We will outline the challenges faced in managing the data sources required for ongoing research activities and the solutions that are being considered and implemented to address those challenges.

instrument data management↗
NASA NTRS2016

Exploring the Use of Alfven Waves in Magnetometer Calibration at Geosynchronous Orbit

An Alfven wave is a type magnetohydrodynamicwave that travels through a conducting fluid under the influence of a magnetic field. Researchers have successfully calculated offset vectors of magnetometers in interplanetary space by optimizing the offset to maximize certain Alfvenic properties of observed waves (Leinweber, Belcher). If suitable Alfven waves can be found in the magnetosphere at geosynchronous altitude then these techniques could be used to augment the overall calibration plan for magnetometers in this region such as on the GOES spacecraft, possibly increasing the time between regular maneuvers. Calibration maneuvers may be undesirable because they disrupt the activities of other instruments. Various algorithms to calculate an offset using Alfven waves were considered. A new variation of the Davis-Smith method was derived because it can be mathematically shown that the Davis-Smith method tolerates filtered data, which expands potential applications. The variant developed was designed to find only the offset in the plane normal to the main field because the overall direction of Earth's magnetic field rarely changes, and theory suggests the Alfvenic disturbances occur transverse to the main field. Other variations of the Davis-Smith method encounter problems with data containing waves that propagate in mostly the same direction. A searching algorithm was then designed to look for periods of time with potential Alfven waves in GOES 15 data based on parameters requiring that disturbances be normal to the main field and not change field magnitude. Final waves for calculation were hand-selected. These waves produced credible two-dimensional offset vectors when input to the Davis-Smith method. Multiple two-dimensional solutions in different planes can be combined to get a measurement of the complete offset. The resulting three dimensional offset did not show sufficient precision over several years to be used as a primary calibration method, but reflected changes in the offset fairly well, suggesting that the method could be helpful in monitoring trends of the offset vector when maneuvers cannot be used.

calibration↗
DOE OSTI2020

Use of the Tool to Support Renewable Energy Auctions Processes (RE Data Explorer)

Renewable energy auctions are now a common competitive approach to procure low-cost renewable power around the world. Ensuring a successful auction process increasingly depends on the capabilities of auction designers and participants to identify actionable and defensible insights from large data sets (on renewable energy resources and complementary data) to both attract potential investors and address stakeholder concerns. The Renewable Energy (RE) Data Explorer is a user-friendly geospatial analysis tool for analyzing renewable energy potential and informing decisions. Developed by the National Renewable Energy Laboratory (NREL) and supported by the U.S. Agency for International Development (USAID), RE Data Explorer performs visualization and analysis of renewable energy potential that can be customized for different scenarios. RE Data Explorer can support prospecting, integrated planning, policymaking, and other decision-making activities to accelerate renewable energy deployment. The broader RE Explorer website provides guidance and information to link the RE Data Explorer geospatial analysis tool to key decision areas. This document provides information on how the RE Data Explorer can be used to support renewable energy auction processes.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
NASA NTRS2004

The Impact of Current and Future Polar Orbiting Satellite Data on Numerical Weather Prediction at NASA/GSFC

The lack of adequate observational data continues to be recognized as a major factor limiting both atmospheric research and numerical prediction on a variety of temporal and spatial scales. Since the advent of meteorological satellites in the 1960's, a considerable research effort has been directed toward the design of space-borne meteorological sensors, the development of optimal methods for the utilization of these data, (and an assessment of the influence of existing satellite data and the potential influence of future satellite observations on numerical weather prediction. This has included both Observing System Experiments (OSEs) and Observing System Simulation Experiments (OSSEs). OSEs are conducted to evaluate the impact of specific observations or classes of observations on analyses and forecasts. While OSEs are performed with existing data, OSSEs are conducted to evaluate the potential for future observing systems to improve-NWP, as well as to evaluate trade-offs in observing system design, and to develop and test improved methods for data assimilation. At the conference, results from OSEs to evaluate satellite data sets that have recently become available to the global observing system, such as AIRS and Seawinds, and results from OSSEs to determine the potential impact of space-based lidar winds will be presented.

Atlas, Robert↗
NASA NTRS1987

Comments on the challenge of using mesoscale data in mesoscale numerical models

The dangers of addressing the initialization issues for limited-area mesoscale models by extending the lessons learned during the development of global analysis and prediction systems are discussed. Lack of impact with data inserts at one time suggests that the lateral boundary conditions imposed on the limited-area models might force the model simulation toward a preferred solution, work against the new data being inserted into the model and, therefore, limit the potential impact that this data can have on the model system. The second potential pitfall involves the imposition of balance constraints on the data that are being inserted into the model to compute winds from temperature data and/or temperature from wind data.

Uccellini, Louis W.↗
DOE OSTI2016

Low-Temperature Hydrothermal Resource Potential Estimate

Compilation of data (spreadsheet and shapefiles) for several low-temperature resource types, including isolated springs and wells, delineated area convection systems, sedimentary basins and coastal plains sedimentary systems. For each system, we include estimates of the accessible resource base, mean extractable resource and beneficial heat. Data compiled from USGS and other sources. The paper (submitted to GRC 2016) describing the methodology and analysis is also included.

15 GEOTHERMAL ENERGY↗
NASA NTRS1978

A three-dimensional potential-flow program with a geometry package for input data generation

Information needed to run a computer program for the calculation of the potential flow about arbitrary three dimensional lifting configurations is presented. The program contains a geometry package which greatly reduces the task of preparing the input data. Starting from a very sparse set of coordinate data, the program automatically augments and redistributes the coordinates, calculates curves of intersection between components, and redistributes coordinates in the regions adjacent to the intersection curves in a suitable manner for use in the potential flow calculations. A brief summary of the program capabilities and options is given, as well as detailed instructions for the data input, a suggested structure for the program overlay, and the output for two test cases.

Halsey, N. D.↗
DOE OSTI2025

Electric Vehicle Charging Data Falsification Attacks Utilizing Behavioral Models

A charging station (CS) and its associated electric vehicle supply equipment (EVSE) and charging electric vehicle (EV) interactions are potential targets for data falsification attacks since CSs are typically unmanned public facilities that are connected to the internet and EVs incorporate the vulnerable CAN bus network, which are both susceptible to remote attacks. The research question being addressed is how is the EV owner and CS negatively affected by CAN bus EV battery current sensor and battery temperature sensor data falsification attacks. Negative effects include economic losses from reduced life span of the battery, battery thermal runaway and fire (and potential loss of surrounding structure), and reduced utilization of the CS due to delayed departure time (longer charging times).

25 ENERGY STORAGE↗
DOE OSTI2023

Artificial Intelligence Guided Studies of van der Waals Magnets

A materials informatics framework to explore a large number of candidate van der Waals (vdW) materials is developed. In particular, in this study a large space of monolayer transition metal halides is investigated by combining high-throughput density functional theory calculations and artificial intelligence (AI) to accelerate the discovery of stable materials and the prediction of their magnetic properties. Here, the formation energy is used as a proxy for chemical stability. Semi-supervised learning is harnessed to mitigate the challenges of sparsely labeled materials data in order to improve the performance of AI models. This approach creates avenues for the rapid discovery of chemically stable vdW magnets by leveraging the ability of AI to recognize patterns in data, to learn mathematical representations of materials from data and to predict materials properties. Using this approach, previously unexplored vdW magnetic materials with potential applications in data storage and spintronics are identified.

36 MATERIALS SCIENCE↗