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  • 1
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    Heliophysics Portal - Multi-Instrument Database of Solar Flares (2017)
    In:  CASI
    Details
    Publication Date: 2019-07-13
    Description: No abstract available
    Keywords: Solar Physics
    Type: ARC-E-DAA-TN48791 , Radiation Characterization from Earth to Moon, Mars, and Beyond; Nov 06, 2017 - Nov 08, 2017; Moffett Field, CA; United States
    Format: application/pdf
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    NASA TECHNICAL REPORTS
  • 2
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    High-End Computing Overview (2019)
    In:  CASI
    Details
    Publication Date: 2019-10-30
    Description: No abstract available
    Keywords: Computer Programming and Software
    Type: ARC-E-DAA-TN72380
    Format: application/pdf
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    NASA TECHNICAL REPORTS
  • 3
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    Semantic Representation and Scale-Up of Integrated Air Traffic Management Data (2016)
    In:  CASI
    Details
    Publication Date: 2019-07-13
    Description: Each day, the global air transportation industry generates a vast amount of heterogeneous data from air carriers, air traffic control providers, and secondary aviation entities handling baggage, ticketing, catering, fuel delivery, and other services. Generally, these data are stored in isolated data systems, separated from each other by significant political, regulatory, economic, and technological divides. These realities aside, integrating aviation data into a single, queryable, big data store could enable insights leading to major efficiency, safety, and cost advantages. In this paper, we describe an implemented system for combining heterogeneous air traffic management data using semantic integration techniques. The system transforms data from its original disparate source formats into a unified semantic representation within an ontology-based triple store. Our initial prototype stores only a small sliver of air traffic data covering one day of operations at a major airport. The paper also describes our analysis of difficulties ahead as we prepare to scale up data storage to accommodate successively larger quantities of data -- eventually covering all US commercial domestic flights over an extended multi-year timeframe. We review several approaches to mitigating scale-up related query performance concerns.
    Keywords: Air Transportation and Safety
    Type: ARC-E-DAA-TN32264 , International Workshop on Semantic Big Data (SBD 2016); Jun 26, 2016 - Jul 01, 2016; San Francisco, CA; United States
    Format: application/pdf
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    NASA TECHNICAL REPORTS
  • 4
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    Semantic Representation and Scale-Up of Integrated Air Traffic Management Data (2016)
    In:  CASI
    Details
    Publication Date: 2019-07-13
    Description: Each day, the global air transportation industry generates a vast amount of heterogeneous data from air carriers, air traffic control providers, and secondary aviation entities handling baggage, ticketing, catering, fuel delivery, and other services. Generally, these data are stored in isolated data systems, separated from each other by significant political, regulatory, economic, and technological divides. These realities aside, integrating aviation data into a single, queryable, big data store could enable insights leading to major efficiency, safety, and cost advantages. In this paper, we describe an implemented system for combining heterogeneous air traffic management data using semantic integration techniques. The system transforms data from its original disparate source formats into a unified semantic representation within an ontology-based triple store. Our initial prototype stores only a small sliver of air traffic data covering one day of operations at a major airport. The paper also describes our analysis of difficulties ahead as we prepare to scale up data storage to accommodate successively larger quantities of data -- eventually covering all US commercial domestic flights over an extended multi-year timeframe. We review several approaches to mitigating scale-up related query performance concerns.
    Keywords: Air Transportation and Safety
    Type: ARC-E-DAA-TN33511 , International Workshop on Semantic Big Data; Jul 01, 2016; San Francisco, CA; United States
    Format: application/pdf
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    NASA TECHNICAL REPORTS
  • 5
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    Interactive Multi-Instrument Database of Solar Flares (2018)
    In:  CASI
    Details
    Publication Date: 2019-07-12
    Description: The fundamental motivation of the project is that the scientific output of solar research can be greatly enhanced by better exploitation of the existing solar/heliosphere space-data products jointly with ground-based observations. Our primary focus is on developing a specific innovative methodology based on recent advances in "big data" intelligent databases applied to the growing amount of high-spatial and multi-wavelength resolution, high-cadence data from NASA's missions and supporting ground-based observatories. Our flare database is not simply a manually searchable time-based catalog of events or list of web links pointing to data. It is a preprocessed metadata repository enabling fast search and automatic identification of all recorded flares sharing a specifiable set of characteristics, features, and parameters. The result is a new and unique database of solar flares and data search and classification tools for the Heliophysics community, enabling multi-instrument/multi-wavelength investigations of flare physics and supporting further development of flare-prediction methodologies.
    Keywords: Solar Physics
    Type: ARC-E-DAA-TN52128
    Format: application/pdf
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  • 6
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    Carbon Nanotube Gas Sensor Using Neural Networks (2019)
    In:  CASI
    Details
    Publication Date: 2019-12-19
    Description: The need to identify the presence and quantify the concentrations of gases and vapors is ubiquitous in NASA missions and societal applications. Sensors for air quality monitoring in crew cabins and ISS have been actively under development (Ref. 1). In particular, measuring the concentration of CO2 and NH3 is important because high concentrations of these gases pose a risk to ISS crew health. Detection of fuel and oxidant leaks in crew vehicles is critical for ensuring mission safety. Accurate gas and vapor concentrations can be measured, but this typically requires bulky and expensive instrumentation. Recently, inexpensive sensors with low power demands have been fabricated for use on the International Space Station (ISS). Carbon Nanotube (CNT) based chemical sensors are one type of these sensors. CNT sensors meet the requirements for low cost and ease of fabrication for deployment on the ISS. However, converting the measured signal from the sensors to human readable indicators of atmospheric air quality and safety is challenging. This is because it is difficult to develop an analytical model that maps the CNT sensor output signal to gas concentration. Training a neural network on CNT sensor data to predict gas concentration is more effective than developing an analytic approach to calculate the concentration from the same data set. With this in mind a neural network was created to tackle this challenge of converting the measured signal into CO2 and NH3 concentration values.
    Keywords: Aeronautics (General)
    Type: ARC-E-DAA-TN75358
    Format: application/pdf
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    NASA TECHNICAL REPORTS
  • 7
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    Predicting Composition of Photo Voltaic Cells Using Neural Networks (2019)
    In:  CASI
    Details
    Publication Date: 2019-12-14
    Description: A better understanding of IV (current voltage) curve data collected from photo voltaic cells may lead to the construction of solar cells with improved electrical properties. With this in mind, IV curve data from different types of solar cells were acquired from the Photovoltaics and Electrochemical Systems Branch, NASA Glenn Research Center. Neural networks were created to predict the chemical composition of three classes of solar cells. The success of these predictions varied with class.
    Keywords: Space Sciences (General)
    Type: ARC-E-DAA-TN75359
    Format: application/pdf
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