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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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7 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS1998

Instrument Remote Control via the Astronomical Instrument Markup Language

The Instrument Remote Control (IRC) project ongoing at NASA's Goddard Space Flight Center's (GSFC) Information Systems Center (ISC) supports NASA's mission by defining an adaptive intranet-based framework that provides robust interactive and distributed control and monitoring of remote instruments. An astronomical IRC architecture that combines the platform-independent processing capabilities of Java with the power of Extensible Markup Language (XML) to express hierarchical data in an equally platform-independent, as well as human readable manner, has been developed. This architecture is implemented using a variety of XML support tools and Application Programming Interfaces (API) written in Java. IRC will enable trusted astronomers from around the world to easily access infrared instruments (e.g., telescopes, cameras, and spectrometers) located in remote, inhospitable environments, such as the South Pole, a high Chilean mountaintop, or an airborne observatory aboard a Boeing 747. Using IRC's frameworks, an astronomer or other scientist can easily define the type of onboard instrument, control the instrument remotely, and return monitoring data all through the intranet. The Astronomical Instrument Markup Language (AIML) is the first implementation of the more general Instrument Markup Language (IML). The key aspects of our approach to instrument description and control applies to many domains, from medical instruments to machine assembly lines. The concepts behind AIML apply equally well to the description and control of instruments in general. IRC enables us to apply our techniques to several instruments, preferably from different observatories.

Sall, Ken↗
DOE OSTI2026

annomate-microsentryai-workflow

Human-in-the-loop AIML powered inspection tools AnnoMate and MicroSentryAI (interrelated)

DeBardeleben, Nathan Andrew [Los Alamos National L↗
DOE OSTI2026

Towards continual machine learning for particle accelerators

This talk covers our work on errant beam prognostics at the Spallation Neutron Source (SNS), focusing on the end-to-end process from data collection to the development and deployment of predictive models in specific. A short overview of AIML work done for accelerators and current trends will be presented. We will walk through key steps involved in creating robust Machine Learning (ML) models, including model training, validation, and deployment in an operational setting. In addition to presenting our technical approach, we will share valuable lessons learned, emphasizing the importance of infrastructure to support the continuous adaptation of models to evolving data and system behaviors. This talk will provide insights into the challenges and solutions involved in applying ML to real-world operational environments, with a particular focus on managing data drift and changes in accelerator setup while ensuring model resilience over time.

Accelerator Physics↗
NASA NTRS2024

The Knowledge-based Digital Platform Concept for Advanced Air Mobility Research and Development

National Aeronautics and Space Administration (NASA) Langley Research Center (LaRC) is spearheading an innovative digital engineering approach to integrate, communicate, and facilitate the research of multi-modal transportation systems. The Knowledge-based Digital Platform (KbDP) is a concept being developed that ties the workflows of Project Managers (PM), Principal Investigators (PI), and System Engineers (SE) together across organizational boundaries. The overarching vision for the KbDP Concept for AAM R&D is a substantial undertaking. The initial concept and implementation will focus on UAM operations to tractably learn and adjust the concept with a manageable database. Lessons learned and best practices with a smaller scope will enable successful scalability to AAM R&D or even to the entire modes of transportation and logistics. The UAM vision is one in which advanced technologies and new operational procedures enable practical and cost-effective air transport as an integrated mode of movement of people and goods throughout metropolitan areas. Initial implementation of three KbDP concepts of use shows promising benefits to NASA’s Air Traffic Management-Exploration (ATM-X) UAM Airspace Subproject. It is envisioned that the KbDP will manage an information database defined by mathematical, data science, and system engineering principles. AIML algorithms play a vital role in this KbDP concept by extracting meaningful knowledge from the information database, which the human user leverages to improve the efficiency and effectiveness of their research greatly.

ATM↗
NASA NTRS2023

AI Ethics Appendix: A Novel Approach to AI Ethics Workforce Development

The "AI Ethics Appendix" is a game-based design fiction for the deliberation of uncertain artificial intelligence (AI) ethical scenarios. The game is intended to be used as a tool for AI practitioners and industry professionals to grow responsible and ethical AI knowledge as they integrate this technology into their development. As AI/ML ethical considerations and governmental compliance develop, it is important to encourage teams to encourage teams to incorporate diverse thinking early in the development cycle and consider how different stakeholders may be affected by the technology. This game accomplishes these goals through storytelling and meaningful game interaction based on methods from game design as well as speculative and design fiction in the field of Human-Centered Design. The game mechanics were informed by the NASA Framework for the Ethical Use of Artificial Intelligence, Executive Order 13960, examples of AI use cases, and colleagues' work experiences with AI/ML.

trustworthy↗