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NESC Technical Bulletin 23-06:Considerations for Software Fault Prevention and Tolerance
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By Space Force
SSC and USC partnered up to pair USC Trojans with SSC Guardians to work within real USSF programs. This partnership team acted as a “living laboratory” to identify strategies for implementing agile development into complex defense projects.
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By NASA
NASA logo NASA has awarded $15.6 million in grant funding to 15 projects supporting the maintenance of open-source tools, frameworks, and libraries used by the NASA science community, for the benefit of all.
The agency’s Open-Source Tools, Frameworks, and Libraries awards provide support for the sustainable development of tools freely available to everyone and critical for the goals of the agency’s Science Mission Directorate.
“We received almost twice the number of proposals this year than we had in the previous call,” said Steve Crawford, program executive, Open Science implementation, Office of the Chief Science Data Officer, NASA Headquarters in Washington. “The NASA science community’s excitement for this program demonstrates the need for sustained support and maintenance of open-source software. These projects are integral to our missions, critical to our data infrastructure, underpin machine learning and data science tools, and are used by our researchers, every day, to advance science that protects our planet and broadens our understanding of the universe.”
This award program is one of several cross-divisional opportunities at NASA focused on advancing open science practices. The grants are funded by NASA’s Office of the Chief Science Data Officer through the agency’s Research Opportunities for Space and Earth Science. The solicitation sought proposals through two types of awards:
Foundational awards: cooperative agreements for up to five years for open-source tools, frameworks, and libraries that have a significant impact on two or more divisions of the Science Mission Directorate. Sustainment awards: grants or cooperative agreements of up to three years for open-source tools, frameworks, and libraries that have significant impact in one or more divisions of the Science Mission Directorate. 2024 awardees are:
Foundation awards:
NASA’s Ames Research Center, Silicon Valley, CaliforniaPrincipal investigator: Ross Beyer “Expanding and Maintaining the Ames Stereo Pipeline” Caltech, Pasadena, CaliforniaPrincipal investigator: Brigitta Sipocz “Enhancement of Infrastructure and Sustained Maintenance of Astroquery” Cornell University, Scarsdale, New YorkPrincipal investigator: Ramin Zabih “Modernize and Expand arXiv’s Essential Infrastructure” NASA’s Goddard Space Flight Center, Greenbelt, MarylandPrincipal investigator: D. Cooley “Enabling SMD Science Using the General Mission Analysis Tool” NumFOCUS, Austin, TexasPrincipal investigator: Thomas Caswell “Sustainment of Matplotlib and Cartopy” NumFOCUSPrincipal investigator: Erik Tollerud “Investing in the Astropy Project to Enable Research and Education in Astronomy” Sustainment awards:
NASA’s Jet Propulsion Laboratory, Southern CaliforniaPrincipal investigator: Cedric David “Sustain NASA’s River Software for the Satellite Data Deluge,” three-year award Pennsylvania State University, University ParkPrincipal investigator: David Radice “AthenaK: A Performance Portable Simulation Infrastructure for Computational Astrophysics,” three-year award United States Geological Survey, Reston, VirginiaPrincipal investigator: Trent Hare “Planetary Updates for QGIS,” one-year award NASA JPLPrincipal investigator: Michael Starch “How To F Prime: Empowering Science Missions Through Documentation and Examples,” three-year award NASA GoddardPrincipal investigator: Albert Shih “Enhancing Consistency and Discoverability Across the SunPy Ecosystem,” three-year award Triad National Security, LLC, Los Alamos, New MexicoPrincipal investigator: Julia Kelliher “Enhancing Analysis Capabilities of Biological Data With the NASA EDGE Bioinformatics Platform,” four-year award iSciences LLC, Burlington, VermontPrincipal investigator: Daniel Baston “Sustaining the Geospatial Data Abstraction Library,” three-year award University of Maryland, College Park,Principal investigator: C Max Stevens “Sustaining the Community Firn Model,” three-year award Quansight, LLC, Austin, TexasPrincipal investigator: Dharhas Pothina “Ensuring a Fast and Secure Core for Scientific Python – Security, Accessibility and Performance of NumPy, SciPy and scikit-learn; Going Beyond NumPy With Accelerator Support,” three-year award For information about open science at NASA, visit:
https://science.nasa.gov/open-science
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Alise Fisher
Headquarters, Washington
202-617-4977
alise.m.fisher@nasa.gov
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By Space Force
Suicide prevention is a top military priority every day, but takes on even greater focus each September, designated since 2008 as National Suicide Prevention month.
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By Space Force
History was made on Aug. 16, as six Space Force students out of basic military training became the first Guardians to graduate technical training at the U.S. Air Force Honor Guard at Joint Base Anacostia-Bolling.
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By NASA
4 min read
Preparations for Next Moonwalk Simulations Underway (and Underwater)
NASA Ames Research Center: ProgPy is an open-source Python package supporting research and development of prognostics, health management, and predictive maintenance tools.
Prognostics is the science of prediction, and the field of Prognostics and Health Management (PHM) aims at estimating the current physical health of a system (e.g., motor, battery, etc.) and predicting how the system will degrade with use. The results of prognostics are used across industries to prevent failure, preserve safety, and reduce maintenance costs.
Prognostics, and prediction in general, is a very difficult and complex undertaking. Accurate prediction requires a model of the performance and degradation of complex systems as a function of time and use, estimation and management of uncertainty, representation of system use profiles, and ability to represent impact of neighboring systems and the environment. Any small discrepancy between the model and the actual system is compounded repeatedly, resulting in a large variation in the resulting prediction. For this reason, prognostics requires complex and capable algorithms, models, and software systems.
The ProgPy architecture can be thought of as three innovations: the Prognostic Models, the Prognostic Engine, Prognostic Support Tools.
The first part of the ProgPy innovation is the Prognostic Models. The model describes the prognostic behavior of the specific system of interest. ProgPy’s architecture includes a spectrum of modeling methodologies, ranging from physics-based models to entirely data-driven or hybrid techniques. Most users develop their own physics-based model, train one of the ProgPy data-driven models (e.g., Neural-Network models), or some hybrid of the two. A set of mature models for systems like batteries, electric motors, pumps, and valves are distributed in ProgPy. For these parameterized models, users tune the model to their specific system using the model tuning tools. The Prognostics Engine and Support Tools are built on top of these models, meaning a user that creates a new model will immediately be able to take advantage of the other features of ProgPy.
The Prognostic Engine is the most important part of ProgPy and forms the backbone of the software. The Prognostics Engine uses a Prognostics Model to perform the key functions of prognostics and health state estimation. The value in this design is that the Prognostics Engine can use any ProgPy model, whether it be a model distributed with ProgPy or a custom model created by users, to perform health state estimation and prognostics in a configurable way. The components of the Prognostics Engine are extendable, allowing users to implement their own state estimation or prediction algorithm for use with ProgPy models or use one distributed with ProgPy. Given the Prognostics Engine and a model, users can start performing prognostics for their application. This flexible and extendable framework for performing prognostics is truly novel and enables the widespread impact of ProgPy in the prognostic community.
The Prognostic Support Tools are a set of features that aid with the development, tuning, benchmarking, evaluation, and visualization of prognostic models and Prognostics Engine results (i.e., predictions). Like the Prognostic Engine, the support tools work equally with models distributed with ProgPy or custom models created by users. A user creating a model immediately has access to a wide array of tools to help them with their task.
Detailed documentation, examples, and tutorials of all these features are available to help users learn and use the software tools.
These three innovations of ProgPy implement architectures and widely used prognostics and health management functionality, supporting both researchers and practitioners. ProgPy combines technologies from across NASA projects and mission directorates, and external partners into a single package to support NASA missions and U.S. industries. Its innovative framework makes it applicable to a wide range of applications, providing enhanced capabilities not available in other, more limited, state-of-the-art software packages.
ProgPy offers unique features and a breadth and depth of unmatched capabilities when compared to other software in the field. It is novel in that it equips users with the tools necessary to do prognostics in their applications as-is, eliminating the need to adapt their use case to comply with the software available. This feature of ProgPy is an improvement upon the current state-of-the-art, as other prognostics software are often developed for specific use cases or based on a singular modeling method (Dadfarina and Drozdov, 2013; Davidson-Pilon, 2022; Schreiber, 2017). ProgPy’s unique approach opens a world of possibilities for researchers, practitioners, and developers in the field of prognostics and health management, as well as NASA missions and U.S. industries.
ProgPy Team:
Adam J Sweet, Aditya Tummala, Chetan Shrikant Kulkarni Christopher Allen Teubert Jason Watkins Kateyn Jarvis Griffith Matteo Corbetta Matthew John Daigle Miryam Stautkalns Portia Banerjee Share
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Last Updated Jul 31, 2024 EditorBill Keeter Related Terms
Office of Technology, Policy and Strategy (OTPS) View the full article
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