MS90: Toward Trustworthy Autonomous Safety-Critical Systems What Makes an Autonomous System Trustworthy?
Explore the source record for details and available documents.
SEARCH · Engineering Papers
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.
Explore the source record for details and available documents.
The exceptional progress in the field of Artificial Intelligence (AI) systems, enabled by Machine Learning (ML) technology in recent years provides historic opportunities for the aviation industry. Current certification standards for avionics were developed prior to the ML renaissance and have several fundamental incompatibilities with the ML technology. WG-114 is working hard to release a new standard as soon as possible but for now there is no recognized means of compliance for ML based systems even of low criticality. In this talk, we present the custom ML workflow that can be used comply with all objectives of the current certification standards for a low-criticality (DAL D and C) ML-based system. To illustrate the practical application of the custom ML workflow we present a case study of a system based on a Deep Neural Network (DNN) intended to detect and identify airport runway signs. We present the system design, data generation, training, and verification in detail and describe how the design assurance objectives can be met for a DAL D and DAL C systems.
Explore the source record for details and available documents.
No abstract provided
Explore the source record for details and available documents.
Explore the source record for details and available documents.
Explore the source record for details and available documents.
Explore the source record for details and available documents.
Explore the source record for details and available documents.
Explore the source record for details and available documents.
Explore the source record for details and available documents.
Explore the source record for details and available documents.
Explore the source record for details and available documents.
Explore the source record for details and available documents.
During a review of the Surplus Plutonium Disposition (SPD) at Savannah River Site (SRS) project Safety Basis Strategy a comment was made about the risk of inadvertent formation of plutonium hexafluoride, PuF6, during a plutonium fire suppressed by the NovecTM 1230 fire suppressant. NovecTM 1230 is a fluorine rich non-hydrogenous extinguishing agent. Hydrogenous materials, like water, increase reactivity of fissile material by adding moderation to the system and thus are generally not used as firefighting agents in fissile material areas.
The Savannah River Site (SRS) has a number of nuclear facilities. Some date back to the mid-1950s while others are actively being built today. As facilities have aged and missions changed, a number of the older nuclear facilities have gone through various stages of deactivation, decontamination, decommissioning, and dismantlement (D4). Due to the nature of the site’s historic missions a number of these facilities processed and utilized significant quantities of fissionable materials. D4 activities have now spanned multiple decades and as such have spanned multiple regulatory and industry standards have variety of documentation levels associated with them.
Explore the source record for details and available documents.
Explore the source record for details and available documents.