PSAM 16 Conference Paper Overview
Welcome to the PSAM 16 Conference paper and speaker overview page.
Lead Author: Ahmad Al Rashdan Co-author(s): Roman Shaffer, romanshaffer@yahoo.com
Edward (Ted) L. Quinn, tedquinn@cox.net
| Fitness of computer vision machine learning in the regulatory framework for safety-related or risk-significant applications |
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| With the advancements made in the field of artificial intelligence (AI) to date, significant potential exists to utilize AI capabilities for nuclear power plant (NPP) applications. AI can replicate human decision making, and AI decision making is usually faster and more accurate. For implementations that impact critical NPP applications (e.g., safety-related or non-safety systems that potentially affect overall plant risk), a deeper regulatory analysis of the AI methods is required. AI applied to NPP operations falls within the category of digital I&C (DI&C) because such applications involve digital computer hardware and custom-designed software that input plant data, execute complex software algorithms, and output the results to a system or licensed human operator to potentially provoke an action. For AI methods to be compliant with current regulatory requirements for DI&C, AI compatibility must be evaluated to identify AI gaps that may exist to prevent effective deployment of AI in NPPs. This work aims to evaluate how example AI technologies, specifically computer vision machine learning, align with the regulatory framework, and discusses some considerations if it is analyzed, modeled, tested, and validated in a manner similar to typical DI&C technologies. |
Paper AH324 Preview
Author and Presentation Info
Presentation only, a full paper is not available.
Lead Author Name: Ahmad Al Rashdan (ahmad.alrashdan@inl.gov)
Bio: Ahmad Al Rashdan, Ph.D., is currently a senior research and development scientist at INL. Dr. Al Rashdan holds a Ph.D. in nuclear engineering from Texas A&M University, a M.Sc. in information technology and automation systems from Esslingen University of Applied Science in Germany, and a B.Sc. in mechanical engineering from Jordan University of Science and Technology in Jordan. He has more than 15 years of industrial and research experience. His experience includes automated work processes using AI methods and advanced analytics, online condition monitoring of nuclear systems, control-systems design and development, anomalies detection, and automated modeling and simulation. Dr. Al Rashdan is an active contributor to and organizer of several DOE events and scientific conferences. He authored or co-authored more than 60 technical reports and journal papers and 7 patent applications and is a senior member of the IEEE and a member of the American Nuclear Society.
Country: United States of America
Company: Idaho National Laboratory
Job Title: Senior Research and Development Scientist
Bio: Ahmad Al Rashdan, Ph.D., is currently a senior research and development scientist at INL. Dr. Al Rashdan holds a Ph.D. in nuclear engineering from Texas A&M University, a M.Sc. in information technology and automation systems from Esslingen University of Applied Science in Germany, and a B.Sc. in mechanical engineering from Jordan University of Science and Technology in Jordan. He has more than 15 years of industrial and research experience. His experience includes automated work processes using AI methods and advanced analytics, online condition monitoring of nuclear systems, control-systems design and development, anomalies detection, and automated modeling and simulation. Dr. Al Rashdan is an active contributor to and organizer of several DOE events and scientific conferences. He authored or co-authored more than 60 technical reports and journal papers and 7 patent applications and is a senior member of the IEEE and a member of the American Nuclear Society.
Country: United States of America
Company: Idaho National Laboratory
Job Title: Senior Research and Development Scientist