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Young Members Group
The Young Members Group works to encourage and enable all young professional members to be actively involved in the efforts and endeavors of the Society at all levels (Professional Divisions, ANS Governance, Local Sections, etc.) as they transition from the role of a student to the role of a professional. It sponsors non-technical workshops and meetings that provide professional development and networking opportunities for young professionals, collaborates with other Divisions and Groups in developing technical and non-technical content for topical and national meetings, encourages its members to participate in the activities of the Groups and Divisions that are closely related to their professional interests as well as in their local sections, introduces young members to the rules and governance structure of the Society, and nominates young professionals for awards and leadership opportunities available to members.
Meeting Spotlight
2024 ANS Annual Conference
June 16–19, 2024
Las Vegas, NV|Mandalay Bay Resort and Casino
Standards Program
The Standards Committee is responsible for the development and maintenance of voluntary consensus standards that address the design, analysis, and operation of components, systems, and facilities related to the application of nuclear science and technology. Find out What’s New, check out the Standards Store, or Get Involved today!
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Latest News
G7 pledges support for nuclear at Italy meeting
The Group of Seven (G7) recommitted its support for nuclear energy in the countries that opt to use it at a Ministerial Meeting on Climate in Italy last month.
In a statement following the April meeting, the group committed to support multilateral efforts to strengthen the resilience of nuclear supply chains, referencing the goal set by 25 countries during last year’s COP28 climate conference in Dubai to triple global nuclear generating capacity by 2050.
Arvind Sundaram, Yeni Li, Hany Abdel-Khalik
Nuclear Technology | Volume 208 | Number 9 | September 2022 | Pages 1365-1381
Technical Paper | doi.org/10.1080/00295450.2022.2027147
Articles are hosted by Taylor and Francis Online.
The widespread digitization of critical industrial systems such as nuclear reactors has led to the development of digital twins and/or the adoption of artificial intelligence techniques for simulating baseline behavior and performing predictive maintenance. Such analytical tools, referred to as anomaly detection techniques, rely on features extracted from data that describe the underlying physical process. While these anomaly detection systems may work well with simulated data, their real-world applications are often hindered by the presence of noise. In some cases, noise may obscure subtle anomalies that may carry information about incipient stages of system faults. These subtle variations may also be the result of malicious intrusion such as so-called false data injection attack, equipment degradation causing sensor drift, or other natural disturbances in the process or the sensors. Consequently, there is a need to extract features that are robust to noise and also denoise data in a manner that aids machine-learning (ML) tools in diagnostics. In this regard, this paper presents a singular value decomposition–based statistical data–driven approach for feature extraction, denoted by randomized window decomposition, to capture the underlying physics of the system. Additionally, the features are used to denoise data to reveal subtle anomalies while also preserving relevant information for ML algorithms. The denoising algorithm is demonstrated using a RELAP5 simulation of a representative nuclear reactor with virtual noise.