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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.
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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
Geological work begins on Poland’s first nuclear plant
Project management firm Bechtel started site geological surveys for Poland’s first nuclear power plant project, the company announced on Wednesday.
Bechtel will conduct in-depth geological surveys at the Lubiatowo-Kopalino site in the Pomeranian municipality of Choczewo, in northern Poland. This is a key milestone for the country’s entry into nuclear power production, as the surveys will inform the suitability of the planned site.
Junyong Bae, Jeeyea Ahn, Seung Jun Lee
Nuclear Technology | Volume 206 | Number 7 | July 2020 | Pages 951-961
Technical Paper – Special section on the 2019 ANS Student Conference | doi.org/10.1080/00295450.2019.1693215
Articles are hosted by Taylor and Francis Online.
Human operators always have the possibility to commit human errors, and in safety-critical infrastructures such as a nuclear power plant, human error could cause serious consequences. Since nuclear plant operations involve highly complex and mentally taxing activities, especially in emergency situations, it is important to detect human errors to maintain plant safety. This work proposes a method to predict the future trends of important plant parameters to determine whether a performed action is an error or not. To achieve this prediction, a recursive strategy is adopted that employs an artificial neural network as its prediction model. Two artificial neural networks were selected and compared: multilayer perceptron and long short-term memory (LSTM). Model training was accomplished using emergency operation data from a nuclear power plant simulator. From the comparison results, it was observed that the future trends of plant parameters were quite accurately predicted through the LSTM model. It is expected that the plant parameter prediction function proposed in this work can give useful information for detecting and recovering human errors.