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Meeting Spotlight
Nuclear Energy Conference & Expo (NECX)
September 8–11, 2025
Atlanta, GA|Atlanta Marriott Marquis
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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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The U.S. Million Person Study of Low-Dose-Rate Health Effects
There is a critical knowledge gap regarding the health consequences of exposure to radiation received gradually over time. While there is a plethora of studies on the risks of adverse outcomes from both acute and high-dose exposures, including the landmark study of atomic bomb survivors, these are not characteristic of the chronic exposure to low-dose radiation encountered in occupational and public settings. In addition, smaller cohorts have limited numbers leading to reduced statistical power.
Wen Si, Jianghai Li, Xiaojin Huang (Tsinghua Univ)
Proceedings | Nuclear Plant Instrumentation, Control, and Human-Machine Interface Technolgies (NPIC&HMIT 2019) | Orlando, FL, February 9-14, 2019 | Pages 1361-1369
This paper focuses on anomaly detection for Instrumentation and Control (I&C) systems at nuclear power plants. Cybersecurity of I&C systems is significant to Nuclear Power Plants (NPPs). When the I&C systems are under cyber-attacks, not only the I&C systems themselves are sabotaged, but also the controlled physical systems may be damaged. Traditional classification-based anomaly detection models are learned from the labeled training data, including normal data instances and abnormal ones. However, during the operation of NPPs, most of the recorded data are normal whereas little abnormal data can be recorded. Therefore, the one-class classification method which assumes all the training data instances only have one class label is suitable for training the anomaly detection model of the I&C systems. A replicator neural network (RNN), as the one-class anomaly detection model, is trained by replicating the input data with one class label to the desired outputs, i.e. the target data. After the RNN training with the recorded data of normal operations, the outputs for the normal data are almost the same as the target data replicated from the inputs. Meanwhile, the outputs for the abnormal data would deviate greatly from the inputs. Therefore, the outliers significant different from normal ones will be detected by the trained RNN. The performance of the RNN-based method are evaluated on the testing datasets consisting of normal data and generated abnormal ones.