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General Kenneth Nichols and the Manhattan Project
Nichols
The Oak Ridger has published the latest in a series of articles about General Kenneth D. Nichols, the Manhattan Project, and the 1954 Atomic Energy Act. The series has been produced by Nichols’ grandniece Barbara Rogers Scollin and Oak Ridge (Tenn.) city historian David Ray Smith. Gen. Nichols (1907–2000) was the district engineer for the Manhattan Engineer District during the Manhattan Project.
As Smith and Scollin explain, Nichols “had supervision of the research and development connected with, and the design, construction, and operation of, all plants required to produce plutonium-239 and uranium-235, including the construction of the towns of Oak Ridge, Tennessee, and Richland, Washington. The responsibility of his position was massive as he oversaw a workforce of both military and civilian personnel of approximately 125,000; his Oak Ridge office became the center of the wartime atomic energy’s activities.”
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.