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Radiation Protection & Shielding
The Radiation Protection and Shielding Division is developing and promoting radiation protection and shielding aspects of nuclear science and technology — including interaction of nuclear radiation with materials and biological systems, instruments and techniques for the measurement of nuclear radiation fields, and radiation shield design and evaluation.
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2024 ANS Annual Conference
June 16–19, 2024
Las Vegas, NV|Mandalay Bay Resort and Casino
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Fusion Science and Technology
Latest News
ANS Standards Committee publishes joint ASME/ANS standard for Level 1/large early release frequency PRA
ANSI/ASME/ANS RA-S-1.1-2024, Standard for Level 1/Large Early Release Frequency Probabilistic Risk Assessment for Nuclear Power Plant Applications, has been published by the American Nuclear Society. The document, which is a joint standard developed with the American Society of Mechanical Engineers by the ANS/ASME Joint Committee on Nuclear Risk Management, received the approval of the American National Standards Institute on February 29, 2024, and was issued on March 15, 2024.
Bin Long, Ying Liu, Fulin Zeng, Jijun Zhou, Yuqian Yang
Fusion Science and Technology | Volume 78 | Number 5 | July 2022 | Pages 379-388
Technical Paper | doi.org/10.1080/15361055.2022.2033061
Articles are hosted by Taylor and Francis Online.
Edge-coherent mode (ECM) is one of the most promising modes in the tokamak fusion experiment, such as the Experimental Advanced Superconducting Tokamak (EAST). This paper presents an efficient convolution neural network model called NoiseNet for ECM recognition from the cross-power spectral data. NoiseNet suppresses the overfitting by applying noise in both the horizontal and vertical directions to the output of each layer of the convolution. And the improvement of the receptive field enables the convolution layer to better learn the difference between the ECM and the turbulence in the data. Experiments show that NoiseNet has better performance in ECM recognition with fewer parameters, and thus improved efficiency, than other major models, such as AlexNet, ResNet, and DenseNet. NoiseNet achieves a test accuracy of 93.94% on the ECM data sets. In addition, compared with the traditional method, this method does not depend on the empirical threshold and its generalization ability will improve with the increase in the amount of data.