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Materials Science & Technology
The objectives of MSTD are: promote the advancement of materials science in Nuclear Science Technology; support the multidisciplines which constitute it; encourage research by providing a forum for the presentation, exchange, and documentation of relevant information; promote the interaction and communication among its members; and recognize and reward its members for significant contributions to the field of materials science in nuclear technology.
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2024 ANS Annual Conference
June 16–19, 2024
Las Vegas, NV|Mandalay Bay Resort and Casino
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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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Fusion Science and Technology
Latest News
X-energy receives federal tax credit for TRISO fuel facility
Advanced reactor company X-energy has been awarded $148.5 million in tax credits under the Inflation Reduction Act for construction of its TRISO-X fuel fabrication facility in Oak Ridge, Tenn.
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.