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Argonne: Where AI research meets education and training
Last September, in the Chicago suburb of Lemont, Ill., Argonne National Laboratory hosted its first AI STEM Education Summit. More than 180 educators from high schools, community colleges, and universities; STEM administrators; and experts in various disciplines convened at “One Ecosystem, Many Pathways–Building an AI-Ready STEM Workforce” to discuss how artificial intelligence is reshaping STEM-related industries, including the implications for the nuclear engineering classroom and workforce.
Yunfeng Zhang, Xiangshun Li, Lin Cai
Nuclear Science and Engineering | Volume 194 | Number 7 | July 2020 | Pages 541-553
Technical Paper | doi.org/10.1080/00295639.2020.1751526
Articles are hosted by Taylor and Francis Online.
Based on the angle and Euclidean distance similarity of the loading matrix, this paper reports on an improved principal component analysis (PCA) modeling method that is successfully applied to identify three different working conditions in the secondary loop of a nuclear power plant (NPP). First, a simulation platform of the secondary loop of the NPP is built in which three kinds of working conditions are set. Second, the multiple-PCA modeling method is used to construct the offline models. Finally, the effectiveness and superiority of the improved method is verified in the simulation platform.