首页|Investigators from Dalian University of Technology Zero in on Machine Learning ( Helium Retention Feature In the Boron Deposited Layer On Tungsten Substrate By L aser-induced Breakdown Spectroscopy and Machine Learning Approach)
Investigators from Dalian University of Technology Zero in on Machine Learning ( Helium Retention Feature In the Boron Deposited Layer On Tungsten Substrate By L aser-induced Breakdown Spectroscopy and Machine Learning Approach)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News – Current study results on Machine Learn ing have been published. According to newsreporting originating in Dalian, Peop le’s Republic of China, by NewsRx journalists, research stated, “ITERis designe d for a burning plasma operation in which Tungsten (W) tiles are used as the fir st wall anddiverter materials. Studying He dynamics in B-coated W wall is essen tial to understanding the effect ofboronized plasma-facing components in fusion reactors, as these post-conditioning materials significantlyinfluence helium r etention and release.”
DalianPeople’s Republic of ChinaAsiaBoronCyborgsEmerging TechnologiesHeliumMachine LearningTransition El ementsTungstenDalian University of Technology
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