RF sheath induced sputtering on Proto-MPEX part 2: Impurity transport modeling and experimental comparison

2021 ◽  
Vol 28 (10) ◽  
pp. 103508
Author(s):  
C. J. Beers ◽  
C. Lau ◽  
J. Rapp ◽  
T. R. Younkin ◽  
T. M. Biewer ◽  
...  
Keyword(s):  
Transport Modeling ◽  
Experimental Comparison ◽  
Impurity Transport

scholarly journals Three-dimensional impurity transport modeling of neon-seeded and nitrogen-seeded LHD plasmas

2018 ◽  
Vol 60 (8) ◽  
pp. 084005 ◽  
Author(s):  
G Kawamura ◽  
H Tanaka ◽  
K Mukai ◽  
B Peterson ◽  
S Y Dai ◽  
...  
Keyword(s):  
Three Dimensional ◽  
Transport Modeling ◽  
Impurity Transport

How to Measure Discount Rates? An experimental comparison of three methods

2010 ◽  
Author(s):  
David J. Hardisty ◽  
Katherine J. Thompson ◽  
David H. Krantz ◽  
Elke U. Weber
Keyword(s):  
Experimental Comparison ◽  
Discount Rates

Analysis of athletes’ stadium stress source based on improved layered K-means algorithm

2020 ◽  
Vol 39 (4) ◽  
pp. 5905-5914
Author(s):  
Chen Gong
Keyword(s):  
Experimental Data ◽  
Machine Learning Algorithms ◽  
User Preference ◽  
Experimental Comparison ◽  
Actual Situation ◽  
Medical Field ◽  
Pressure Source ◽  
Preference Model ◽  
Related Research ◽  
Analysis System

Most of the research on stressors is in the medical field, and there are few analysis of athletes’ stressors, so it can not provide reference for the analysis of athletes’ stressors. Based on this, this study combines machine learning algorithms to analyze the pressure source of athletes’ stadium. In terms of data collection, it is mainly obtained through questionnaire survey and interview form, and it is used as experimental data after passing the test. In order to improve the performance of the algorithm, this paper combines the known K-Means algorithm with the layering algorithm to form a new improved layered K-Means algorithm. At the same time, this paper analyzes the performance of the improved hierarchical K-Means algorithm through experimental comparison and compares the clustering results. In addition, the analysis system corresponding to the algorithm is constructed based on the actual situation, the algorithm is applied to practice, and the user preference model is constructed. Finally, this article helps athletes find stressors and find ways to reduce stressors through personalized recommendations. The research shows that the algorithm of this study is reliable and has certain practical effects and can provide theoretical reference for subsequent related research.

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