Initialising ...
Initialising ...
Initialising ...
Initialising ...
Initialising ...
Initialising ...
Initialising ...
Tokuda, Shinji
Purazuma, Kaku Yugo Gakkai-Shi, 78(9), p.913 - 924, 2002/09
An introductory review is given on recent developments in the methods for stability analysis of a toroidally confined plasma. Emphasis is put on the perturbation analysis of a magnetohydrodynamic system that has the marginally stable state as a terminal point of continuous spectra. We address ourselves to the asymptotic matching method pertinent to such a problem. The Newcomb equation and inner-layer equations are essential ingredients in the methods and the numerical methods for solving them are discussed.
;
JAERI-M 82-009, 43 Pages, 1982/03
no abstracts in English
; ;
JAERI-M 9761, 79 Pages, 1981/10
no abstracts in English
Oba, Masaki
no journal, ,
As a method of analyzing multi-element spectral data obtained by LIBS, etc., we are constructing an analysis system using a neural network. More learning data is expected to improve accuracy, but it takes time and effort to prepare many actual samples. Therefore, the spectral data of Gd
O
, TiO and ZrO were mixed on the data by changing the ratio to create 462 types of processed learning data, and the data were learned. After that, we analyzed the content ratio between each element of 62 kinds of data of real samples obtained by microwave LIBS measurement and examined its characteristics. As a result, the content ratio was obtained with a difference of about 10% from the true value.
Oba, Masaki
no journal, ,
Dimension reduction was performed using PCA on 462 types of training data obtained by processing GdO, TiO, ZrOdata. After learning, the content rates of elements were analyzed using 62 types of data from actual samples as test data. Similar to last time, create a calibration curve of the true value and analytical value of the actual sample and analyze the content ratio. The neural network used this time had a configuration of input layer, middle layer (2 layers), and output layer, and the middle layer used 2 layers with 100 nodes each. As a result of PCA on the training data, we were able to significantly reduce the 7944 dimensions (pixels) of the training data to 5 dimensions. As a result of training using this and analyzing test data, the difference from the true value was approximately 10%, which was almost the same as the previous value.
