Classification of emission lines of the Group IIIB elements, aluminium, gallium and indium, excited by Grimm glow discharge plasmas using several different plasma gases

1996 ◽  
Vol 11 (10) ◽  
pp. 957 ◽  
Author(s):  
Kazuaki Wagatsuma
Keyword(s):  
Glow Discharge ◽  
Emission Lines ◽  
Discharge Plasmas

Characterization of atomic emission lines from argon, neon, and nitrogen glow discharge plasmas

1985 ◽  
Vol 57 (14) ◽  
pp. 2901-2907 ◽  
Author(s):  
Kazuaki. Wagatsuma ◽  
Kichinosuke. Hirokawa
Keyword(s):  
Glow Discharge ◽  
Atomic Emission ◽  
Emission Lines ◽  
Discharge Plasmas

Detection of fluorine emission lines from Grimm-type glow-discharge plasmas — use of neon as the plasma gas

1996 ◽  
Vol 324 (2-3) ◽  
pp. 147-154 ◽  
Author(s):  
Kazuaki Wagatsuma ◽  
Kichinosuke Hirokawa ◽  
Noboru Yamashita
Keyword(s):  
Glow Discharge ◽  
Emission Lines ◽  
Discharge Plasmas ◽  
Fluorine Emission

Self-Adjusting Characterization for Steady-State, Direct Current Cathode-Dominated Glow Discharge Plasmas at High Pressures

2013 ◽  
Vol 30 (8) ◽  
pp. 085201 ◽  
Author(s):  
Fang Ding ◽  
Shi-Jian Zheng ◽  
Bo Ke ◽  
Zhong-Liang Tang ◽  
Yi-Chuan Zhang ◽  
...  
Keyword(s):  
Steady State ◽  
Glow Discharge ◽  
Direct Current ◽  
High Pressures ◽  
Discharge Plasmas

Classification of Alloys with an Artificial Neural Network and Multivariate Calibration of Glow-Discharge Emission Spectra

1991 ◽  
Vol 45 (10) ◽  
pp. 1706-1716 ◽  
Author(s):  
Mark Glick ◽  
Gary M. Hieftje
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Glow Discharge ◽  
Multivariate Calibration ◽  
Emission Spectra ◽  
K Nearest Neighbor ◽  
The Neural Network ◽  
Calibration Sets ◽  
Artificial Neural

Artificial neural networks were constructed for the classification of metal alloys based on their elemental constituents. Glow discharge-atomic emission spectra obtained with a photodiode array spectrometer were used in multivariate calibrations for 7 elements in 37 Ni-based alloys (different types) and 15 Fe-based alloys. Subsets of the two major classes formed calibration sets for stepwise multiple linear regression. The remaining samples were used to validate the calibration models. Reference data from the calibration sets were then pooled into a single set to train neural networks with different architectures and different training parameters. After the neural networks learned to discriminate correctly among alloy classes in the training set, their ability to classify samples in the testing set was measured. In general, the neural network approach performed slightly better than the K-nearest neighbor method, but it suffered from a hidden classification mechanism and nonunique solutions. The neural network methodology is discussed and compared with conventional sample-classification techniques, and multivariate calibration of glow discharge spectra is compared with conventional univariate calibration.

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