Feature selection of laser-induced breakdown spectroscopy data for steel aging estimation

2018 ◽  
Vol 150 ◽  
pp. 49-58 ◽  
Author(s):  
Shengzi Lu ◽  
Shen Shen ◽  
Jianwei Huang ◽  
Meirong Dong ◽  
Jidong Lu ◽  
...  
Author(s):  
Weiran Song ◽  
Zongyu Hou ◽  
Muhammad Sher Afgan ◽  
Weilun Gu ◽  
Hui Wang ◽  
...  

Variable selection based on ensemble learning and validation for rapid and low-cost analysis of coal properties using laser-induced breakdown spectroscopy.


2017 ◽  
Vol 32 (2) ◽  
pp. 277-288 ◽  
Author(s):  
P. Pořízka ◽  
J. Klus ◽  
A. Hrdlička ◽  
J. Vrábel ◽  
P. Škarková ◽  
...  

Normalization of data is significant and should be chosen according to the sample matrix under investigation.


Data in Brief ◽  
2020 ◽  
Vol 33 ◽  
pp. 106483
Author(s):  
Liang Yang ◽  
Liuwei Meng ◽  
Huaqi Gao ◽  
Jingyu Wang ◽  
Can Zhao ◽  
...  

2019 ◽  
Vol 34 (3) ◽  
pp. 460-468 ◽  
Author(s):  
Lingxia Huang ◽  
Liuwei Meng ◽  
Liang Yang ◽  
Jingyu Wang ◽  
Shaojia Li ◽  
...  

Laser-induced breakdown spectroscopy (LIBS) data generally contain abundant “fingerprint” variables.


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