Recognition of Ethylene Plasma Spectra 1D Data Based on Deep Convolutional Neural Networks

被引：0

作者：

Li, Baoxia ^{[1
]}

Chen, Wenzhuo ^{[1
]}

Bian, Shaohuang ^{[1
]}

Lusi, A. ^{[1
]}

Tang, Xiaojiang ^{[1
]}

Liu, Yang ^{[2
,3
]}

Guo, Junwei ^{[2
]}

Zhang, Dan ^{[2
]}

Yang, Cheng ^{[2
]}

Huang, Feng ^{[2
]}

机构：

[1] China Agr Univ, Coll Informat & Elect Engn, Beijing 100083, Peoples R China

[2] China Agr Univ, Coll Sci, Beijing 100083, Peoples R China

[3] Christian Albrechts Univ Kiel, Inst Expt & Appl Phys, D-24098 Kiel, Germany

来源：

ELECTRONICS | 2024年 / 13卷 / 05期

基金：

中国国家自然科学基金;

关键词：

ethylene plasma; spectral recognition; deep convolutional neural network; ATMOSPHERIC-PRESSURE;

D O I：

10.3390/electronics13050983

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

As a commonly used plasma diagnostic method, the spectral analysis methodology generates a large amount of data and has a complex quantitative relationship with discharge parameters, which result in low accuracy and time-consuming operation of traditional manual spectral recognition methods. To quickly and efficiently recognize the discharge parameters based on the collected spectral data, a one-dimensional (1D) deep convolutional neural network was constructed, which can learn the data features of different classes of ethylene plasma spectra to obtain the corresponding discharge parameters. The results show that this method has a higher recognition accuracy of higher than 98%. This model provides a new idea for plasma spectral diagnosis and its related application.

引用

页数：14

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