Machine learning for real-time diagnostics of cold atmospheric plasma sources

Dogan Gidon; Xuekai Pei; Angelo D. Bonzanini; David B. Graves; Ali Mesbah

doi:10.1109/TRPMS.2019.2910220

Machine learning for real-time diagnostics of cold atmospheric plasma sources

Dogan Gidon, Xuekai Pei, Angelo D. Bonzanini, David B. Graves, Ali Mesbah

Research output: Contribution to journal › Article › peer-review

47 Scopus citations

Abstract

Real-time diagnostics of cold atmospheric plasma (CAP) sources can be challenging due to the requirement for expensive equipment and complicated analysis. Data analytics that rely on machine learning (ML) methods can help address this challenge. In this paper, we demonstrate the application of several ML methods for real-time diagnosis of CAPs using informationrich optical emission spectra and electro-acoustic emission. We show that data analytics based on ML can provide a simple and effective means for estimation of operation-relevant parameters such as rotational and vibrational temperature and substrate characteristic in real-time. Our findings indicate a great potential promise for ML for real-time diagnostics of CAPs.

Original language	English (US)
Article number	8685139
Pages (from-to)	597-605
Number of pages	9
Journal	IEEE Transactions on Radiation and Plasma Medical Sciences
Volume	3
Issue number	5
DOIs	https://doi.org/10.1109/TRPMS.2019.2910220
State	Published - Sep 2019
Externally published	Yes

All Science Journal Classification (ASJC) codes

Atomic and Molecular Physics, and Optics
Instrumentation
Radiology Nuclear Medicine and imaging

Keywords

Cold atmospheric plasma (CAP)
Electroacoustic signal
Gaussian process (GP)
K-means clustering
Linear regression
Machine learning (ML)
Optical emission spectrum (OES)
Real-time diagnostics

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10.1109/TRPMS.2019.2910220

Cite this

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title = "Machine learning for real-time diagnostics of cold atmospheric plasma sources",

abstract = "Real-time diagnostics of cold atmospheric plasma (CAP) sources can be challenging due to the requirement for expensive equipment and complicated analysis. Data analytics that rely on machine learning (ML) methods can help address this challenge. In this paper, we demonstrate the application of several ML methods for real-time diagnosis of CAPs using informationrich optical emission spectra and electro-acoustic emission. We show that data analytics based on ML can provide a simple and effective means for estimation of operation-relevant parameters such as rotational and vibrational temperature and substrate characteristic in real-time. Our findings indicate a great potential promise for ML for real-time diagnostics of CAPs.",

keywords = "Cold atmospheric plasma (CAP), Electroacoustic signal, Gaussian process (GP), K-means clustering, Linear regression, Machine learning (ML), Optical emission spectrum (OES), Real-time diagnostics",

author = "Dogan Gidon and Xuekai Pei and Bonzanini, {Angelo D.} and Graves, {David B.} and Ali Mesbah",

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year = "2019",

month = sep,

doi = "10.1109/TRPMS.2019.2910220",

language = "English (US)",

volume = "3",

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T1 - Machine learning for real-time diagnostics of cold atmospheric plasma sources

AU - Gidon, Dogan

AU - Pei, Xuekai

AU - Bonzanini, Angelo D.

AU - Graves, David B.

AU - Mesbah, Ali

PY - 2019/9

Y1 - 2019/9

N2 - Real-time diagnostics of cold atmospheric plasma (CAP) sources can be challenging due to the requirement for expensive equipment and complicated analysis. Data analytics that rely on machine learning (ML) methods can help address this challenge. In this paper, we demonstrate the application of several ML methods for real-time diagnosis of CAPs using informationrich optical emission spectra and electro-acoustic emission. We show that data analytics based on ML can provide a simple and effective means for estimation of operation-relevant parameters such as rotational and vibrational temperature and substrate characteristic in real-time. Our findings indicate a great potential promise for ML for real-time diagnostics of CAPs.

AB - Real-time diagnostics of cold atmospheric plasma (CAP) sources can be challenging due to the requirement for expensive equipment and complicated analysis. Data analytics that rely on machine learning (ML) methods can help address this challenge. In this paper, we demonstrate the application of several ML methods for real-time diagnosis of CAPs using informationrich optical emission spectra and electro-acoustic emission. We show that data analytics based on ML can provide a simple and effective means for estimation of operation-relevant parameters such as rotational and vibrational temperature and substrate characteristic in real-time. Our findings indicate a great potential promise for ML for real-time diagnostics of CAPs.

KW - Cold atmospheric plasma (CAP)

KW - Electroacoustic signal

KW - Gaussian process (GP)

KW - K-means clustering

KW - Linear regression

KW - Machine learning (ML)

KW - Optical emission spectrum (OES)

KW - Real-time diagnostics

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Machine learning for real-time diagnostics of cold atmospheric plasma sources

Abstract

All Science Journal Classification (ASJC) codes

Keywords

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