Machine Learning for Real-Time Diagnostics of Cold Atmospheric Plasma Sources
Machine Learning for Real-Time Diagnostics of Cold Atmospheric Plasma Sources
复制标题
DOI：
10.1109/trpms.2019.2910220
复制
发表时间：
2019-09-01
影响因子：
4.4
通讯作者：
Mesbah, Ali
中科院分区：
文献类型：
--
作者：
Gidon, Dogan;Pei, Xuekai;Mesbah, Ali
由于需要昂贵的设备和复杂的分析，冷大气等离子体(CAP)源的实时诊断可能具有挑战性。依赖机器学习(ML)方法的数据分析可以帮助应对这一挑战。在本文中，我们展示了几种最大似然方法的应用，利用信息丰富的光发射光谱和电声发射对CAPS进行实时诊断。结果表明，基于最大似然法的数据分析可以为实时估计旋转和振动温度以及基片特性等与操作相关的参数提供一种简单而有效的手段。我们的发现表明ML在CAPS的实时诊断方面有很大的潜力。
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 information-rich 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.