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Machine Learning for Real-time Diagnostics of Cold Atmospheric Plasma Soruces

This repository includes the data and the code relating to results presented in the paper. Each Jupyter notebook corresponds to one of the case studies investigated in detail in the paper using the datasets described below.

Datasets

dat_train.csv - optical emission spectra for the N2 second positive transition, fitted rotational and vibrational temperatures and operating conditions (power, flow, substrate type) compiled into comma seperated values file

dat_test.csv - optical emission spectra for the N2 second positive transition, fitted rotational and vibrational temperatures and operating conditions (power, flow, substrate type) compiled into comma seperated values file

dat_test_dsep.csv - optical emission spectra for the N2 second positive transition, fitted rotational and vibrational temperatures and operating conditions (power, flow, substrate type, seperation distance) compiled into comma seperated values file

dat_test_o2.csv - optical emission spectra for the N2 second positive transition, fitted rotational and vibrational temperatures and operating conditions (power, flow, substrate type, additive o2 concentration (%) compiled into comma seperated values file

dat_test_pull.csv - optical emission spectra for the N2 second positive transition, fitted rotational and vibrational temperatures and operating conditions (power, flow, substrate type) recorded as glass substrate pulled from under the APPJ

sounnd_train2.csv - Fast Fourier transform of the electroacustic emission of the plasma flashlight and the measured device-tip-to-substrate seperation distance compiled into comma seperated values file

sounnd_test.csv - Fast Fourier transform of the electroacustic emission of the plasma flashlight and the measured device-tip-to-substrate seperation distance compiled into comma seperated values file

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This repository includes the code utilized for real-time diagnostics of plasma devices based on machine learning methods.

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