Machine learning combined with Langmuir probe measurements for diagnosis of dusty plasma of a positive column

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Machine learning combined with Langmuir probe measurements for diagnosis of dusty plasma of a positive column. / Ding, Zhe; Yao, Jingfeng; Wang, Ying; Yuan, Chengxun; Zhou, Zhongxiang; Kudryavtsev, Anatoly A.; Gao, Ruilin; Jia, Jieshu.

In: Plasma Science and Technology, Vol. 23, No. 9, 095403, 09.2021.

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

Author

Ding, Zhe ; Yao, Jingfeng ; Wang, Ying ; Yuan, Chengxun ; Zhou, Zhongxiang ; Kudryavtsev, Anatoly A. ; Gao, Ruilin ; Jia, Jieshu. / Machine learning combined with Langmuir probe measurements for diagnosis of dusty plasma of a positive column. In: Plasma Science and Technology. 2021 ; Vol. 23, No. 9.

BibTeX

@article{5c59268e144641399e7a2486ca411179,

title = "Machine learning combined with Langmuir probe measurements for diagnosis of dusty plasma of a positive column",

abstract = "This paper reports the use of machine learning to enhance the diagnosis of a dusty plasma. Dust in a plasma has a large impact on the properties of the plasma. According to a probe diagnostic experiment on a dust-free plasma combined with machine learning, an experiment on a dusty plasma is designed and carried out. Using a specific experimental device, dusty plasma with a stable and controllable dust particle density is generated. A Langmuir probe is used to measure the electron density and electron temperature under different pressures, discharge currents, and dust particle densities. The diagnostic result is processed through a machine learning algorithm, and the error of the predicted results under different pressures and discharge currents is analyzed, from which the law of the machine learning results changing with the pressure and discharge current is obtained. Finally, the results are compared with theoretical simulations to further analyze the properties of the electron density and temperature of the dusty plasma.",

keywords = "Dusty plasma, Langmuir probe, Machine learning, dusty plasma, machine learning, GLOW-DISCHARGE",

author = "Zhe Ding and Jingfeng Yao and Ying Wang and Chengxun Yuan and Zhongxiang Zhou and Kudryavtsev, {Anatoly A.} and Ruilin Gao and Jieshu Jia",

note = "Publisher Copyright: {\textcopyright} 2021 Hefei Institutes of Physical Science.",

year = "2021",

month = sep,

doi = "10.1088/2058-6272/ac125d",

language = "English",

volume = "23",

journal = "Plasma Science and Technology",

issn = "1009-0630",

publisher = "IOP Publishing Ltd.",

number = "9",

}

RIS

TY - JOUR

T1 - Machine learning combined with Langmuir probe measurements for diagnosis of dusty plasma of a positive column

AU - Ding, Zhe

AU - Yao, Jingfeng

AU - Wang, Ying

AU - Yuan, Chengxun

AU - Zhou, Zhongxiang

AU - Kudryavtsev, Anatoly A.

AU - Gao, Ruilin

AU - Jia, Jieshu

PY - 2021/9

Y1 - 2021/9

N2 - This paper reports the use of machine learning to enhance the diagnosis of a dusty plasma. Dust in a plasma has a large impact on the properties of the plasma. According to a probe diagnostic experiment on a dust-free plasma combined with machine learning, an experiment on a dusty plasma is designed and carried out. Using a specific experimental device, dusty plasma with a stable and controllable dust particle density is generated. A Langmuir probe is used to measure the electron density and electron temperature under different pressures, discharge currents, and dust particle densities. The diagnostic result is processed through a machine learning algorithm, and the error of the predicted results under different pressures and discharge currents is analyzed, from which the law of the machine learning results changing with the pressure and discharge current is obtained. Finally, the results are compared with theoretical simulations to further analyze the properties of the electron density and temperature of the dusty plasma.

AB - This paper reports the use of machine learning to enhance the diagnosis of a dusty plasma. Dust in a plasma has a large impact on the properties of the plasma. According to a probe diagnostic experiment on a dust-free plasma combined with machine learning, an experiment on a dusty plasma is designed and carried out. Using a specific experimental device, dusty plasma with a stable and controllable dust particle density is generated. A Langmuir probe is used to measure the electron density and electron temperature under different pressures, discharge currents, and dust particle densities. The diagnostic result is processed through a machine learning algorithm, and the error of the predicted results under different pressures and discharge currents is analyzed, from which the law of the machine learning results changing with the pressure and discharge current is obtained. Finally, the results are compared with theoretical simulations to further analyze the properties of the electron density and temperature of the dusty plasma.

KW - Dusty plasma

KW - Langmuir probe

KW - Machine learning

KW - dusty plasma

KW - machine learning

KW - GLOW-DISCHARGE

UR - http://www.scopus.com/inward/record.url?scp=85112282532&partnerID=8YFLogxK

UR - https://www.mendeley.com/catalogue/78ae13ba-8beb-3ddc-9dfb-83911197c80e/

U2 - 10.1088/2058-6272/ac125d

DO - 10.1088/2058-6272/ac125d

M3 - Article

AN - SCOPUS:85112282532

VL - 23

JO - Plasma Science and Technology

JF - Plasma Science and Technology

SN - 1009-0630

IS - 9

M1 - 095403

ER -

ID: 88380776