Work

Deep learning via CNN for identification of blue core phenomenon in helicon plasma discharge

Year: 2024

Type: article

Abstract: Based on deep learning image recognition techniques, a convolutional neural network model for discharge mode recognition of helicon plasma was trained. The accuracy of the model was evaluated using functions such as F1-scores and the confusion matrix. The final recognition accuracy was more than 98.18% after 30 iterations. Interpretable analysis was done using methods such as gradient-weighted class activation mapping to verify the model's robustness as well as repeatability. The model identification results were compared with Langmuir probe diagnostic results. It was found a good fit between the model and the probe results, corroborating the correctness of the model. The present model can well identify the critical power of entering W mode in the discharge process of helicon plasma. As the discharge database expands, it has great potential for recognizing the higher-order discharge modes based on deep learning.

Source: Physics of Plasmas

DOI: 10.1063/5.0233814

Authors: Xianyi Yin, Qinlong Song, Shiyang Cheng, Haibao Zhang

Institution: Beijing Institute of Graphic Communication

Language: English

Topic: Plasma Diagnostics and Applications

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: hybrid

Funder: Beijing Municipal Natural Science Foundation

Award: G2122395371