التفاصيل البيبلوغرافية
| العنوان: |
Microparticle cloud imaging and tracking for data-driven plasma science. |
| المؤلفون: |
Wang, Zhehui, Xu, Jiayi, Kovach, Yao E., Wolfe, Bradley T., Thomas, Edward, Guo, Hanqi, Foster, John E., Shen, Han-Wei |
| المصدر: |
Physics of Plasmas; Mar2020, Vol. 27 Issue 3, p1-13, 13p, 8 Diagrams, 1 Chart, 4 Graphs |
| مصطلحات موضوعية: |
ARTIFICIAL neural networks, FAST Fourier transforms, PLASMA physics, SELF-organizing maps, DUSTY plasmas, PLASMA interactions |
| مستخلص: |
Oceans of image and particle track data encountered in plasma interactions with microparticle clouds motivate development and applications of machine-learning (ML) algorithms. A local-constant-velocity tracker, a Kohonen neural network or self-organizing map, the feature tracking kit, and U-Net are described and compared with each other for microparticle cloud datasets generated from exploding wires, dusty plasmas, and atmospheric plasmas. Particle density and the signal-to-noise ratio have been identified as two important factors that affect the tracking accuracy. Fast Fourier transform is used to reveal how U-Net, a deep convolutional neural network developed for non-plasma applications, achieves the improvements for noisy scenes. Viscous effects are revealed in the ballistic motions of the particles from the exploding wires and atmospheric plasmas. Subdiffusion of microparticles satisfying Δ r 2 ∝ t k (k = 0.84 ± 0.02) is obtained from the dusty plasma datasets. Microparticle cloud imaging and tracking, when enhanced with data and ML models, present new possibilities for plasma physics. [ABSTRACT FROM AUTHOR] |
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| قاعدة البيانات: |
Complementary Index |
