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J. Imaging 2019, 5(3), 36; https://doi.org/10.3390/jimaging5030036

Identification of the Interface in a Binary Complex Plasma Using Machine Learning

1
College of Science, Donghua University, Shanghai 201620, China
2
Institut für Materialphysik im Weltraum, Deutsches Zentrum für Luft- und Raumfahrt (DLR), 82234 Weßling, Germany
3
Magnetic Confinement Fusion Research Centre, Ministry of Education, Shanghai 201620, China
*
Author to whom correspondence should be addressed.
Received: 18 February 2019 / Revised: 26 February 2019 / Accepted: 6 March 2019 / Published: 12 March 2019
(This article belongs to the Special Issue Image Processing in Soft Condensed Matter)
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Abstract

A binary complex plasma consists of two different types of dust particles in an ionized gas. Due to the spinodal decomposition and force imbalance, particles of different masses and diameters are typically phase separated, resulting in an interface. Both external excitation and internal instability may cause the interface to move with time. Support vector machine (SVM) is a supervised machine learning method that can be very effective for multi-class classification. We applied an SVM classification method based on image brightness to locate the interface in a binary complex plasma. Taking the scaled mean and variance as features, three areas, namely small particles, big particles and plasma without dust particles, were distinguished, leading to the identification of the interface between small and big particles. View Full-Text
Keywords: complex plasma; machine learning complex plasma; machine learning
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Huang, H.; Schwabe, M.; Du, C.-R. Identification of the Interface in a Binary Complex Plasma Using Machine Learning. J. Imaging 2019, 5, 36.

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