Huang He, Schwabe Mierk, Du Cheng-Ran
College of Science, Donghua University, Shanghai 201620, China.
Institut für Materialphysik im Weltraum, Deutsches Zentrum für Luft- und Raumfahrt (DLR), 82234 Weßling, Germany.
J Imaging. 2019 Mar 12;5(3):36. doi: 10.3390/jimaging5030036.
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.
二元复合等离子体由电离气体中的两种不同类型的尘埃颗粒组成。由于旋节线分解和力不平衡，不同质量和直径的颗粒通常会发生相分离，从而形成一个界面。外部激发和内部不稳定性都可能导致界面随时间移动。支持向量机（SVM）是一种监督式机器学习方法，对多类分类非常有效。我们应用了一种基于图像亮度的支持向量机分类方法来定位二元复合等离子体中的界面。以缩放后的均值和方差为特征，区分出三个区域，即小颗粒、大颗粒和无尘埃颗粒的等离子体，从而确定了小颗粒和大颗粒之间的界面。
