Name
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. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.3390/jimaging5030036 | JOURNAL OF IMAGING |
Keywords | Field | DocType |
complex plasma,machine learning | Dust particles,Spinodal decomposition,Instability,Support vector machine,Excitation,Artificial intelligence,Plasma,Machine learning,Brightness,Mathematics,Binary number | Journal |
Volume | Issue | ISSN |
5 | 3 | 2313-433X |
Citations | PageRank | References |
0 | 0.34 | 9 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| He Huang | 1 | 79 | 18.92 |
| Mierk Schwabe | 2 | 0 | 0.68 |
| Cheng-Ran Du | 3 | 0 | 0.34 |