Deep Learning for Automatic Recognition of Magnetic Type in Sunspot Groups
文献类型：期刊论文
| 作者 | Fang, Yuanhui; Cui, Yanmei; Ao, Xianzhi |
| 刊名 | ADVANCES IN ASTRONOMY
 |
| 出版日期 | 2019 |
| 卷号 | 2019页码:9196234 |
| ISSN号 | 1687-7969 |
| DOI | 10.1155/2019/9196234 |
| 英文摘要 | Sunspots are darker areas on the Sun's photosphere and most of solar eruptions occur in complex sunspot groups. The Mount Wilson classification scheme describes the spatial distribution of magnetic polarities in sunspot groups, which plays an important role in forecasting solar flares. With the rapid accumulation of solar observation data, automatic recognition of magnetic type in sunspot groups is imperative for prompt solar eruption forecast. We present in this study, based on the SDO/HMI SHARP data taken during the time interval 2010-2017, an automatic procedure for the recognition of the predefined magnetic types in sunspot groups utilizing a convolutional neural network (CNN) method. Three different models (A, B, and C) take magnetograms, continuum images, and the two-channel pictures as input, respectively. The results show that CNN has a productive performance in identification of the magnetic types in solar active regions (ARs). The best recognition result emerges when continuum images are used as input data solely, and the total accuracy exceeds 95%, for which the recognition accuracy of Alpha type reaches 98% while the accuracy for Beta type is slightly lower but maintains above 88%. |
| 语种 | 英语 |
| 源URL | [http://ir.nssc.ac.cn/handle/122/7245]  |
| 专题 | 国家空间科学中心_空间环境部 |
| 推荐引用方式 GB/T 7714 | Fang, Yuanhui,Cui, Yanmei,Ao, Xianzhi. Deep Learning for Automatic Recognition of Magnetic Type in Sunspot Groups[J]. ADVANCES IN ASTRONOMY,2019,2019:9196234. |
| APA | Fang, Yuanhui,Cui, Yanmei,&Ao, Xianzhi.(2019).Deep Learning for Automatic Recognition of Magnetic Type in Sunspot Groups.ADVANCES IN ASTRONOMY,2019,9196234. |
| MLA | Fang, Yuanhui,et al."Deep Learning for Automatic Recognition of Magnetic Type in Sunspot Groups".ADVANCES IN ASTRONOMY 2019(2019):9196234. |
入库方式： OAI收割
来源：国家空间科学中心
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。