TY - JOUR TI - 深度学习的磁吸式的黑子群VL自动识别 - 2019 PY - 2019 DA - 2019年8月1日DO - 10.1155 /九百一十九万六千二百三十四分之二千〇一十九UR - https://doi.org/10.1155/2019/9196234 AB - 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%. JF - Advances in Astronomy SN - 1687-7969 PB - Hindawi SP - 9196234 KW - A2 - Kovacs, Geza AU - Fang, Yuanhui AU - Cui, Yanmei AU - Ao, Xianzhi ER -