Auto Recognition of Solar Radio Bursts Using the C-DCGAN Method
Solar radio bursts can be used to study the properties of solar activities and the underlying coronal conditions on the basis of the present understanding of their emission mechanisms. With the construction of observational instruments, around the world, a vast volume of solar radio observational da...
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Frontiers Media S.A.
2021-09-01
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| Series: | Frontiers in Physics |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fphy.2021.646556/full |
| _version_ | 1818678496207044608 |
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| author | Weidan Zhang Fabao Yan Fabao Yan Fuyun Han Ruopu He Enze Li Zhao Wu Yao Chen |
| author_facet | Weidan Zhang Fabao Yan Fabao Yan Fuyun Han Ruopu He Enze Li Zhao Wu Yao Chen |
| author_sort | Weidan Zhang |
| collection | DOAJ |
| description | Solar radio bursts can be used to study the properties of solar activities and the underlying coronal conditions on the basis of the present understanding of their emission mechanisms. With the construction of observational instruments, around the world, a vast volume of solar radio observational data has been obtained. Manual classifications of these data require significant efforts and human labor in addition to necessary expertise in the field. Misclassifications are unavoidable due to subjective judgments of various types of radio bursts and strong radio interference in some events. It is therefore timely and demanding to develop techniques of auto-classification or recognition of solar radio bursts. The latest advances in deep learning technology provide an opportunity along this line of research. In this study, we develop a deep convolutional generative adversarial network model with conditional information (C-DCGAN) to auto-classify various types of solar radio bursts, using the solar radio spectral data from the Culgoora Observatory (1995, 2015) and the Learmonth Observatory (2001, 2019), in the metric decametric wavelengths. The technique generates pseudo images based on available data inputs, by modifying the layers of the generator and discriminator of the deep convolutional generative adversarial network. It is demonstrated that the C-DCGAN method can reach a high-level accuracy of auto-recognition of various types of solar radio bursts. And the issue caused by inadequate numbers of data samples and the consequent over-fitting issue has been partly resolved. |
| first_indexed | 2024-12-17T09:16:11Z |
| format | Article |
| id | doaj.art-9b4b6bb7a5754f54a188d1f01fe876d2 |
| institution | Directory Open Access Journal |
| issn | 2296-424X |
| language | English |
| last_indexed | 2024-12-17T09:16:11Z |
| publishDate | 2021-09-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Physics |
| spelling | doaj.art-9b4b6bb7a5754f54a188d1f01fe876d22022-12-21T21:54:58ZengFrontiers Media S.A.Frontiers in Physics2296-424X2021-09-01910.3389/fphy.2021.646556646556Auto Recognition of Solar Radio Bursts Using the C-DCGAN MethodWeidan Zhang0Fabao Yan1Fabao Yan2Fuyun Han3Ruopu He4Enze Li5Zhao Wu6Yao Chen7Laboratory for ElectromAgnetic Detection (LEAD), Institute of Space Sciences, Shandong University, Weihai, ChinaLaboratory for ElectromAgnetic Detection (LEAD), Institute of Space Sciences, Shandong University, Weihai, ChinaSchool of Mechanical, Electrical & Information Engineering, Shandong University, Weihai, ChinaSchool of Mechanical, Electrical & Information Engineering, Shandong University, Weihai, ChinaSchool of Mechanical, Electrical & Information Engineering, Shandong University, Weihai, ChinaSchool of Mechanical, Electrical & Information Engineering, Shandong University, Weihai, ChinaLaboratory for ElectromAgnetic Detection (LEAD), Institute of Space Sciences, Shandong University, Weihai, ChinaLaboratory for ElectromAgnetic Detection (LEAD), Institute of Space Sciences, Shandong University, Weihai, ChinaSolar radio bursts can be used to study the properties of solar activities and the underlying coronal conditions on the basis of the present understanding of their emission mechanisms. With the construction of observational instruments, around the world, a vast volume of solar radio observational data has been obtained. Manual classifications of these data require significant efforts and human labor in addition to necessary expertise in the field. Misclassifications are unavoidable due to subjective judgments of various types of radio bursts and strong radio interference in some events. It is therefore timely and demanding to develop techniques of auto-classification or recognition of solar radio bursts. The latest advances in deep learning technology provide an opportunity along this line of research. In this study, we develop a deep convolutional generative adversarial network model with conditional information (C-DCGAN) to auto-classify various types of solar radio bursts, using the solar radio spectral data from the Culgoora Observatory (1995, 2015) and the Learmonth Observatory (2001, 2019), in the metric decametric wavelengths. The technique generates pseudo images based on available data inputs, by modifying the layers of the generator and discriminator of the deep convolutional generative adversarial network. It is demonstrated that the C-DCGAN method can reach a high-level accuracy of auto-recognition of various types of solar radio bursts. And the issue caused by inadequate numbers of data samples and the consequent over-fitting issue has been partly resolved.https://www.frontiersin.org/articles/10.3389/fphy.2021.646556/fulldeep learningdeep convolution generation confrontation networkimage reconstructionconvolutional neural networksspace weather |
| spellingShingle | Weidan Zhang Fabao Yan Fabao Yan Fuyun Han Ruopu He Enze Li Zhao Wu Yao Chen Auto Recognition of Solar Radio Bursts Using the C-DCGAN Method Frontiers in Physics deep learning deep convolution generation confrontation network image reconstruction convolutional neural networks space weather |
| title | Auto Recognition of Solar Radio Bursts Using the C-DCGAN Method |
| title_full | Auto Recognition of Solar Radio Bursts Using the C-DCGAN Method |
| title_fullStr | Auto Recognition of Solar Radio Bursts Using the C-DCGAN Method |
| title_full_unstemmed | Auto Recognition of Solar Radio Bursts Using the C-DCGAN Method |
| title_short | Auto Recognition of Solar Radio Bursts Using the C-DCGAN Method |
| title_sort | auto recognition of solar radio bursts using the c dcgan method |
| topic | deep learning deep convolution generation confrontation network image reconstruction convolutional neural networks space weather |
| url | https://www.frontiersin.org/articles/10.3389/fphy.2021.646556/full |
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