Prediction of Extreme Ultraviolet Variability Experiment (EVE)/Extreme Ultraviolet Spectro-Photometer (ESP) Irradiance from Solar Dynamics Observatory (SDO)/Atmospheric Imaging Assembly (AIA) Images Using Fuzzy Image Processing and Machine Learning
Prediction of Extreme Ultraviolet Variability Experiment (EVE)/Extreme Ultraviolet Spectro-Photometer (ESP) Irradiance from Solar Dynamics Observatory (SDO)/Atmospheric Imaging Assembly (AIA) Images Using Fuzzy Image Processing and Machine Learning
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DOI：
10.1007/s11207-011-9880-9
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发表时间：
2011-11
期刊：
影响因子：
2.8
通讯作者：
T. Colak;R. Qahwaji
中科院分区：
文献类型：
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作者：
T. Colak;R. Qahwaji
随着STEREO和SDO等新航天器的发射，太阳图像的节奏和分辨率急剧增加。数据量的增加为太阳能研究人员提供了新的机会，但这些数据的有效处理和分析也带来了新的挑战。本文介绍了一种基于模糊理论的太阳特征检测系统。建议的系统处理SDO/AIA图像使用模糊规则来检测冕洞和活动区域。该系统速度快，可以处理不同尺寸的图像。该系统在六个月的太阳数据（2010年10月1日至2011年3月31日）上进行测试，以生成活动区和冕洞的填充因子（太阳特征面积与太阳盘其余面积的比率）。然后将这些填充因子与SDO/EVE/ESP辐照度测量值进行比较。活跃区域填充因子和辐照度测量之间的相关性被发现是非常高的，这鼓励我们设计一个时间序列预测系统，使用径向基函数网络来预测ESP辐照度测量从我们生成的填充因子。
The cadence and resolution of solar images have been increasing dramatically with the launch of new spacecraft such as STEREO and SDO. This increase in data volume provides new opportunities for solar researchers, but the efficient processing and analysis of these data create new challenges. We introduce a fuzzy-based solar feature-detection system in this article. The proposed system processes SDO/AIA images using fuzzy rules to detect coronal holes and active regions. This system is fast and it can handle different size images. It is tested on six months of solar data (1 October 2010 to 31 March 2011) to generate filling factors (ratio of area of solar feature to area of rest of the solar disc) for active regions and coronal holes. These filling factors are then compared to SDO/EVE/ESP irradiance measurements. The correlation between active-region filling factors and irradiance measurements is found to be very high, which has encouraged us to design a time-series prediction system using Radial Basis Function Networks to predict ESP irradiance measurements from our generated filling factors.