Titlebook: Deep Learning in Solar Astronomy; Long Xu,Yihua Yan,Xin Huang Book 2022 The Editor(s) (if applicable) and The Author(s), under exclusive l

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書目名稱Deep Learning in Solar Astronomy影響因子(影響力)




書目名稱Deep Learning in Solar Astronomy影響因子(影響力)學(xué)科排名




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書目名稱Deep Learning in Solar Astronomy被引頻次學(xué)科排名




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書目名稱Deep Learning in Solar Astronomy讀者反饋學(xué)科排名

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Deep Learning in Solar Object Detection Tasks,ellite continuously record high-resolution and high-cadence full-disk solar images. These images are used for solar activity forecasting and statistical analysis. Usually, it is required to mine key information from full-disk images firstly. Then, over extracted information, one can establish classi

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Deep Learning in Solar Image Generation Tasks,ty of image generation which is more challenging than classification. In this chapter, several applications of deep learning in solar image enhancement, reconstruction and processing are presented, including image deconvolution of solar radioheliograph, desaturation of solar imaging, generating magn

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Deep Learning in Solar Forecasting Tasks,ecifically designed for handling time series input, e.g., video sequence, natural language processing. As the best representative of RNN, LSTM has been widely exploited in various of time series analysis, achieving big success. In this chapter, it is applied to solar activity/event forecasting and s

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Classical Deep Learning Models, and excitation (SE), global context (GC), and most popular transformer), graph convolution network (GCN), self-supervised learning and contrastive learning. They can further boost model performance, extend application filed and break the limits of lack of labelled data, noise data and etc.

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