...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Multimedia Tools and Applications >Imaging and representation learning of solar radio spectrums for classification
【24h】

Imaging and representation learning of solar radio spectrums for classification

机译：太阳无线电频谱的成像和表示学习以进行分类

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

In this paper, the authors make the first attempt to employ the deep learning method for the representation learning of the solar radio spectrums. The original solar radio spectrums are pre-processed, including normalization, enhancement and etc., to generate new images for the next processing. With the expertise of solar radio astronomy for identifying solar radio activity, we build a solar radio activity database, which contains solar radio spectrums as well as their labels indicating the types of solar radio bursts. The employed deep learning network is firstly pre-trained based on the available massive of unlabeled radio solar images. Afterwards, the weights of the network are further fined-tuned based on the labeled data. Experimental results have demonstrated that the employed network can effectively classify the solar radio image into the labeled categories. Moreover, the pre-training process can help improve the classification accuracy.
机译：在本文中，作者们首次尝试将深度学习方法用于太阳无线电频谱的表示学习。原始的太阳无线电频谱经过预处理（包括归一化，增强等），以生成新图像以用于下一步处理。利用太阳射电天文学的专业知识来识别太阳射电活动，我们建立了太阳射电活动数据库，该数据库包含太阳射电频谱及其标签，指示太阳射电爆发的类型。首先根据可用的大量未标记的无线电太阳图像对所使用的深度学习网络进行预训练。之后，基于标记数据进一步调整网络的权重。实验结果表明，所使用的网络可以有效地将太阳无线电图像分类为标记的类别。此外，预训练过程可以帮助提高分类准确性。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号