應用Spatial Attention U-Net神經網路於VIPIR垂直電離圖的自動判圖處理

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姓名

鄭子琳(Zih-Lin Cheng) 查詢紙本館藏

畢業系所

太空科學與工程學系

論文名稱

應用Spatial Attention U-Net神經網路於VIPIR垂直電離圖的自動判圖處理
(Application of Spatial Attention U-Net Neural Network in autoprocessing of VIPIR vertical ionograms)

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摘要(中)

在本研究中，提出了一種基於Spatial Attention U-Net (SA U-Net)神經網路從電離圖中提取電離層回波訊號的方法。電離圖資料由位於台灣花蓮(北緯 23.99°，東經 131.61°)的垂直入射脈衝電離層雷達(Vertical Incidence Pulsed Ionospheric Radar，VIPIR)提供，每天可產生288張電離圖。SA U-Net是一種以圖像分割為目的而開發的卷積神經網路。目前，我們收集了2013年8月至2014年6月的常態數據，使用SA U-Net訓練以及自動處理這些數據，並以各訊號的臨界頻率以及虛擬高度準確度作為判斷SA U-Net性能的方式。最後，我們還對訓練資料進行分類處理，嘗試使用分類後的資料訓練神經網路模型，期望能提高模型對該類電離圖訊號的判斷成功率。

摘要(英)

In this study, we propose a method for extracting ionospheric echo signals from ionograms based on Spatial Attention U-Net (SA U-Net) neural network. Ionogram data are provided by the Vertical Incidence Pulsed Ionospheric Radar (VIPIR) located in Hualien, Taiwan (23.99° north latitude, 131.61° east longitude), which can produce 288 ionization maps every day. SA U-Net is a convolutional neural network developed for the purpose of image segmentation. Currently, we have collected normal data from August 2013 to June 2014, used SA U-Net to train and automatically process the data, and used the critical frequency and virtual height accuracy of each signal as a criterion to judge the performance of SA U-Net. Finally, we classify the training data and try to use the classified data to train the neural network model, hoping to improve the judgment success rate of the model.

關鍵字(中)

★ 電離圖
★ 神經網路

關鍵字(英)

論文目次

中文摘要 I
英文摘要 II
誌謝 III
目錄 IV
圖目錄 VI
表目錄 VIII
一、緒論 1
二、電離層簡介 3
2-1 電離層基本結構 3
2-2 查普曼層理論(Chapman Layer Theory) 4
三、電離層觀測儀與電離圖 8
3-1 系統 8
3-2 觀測原理 10
3-2-1 電漿頻率 10
3-2-2 迴旋頻率 11
3-2-3 電磁波在電漿中的傳播 12
3-3 電離圖 14
四、 Spatial Attention U-Net神經網路 18
4-1 神經網路的基本架構 18
4-2 Spatial Attention U-Net神經網路模型 26
五、應用SA U-Net的電離圖判圖 34
5-1 資料來源 34
5-2 環境建置 36
5-3 神經網路模型的訓練 40
5-4 判斷模型表現的標準 42
六、研究成果 45
七、討論 49
八、結論 50
參考文獻 51

參考文獻

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指導教授

蔡龍治(Lung-Chih Tsai)

審核日期

2024-8-23

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