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

NCUIR > College of Earth Sciences > Graduate Institute of Space Science > Department of Earth Sciences > Item 987654321/94679

Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/94679

Title:	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
Authors:	鄭子琳;Cheng, Zih-Lin
Contributors:	太空科學與工程學系
Keywords:	電離圖;神經網路
Date:	2024-08-23
Issue Date:	2024-10-09 15:23:22 (UTC+8)
Publisher:	國立中央大學
Abstract:	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。;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.
Appears in Collections:	[Graduate Institute of Space Science] Department of Earth Sciences

Files in This Item:

File	Description	Size	Format
index.html		0Kb	HTML	34	View/Open

社群 sharing

Loading...