應用先進電離層探測儀與類神經網路以建立初步電漿泡預測模型

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姓名

謝岳均(Yueh-Chun Hsieh) 查詢紙本館藏

畢業系所

太空科學研究所

論文名稱

應用先進電離層探測儀與類神經網路以建立初步電漿泡預測模型

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摘要(中)

電漿泡作為一種電離層不規則體，容易因垂直電漿流破壞高層電漿濃度高、低層電漿濃度低的Rayleigh-Taylor (R-T) 不穩定環境而形成，導致電磁波訊號穿透時發生訊號閃爍。福衛五號上的先進電離層探測儀 (AIP) 提供了距地720公里高的電離層現地量測資料，可有效觀測全球中低緯度的電漿泡並分析垂直電漿流與電漿泡出現之間的關係以及對應的地理位置分布。
本論文將使用AIP從2017年10月31日到2019年10月31日之間量測的電漿資料：經度、緯度、離子濃度標準差、垂直離子流速與實時太陽輻射通量 (F10.7 index) 對類神經網路進行訓練。欲利用類神經網路學習並重現對應環境下電漿泡的發生傾向，旨在建立有能力反映出電漿泡發生潛勢的初步模型，並利用網路模型反映出的預測能力來探討資料處理流程與網路模型的定義取捨。
最終訓練出的預測模型中，二月、十月與十二月的電漿泡發生潛勢預測模型的地理分布預測表現最理想。其中，十二月的預測模型表現更為優異，甚至可以取代十一月與一月的預測模型。根據訓練的結果可對十月至二月的全球中低緯度的電漿泡發生潛勢提供具有參考性的預測。本篇研究結果亦會呈現處理過後的資料特徵及在本研究中利用泛化能力的表現來改善電漿泡預測模型的過程。

摘要(英)

Equatorial plasma bubbles (EPBs) usually cause stronger signal scintillation in space-earth communications compared to other ionospheric irregularities. The most commonly accepted theory for the EPBs formation mechanism is that they are developed from a Rayleigh-Taylor instability (R-T instability) . But to date, predicting the likelihood EPBs’ appearance is still challenging, despite scientists academics already having a basic consensus about the formation mechanism of EPBs.
The Advanced Ionospheric Probe (AIP) is a science payload installed on FORMOSAT-5, providing high resolution in-situ plasma measurement at altitude 720 km. According to the factors contributing to the EPBs’ triggering process, this research collected vertical ion drift velocity, ion number density, the corresponding latitude and longitude from AIP, attached with real-time F10.7 index. After collecting and filtering the data, an empirical model was built to predict the potential appearance of EPBs via Artificial Neural Network (ANN) .
Among the prediction models finally trained, the prediction models for the occurrence potential of plasma bubbles in February, October and December performed best in terms of geographic distribution. Among them, the forecast model for December performed even better and even replaced the forecast models for November and January. According to the training results, it can provide a reference prediction for the occurrence potential of plasma bubbles in the middle and low latitudes of the world from October to February. The results of this study will also present the characteristics of the processed data and the process of using the performance of the generalization ability to improve the plasma bubble prediction model in this study.

關鍵字(中)

★ 電離層
★ 電漿泡
★ 經驗模型
★ 機器學習
★ 數據分析
★ 模型推論

關鍵字(英)

★ Ionosphere
★ Equatorial Plasma Bubbles
★ Experiential Model
★ Machine Learning
★ Data Analysis
★ Model Inference

論文目次

中文摘要 vii
英文摘要 vii
誌謝 viii
目錄 x
圖目錄 xi
表目錄 xviiii
第一章緒論 1
1.1 電離層 1
1.2 電漿泡 3
1.3 先進電離層探測儀 6
1.4 類神經網路 7
1.5 研究動機與目的 17
1.6 論文內容架構 17
第二章研究方法 18
2.1 定義模型 18
2.2 資料採用 20
2.3 訓練資料分配 20
第三章資料處理流程與訓練 21
3.1 資料處理架構 21
3.2 資料前處理 24
3.3 前饋網路訓練 35
3.4 訓練後處理 39
3.5 模型推論 45
第四章結果與討論 50
4.1 AIP資料分析 50
4.2 改進預測能力 55
4.3 現階段模型表現 65
第五章結論與未來改進方向 66
參考文獻 68
附錄一 73
附錄二 86
附錄三 92
附錄四 98
附錄五 105

參考文獻

〔1〕 Ratcliffe, J. A., Introduction to the Ionospheric and Magnetosphere, Cambridge
University Press, London, 1972, https://doi.org/10.1002/qj.49709941923.
〔2〕 Davies, K., Ionospheric Radio, Peter Peregrinus Ltd., London, 1990,
http://dx.doi.org/10.1049/PBEW031E.
〔3〕 Basu, S., S. Basu, J. Aarons, J. P. McClure, and M. D. Cousins, On the coexistence of kilometer- and meter-scale irregularities in the nighttime equatorial F region, J. Geophys. Res., 83,4219 (1978), https://doi.org/10.1029/JA083iA09p04219
〔4〕 Kelly, M. A., J. M. Comberiate, E. S. Miller, and L. J. Paxton, Progress toward forecasting of space weather effects on UHF SATCOM after Operation Anaconda, Space Weather, 12, 601 (2014) , https://doi.org/10.1002/2014SW001081
〔5〕 Basu, S., K. M. Groves, Su. Basu, and P. J. Sultan, Specification and forecasting of scintillations in communication/navigation links: current status and future plans, J. Atmos. Solar Terr. Phys., 64, 1,745 (2002) , https://doi.org/10.1016/S1364-6826(02)00124-4
〔6〕 Dungey, J. W. (1956), Convective diffusion in equatorial F-region, J. Atmos. Terr.
Phys., 9, 304-310, https://doi.org/10.1016/0021-9169(56)90148-9.
〔7〕 Ossakow, S. L., S. T. Zalesak, and B. E. McDonald (1979), Nonlinear equatorial
spread F: Dependence on altitude of the F peak and bottomside background electron density gradient scale length, J. Geophys. Res., 84, 17-29, https://doi.org/10.1029/JA084iA01p00017.
〔8〕 Schunk, R. W., and A. F. Nagy (2000), Ionosphere: Physics, Plasma Physics, and
Chemistry, Cambridge University Press, Cambridge, UK. DOI:10.1017/CBO9780511551772.

〔9〕 Tsunoda, R. T., T. T. Nguyen, and M. H. Le (2015), Effects of tidal forcing,
conductivity gradient, and active seeding on the climatology of equatorial spread F
over Kwajalein, J. Geophys. Res. Space Physics, 120, 632–653,
doi:10.1002/2014JA020762.
〔10〕L. C. Gentile, W. J. Burke, and F. J. Rich, A global climatology for equatorial plasma
bubbles in the topside ionosphere
〔11〕Li, Shengtai and Hui Li. Parallel AMR Code for Compressible MHD or HD
Equations. Los Alamos National Laboratory. [2006-09-05]
〔12〕Charles Rino, Tatsuhiro Yokoyama & Charles Carrano. Dynamic spectral
characteristics of high-resolution simulated equatorial plasma bubbles Progress in
Earth and Planetary Science volume 5, Article number: 83 (2018)
〔13〕Lin, Z. W., C. K. Chao, J. Y. Liu, C. M. Huang, Y. H. Chu, C. L. Su, Y. C. Mao, and
Y. S. Chang, Advanced Ionospheric Probe scientific mission onboard FORMOSAT-5
satellite. Terr. Atmos. Ocean. Sci., 28, 99 (2017)
〔14〕Chao, C. K., Z. W. Lin, Y. C. Mao, and Y. S. Chang, System architecture of Advanced
Ionospheric Probe onboard FORMOSAT-5 satellite, 2016.
〔15〕先進電離層探測儀探頭，from -
http://spl.ss.ncu.edu.tw/Products/Payloads/AIP/index.html
〔16〕先進電離層探測儀完整系統架構，from -
http://spl.ss.ncu.edu.tw/Missions/Satellites/FS-5/index.html
〔17〕羅華強：《類神經網路—MATLAB的應用》，第二版，台北縣 (現新北市) ：高立
圖書有限公司，民國九十七年。
〔18〕Iñiguez-Jarrín, C. A conceptual modelling-based approach to generate data value
through the end-user interactions: A case study in the genomics domain. CEUR
Workshop Proceedings, 1765, 14–21. (2016).

〔19〕Zhe Wang, Fei Wang, Shi Su, Solar Irradiance Short-Term Prediction Model Based on
BP Neural Network, December 2011 Energy Procedia 12:488–494
DOI:10.1016/j.egypro.2011.10.065
〔20〕Anzy Lee, Zong Woo Geem and Kyung-Duck Suh, Determination of Optimal Initial
Weights of an Artificial Neural Network by Using the Harmony Search Algorithm:
Application to Breakwater Armor Stones Appl. Sci. 2016, 6(6), 164;
https://doi.org/10.3390/app6060164
〔21〕MATLAB類神經網路過度訓練案例，from -
https://www.mathworks.com/help/deeplearning/ug/improve-neural-network-generalization-and-avoid-overfitting.html
〔22〕David E. Rumelhart, Geoffrey E. Hinton, Ronald J. Williams. Learning
representations by back-propagating errors, 09 October 1986.
〔23〕“Generate feedforward neural network”, MATLAB MathWorks, from -
https://www.mathworks.com/help/deeplearning/ref/feedforwardnet.html;jsessionid=2d
905565ae95740b43a7530ff92d
〔24〕轉移函數，from - https://documen.site/download/chapter2-5ae0d72e8a9ec_pdf
〔25〕MATLAB硬轉移函數介紹，from -
http://matlab.izmiran.ru/help/toolbox/nnet/hardlim.html
〔26〕線性轉移函數，from - https://www.researchgate.net/figure/Figure-215-show-linear-
transfer-function_fig12_323382651
〔27〕MATLAB對數雙彎曲轉移函數，from -
http://matlab.izmiran.ru/help/toolbox/nnet/logsig.html
〔28〕Chen, S.-P., D. Bilitza, J.-Y. Liu, R. Caton, L.C. Chang, W.-H. Yeh, An Empirical
Model of L-band Scintillation S4 index Constructed by Using FORMOSAT-3
/COSMIC Data, Adv. Space Res., 60, doi:10.1016/j.asr.2017.05.031.

〔29〕Chi-Kuang Chao, Mei-Hua Hsu, Tie-Yu Liu, Cheng-Yung Huang
ISP-07, FORMOSAT-5 Advanced Ionospheric Probe Science Data Center, 2021
〔30〕國家太空中心福爾摩沙衛星五號先進電離層探測儀科學資料中心，from -
http://sdc.ss.ncu.edu.tw/Data/index.html
〔31〕Tapping, K.F.(2013), The 10.7 cm solar radio flux (F10.7), Space Weather,
11,394–406,doi:10.1002/swe.20064.
〔32〕British Geological Survey, from -
http://www.geomag.bgs.ac.uk/education/earthmag.htm
〔33〕Campbell, W. H., 2003. Introduction to Geomagnetic Fields (2nd Ed). Cambridge
University Press.
〔34〕MATLAB 深度學習訓練指南，from -
https://www.mathworks.com/help/deeplearning/ug/deep-learning-tips-and-tricks.html
〔35〕MATLAB前饋網路與擬合網路的差異，from -
https://www.mathworks.com/matlabcentral/answers/35772-what-is-the-difference-
between-feedforwardnet-with-fitnet
〔36〕Applying Neural Networks - A Practical Guide, Kevin Swingler, 1996. (p.53) .
〔37〕Jeffheaton, 2008, Chapter.5, Introduction to Neural Networks for Java, Second
Edition.
〔38〕C. Spearman, "The proof and measurement of association between two things" Amer.
J. Psychol., 15 (1904) pp. 72–101
〔39〕“Spearman Rank Correlation Demonstration”, Skbkekas, 2009. from -
https://zh.wikipedia.org/wiki/File:Spearman_fig1.svg
〔40〕B. G. Fejer,L. Scherliess,E. R. de Paula, Effects of the vertical plasma drift velocity on
the generation and evolution of equatorial spread F.

指導教授

張起維(Loren Chang)

審核日期

2022-9-29

推文