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研究生:劉思函
研究生(外文):LIU, SSU-HAN
論文名稱:運用深度學習與液晶技術於光纖光柵解讀儀之研究
論文名稱(外文):Using Deep Learning and Liquid Crystal Technology in Fiber Bragg Grating Interrogation
指導教授:彭朋群
指導教授(外文):PENG, PENG-CHUN
口試委員:彭朋群詹明哲李穎玟
口試委員(外文):PENG, PENG-CHUNCHAN, MING-CHELEE, YIN-WEN
口試日期:2022-07-27
學位類別:碩士
校院名稱:國立臺北科技大學
系所名稱:光電工程系
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2022
畢業學年度:110
語文別:中文
論文頁數:41
中文關鍵詞:光譜解讀儀光譜分析儀深度學習液晶技術
外文關鍵詞:SpectrometerFBG InterrogationDeep LearningLiquid Crystal Technology
相關次數:
  • 被引用被引用:0
  • 點閱點閱:130
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  • 收藏至我的研究室書目清單書目收藏:0
本論文研究運用深度學習與液晶技術於光纖光柵解讀儀,光纖布拉格光柵解讀儀使用液晶片建構法布里-珀羅之共振腔,透過驅動電壓改變等效折射率來實現光譜儀的掃頻功能,液晶片與精簡的光路設計促使架構微小化且搭配光纖為連接端口,提升使用上的穩定性與便利性。此研究的解讀儀主要利用基因演算法、差分進化演算法、粒子群演算法,三種演算法達成重建光譜,並用深度學習中的循環神經網路、長短期記憶神經網路、門控循環神經網路,三種神經網路的模型達成光纖光柵的峰值偵測。
The application of deep learning and liquid crystal technique in the fiber Bragg grating (FBG) interrogation is proposed in this paper. The FBG interrogation uses a liquid crystal sheet to construct a Fabry-Perot resonant cavity and changes the equivalent refractive index by driving voltage to realize the frequency sweep function of the spectrometer. The liquid crystal panel and the simplified optical path design promote the miniaturization of the structure and use the optical fiber as the connection port to improve the stability and convenience of use. In this study, we compare three algorithms in interpreter to reconstruct the spectrum: genetic algorithm, differential evolution algorithm, and particle swarm optimization, respectively. Moreover, we use the recurrent neural network, long short-term memory neural network, and gated recurrent neural network in the deep learning network to realize peak detection of fiber grating.
摘要 i
ABSTRACT ii
誌謝 iv
目錄 v
表目錄 vii
圖目錄 viii
第一章 緒論 1
1.1 研究背景 1
1.2 研究動機 6
1.3 論文結構 8
第二章 研究理論與架構 9
2.1 實驗原理 9
2.1.1 法布里-珀羅原理 10
2.1.2 進化演算法 12
2.1.3 遞迴神經網路 15
2.2 光學系統架構 18
2.3 實驗流程與步驟 20
第三章 結果與討論 23
3.1 光譜量測與計算結果 23
3.1.1 使用基因演算法量測寬頻光源光譜 24
3.1.2 使用差分進化演算法量測寬頻光源光譜 25
3.1.3 使用粒子群演算法量測寬頻光源光譜 25
3.1.4 比較三種演算法計算結果 26
3.2 使用深度學習找出光纖光柵光譜中心波長 27
3.2.1 使用循環神經網路的預測結果 29
3.2.2 使用長短期記憶神經網路的預測結果 30
3.2.3 使用門控循環神經網路的預測結果 31
3.2.4 三種神經網路的預測結果比較 32
3.3 使用門控循環網路預測寬頻光纖光柵的結果 33
第四章 結論 36
參考文獻 37

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