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基於Pixel2PixelGAN的光纖干涉感測器強健解調方法 = = T...

李晟鋒

基於Pixel2PixelGAN的光纖干涉感測器強健解調方法 = = The Robust Demodulation Method for Fiber Optic Interferometric Sensors by Pixel2Pixel GAN /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	基於Pixel2PixelGAN的光纖干涉感測器強健解調方法 =/ 李晟鋒.
Reminder of title:	The Robust Demodulation Method for Fiber Optic Interferometric Sensors by Pixel2Pixel GAN /
remainder title:	The Robust Demodulation Method for Fiber Optic Interferometric Sensors by Pixel2Pixel GAN.
Author:	李晟鋒
Published:	雲林縣 :國立虎尾科技大學 , : 民113.07.,
Description:	[8], 57面 :圖, 表 ; : 30公分.;
Notes:	指導教授: 鄭旭志.
Subject:	Pixel2PixelGAN. -
Online resource:	電子資源

基於Pixel2PixelGAN的光纖干涉感測器強健解調方法 = = The Robust Demodulation Method for Fiber Optic Interferometric Sensors by Pixel2Pixel GAN /
李晟鋒

基於Pixel2PixelGAN的光纖干涉感測器強健解調方法 =The Robust Demodulation Method for Fiber Optic Interferometric Sensors by Pixel2Pixel GAN /The Robust Demodulation Method for Fiber Optic Interferometric Sensors by Pixel2Pixel GAN.李晟鋒. - 初版. - 雲林縣 :國立虎尾科技大學 ,民113.07. - [8], 57面 :圖, 表 ;30公分.

指導教授: 鄭旭志.

碩士論文--國立虎尾科技大學光電工程系光電與材料科技碩士班.

含參考書目.

本研究探討麥克生干涉(Michelson Interference, MI)感測器的非理想干涉訊號解調，透過電光調變器(Electro-Optic Modulation, EOM)模擬非理想干涉訊號中光源強度干擾的影響，當採用傳統解調方法，如相位生成載波(Phase Generated Carrier, PGC)中微分交叉相乘( Differentiation and Cross Multiplication, DCM)，會有相位延遲、調變深度問題，這些問題會造成解調後的待測訊號波形失真。為了改善這些問題，以採用了深度學習(Deep Learning, DL)中的Pixel2Pixel生成對抗網路(Generative Adversarial Network, GAN)方法實現解調，將模擬麥克生干涉感測器的非理想干涉訊號與目標訊號作為樣本圖像進行訓練模型，再以經過訓練模型生成的圖像與目標圖像做成功率的判斷。將非理想干涉訊號結果與傳統麥克生干涉感測器的理想干涉訊號做比較與分析。.

(平裝)Subjects--Topical Terms:

1449869
Pixel2PixelGAN.

基於Pixel2PixelGAN的光纖干涉感測器強健解調方法 = = The Robust Demodulation Method for Fiber Optic Interferometric Sensors by Pixel2Pixel GAN /
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245 1 0 $a 基於Pixel2PixelGAN的光纖干涉感測器強健解調方法 = $b The Robust Demodulation Method for Fiber Optic Interferometric Sensors by Pixel2Pixel GAN / $c 李晟鋒.
246 1 1 $a The Robust Demodulation Method for Fiber Optic Interferometric Sensors by Pixel2Pixel GAN.
250 $a 初版.
260 # $a 雲林縣 : $b 國立虎尾科技大學 , $c 民113.07.
300 $a [8], 57面 : $b 圖, 表 ; $c 30公分.
500 $a 指導教授: 鄭旭志.
500 $a 學年度: 112.
502 $a 碩士論文--國立虎尾科技大學光電工程系光電與材料科技碩士班.
504 $a 含參考書目.
520 3 $a 本研究探討麥克生干涉(Michelson Interference, MI)感測器的非理想干涉訊號解調，透過電光調變器(Electro-Optic Modulation, EOM)模擬非理想干涉訊號中光源強度干擾的影響，當採用傳統解調方法，如相位生成載波(Phase Generated Carrier, PGC)中微分交叉相乘( Differentiation and Cross Multiplication, DCM)，會有相位延遲、調變深度問題，這些問題會造成解調後的待測訊號波形失真。為了改善這些問題，以採用了深度學習(Deep Learning, DL)中的Pixel2Pixel生成對抗網路(Generative Adversarial Network, GAN)方法實現解調，將模擬麥克生干涉感測器的非理想干涉訊號與目標訊號作為樣本圖像進行訓練模型，再以經過訓練模型生成的圖像與目標圖像做成功率的判斷。將非理想干涉訊號結果與傳統麥克生干涉感測器的理想干涉訊號做比較與分析。.
520 3 $a This research explores the demodulation of non-ideal interference signals from the Michelson Interference (MI) sensor and simulates the impact of light intensity disturbance on the non-ideal interference signal using Electro-Optic Modulation (EOM). Traditional demodulation methods, such as Differentiation and Cross Multiplication (DCM) in Phase Generated Carrier (PGC), may lead to phase delay and modulation depth issues, these issues will result in waveform distortion in the measured demodulated signal. In order to address these issues, the Pixel2Pixel Generative Adversarial Network (GAN) method in Deep Learning (DL) is employed for demodulation. This method involves combining the non-ideal interference signal from the simulated Michelson interference sensor with the target signal to create a sample image for training the model. Subsequently, the model generates an image, which is then compared with the target image to evaluate efficiency. Compare and analyze the non-ideal interference signal results with the ideal interference signal of the traditional Michelson interference sensor..
563 $a (平裝)
650 # 4 $a Pixel2PixelGAN. $3 1449869
650 # 4 $a Deep Learning. $3 1127423
650 # 4 $a Michelson Interferometer. $3 1215517
650 # 4 $a Pixel2Pixel生成對抗網路. $3 1449868
650 # 4 $a 深度學習. $3 1127425
650 # 4 $a 麥克生干涉感測器. $3 1215513
856 7 # $u https://handle.ncl.edu.tw/11296/vud5td $z 電子資源 $2 http