期刊
IEEE PHOTONICS JOURNAL
卷 13, 期 1, 页码 -出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JPHOT.2021.3050298
关键词
Fiber gratings; Reflection; Strain; Wavelength division multiplexing; Wavelength measurement; Sensor systems; Convolution; Intensity and wavelength division multiplexing (IWDM); Fiber Bragg gratings (FBG); Machine learning
资金
- Ministry of Science and Technology, Taiwan [MOST 108-2221-E-027 -040 -MY2, MOST 109-2813-C-027017-E]
The paper proposes a new fiber Bragg grating central wavelength interrogation system by combining an unsupervised autoencoder pre-training convolutional neural network with a differential evolutionary algorithm. This system achieves good accuracy and can speed up computational time by a maximum of 30 times compared to the DE algorithm.
This paper proposes a new fiber Bragg grating central wavelength interrogation system by combining evolutionary algorithm and machine learning techniques integrated with an unsupervised autoencoder (AE) pre-training strategy. The proposed unsupervised AE pre-training convolution neural network (CNN) allows training of the convolutional layers independently from a regression task in order to learn a new data representation and give better generalization. It is also used to improve the system accuracy by four times without extra-labeled data. Moreover, AE is combined with a differential evolutionary (DE) algorithm to automate the human labeling task. The proposed autoencoder pre-training convolution neural network and differential evolutionary (AECNNDE) interrogation system achieve good accuracy and can speed-up the computational time by a maximum of 30 times than DE algorithm.
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