AI Article Synopsis
- The study presents a method using ensemble learning to effectively demodulate low SNR signals from Fabry-Perot sensors.
- It utilizes multilevel discrete wavelet transform coefficients as input features and combines multiple SVM and KNN models for improved accuracy.
- The proposed model achieved a remarkable accuracy with a 0.46% F.S. relative mean error, offering potential for scaling up fiber-based Fabry-Perot sensor networks.
Article Abstract
We demonstrate an ensemble learning based method to solve the problem of low SNR Fabry-Perot sensor spectrum signal demodulation. Taking the eight-layer approximate coefficients of a multilevel discrete wavelet transform as input features, an ensemble model that combines multiple SVM and KNN learners is trained. Bootstrap and booting techniques are introduced for better modeling performance and stability. It is shown that the performance of the proposed ensemble model based on SVM-KNN regressors is excellent; an accuracy of 0.46%F.S. relative mean error is achieved. This method could provide insight into the construction of a large scale fiber based Fabry-Perot sensor network.
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| http://dx.doi.org/10.1364/AO.509671 | DOI Listing |
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