RESUMO
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.
RESUMO
An all-fiber seawater salinity sensor based on intracavity loss-modulated sensing in a fiber ring laser is proposed and experimentally demonstrated. An optical fiber multimode interferometer, which is based on single-mode-no-core-single-mode fiber structure, is cascaded with a fiber reflector and used as a reflected sensing head to enhance loss-modulated depth. It is inserted in a fiber ring laser and the intracavity loss-modulated salinity sensing is induced for the fiber laser's output intensity. The salinity sensitivity is measured to be 0.1 W/ with a high signal-to-noise ratio more than 49 dB and narrow full width at half maximum less than 40 pm. The temperature cross-sensitivity characteristic and stability are also analyzed. Considering the errors from cross-sensitivity, stability and resolution of the photodetector, the detection limit of the sensor system is 0.0023 (0.0002 S/m), which is comparable to the most advanced commercial electronic salinity sensor.