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We propose and demonstrate a new scheme for enhancing the sensitivity of an optical fiber vibration sensor based on microwave interferometry, which is realized by an incoherent optical Michelson interferometer (MI). The sensing arm of the MI is sensitive to environmental vibration; this will cause changes in the phase of the reflection spectra in the microwave domain. The phase sensitivity can be improved by adjusting the power ratio of the two beams in the interferometer and the driving frequency of the modulator. The system can overcome the problem of interference fading so that it is immune to environmental disturbance. The proposed scheme has merits of simplicity and compact configuration, and may provide a new type of high-precision fiber sensor for measuring vibration, temperature, strain, and so on.

A radio frequency (RF)-assisted Sagnac interferometer based on a dual-loop optoelectronic oscillator (OEO) is experimentally demonstrated for high-precision magnetic field measurement, in which the tapered fiber covered with the magnetic fluid (MF) as the magnetic field sensing head is embedded in the Sagnac interferometer. The evanescent field of the tapered fiber can interact with the MF under the external magnetic field to cause the birefringence variation of the fundamental mode, leading to the change in the free spectral range (FSR) of the interferometer, which can be mapped to the oscillation frequency shift of the OEO in the microwave domain. By the above converting, the magnetic field measurement with high interrogation speed and resolution can be realized. In addition, the designed device shows a certain measurement directionality of the magnetic field due to two orthogonally polarized fundamental modes asymmetric to the magnetic field, obtaining a good conformity with the constructed theoretical models. The experimental results show the maximum magnetic field sensitivities of 159.4 Hz/mT in the range of 8.48-27.83 mT, and 350.8 Hz/mT in the range of 0-5.14 mT, corresponding to the light wave vector parallel and perpendicular to the magnetic field, respectively.

It is difficult to measure the surface temperature of continuous casting billet, which results in the lack of important feedback parameters for further scientific control of the billet quality. This paper proposes a sparrow search algorithm to optimize the Least Square Support Vector Machine (LSSVM) model for surface temperature prediction of the billet, which is further improved by Logistic Chaotic Mapping and Golden Sine Algorithm (Improve Logistic Golden Sine Sparrow Search Algorithm LSSVM, short name ILGSSA-LSSVM). Using the Improved Logistic Chaos Mapping and Golden Sine Algorithm to find the optimal initial sparrow population, the value of penalty factor $$\gamma$$ γ and kernel parameter $$\sigma$$ σ for LSSVM are calculated. Global optimization method is adopted to find the optimal parameter combination, so that the negative influence of randomly initializing parameters on the prediction accuracy would be reduced. Our proposed ILGSSA-LSSVM soft sensing model is compared respectively with traditional Least Square Support Vector Machine, BP neural network and Gray Wolf optimized Least Square Support Vector Machine, results show that proposed model outperformed the others. Experiments show that the maximum error of ILGSA-LSSVM soft sensing model is 3.85733 °C, minimum error is 0.0174 °C, average error is 0.05805 °C, and generally outperformed other comparison models.