Ms. Jiamin Li Profile

Jiamin Li

at Guilin Univ of Electronic Technology

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Publications (3)

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Proceedings Article | 24 November 2021 Paper

Recognition of sound vibration by DCNN based on φ-OTDR system

Cong Chen, Jiamin Li, Zujun Qin, Xianming Xiong, Wentao Zhang

Proceedings Volume 12066, 120660K (2021) https://doi.org/10.1117/12.2603867

KEYWORDS: Signal detection, Signal processing, Fiber lasers, Bragg cells, Backscatter, Sensors, Sensing systems, Reflectometry

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Phase-sensitive optical time-domain reflectometer (φ-OTDR) is widely used for safety monitoring of large-scale civil objects, by which external vibrations along the sensing fiber can be detected. It has to be noticed that the category of vibration signals should be accurately distinguished for many real applications. At present, an extensively approach of signal recognition is deep convolutional neural network (DCNN). In the work, the one-dimensional DCNN (1D-DCNN) is applied to recognize different sound-induced vibrations based on their time-domain intensity signals detected by an amplitude-demodulated φ-OTDR system. It is turned out that the DCNN successfully shows the capability of recognizing walking, rock drill, explosion, hand hammer, car siren, and background noises with a high accuracy. Additionally, the 1D time-domain intensity vectors are rearranged into 2D matrices and the 2D-DCNN is accordingly employed to identify these vibration signals. The confusion matrices demonstrate that the 1D-DCNN has a higher average recognition accuracy to identify the concerned sounds with respect to the 2D-DCNN.

Proceedings Article | 1 November 2021 Paper

Mode-conversion in few-mode fibers based on PC-FWM

Longtao Yang, Jiamin Li, Cong Chen, Zhenhua Xu, Zujun Qin, Jianyuan Li

Proceedings Volume 12057, 120572C (2021) https://doi.org/10.1117/12.2605857

KEYWORDS: Optical fibers, Phase matching, Signal processing, Refractive index, Phase conjugation

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Four-wave mixing (FWM) in few-mode fibers (FMFs) has been extensively investigated to develop mode-related alloptical signal processing, such as wavelength conversion, parametric amplification and mode conversion. Compared to the FWM processes in single-mode fibers, intermodal FWM in FMFs shows more flexible phase-matching condition by tailoring the modal dispersion of each optical mode. Generally, there are two mainly different types of FWM processes, namely, Bragg scattering (BS) and phase conjugation (PC). In this paper, we focus our interest on the PC-FWM in both graded-index (GI) and step-index (SI) FMFs to probe mode conversion. In the PC-FWM, the energy transfers from pump modes to both signal and idler waves. From the point of phase matching, the modal dispersions of the two FMFs is firstly optimized by genetic algorithm (GA) to design optimal core radius and core-cladding refractive difference. We then investigate the effect of the small deviations of these two parameters from their optimal values upon the phase mismatch. Numerical results show that both SI and GI fibers are able to convert the LP₀₁ mode to the LP₀₂ mode with the phase matching condition of the SI fiber being more sensitive to the changes of fiber parameters. In addition, we analyze the dependence of mode conversion performance (bandwidth and efficiency) on fiber length and pump level. It is shown that the 3dB-bandwidth increases with the pump power in the PC-FWM, which can be attributed to the nonlinear phase shift induced by the high pump power compensate for the linear phase mismatch.

Proceedings Article | 8 January 2021 Paper

An approach for vibration positioning and recognition in OFDR

Yiwei Hu, Zujun Qin, Jiamin Li, Yinghua Lu, Jinpeng Li, Wentao Zhang, Xianming Xiong

Proceedings Volume 11607, 1160704 (2021) https://doi.org/10.1117/12.2583565

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Optical frequency-domain reflectance (OFDR) has been widely used in vibration measurement due to its unique advantages over optical time-domain reflectometry (OTDR). It should be noted that, however, OFDR requires long measurement time and shows poor sensitivity when applied to measure vibration signal over long distance. In the work, an algorithm is presented to automatically detect and locate the vibration signals. Firstly, we perform cross-correlation analysis in a moving window between the beating signals without and with vibration, and find the maximum cross-correlation coefficients in all windows to reconstruct them into a cross-correlation curve. Secondly, an automatic decision threshold curve is designed to conclude whether there is any vibration over the sensing fiber. Lastly, the cross-correlation curve is compared with the threshold to locate the vibration. We experimentally test the algorithm in an OFDR system and locate a PZT vibration at 26.96 km, which demonstrates its validity in terms of detecting external disturbances over a relative long distance.

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