解调
人工神经网络
干扰(通信)
灵敏度(控制系统)
光纤
计算机科学
温度测量
环境科学
生物系统
电子工程
电信
人工智能
频道(广播)
物理
工程类
量子力学
生物
作者
Lei Ren,Jincheng Zhao,Yifan Zhou,Like Li,Yanan Zhang
标识
DOI:10.1016/j.sna.2023.114958
摘要
The observation of seawater salinity and temperature is indispensable for sustainable development and utilization of marine resources. In this research, a simple and low-cost single-mode fiber (SMF) - no-core fiber (NCF) - SMF structure based on Mach-Zehnder interference (MZI) is proposed to measure two variables with the assistance of an artificial neural network (ANN). In contrast to traditional wavelength linear fitting, direct matching of the entire spectrum to the variables through machine learning analysis effectively improves the accuracy of the output predictions. Specifically, mean absolute errors of salinity and temperature reduce from 1.540‰ to 0.808‰ and from 1.061 ℃ to 0.154 ℃, respectively. The relaxation of the light source and the optical spectrum analyzer (OSA) requirements, the fluctuation of salinity or temperature, the disturbance of environmental parameters will all not weaken the demodulation performance of the new method. Furthermore, the accuracy of the predicted results on the new probes demonstrates the adaptability of the demodulation approach. Importantly, ANN can simultaneously demodulate two parameters from a single spectrum, avoiding the cross-sensitivity problem in traditional methods. The strategy is highly generalizable and promising to be extended to any other parameters measured by optical fiber sensors.
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