Performance enhancement of arrayed waveguide grating-based fibre Bragg grating interrogation assisted by random forest

被引：5

作者：

Yue, Zizheng ^{[1
]}

Li, Wenbo ^{[1
]}

Zheng, Di ^{[1
]}

Xie, Changjian ^{[1
]}

Pan, Wei ^{[1
]}

Zou, Xihua ^{[1
]}

机构：

[1] Southwest Jiaotong Univ, Ctr Informat Photon & Commun, Sch Informat Sci & Technol, Chengdu, Peoples R China

来源：

ELECTRONICS LETTERS | 2023年 / 59卷 / 01期

基金：

中国国家自然科学基金;

关键词：

arrayed waveguide gratings; demodulation;

D O I：

10.1049/ell2.12682

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

The random forest, a powerful machine learning algorithm, is introduced to improve the performance of silicon arrayed waveguide grating (AWG)-based fibre Bragg grating (FBG) wavelength interrogation. The experimental results show the proposed method has high interrogation accuracy with the root mean squared error (RMSE) of 0.73 pm in the whole demodulation range.

引用

页数：2

共 12 条

[1]

Adele C., 2012, ENSEMBLE MACHINE LEA

[2] Random forests [J].

Breiman, L .

MACHINE LEARNING, 2001, 45 (01) :5-32

[3] Cost-effective improvement of the performance of AWG-based FBG wavelength interrogation via a cascaded neural network [J].

Chen, Shengchao ;

Yao, Feifan ;

Ren, Sufen ;

Wang, Guanjun ;

Huang, Mengxing .

OPTICS EXPRESS, 2022, 30 (05) :7647-7663

[4] Random forest assisted vector displacement sensor based on a multicore fiber [J].

Cui, Jingxian ;

Luo, Huaijian ;

Lu, Jianing ;

Cheng, Xin ;

Tam, Hwa-Yaw .

OPTICS EXPRESS, 2021, 29 (10) :15852-15864

[5] Random forest regression evaluation model of regional flood disaster resilience based on the whale optimization algorithm [J].

Liu, Dong ;

Fan, Zhongrui ;

Fu, Qiang ;

Li, Mo ;

Faiz, Muhammad Abrar ;

Ali, Shoaib ;

Li, Tianxiao ;

Zhang, Liangliang ;

Khan, Muhammad Imran .

JOURNAL OF CLEANER PRODUCTION, 2020, 250

[6]

Muhammad A.R., 2020, IEEE SENS J, V20, P7614

[7] Review and Analysis of Peak Tracking Techniques for Fiber Bragg Grating Sensors [J].

Tosi, Daniele .

SENSORS, 2017, 17 (10)

[8]

Wenda Z., 2020, APPL OPTICS, V33, P10532

[9] SOI-Based Multi-Channel AWG with Fiber Bragg Grating Sensing Interrogation System [J].

Weng, Siming ;

Yuan, Pei ;

Zhuang, Wei ;

Zhang, Dongliang ;

Luo, Fei ;

Zhu, Lianqing .

PHOTONICS, 2021, 8 (06)

[10] Accurate Strain Extraction via Kernel Extreme Learning Machine for Fiber Bragg Grating Sensor [J].

Xu, Xin ;

Wang, Yiping ;

Zhu, Dan ;

Shi, Jingzhan .

IEEE SENSORS JOURNAL, 2022, 22 (08) :7792-7797

← 1 2 →