Mid-infrared methane standoff sensor using a frequency channel attention based convolutional neural network filter-Reference-Cited by-同舟云学术

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Mid-infrared methane standoff sensor using a frequency channel attention based convolutional neural network filter

Published:2024-11 Issue: Volume:419 Page:136371
ISSN:0925-4005
Container-title:Sensors and Actuators B: Chemical
language:en
Short-container-title:Sensors and Actuators B: Chemical

Author:

Wang Senyuan,Liu Shijie,He Xin,Tang Guoliang,Zhu Shouzheng,Yang Shicheng,Li Chunlai,Wang Jianyu

Funder

Shanghai Institute of Technical Physics

Publisher

Elsevier BV

Reference42 articles.

1. A global review of methane policies reveals that only 13% of emissions are covered with unclear effectiveness;Olczak;One Earth,2023

2. A risk assessment method to quantitatively investigate the methane explosion in underground coal mine;Shi;Process Saf. Environ. Prot.,2017

3. Coalbed methane emissions and drainage methods in underground mining for mining safety and environmental benefits: a review;Zheng;Process Saf. Environ. Prot.,2019

4. Integrated coaxial graphene-based yarn with multidimensional architecture for self-powered photoelectrochemical methane sensor;Qin;Sens. Actuators B Chem.,2022

5. A selective methane gas sensor with printed catalytic films as active filters;Lu;Sens. Actuators B Chem.,2021

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