Work

Qualitative identification of passive Fourier transform infrared spectra via an attention mechanism based deep learning approach

Year: 2024

Type: article

Abstract: Passive Fourier transform infrared spectroscopy, used to detect chemical pollutants in the air, works with extremely weak signals with complex and varying background interference. This significantly challenges gas identification in terms of precision. Consequently, limited research progress has been made in this area. To address this issue, this study proposes a model that leverages the Transformer, a self-attention-based neural network, and a coattention mechanism for gas identification. The architecture of this model facilitates joint feature learning and fusion, rendering the prediction performance robust to background interference. Extensive experiments demonstrate its significant improvements and feasibility, underlining the potential application in hazardous gas warning systems.

Source: Optics Express

DOI: 10.1364/oe.543450

Authors: Yuxi Zhang, Liang Xu, Chenguang Huang, Haotian Wu

Institutions: Chinese Academy of Sciences, Hefei Institutes of Physical Science, University of Chinese Academy of Sciences

Language: English

Topic: Spectroscopy and Laser Applications

Subfield: Spectroscopy

Field: Chemistry

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,270

Funder: Anhui Institute of Optics and Fine Mechanics, Chinese Academy of Sciences