.png){kind=icon}
.png){kind=icon}
Return
AMTrans: Auto-Correlation Multi-Head Attention Transformer for Infrared Spectral Deconvolution
.png){kind=icon}
PRE
AI
L
L
S
邓
X
H
DOI：10.26599/TST.2024.9010131.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Infrared spectroscopy analysis has found widespread applications in various fields due to advancements in technology and industry convergence. To improve the quality and reliability of infrared spectroscopy signals, deconvolution is a crucial preprocessing step. Inspired by the transformer model, we propose an Auto-correlation Multi-head attention Transformer (AMTrans) for infrared spectrum sequence deconvolution. The auto-correlation attention model improves the scaled dot-product attention in the transformer. It utilizes attention mechanism for feature extraction and implements attention computation using the auto-correlation function. The auto-correlation attention model is used to exploit the inherent sequence nature of spectral data and to effectively recovery spectra by capturing auto-correlation patterns in the sequence. The proposed model is trained using supervised learning and demonstrates promising results in infrared spectroscopic restoration. By comparing the experiments with other deconvolution techniques, the experimental results show that the method has excellent deconvolution performance and can effectively recover the texture details of the infrared spectrum.
Keywords:
Spectroscopy
Deconvolution
Computational modeling
Supervised learning
Noise reduction
Noise
Transformers
Data processing
Data models
Reliability
spectroscopy
spectral deconvolution
transformer
auto-correlation mechanism
Journal
T
IF:
3.5
Papers:
987
Citations:
2.5K
Organization
Cited Papers