Discover the SciOpen Platform and Achieve Your Research Goals with Ease.
Search articles, authors, keywords, DOl and etc.
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.
E. Al Ibrahim and A. Farooq, Augmentations for selective multi-species quantification from infrared spectroscopic data, Chemom. Intell. Lab. Syst., vol. 240, p. 104913, 2023.
U. Blazhko, V. Shapaval, V. Kovalev, and A. Kohler, Comparison of augmentation and pre-processing for deep learning and chemometric classification of infrared spectra, Chemom. Intell. Lab. Syst., vol. 215, p. 104367, 2021.
H. Guan, M. Yu, X. Ma, L. Li, C. Yang, and J. Yang, A recognition method of mushroom mycelium varieties based on near-infrared spectroscopy and deep learning model, Infrared Phys. Technol., vol. 127, p. 104428, 2022.
P. Fu, Y. Wen, Y. Zhang, L. Li, Y. Feng, L. Yin, and H. Yang, SpectraTr: A novel deep learning model for qualitative analysis of drug spectroscopy based on transformer structure, J. Innov. Opt. Health Sci., vol. 15, no. 3, p. 2250021, 2022.
L. Li, J. Pan, W. S. Lai, C. Gao, N. Sang, and M. H. Yang, Blind image deblurring via deep discriminative priors, Int. J. Comput. Vis., vol. 127, no. 8, pp. 1025–1043, 2019.
F. Liu, C. Gao, F. Chen, D. Meng, W. Zuo, and X. Gao, Infrared small and dim target detection with transformer under complex backgrounds, IEEE Trans. Image Process., vol. 32, pp. 5921–5932, 2023.
Z. Wang, X. Wang, K. Tan, B. Han, J. Ding, and Z. Liu, Hyperspectral anomaly detection based on variational background inference and generative adversarial network, Pattern Recognit., vol. 143, p. 109795, 2023.
S. Yu, Z. Dou, and S. Wang, Prompting and tuning: A two-stage unsupervised domain adaptive person re-identification method on vision transformer backbone, Tsinghua Science and Technology, vol. 28, no. 4, pp. 799–810, 2023.
D. Zhu, W. Zeng, and J. Su, Construction of transformer substation fault knowledge graph based on a depth learning algorithm, Int. J. Model., Simul., Sci. Comput., vol. 14, no. 1, p. 2341017, 2023.
W. Huang, Y. Deng, S. Hui, Y. Wu, S. Zhou, and J. Wang, Sparse self-attention transformer for image inpainting, Pattern Recognit., vol. 145, p. 109897, 2024.
X. Wang, H. Liu, J. Du, Z. Yang, and X. Dong, Clformer: Locally grouped auto-correlation and convolutional transformer for long-term multivariate time series forecasting, Eng. Appl. Artif. Intell., vol. 121, p. 106042, 2023.
L. Deng, G. Xu, J. Pi, H. Zhu, and X. Zhou, Unpaired self-supervised learning for industrial cyber-manufacturing spectrum blind deconvolution, ACM Trans. Internet Technol., vol. 23, no. 4, p. 52, 2023.
H. Zhu, L. Deng, H. Li, and Y. Li, Deconvolution methods based on convex regularization for spectral resolution enhancement, Comput. Electr. Eng., vol. 70, pp. 959–967, 2018.
L. Deng, G. Xu, Y. Dai, and H. Zhu, A dual stream spectrum deconvolution neural network, IEEE Trans. Ind. Inf., vol. 18, no. 5, pp. 3086–3094, 2022.
B. Jacobs, H. Tobi, and G. M. Hengeveld, Linking error measures to model questions, Ecol. Modell., vol. 487, p. 110562, 2024.
E. Temizhan, H. Mirtagioglu, and M. Mendes, Which correlation coefficient should be used for investigating relations between quantitative variables, Am. Acad. Sci. Res. J. Eng. Technol. Sci., vol. 85, pp. 265–277, 2022.
R. M. Zulqarnain, I. Siddique, M. Asif, H. Ahmad, S. Askar, and S. H. Gurmani, Extension of correlation coefficient based TOPSIS technique for interval-valued pythagorean fuzzy soft set: A case study in extract, transform, and load techniques, PLoS One, vol. 18, no. 10, p. e0287032, 2023.
263
Views
46
Downloads
0
Crossref
0
Web of Science
0
Scopus
0
CSCD
Altmetrics
The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).