A5015786605- Spectroscopy and Chemometric Analyses
- Spectroscopy Techniques in Biomedical and Chemical Research
- Advanced Chemical Sensor Technologies
- Fault Detection and Control Systems
- Remote-Sensing Image Classification
Chonnam National University
2022-2025
Most spectral data, such as those obtained via infrared, Raman, and mass spectroscopy, have baseline drifts due to fluorescence or other reasons, which an adverse impact on subsequent analyses. Therefore, several researchers proposed the use of various baseline-correction methods address aforementioned issue. However, most require manual adjustment parameters achieve desirable performance. In this study, we propose a method based deep-learning model that combines ResNet UNet. The uses...
Spectral identification is an essential technology in various spectroscopic applications, often requiring large spectral databases. However, the reliance on databases significantly increases computational complexity. To address this issue, we propose a novel fast search algorithm that substantially reduces demands compared to existing methods. The proposed method employs principal component transformation (PCT) as its foundational framework, similar techniques. A running average filter...
Raman spectroscopy requires baseline correction to address fluorescence and instrumentation-related distortions. Existing methods can be broadly classified into traditional mathematical approaches deep learning-based techniques. While methods...
Raman spectroscopy is widely used for its exceptional identification capabilities in various fields. Traditional methods target using rely on signal correlation with moving windows, requiring data preprocessing that can significantly impact performance. In recent years, deep-learning approaches have been proposed to leverage augmentation techniques, such as baseline and additive noise addition, order overcome scarcity. However, these are limited the spectra encountered during training...