主要提出了一种从多谱线特征层融合角度提出了基于格拉姆角场(Gramian angular field)与多尺度卷积神经网络(multi-scale convolutional neural network)的光谱特征提取方法,使用格拉姆角场将一维光谱信号变换为二维图像增强了信号特征,使用多尺度卷积神经网络提取图像特征,利用特征向量回归计算金属离子浓度。
A spectral feature extraction method based on the Gramian Angular Field (GAF) and a multi-scale convolutional neural network (CNN) is primarily proposed from the perspective of fusing features across multiple spectral lines. The Gramian Angular Field is employed to transform one-dimensional spectral signals into two-dimensional images, enhancing signal characteristics. The multi-scale convolutional neural network is then used to extract features from the images, and feature vectors are utilized for the regression calculation of metal ion concentrations.
配合./doc中的中文论文原稿和T. Liu, C. Zhou, C. Fang, H. Zhu, Y. Li and J. Wu, "A Scattered Liquid Component Analysis Approach Based on Spectral Visual Encoding and Fusion," in IEEE Sensors Journal, doi: 10.1109/JSEN.2023.3336797. 正式发表论文参考
- 此目录主要存放原始数据和各种处理后的数据
- 以及各种处理脚本
- process_2d_new.py 为批量将目标库中的csv转为格拉姆角场(CNN训练中只使用目标库数据) ref_2d_process.py 处理参考库中的csv转为格拉姆(对比测试用,实际训练中未使用)
- 此目录存放神经网络代码
- 此目录存放格拉姆角场可视化文件
- 此目录下为光谱矫正和原始光谱误差等预处理文件