论文标题
深度光谱CNN用于激光诱导的分解光谱法
Deep Spectral CNN for Laser Induced Breakdown Spectroscopy
论文作者
论文摘要
这项工作提出了在激光诱导的分解光谱（LIB）信号上运行的光谱卷积神经网络（CNN），以学会学会（1）从传感器不确定性的来源（即，处理前）和（2）获得定性和定量测量的频谱含量（即，获得光谱的化学含量）的频谱（即（2），给出了光谱的化学含量（即给出了信号的化学量）。一旦训练了频谱CNN，它就可以通过单个进率通过，具有实时好处，并且没有任何其他侧面信息要求，包括黑电流，系统响应，温度和检测器到目标范围。我们的实验表明，所提出的方法的表现优于Mars Science Lab使用的现有方法，用于预处理和校准，用于从火星漫游车“好奇心”中进行遥感观察。
This work proposes a spectral convolutional neural network (CNN) operating on laser induced breakdown spectroscopy (LIBS) signals to learn to (1) disentangle spectral signals from the sources of sensor uncertainty (i.e., pre-process) and (2) get qualitative and quantitative measures of chemical content of a sample given a spectral signal (i.e., calibrate). Once the spectral CNN is trained, it can accomplish either task through a single feed-forward pass, with real-time benefits and without any additional side information requirements including dark current, system response, temperature and detector-to-target range. Our experiments demonstrate that the proposed method outperforms the existing approaches used by the Mars Science Lab for pre-processing and calibration for remote sensing observations from the Mars rover, 'Curiosity'.
