CAJ | 학술논문

基于分段光谱特征值提取法和小波变换算法等多个数据预处理方法，分别针对分段基线差异及光谱噪声等严重影响激光诱导击穿光谱(LIBS)信号质量的主要影响因素，开展光谱信号预处理研究。基于实验室LIBS实验装置，通过实验验证，基于多通道光谱仪不同波段光谱特征值提取，提出了一种简单易行的多组数据中特征值点连接的方法，有效地提高了LIBS光谱信号的基线平直度，并得出以小波变换算法进行LIBS谱线信号去噪的最佳算法参数。在上述工作的基础上，使用基于误差反向传播的人工神经网络方法，实现了纯铜和不锈钢等物质种类的有效识别，研究结果表明，综合利用多数据处理方法进行LIBS技术中光谱信号处理可以有效提高谱线分析和识别的质量。
기우분단광보특정치제취법화소파변환산법등다개수거예처리방법，분별침대분단기선차이급광보조성등엄중영향격광유도격천광보(LIBS)신호질량적주요영향인소，개전광보신호예처리연구。기우실험실LIBS실험장치，통과실험험증，기우다통도광보의불동파단광보특정치제취，제출료일충간단역행적다조수거중특정치점련접적방법，유효지제고료LIBS광보신호적기선평직도，병득출이소파변환산법진행LIBS보선신호거조적최가산법삼수。재상술공작적기출상，사용기우오차반향전파적인공신경망락방법，실현료순동화불수강등물질충류적유효식별，연구결과표명，종합이용다수거처리방법진행LIBS기술중광보신호처리가이유효제고보선분석화식별적질량。
Based on multiple signal process methods, such as segmented spectral feature extraction and wavelet transform algorithm, the pre﹣spectrum signal treatment technique was investigated to decrease the difference of segmented spectral baseline and lower the spectral noise, and thus the signal quality in laser﹣induced breakdown spectroscopy (LIBS) was improved. Based on extracting the characteristic value in different spectral bands of multi﹣channel spectrometers, a simple method was presented to connect the characteristic value in different segmented data and effectively flats the signal baseline. Through analyzing the experimental data, the wavelet transform was used to lower the noise and obtain the optimum parameters. On the basis of the above work, artificial neural network based error back propagation was adopted to identify spectral line of the copper and stainless steel sample successfully. All the results illustrate that the utilization of multiple data processing method for spectral signal processing in LIBS technique can improve the quality of line's analysis and recognition.