A method derived from genetic algorithm, principal component analysis and artificial neural networks to enhance classification capability of laser-Induced breakdown spectroscopy
文献类型：会议论文
| 作者 | Guo MT(郭美亭); Yu HB(于海斌) ; Kong HY(孔海洋); Sun LX(孙兰香); Zhang P(张鹏); Zeng P(曾鹏)
|
| 出版日期 | 2017 |
| 会议名称 | Applied Optics and Photonics China: Optical Spectroscopy and Imaging, AOPC 2017 |
| 会议日期 | June 4-6, 2017 |
| 会议地点 | Beijing, China |
| 关键词 | Laser Induced Breakdown Spectroscopy Genetic Algorithm Principal Component Analysis Artificial Neural Networks spectral segment selection classification |
| 页码 | 1-10 |
| 通讯作者 | Sun LX(孙兰香) |
| 中文摘要 | Selection of characteristic lines is a critical work for both qualitative and quantitative analysis of laser-induced breakdown spectroscopy; it usually needs a lot of time and effort. A novel method combining genetic algorithm, principal component analysis and artificial neural networks (GA-PCA-ANN) is proposed to automatically extract the characteristic spectral segments from the original spectra, with ample feature information and less interference. On the basis of this method, three selection manners: selecting the whole spectral range, optimizing a fixed-length segment and optimizing several non-fixed-length sub-segments were analyzed; and their classification results of steel samples were compared. It is proved that selecting a fixed-length segment with an appropriate segment length achieves better results than selecting the whole spectral range; and selecting several non-fixed-length sub-segments obtains the best result with smallest amount of data. The proposed GA-PCA-ANN method can reduce the workload of analysis, the usage of bandwidth and cost of spectrometers. As a result, it can enhance the classification capability of laser-induced breakdown spectroscopy. |
| 英文摘要 | |
| 收录类别 | EI ; CPCI（ISTP） |
| 产权排序 | 1 |
| 会议录 | AOPC 2017: Optical Spectroscopy and Imaging
 |
| 会议录出版者 | SPIE |
| 会议录出版地 | Bellingham, WA |
| 语种 | 英语 |
| ISSN号 | 0277-786X |
| ISBN号 | 978-1-5106-1403-1 |
| WOS记录号 | WOS:000425516200006 |
| 源URL | [http://ir.sia.cn/handle/173321/21538]  |
| 专题 | 沈阳自动化研究所_工业控制网络与系统研究室 |
| 作者单位 | 1.Key Laboratory of Networked Control System, CAS, Shenyang 110016, China 2.Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China 3.University of Chinese Academy of Sciences, Beijing 100049, China |
| 推荐引用方式 GB/T 7714 | Guo MT,Yu HB,Kong HY,et al. A method derived from genetic algorithm, principal component analysis and artificial neural networks to enhance classification capability of laser-Induced breakdown spectroscopy[C]. 见:Applied Optics and Photonics China: Optical Spectroscopy and Imaging, AOPC 2017. Beijing, China. June 4-6, 2017. |
入库方式： OAI收割
来源：沈阳自动化研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。