Study on neural network algorithm for detecting respirable dust in photoacoustic cavity
文献类型：期刊论文
| 作者 | Jin, Huawei1,2,3,4; Luo, Ping1,3; Dou, Juan1; Bai, Huachun5 |
| 刊名 | AIP ADVANCES
 |
| 出版日期 | 2021-12-01 |
| 卷号 | 11 |
| DOI | 10.1063/5.0073112 |
| 通讯作者 | Jin, Huawei(hwjin@mail.ustc.edu.cn) |
| 英文摘要 | The traditional photoacoustic cavity has the advantages of simple structure, low cost, and easy integration with optical cavity technology, so it has significant advantages in the measurement of the optical characteristics of respirable dust. In order to meet the demand of high-precision respirable dust measurements in practical applications, it is necessary to improve the measurement accuracy of respirable dust by traditional photoacoustic spectroscopy technology. Therefore, the structure size of the photoacoustic cavity was determined by theoretical and simulation analysis. A system for measuring respirable dust by photoacoustic spectroscopy was designed, which was applied to the atmospheric respirable dust detection simultaneously with the cavity ring-down spectroscopy system. The results showed that the correlation between the two systems was poor. Therefore, the three-layer back propagation neural network algorithm was used to correct the photoacoustic response values, and the measured value of the cavity ring-down spectroscopy system was used as the reference truth value. The calibration results showed that the output value of the neural network model was in good agreement with the reference true value: the slope was above 0.96. The results showed that the neural network algorithm could effectively improve the measurement accuracy of the photoacoustic spectroscopy system to respirable dust, improve the linearity, and reduce the detection error.& nbsp;(C)2021 Author(s). All article content, except where otherwise noted, is licensed under a Creative Commons Attribution (CC BY) license(http://creativecommons.org/licenses/by/4.0/).. |
| WOS关键词 | SPECTROSCOPY |
| 资助项目 | Open Foundation of State Key Laboratory of Coal Resources in Western China of Xi'an University of Science and Technology[SKLCRKF20-14] ; Research and Development Project of Wuhu Research Institute of Anhui University of Science and Technology[ALW2020YF17] ; Major Scientific Research Project of Anhui Universities[KJ2021ZD] |
| WOS研究方向 | Science & Technology - Other Topics ; Materials Science ; Physics |
| 语种 | 英语 |
| WOS记录号 | WOS:000727618700006 |
| 出版者 | AIP Publishing |
| 资助机构 | Open Foundation of State Key Laboratory of Coal Resources in Western China of Xi'an University of Science and Technology ; Research and Development Project of Wuhu Research Institute of Anhui University of Science and Technology ; Major Scientific Research Project of Anhui Universities |
| 源URL | [http://ir.hfcas.ac.cn:8080/handle/334002/126412]  |
| 专题 | 中国科学院合肥物质科学研究院 |
| 通讯作者 | Jin, Huawei |
| 作者单位 | 1.Xian Univ Sci & Technol, State Key Lab Coal Resources Western China, Xian 710054, Peoples R China 2.Anhui Univ Sci & Technol, Inst Environm Friendly Mat & Occupat Hlth, Wuhu 241000, Peoples R China 3.Chinese Acad Sci, Anhui Inst Opt & Fine Mech, Key Lab Environm Opt & Technol, Hefei 230031, Peoples R China 4.Anhui Univ Sci & Technol, State Key Lab Min Response & Disaster Prevent & C, Huainan 232001, Peoples R China 5.Anhui Huainan Tech Sch, Huainan 232001, Peoples R China |
| 推荐引用方式 GB/T 7714 | Jin, Huawei,Luo, Ping,Dou, Juan,et al. Study on neural network algorithm for detecting respirable dust in photoacoustic cavity[J]. AIP ADVANCES,2021,11. |
| APA | Jin, Huawei,Luo, Ping,Dou, Juan,&Bai, Huachun.(2021).Study on neural network algorithm for detecting respirable dust in photoacoustic cavity.AIP ADVANCES,11. |
| MLA | Jin, Huawei,et al."Study on neural network algorithm for detecting respirable dust in photoacoustic cavity".AIP ADVANCES 11(2021). |
入库方式： OAI收割
来源：合肥物质科学研究院
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。