| 光谱学与光谱分析 |
|
|
|
|
|
| Line Index Stellar Atmospheric Physical Parameter Measurement Based on Artificial Neural Network |
| TAN Xin1, PAN Jing-chang1*, WANG Jie1, LUO A-li2, TU Liang-ping3 |
1. School of Mechanical, Electrical & Information Engineering, Shandong University at Weihai, Weihai 264209, China
2. Key Laboratory of Optical Astronomy, National Astronomical Observatories, Chinese Academy of Sciences, Beijing 100012, China
3. School of Science, Liaoning University of Science and Technology, Anshan 144051, China |
|
|
|
|
Abstract By using the artificial neural network method based on the Lick line index to develop atmospheric physical parameters measurement, Kurucz synthetic spectra preprocessing was used to adapt to the requirements of the final LAMOST spectral data, Lick line index with the corresponding atmospheric physical parameter was input, and training artificial neural network was used to get the training model through DR8 spectroscopic data for testing. The artificial neural network is aimed to achieve the best experimental effect by adjusting the relevant parameters. The experimental results show that through the artificial neural network method to get the atmospheric physical parameters is feasible.
|
|
Received: 2012-10-24
Accepted: 2013-02-20
|
|
|
|
Corresponding Authors:
PAN Jing-chang
E-mail: pjc@sdu.edu.cn
|
|
[1] Stuart I Muldrew, Darren J Croton, Ramin A Skibba, et al. Mon. Not. R. Astron. Soc., 2012, 419: 2670.
[2] Beth A Reid, Lado Samushia, Martin White, et al. Mon. Not. R. Astron. Soc., 2012, 426: 2719.
[3] Ariel G Sánchez, Scóccola C G, Ross A J, et al. Mon. Not. R. Astron. Soc., 2012, 425: 415.
[4] Masamune Oguri, Matthew B Bayliss, Hakon Dahle, et al. Mon. Not. R. Astron. Soc., 2012, 420: 3213.
[5] Cui Xiangqun, Zhao Yongheng, Chu Yaoquan, et al. Research in Astron. Astrophys, 2012, 12(9): 1197.
[6] Luo Ali, Zhang Haotong, Zhao Yongheng, et al. Research in Astronomy and Astrophysics, 2012, 12(9): 1243.
[7] ZHao Gang, Zhao Yongheng, Chu Yaoquan, et al. Research in Astron. Astrophys, 2012, 12(7): 723.
[8] Silva L, Schurer A, Granato G L, et al. Mon. Not. R. Astron. Soc., 2011, 410: 2043.
[9] Bonfield D G, Sun Y, Davey N, et al. Mon. Not. R. Astron. Soc., 2010, 405: 987.
[10] Shankar Agarwal, Filipe B Abdalla, Hume A Feldman, et al. Mon. Not. R. Astron. Soc., 2012, 424: 1409.
[11] Padmapriya Praveenkumar, Rengarajan Amirtharajan, Thenmozhi K, et al. Journal of Applied Sciences, 2012, 12(4): 301.
[12] Mamum Bin Ibne Reaz, Md. Syedul Amin, Fazida Hanim Hashim, et al. Journal of Applied Sciences, 2011, 11(16): 2984.
[13] Shabani, Mohsen Ostad, Mazahery Ali. The Minerals, Metals & Materials Society and ASM International, 2012, 43(6): 2158.
[14] Guy Worthey, Faber S M, Jesús González J, et al. The Astrophysical Journal Supplement Series, 1994, 95: 687.
[15] Franchini C Morossi, Di Marcantonio P, et al. The Astrophysical Journal, 2011, 730: 117. |
| [1] |
LUO Li, WANG Jing-yi, XU Zhao-jun, NA Bin*. Geographic Origin Discrimination of Wood Using NIR Spectroscopy
Combined With Machine Learning Techniques[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3372-3379. |
| [2] |
SUN Cheng-yu1, JIAO Long1*, YAN Na-ying1, YAN Chun-hua1, QU Le2, ZHANG Sheng-rui3, MA Ling1. Identification of Salvia Miltiorrhiza From Different Origins by Laser
Induced Breakdown Spectroscopy Combined with Artificial Neural
Network[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3098-3104. |
| [3] |
LIU Shu1, JIN Yue1, 2, SU Piao1, 2, MIN Hong1, AN Ya-rui2, WU Xiao-hong1*. Determination of Calcium, Magnesium, Aluminium and Silicon Content in Iron Ore Using Laser-Induced Breakdown Spectroscopy Assisted by Variable Importance-Back Propagation Artificial Neural Networks[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3132-3142. |
| [4] |
LIU Zhao1, 2, LI Hua-peng1, CHEN Hui1, 2, ZHANG Shu-qing1*. Maize Yield Forecasting and Associated Optimum Lead Time Research Based on Temporal Remote Sensing Data and Different Model[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(08): 2627-2637. |
| [5] |
BAO Pei-jin1, CHEN Quan-li1, 3*, ZHAO An-di1, REN Yue-nan2. Identification of the Origin of Bluish White Nephrite Based on
Laser-Induced Breakdown Spectroscopy and Artificial
Neural Network Model[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(01): 25-30. |
| [6] |
WANG Dong-sheng1, WANG Hai-long1, 2, ZHANG Fang1, 3*, HAN Lin-fang1, 3, LI Yun1. Near-Infrared Spectral Characteristics of Sandstone and Inversion of Water Content[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(11): 3368-3372. |
| [7] |
DUAN Hong-wei1, 2, GUO Mei3, ZHU Rong-guang3, NIU Qi-jian1, 2. LIBS Quantitative Analysis of Calorific Value of Straw Charcoal Based on XY Bivariate Feature Extraction Strategy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(11): 3435-3440. |
| [8] |
ZHAO Zhi-lei1, 2, 3, 4，WANG Xue-mei1, 2, 3，LIU Dong-dong1, 2, 3，WANG Yan-wei1, 2, 3，GU Yu-hong5，TENG Jia-xin1，NIU Xiao-ying1, 2, 3, 4*. Quantitative Analysis of Soluble Solids and Titratable Acidity Content in Angeleno Plum by Near-Infrared Spectroscopy With BP-ANN and PLS[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(09): 2836-2842. |
| [9] |
ZHU Li-wei, YAN Jin-xin, HUANG Juan, SHI Tao-xiong, CAI Fang, LI Hong-you, CHEN Qing-fu*, CHEN Qi-jiao*. Rapid Determination of Amino Acids in Golden Tartary Buckwheat Based on Near Infrared Spectroscopy and Artificial Neural Network[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(01): 49-55. |
| [10] |
ZHAO Wen-ya1, 2, MIN Hong2, LIU Shu2*, AN Ya-rui1*, YU Jin3. Application Progress of Artificial Neural Network in Laser-Induced Breakdown Spectral Data Analysis[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(07): 1998-2004. |
| [11] |
MA Yang, ZHANG Ji-fu, CAI Jiang-hui, YANG Hai-feng, ZHAO Xu-jun*. Parallel Extraction and Analysis of Abnormal Features of QSO Spectra Based on Sparse Subspace[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(04): 1086-1091. |
| [12] |
QU Cai-Xia, YANG Hai-feng*, CAI Jiang-hui, XUN Ya-ling. P-Cygni Profile Analysis of the Spectrum: LAMOST J152238.11+333136.1[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2020, 40(04): 1304-1308. |
| [13] |
XIE Huan1, CHEN Zheng-guang1*, ZHANG Qing-hua2. Rapid Discrimination of Japonica Rice Seeds Based on Near Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2019, 39(10): 3267-3272. |
| [14] |
YANG Si-cheng1, 2, SHU Zai-xi2, CAO Yang1*. Identification of Rice Varieties Based on Hyperspectral Image[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2019, 39(10): 3273-3280. |
| [15] |
XUE Ren-zheng1, CHEN Shu-xin1*, HUANG Hong-ben2. Line Index of A-Type Stellar Astronomical Spectrum Predict Effective Temperature by Ridge Regression Model[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2019, 39(08): 2624-2629. |
|
|
|
|
