|
|
|
|
|
|
Research on Data Migration Modeling Method for Bending Strength of
Solid Wood Based on SWCSS-GFK-SVM |
CHEN Jin-hao, JIANG Da-peng, ZHANG Yi-zhuo*, WANG Ke-qi* |
College of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin 150040, China
|
|
|
Abstract The modulus of elasticity is an important mechanical index of wood. The advantages of spectral analysis technology include a simple, convenient and fast operation process, which has become an important tool for wood testing. However, in practical applications, we often face changes in temperature and humidity of near-spectrometer testing conditions or aging of instrument components and replacement of accessories when the collected spectral data will be shifted. In order to solve this problem, this paper proposes a near-infrared spectral modeling method combining migration learning and spectral transfer calibration to address the poor generalization of the master model due to the difference data from different types of spectrometers,taking the near-infrared spectral prediction model of larch bending strength as the research object. Firstly, 200 sets of Larch test specimens were processed. Two kinds of spectrometers, the NIRQuest512 spectrometer as the master instrument and the One-chip as the slave, were used to collect the spectral data of Larch test specimens respectively. And the true values of the test specimens were detected by the mechanical universal testing machine. Secondly, the preprocessing of SNV, S-G and spectral shearing was employed, and then the method of PDS transfer correction was applied to complete the linear transformation from the slave instrument to the master. Thirdly, the SWCSS was used to extract the features of two kinds of spectral data, and the stable wave points were optimized. Finally, the GFK-SVM model was established by using two types of near-infrared spectral data of 100 sets of specimens. 100 sets of data were applied to test and compare the modeling methods such as DS-PLS, PDS-PLS, DS-SWCSS-GFK-SVM, and PDS-SWCSS-GFK-SVM. The experimental results show that PDS, compared with DS, can better complete the linear mapping of spectral data due to the sliding window, which could unify the optical length and wave points between the two spectrometers, and improve the modeling accuracy to a certain extent; As a feature extraction method, SWSS can select wavebands according to the differences and similarities of the two groups of spectral data sets, which can ensure the effectiveness and stability of features, and improve the modeling accuracy; The GFK-SVM is suitable for the migration of different spectral data. It can realize high-dimensional mapping of different types of spectral data through reasonable kernel function parameters. A generalized model for different datasets is constructed to realize the generalization of the master model on the slave spectral prediction, which improves the data efficiency, and the test set correlation coefficient Rp reaches 0.875, and the root mean square error RMSEP is 11.975.
|
Received: 2021-04-23
Accepted: 2021-09-08
|
|
Corresponding Authors:
ZHANG Yi-zhuo, WANG Ke-qi
E-mail: zdhwkq@163.com;nefuzyz@163.com
|
|
[1] LIU Ya-na, YANG Zhong, LÜ Bin, et al(刘亚娜, 杨 忠, 吕 斌, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2014, 34(3): 648.
[2] WANG Cheng-kun, ZHAO Peng(王承琨, 赵 鹏). Journal of Infrared and Millimeter Waves(红外与毫米波学报), 2020, 39(1): 72.
[3] CHAI Yu-bo, SUN Bo-ling, LIU Jun-liang, et al(柴宇博, 孙柏玲, 刘君良, 等). Scientia Silvae Sinicae(林业科学), 2014, 50(9): 124.
[4] Workman J. J. Applied Spectroscopy, 2018, 72(3): 340.
[5] Shi Yunying, Li Jingyan, Chu Xiaoli. Chinese Journal of Analytical Chemistry, 2019, 47(4): 479.
[6] ZHANG Jin, CAI Wen-sheng, SHAO Xue-guang(张 进, 蔡文生, 邵学广). Progress in Chemistry(化学进展), 2017, 29(8): 902.
[7] Chen W R, Bin J, Lu H M, et al. Analyst, 2016, 141(6): 1973.
[8] Greensill C V, Wolfs P J, Spiegelman C H, et al. Applied Spectroscopy, 2001, 55(5): 647.
[9] Pu Y Y, Sun D W, Riccioli C, et al. Food Analytical Methods, 2018, 11(4): 1021.
[10] Qin Y, Gong H. Infrared Physics & Technology, 2016, 77: 239.
[11] Luoma P, Natschläger T, Malli B, et al. Analytica Chimica Acta, 2018, 1007: 10.
[12] Fernandez L, Guney S, Gutierrez-Galvez A, et al. Sensors and Actuators B: Chemical, 2016, 231: 276.
[13] Zhang F, Zhang R, Ge J, et al. Analytical Methods, 2018, 10(18): 2169.
[14] Workman J J. Applied Spectroscopy, 2018, 72(3): 340.
[15] Chen Y, Wang Z. Chemometrics and Intelligent Laboratory Systems, 2019, 192: 103824.
[16] Poerio D V, Brown S D. Applied Spectroscopy, 2018, 72(3): 378.
[17] Yu B, Ji H. Analytical Methods, 2015, 7(6): 2714.
[18] ZHUANG Fu-zhen, LUO Ping, HE Qing, et al(庄福振, 罗 平, 何 清, 等). Journal of Sofeware(软件学报), 2015, 26(1): 26.
[19] Gong B, Shi Y, Sha F, et al. Geodesic Flow Kernel for Unsupervised Domain Adaptation. 2012 IEEE Conference on Computer Vision and Pattern Recognition. IEEE, 2012, 2066.
[20] Ni L, Han M, Luan S, et al. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 2019, 206: 350.
[21] NI Li-jun, HAN Ming-yue, ZHANG Li-guo, et al(倪力军, 韩明月, 张立国, 等). Chinese Journal of Analytical Chemistry(分析化学), 2018, 46(10): 1660.
[22] Teye E, Anyidoho E, Agbemafle R, et al. Infrared Physics & Technology, 2020, 104: 103127.
|
[1] |
MA Qian1, 2, YANG Wan-qi1, 2, LI Fu-sheng1, 2*, CHENG Hui-zhu1, 2, ZHAO Yan-chun1, 2. Research on Classification of Heavy Metal Pb in Honeysuckle Based on XRF and Transfer Learning[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2729-2733. |
[2] |
CAI Jian-rong1, 2, HUANG Chu-jun1, MA Li-xin1, ZHAI Li-xiang1, GUO Zhi-ming1, 3*. Hand-Held Visible/Near Infrared Nondestructive Detection System for Soluble Solid Content in Mandarin by 1D-CNN Model[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2792-2798. |
[3] |
ZHANG Qian1, YANG Ying1*, LIU Gang1, 2, 3, WU Xiao1, NING Yuan-lin1. Detection of Dairy Cow Mastitis From Thermal Images by Data Enhancement and Improved ResNet34[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(01): 280-288. |
[4] |
FU Peng-you1, 2, WEN Yue2, ZHANG Yu-ke3, LI Ling-qiao1*, YANG Hui-hua1, 2*. Deep Learning Modelling and Model Transfer for Near-Infrared Spectroscopy Quantitative Analysis[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(01): 310-319. |
[5] |
ZHENG Kai-yi, SHEN Ye, ZHANG Wen, ZHOU Chen-guang, DING Fu-yuan, ZHANG Yang, ZHANG Rou-jia, SHI Ji-yong, ZOU Xiao-bo*. Interval Genetic Algorithm for Double Spectra and Its Applications in Calibration Transfer[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(12): 3783-3788. |
[6] |
WANG Guang-lai, WANG En-feng, WANG Cong-cong, LIU Da-yang*. Early Bruise Detection of Crystal Pear Based on Hyperspectral Imaging Technology and Transfer Learning[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(11): 3626-3630. |
[7] |
WU Yun-fei, LUAN Xiao-li*, LIU Fei. Transfer Learning Modeling of 2,6-Dimethylphenol Purity Based on PLS Subspace Alignment[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(11): 3608-3614. |
[8] |
ZHENG Kai-yi1, ZHANG Wen1, DING Fu-yuan1, ZHOU Chen-guang1, SHI Ji-yong1, Yoshinori Marunaka2, ZOU Xiao-bo1*. Using Ensemble Refinement (ER) Method to OptimizeTransfer Set of Near-Infrared Spectra[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(04): 1323-1328. |
[9] |
ZHENG Kai-yi, FENG Yu-hang, ZHANG Wen, HUANG Xiao-wei, LI Zhi-hua, ZHANG Di, SHI Ji-yong, ZOU Xiao-bo*. Iterative Interval Backward Selection Algorithm and Its Application in Calibration Transfer of Near Infrared Spectra[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(06): 1789-1794. |
[10] |
LI Xue-ying1, 2, 3, 4, FAN Ping-ping1, 3, 4*, HOU Guang-li1, 3, 4, QIU Hui-min1, 3, 4, LÜ Hong-min1, 3, 4. A Review of Calibration Transfer Based on Spectral Technology[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(04): 1114-1118. |
[11] |
ZHENG Kai-yi1, FENG Tao1, ZHANG Wen1, HUANG Xiao-wei1, LI Zhi-hua1, ZHANG Di1, SHI Ji-yong1, Yoshinori Marunaka2, ZOU Xiao-bo1*. Weighted SPXYE (WSPXYE) and Its Application to Transfer Set Selection in Near Infrared Spectra[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(03): 984-989. |
[12] |
LIU Zhen-wen1, XU Ling-jie2*, CHEN Xiao-jing3. Near Infrared Spectroscopy Transfer Based on Deep Autoencoder[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2020, 40(07): 2313-2318. |
[13] |
HU Yun1, LI Bo-yan2*, ZHANG Jin2, PENG Qian-rong1. A New NIR Calibration Transfer Method Based on Parameter Correction[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2020, 40(06): 1804-1808. |
[14] |
LI Zhen-bo1, 2, 3, NIU Bing-shan1, PENG Fang1, LI Guang-yao1. Estimation Method of Fry Body Length Based on Visible Spectrum[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2020, 40(04): 1243-1250. |
[15] |
TAO Chao1, CUI Wen-bo1, WANG Ya-jin1, ZOU Bin1, 2*, ZOU Zheng-rong1. Soil Heavy Metal Qualitative Classification Model Based on Hyperspectral Measurements and Transfer Learning[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2019, 39(08): 2602-2607. |
|
|
|
|