|
|
|
|
|
|
The Inversion of Knots in Solid Wood Plates Based on Near-Infrared Spectroscopy |
YU Hui-ling1, ZHANG Miao2, HOU Hong-yi2, ZHANG Yi-zhuo2* |
1. College of Information and Computer Engineering, Northeast Forestry University, Harbin 150040, China
2. College of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin 150040, China |
|
|
Abstract Knots affect the mechanical properties of solid wood plates.The accurate description of knots in wood plates and the calculation of wood board mechanical properties have been issues with great practical value. Nowadays, machine vision method is used to detect the defects on wood surface, ultrasonic testing is used to determine the existence of defects, and X-ray method can give a full description of solid wood, but the cost is high. The near infrared spectroscopy analysis technology has the characteristics of rich structure, convenient testing and being nondestructive, but the redundancy and nonlinear information in the spectrum affect the precision of the modeling. In this paper, a method of identifying the knots based on the fusion of Isomap and wavelet neural networks is proposed, and the nonlinear dimensionality reduction is completed by Isomap. The nonlinear relation is modelled by the wavelet neural network between the material and the angle of the knot edge, and the shape structure of the knot inside the wood plate is performed by the multi-point angle of the edge. First, the method uses the cone model to express the knot structure proposed by Pablo. Knots are extracted by machine vision method from the image, and their center positions are obtained by calculation. Then, the information of multi-point position about the edges of knots is extracted and processed by the baseline drift and denoising methods. After that, abnormal spectrums are eliminated by combining PCA and mahalanobis distance, the calibration sample sets are divided by K-S, and effective spectral information are extracted through Isomap, which set the dimensionality reduction and adjacent number, and the fast modeling of different spectral dimensions is completed through PLS, and then the ideal spectral feathers are iterated. Finally, wavelet network is used to establish the relationship between the edge spectrum and their inclination angel of the knots, and the 3D status of these knots is realized by Solidworks software. In this experiment, 160 sets of spectral data of 40 knots were collected from Larix gmelinii plates. After measuring the relative spatial position of the upper and lower surfaces of knots, true inclination angles of every point were obtained. The result of the experiment reveals that S-G smoothing and first order derivative can give clear outline in spectral pre-processing and the absorption peak is more obvious. When Isomap method is for dimension reduction, with non-linear dimension reduction number d=12, the nearest neighbor number k=19, the SECV is the minimum and the redundant data of spectrum is eliminated. When wavelet neural network is used to build the model of nonlinear inclination angles of knots, the correlation coefficient is 0.88, the prediction standard deviation is 7.65, and the relative analysis error is 2.14. This method can realize the inversion of knot structure in wood plates, and can provide quantitative analysis means for the prediction of mechanical properties.
|
Received: 2018-07-13
Accepted: 2018-11-20
|
|
Corresponding Authors:
ZHANG Yi-zhuo
E-mail: Zhangyz@nefu.edu.cn
|
|
[1] Ruz G A, Estevez P A,Ramirez P A. International Journal of Systems Science, 2009, 40(2): 163.
[2] Zhang Yizhuo, Xu Chao, Li Chao, et. al. Journal of Forestry Research, 2015, 26(3): 745.
[3] Zhang Yizhuo, Liu Sijia, Cao Jun, et. al. Wood Science and Technology, 2016, 50(3): 297.
[4] PENG Hui, JIANG Jia-li, ZHAN Tian-yi, et al(彭 辉,蒋佳荔,詹天翼,等). Scientia Silvae Sinicae(林业科学), 2016, 52(10):117.
[5] YANG Hui-min, WANG Li-hai(杨慧敏,王立海). Journal of Northeast Forestry University(东北林业大学), 2015, 43(8): 114.
[6] Olsson A, Oscarsson J, Serrano E,et al. European Journal of Wood Products,2013, 71(3): 319.
[7] Hittawe M M, Sidibe D, Meriaudeau F. Proceedings of SPIE, 2015, 9534: 95340L.
[8] LIANG Hao, CAO Jun, LIN Xue, et al(梁 浩,曹 军,林 雪,等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2017, 37(7): 2041.
[9] Yang Zhong, Zhang Mao Mao, Chen Ling, et al. Bioresources, 2015,10(2):3314.
[10] ZHANG Yi-zhuo, SU Yao-wen, LI Chao, et al(张怡卓,苏耀文,李 超, 等). Journal of Forestry Engineering(林业工程学报), 2016, 1(6): 17.
[11] ZHANG Yi-zhuo, SU Yao-wen, LI Chao, et al(张怡卓,苏耀文,李 超,等). Journal of Beijing Forestry University(北京林业大学学报),2016, 38(8): 99.
[12] Pablo Guindos, Manuel Guaita. Wood Science and Technology, 2013, 47(3): 585.
[13] SHU Xiao-hua, SHEN Zhen-kang, ZENG Guang-sheng,et al(舒小华,沈振康,曾广胜,等). Computer Engineering and Applications(计算机工程与应用),2012, 48(31):191. |
[1] |
BAO Hao1, 2,ZHANG Yan1, 2*. Research on Spectral Feature Band Selection Model Based on Improved Harris Hawk Optimization Algorithm[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 148-157. |
[2] |
WANG Yu-qi, LI Bin, ZHU Ming-wang, LIU Yan-de*. Optimizations of Sample and Wavelength for Apple Brix Prediction Model Based on LASSOLars Algorithm[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(05): 1419-1425. |
[3] |
JIANG Xiao-gang1, ZHU Ming-wang1, YAO Jin-liang1, LI Bin1, LIAO Jun1, LIU Yan-de1*, ZHANG Jian-yi2, JING Han-song2. Research on Parameter Optimization of Apple Sugar Model Based on Near-Infrared On-Line Device[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(01): 116-121. |
[4] |
LIU Meng-xuan1, 2, 3, 4, WU Qiong5, WANG Xu-quan1, 2, 4, CHEN Qi5, ZHANG Yong-gang1, 2, HUANG Song-lei1, 2*, FANG Jia-xiong1, 2*. Validity and Redundancy of Spectral Data in the Detection Algorithm of Sucrose-Doped Content in Tea[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(11): 3647-3652. |
[5] |
CHEN Miao, HOU Ming-yu, CUI Shun-li, LI Zhen, MU Guo-jun, LIU Ying-ru, LI Xiu-kun, LIU Li-feng*. Construction of Near-Infrared Model of Peanut Sugar Content in
Different Seed Coat Colors[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(09): 2896-2902. |
[6] |
ZHAO Si-meng1, YU Hong-wei1, GAO Guan-yong2, CHEN Ning2, WANG Bo-yan3, WANG Qiang1*, LIU Hong-zhi1*. Rapid Determination of Protein Components and Their Subunits in Peanut Based on Near Infrared Technology[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(03): 912-917. |
[7] |
MA Ben-xue1,2*, YU Guo-wei1,2, WANG Wen-xia1,2, LUO Xiu-zhi1,2, LI Yu-jie1,2, LI Xiao-zhan1,2, LEI Sheng-yuan1,2. Recent Advances in Spectral Analysis Techniques for Non-Destructive Detection of Internal Quality in Watermelon and Muskmelon: A Review[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2020, 40(07): 2035-2041. |
[8] |
YU Hui-ling1, MEN Hong-sheng2, LIANG Hao2, ZHANG Yi-zhuo2*. Near Infrared Spectroscopy Identification Method of Wood Surface Defects Based on SA-PBT-SVM[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(06): 1724-1728. |
[9] |
YUAN Jing-ze1, 2, LU Qi-peng1*, WU Chun-yang1, 2, DING Hai-quan1, GAO Hong-zhi1, LI Wan-xia1, 2, WANG Yang3. Noninvasive Human Triglyceride Detecting with Near-Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(01): 42-48. |
[10] |
ZHU Wei-hua1, CHEN Guo-qing2, ZHU Zhuo-wei2, ZHU Feng1, GENG Ying1, HE Xiang1,TANG Chun-mei1. Year Prediction of a Mild Aroma Chinese Liquors Based on Fluorescence Spectra and Quantum Genetic Algorithm[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2017, 37(05): 1431-1436. |
[11] |
ZHENG Yu1, CHEN Xiong1, ZHOU Mei2, WANG Meng-jun3, WANG Jin-hai1*, LI Gang2, CUI Jun1 . The Influence of Different Ionic Concentration in Cell Physiological Solution on Temperature Measurement by Near Infrared[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2015, 35(10): 2718-2722. |
[12] |
ZHANG Zhuo-yong . Studies on Cancer Diagnosis by Using Spectroscopy Combined with Chemometrics[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2015, 35(09): 2388-2392. |
[13] |
WANG Dong-min1, JI Jun-min1, GAO Hong-zhi2 . The Effect of MSC Spectral Pretreatment Regions on Near Infrared Spectroscopy Calibration Results[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2014, 34(09): 2387-2390. |
[14] |
LEI Meng, LI Ming*, MA Xiao-ping, MIAO Yan-zi, WANG Jian-sheng . Spectral Scatter Correction of Coal Samples Based on Quasi-Linear Local Weighted Method [J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2014, 34(07): 1816-1820. |
[15] |
HUANG Hua-jun1, YAN Yan-lu1, SHEN Bing-hui1, LIU Zhe1, GU Jian-cheng2, LI Shao-ming1, ZHU De-hai1, ZHANG Xiao-dong1, MA Qin1, LI Lin1, AN Dong1*. Near Infrared Spectroscopy Analysis Method of Maize Hybrid Seed Purity Discrimination [J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2014, 34(05): 1253-1258. |
|
|
|
|