|
|
|
|
|
|
Wood Quality of Chinese Zither Panels Based on Convolutional Neural Network and Near-Infrared Spectroscopy |
MENG Shi-yu1, HUANG Ying-lai1*, ZHAO Peng1, LI Chao1, LIU Zhen-bo2, LIU Yi-xing2, XU Yan3 |
1. College of Information and Computer Engineering, Northeast Forestry University, Harbin 150040, China
2. College of Materials Science and Engineering, Northeast Forestry University, Harbin 150040, China
3. Yangzhou Liangjiang Ancient Zither Making Academe Co., Ltd., Yangzhou 225001, China |
|
|
Abstract Currently, the instrument production industry relies mainly on the subjective judgment of instrumental technicians when selecting the wood for Chinese zither panels. However, this method lacks a summary of scientific theories and is inefficient, which limits the objectivity of the selection and the improvement of the yield. Moreover, the current model for judging the wood grade cannot satisfy the large demand of the musical instrument market. Therefore, achieving rapid and intelligent grading of wood for Chinese zither panels is an urgent problem to be solved. Near-infrared spectroscopy contains information about the molecular structure of an object and is very suitable for measuring organic substances containing hydrogen. The chemical bonds of the main chemical components of wood used in Chinese zither panels are composed of hydrogen-containing groups, and the chemical compositions of the panels of different grades are different. These differences are reflected in near-infrared spectral data by light, which makes it possible to judge the wood grade. Simultaneously, convolutional neural network (CNN) has a strong feature extraction ability for nonlinear data. Therefore, this paper proposes a method to analyze the spectral data by using the CNN model to determine the wood grade. In the experiment, this paper applied two spectral preprocessing methods, like the Savitzky Golay first-derivative and second-derivative preprocessing methods, and two data compression methods, like kernel principal component analysis (KPCA) and successive projections algorithm. Through the CNN model designed in the paper, the optimal preprocessing and data compression methods were selected by using the classification accuracy rate of samples and the loss value in the model construction process as the judgment indicators. In order to improve the ability of the experimental model to extract and analyze spectral data and avoid overfitting, this experiment applied multi-channel convolution kernel, batch normalization and early stopping strategies. Finally, the feature information extracted by the two convolution layers was sent into the fully connected layers to extract other residual features, and the prediction grade of the panel was obtained using the softmax function. Thus, the final experimental model was determined. Finally, Savitzky Golay first-derivative and KPCA were the optimal data processing methods. At the same time, the main key bands for distinguishing different wood grades were obtained, which were 1 163~1 243 and 1 346~1 375 and 1 525~1 584 nm, respectively. Applying the proposed model to the test set samples, the grade classification accuracy of the wood for Chinese zither panels was 95.5%. Experimental results revealed that the proposed method can efficiently process spectral data and identify the key features of different grades of wood for Chinese zither panels. Therefore, it can provide specific technical support for the broad instrument market.
|
Received: 2019-07-10
Accepted: 2019-11-02
|
|
Corresponding Authors:
HUANG Ying-lai
E-mail: nefuhyl@163.com
|
|
[1] Beena G Sood, Kathleen McLaughlin, Josef Cortez. Seminars in Fetal and Neonatal Medicine, 2015, 20(3): 164.
[2] Hwang S W, Horikawa Y, Lee W H, et al. Journal of Wood Science, 2016, 62(2): 156.
[3] Inagaki Tetsuya, Matsuo Miyuki, Tsuchikawa Satoru. Applied Physics A, 2016, 122: 208.
[4] PANG Xiao-yu, YANG Zhong, LÜ Bin(庞晓宇, 杨 忠, 吕 斌, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2016, 36(11): 3552.
[5] Lee Hyungtae, Kwon Heesung. IEEE Transactions on Image Processing, 2017, 26(10): 4843.
[6] Justin Salamon, Juan Pablo Bello. IEEE Signal Processing Letters, 2017, 24(3): 279.
[7] Bulat Ibragimov, Lei Xing. Medical Physics, 2017, 44(2): 547.
[8] XU Shan-shan, LIU Ying-an, XU Sheng(徐姗姗,刘应安,徐 昇). Journal of Shandong University·Engineering Science(山东大学学报·工学版), 2013, 43(2): 23.
[9] WEI Xian(魏 弦). Journal of Electronic Measurement and Instrument (电子测量与仪器学报), 2017, 31(12): 2017.
[10] Diniz P H G D, Pistonesi M F, Alvarez M B. et al. Journal of Food Composition and Analysis, 2015, 39: 103.
[11] Manfred Schwanninger, José Carlos Rodrigues, Karin Fackler. Journal of Near Infrared Spectroscopy, 2011, 19(5): 287.
[12] ZHOU Zhu, YIN Jian-xin, ZHOU Su-yin, et al(周 竹, 尹建新, 周素茵,等). Laser & Optoelectronics Progress(激光与光电子学进展), 2017, 54(2): 311. |
[1] |
ZHENG Pei-chao, YIN Yi-tong, WANG Jin-mei*, ZHOU Chun-yan, ZHANG Li, ZENG Jin-rui, LÜ Qiang. Study on the Method of Detecting Phosphate Ions in Water Based on
Ultraviolet Absorption Spectrum Combined With SPA-ELM Algorithm[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 82-87. |
[2] |
LI Xin-ting, ZHANG Feng, FENG Jie*. Convolutional Neural Network Combined With Improved Spectral
Processing Method for Potato Disease Detection[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 215-224. |
[3] |
LAN Yan1,WANG Wu1,XU Wen2,CHAI Qin-qin1*,LI Yu-rong1,ZHANG Xun2. Discrimination of Planting and Tissue-Cultured Anoectochilus Roxburghii Based on SMOTE and Inception-CNN[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 158-163. |
[4] |
WANG Cai-ling1,ZHANG Jing1,WANG Hong-wei2*, SONG Xiao-nan1, JI Tong3. A Hyperspectral Image Classification Model Based on Band Clustering and Multi-Scale Structure Feature Fusion[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 258-265. |
[5] |
WANG Qi-biao1, HE Yu-kai1, LUO Yu-shi1, WANG Shu-jun1, XIE Bo2, DENG Chao2*, LIU Yong3, TUO Xian-guo3. Study on Analysis Method of Distiller's Grains Acidity Based on
Convolutional Neural Network and Near Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3726-3731. |
[6] |
HUANG Meng-qiang1, KUANG Wen-jian2, 3*, LIU Xiang1, HE Liang4. Quantitative Analysis of Cotton/Polyester/Wool Blended Fiber Content by Near-Infrared Spectroscopy Based on 1D-CNN[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3565-3570. |
[7] |
JIA Zong-chao1, WANG Zi-jian1, LI Xue-ying1, 2*, QIU Hui-min1, HOU Guang-li1, FAN Ping-ping1*. Marine Sediment Particle Size Classification Based on the Fusion of
Principal Component Analysis and Continuous Projection Algorithm[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3075-3080. |
[8] |
AN Bai-song1, 2, WANG Xue-mei1, 2*, HUANG Xiao-yu1, 2, KAWUQIATI Bai-shan1, 2. Hyperspectral Estimation of Soil Lead Content Based on Random Frog Band Selection Algorithm[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3302-3309. |
[9] |
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. |
[10] |
PU Shan-shan, ZHENG En-rang*, CHEN Bei. Research on A Classification Algorithm of Near-Infrared Spectroscopy Based on 1D-CNN[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(08): 2446-2451. |
[11] |
TANG Ting, PAN Xin*, LUO Xiao-ling, GAO Xiao-jing. Fusion of ConvLSTM and Multi-Attention Mechanism Network for
Hyperspectral Image Classification[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(08): 2608-2616. |
[12] |
LI Wen-xia1, DU Yu-jun2, WANG Yue1, LIU Zheng-dong3*, ZHENG Jia-hui1, DU Wen-qian1, WANG Hua-ping4. Research on On-Line Efficient Near-Infrared Spectral Recognition and Automatic Sorting Technology of Waste Textiles Based on Convolutional Neural Network[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(07): 2139-2145. |
[13] |
LIANG Wan-jie1, FENG Hui2, JIANG Dong3, ZHANG Wen-yu1, 4, CAO Jing1, CAO Hong-xin1*. Early Recognition of Sclerotinia Stem Rot on Oilseed Rape by Hyperspectral Imaging Combined With Deep Learning[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(07): 2220-2225. |
[14] |
ZHANG Hai-liang1, XIE Chao-yong1, TIAN Peng1, ZHAN Bai-shao1, CHEN Zai-liang1, LUO Wei1*, LIU Xue-mei2*. Measurement of Soil Organic Matter and Total Nitrogen Based on Visible/Near Infrared Spectroscopy and Data-Driven Machine Learning Method[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(07): 2226-2231. |
[15] |
JIANG Xia*, QIU Bo, WANG Lin-qian, GUO Xiao-yu. Automatic Classification Method of Star Spectra Based on
Semi-Supervised Mode[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(06): 1875-1880. |
|
|
|
|