近红外光谱的古筝面板用木材等级识别研究
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摘要
目前民族乐器古筝面板用板材的等级主要依靠乐器技师凭借个人经验进行判断,此方法受限于有丰富经验的技师且容易受其主观判断影响。针对此现状,以用于制作古筝面板的泡桐木材为实验样本,提出了一种利用近红外光谱结合改进的BP神经网络方法,实现快速识别古筝面板用板材的不同等级。近红外光谱可以表征丰富的物质结构与组成信息,并且测量仪器成本较低,附件形式多样化,所以针对泡桐板材的近红外光谱实验分析有实用意义首先进行光谱去噪,消除系统误差等以提高光谱分辨率,根据均方根误差与信号平方和作为多种预处理方法评价指标,选取一阶导数为本实验最终预处理方式, 15为合适的滤波去噪窗口大小,然后通过主成分分析法压缩数据以及马氏距离法剔除建模集异常样本,从而建立更具代表性的建模集。然后通过聚类分析无监督学习方法进行板材等级分析,证明板材分级的可行性。由于H_2O在近红外光谱区域具有较大吸收,根据实验光谱分析结果,不考虑其基频振动波段5 396.0～4 978.0 cm~(-1)区域和第一泛音振动波段6 800～7 000 cm~(-1)区域,仅考虑剩余近红外光谱波段信息,将不同光谱信息波段组合,共七种组合波段区域作为神经网络模型的输入,进行面板板材等级识别模型实验。对传统的BP神经网络模型作改进。BP神经网络中学习率的设置采用自适应学习率优化策略,弥补传统神经网络训练速率慢等劣势。同时采用交叉熵函数作为代价函数,从而加快权重的更新速度。选取Relu函数作为输入层与隐藏层之间的传递函数,提高了模型训练速度,有效防止过拟合的发生。选取Softmax函数作为最后一层的传递函数,以此减少复杂计算,构成该研究最终BP神经网络模型。选取不同数量的主成分变量所能提取的光谱信息量不同,通过不断增加主成分个数和调整参与模型的光谱波段区间,调整BP神经网络模型的输入,当主成分个数为11和光谱区间为10 000～7 000和4 976～4 000 cm~(-1)时,未知样本识别率达到99.7%,所选光谱区间涵盖C—H等基团全部特征信息。研究结果表明,近红外光谱结合神经网络可以对不同等级的泡桐木材进行有效的识别,降低人工检测误差,缩短板材分级时间,更好地满足乐器市场需求。
At present,the wood grades of the national musical instrument Chinese zither panel mainly rely on the personal experience of the musical instrument technician.This method relies on experienced technicians and is susceptible to subjective judgment. In view of this situation, we use the Paulownia wood used to make the Chinese zither panel as an experimental sample. We propose a method of using near-infrared spectroscopy and the improved BP neural network to rapidly identify different grades of Chinese zither panels.Because Near-infrared spectroscopy can characterize a number of material structure and composition information, with the low cost of measuring instruments and many measuring accessories,we conduct an experimental analysis of the near-infrared spectral data of Paulownia panel. In the experiment, spectral denoising is first performed to eliminate system errors and improve spectral resolution, regarding the root mean square error and the square sum of signals as the evaluation criterions of various pretreatment methods.Therefore,the first derivative is selected as the final pretreatment method, and 15 is the appropriate filter denoising window size.The principal component analysis is then used to compress the data and the Mahalanobis distance is used to eliminate the modeling set's abnormal samples to create a more representative modeling set. Then, an unsupervised clustering is used to analyze the Paulownia panel grades, which proves the feasibility of grade classification.Since H_2O has a large absorption in the near-infrared spectral region, according to experimental spectral analysis results,we do not consider the fundamental frequency vibration band(5 396 to 4 978 cm~(-1)) and the first overtone vibration band(6 800 to 7 000 cm~(-1)),but consider only the remaining near-infrared spectral band. Different spectral bands are combined, and seven bands are used as input to the neural network to carry out the panel grade recognition.We also improve the traditional BP neural network model. The learning rate of BP neural network is set by an adaptive optimization strategy to speed up the traditional neural network's training rate.At the same time, the cross entropy function is used as the cost function to speed up the updating of the weight.The Relu function is selected as the transfer function between the input layer and the hidden layer, which improves the training speed of the model and effectively prevents over-fitting.The Softmax function is chosen as the transfer function of the last layer to reduce complex calculations. By this way, the final BP neural network is constructed.The amount of spectral information that can be extracted by different principal component variables is different.We adjust the input of the BP neural network model by increasing the number of principal components and adjusting the spectral band interval.When the number of principal components is 11 and the spectral intervals are 10 000 to 7 000 cm~(-1) and 4 976 to 4 000 cm~(-1), the unknown sample's recognition rate reaches 99.7%, and the selected spectral range covers all the characteristis of C—H bond and other bond information.The results show that near-infrared spectroscopy combined with BP neural network can effectively identify different grades of Paulownia panel, thereby reducing manual detection errors, shortening the processing time, and better meeting the needs of the instrument market.
At present,the wood grades of the national musical instrument Chinese zither panel mainly rely on the personal experience of the musical instrument technician.This method relies on experienced technicians and is susceptible to subjective judgment. In view of this situation, we use the Paulownia wood used to make the Chinese zither panel as an experimental sample. We propose a method of using near-infrared spectroscopy and the improved BP neural network to rapidly identify different grades of Chinese zither panels.Because Near-infrared spectroscopy can characterize a number of material structure and composition information, with the low cost of measuring instruments and many measuring accessories,we conduct an experimental analysis of the near-infrared spectral data of Paulownia panel. In the experiment, spectral denoising is first performed to eliminate system errors and improve spectral resolution, regarding the root mean square error and the square sum of signals as the evaluation criterions of various pretreatment methods.Therefore,the first derivative is selected as the final pretreatment method, and 15 is the appropriate filter denoising window size.The principal component analysis is then used to compress the data and the Mahalanobis distance is used to eliminate the modeling set's abnormal samples to create a more representative modeling set. Then, an unsupervised clustering is used to analyze the Paulownia panel grades, which proves the feasibility of grade classification.Since H_2O has a large absorption in the near-infrared spectral region, according to experimental spectral analysis results,we do not consider the fundamental frequency vibration band(5 396 to 4 978 cm~(-1)) and the first overtone vibration band(6 800 to 7 000 cm~(-1)),but consider only the remaining near-infrared spectral band. Different spectral bands are combined, and seven bands are used as input to the neural network to carry out the panel grade recognition.We also improve the traditional BP neural network model. The learning rate of BP neural network is set by an adaptive optimization strategy to speed up the traditional neural network's training rate.At the same time, the cross entropy function is used as the cost function to speed up the updating of the weight.The Relu function is selected as the transfer function between the input layer and the hidden layer, which improves the training speed of the model and effectively prevents over-fitting.The Softmax function is chosen as the transfer function of the last layer to reduce complex calculations. By this way, the final BP neural network is constructed.The amount of spectral information that can be extracted by different principal component variables is different.We adjust the input of the BP neural network model by increasing the number of principal components and adjusting the spectral band interval.When the number of principal components is 11 and the spectral intervals are 10 000 to 7 000 cm~(-1) and 4 976 to 4 000 cm~(-1), the unknown sample's recognition rate reaches 99.7%, and the selected spectral range covers all the characteristis of C—H bond and other bond information.The results show that near-infrared spectroscopy combined with BP neural network can effectively identify different grades of Paulownia panel, thereby reducing manual detection errors, shortening the processing time, and better meeting the needs of the instrument market.
引文
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[2]FU Ming-jian(傅明鉴).Entertainment Technology(演艺科技),2016,(5):11.
[3]Standards for Light Industry of the People’s Republic of China(中华人民共和国轻工业行业标准).QB/T 1207.3-2011Zheng(筝),2011.
[4]ZHANG En-yang,XIA Wei-gao,YAN Xiao-jian(张恩阳,夏维高,闫晓剑).Information Recording Materials(信息记录材料),2017,18(6):26.
[5]Tsuchikawa S,Kobori H.Journal of Wood Science,2015,61(3):213.
[6]Horikawa Y,Mizuno-Tazuru S,Sugiyama J.J Wood Sci.,2015,61:251.
[7]PANG Xiao-yu,YANG Zhong,LBin,et al(庞晓宇,杨忠,吕斌,等).Spectroscopy and Spectral Analysis(光谱学与光谱分析),2016,36(11):3552.
[8]Hwang S W,Horikawa Y,Lee W H,et al.Journal of Wood Science,2016,62(2),156.
[9]WANG Xue-shun,SUN Yi-dan,HUANG An-min(王学顺,孙一丹,黄安民,等).Journal of Northeast Forestry University(东北林业大学学报),2015,43(12):82.
[10]WANG Xue-shun,SUN Yi-dan,HUANG An-min(王学顺,孙一丹,黄安民).Forest Engineering(森林工程),2015,31(6):65.
[11]YANG Zhong,HUANG An-min,JIANG Ze-hui(杨忠,黄安民,江泽慧).Scientia Silvae Sinicae(林业科学),2012,48(10):120.
[12]Alves A,Simoes R,Stackpole D J,et al.J Near Infrared Spectrosc.,2011,19:343.
[13]Brigitte Loblon,Oluantosin Adedipe,Guillaume Hans,et al.The Forestry Chronicle,2013,89(5):595..
[14]XU Xiu-qin,CHEN Guo,ZHANG Hao,et al(许秀琴,陈国,章豪,等).Anhui Chemical Industry(安徽化工),2017,43(4):7.