Wood quality of Chinese zither panel based on convolutional neural network and near-infrared spectroscopy

Huang Yinglai; Meng Shiyu; Zhao Peng; Li Chao

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Wood quality of Chinese zither panel based on convolutional neural network and near-infrared spectroscopy

机译：基于卷积神经网络和近红外光谱的中国古筝板木材品质

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Currently, the wood grade used for Chinese zither panels is mainly manually determined. This method discriminates slowly and is subject to subjective influences, which cannot meet the requirements of mass production in the musical instrument market. This paper proposes a method by combining a convolutional neural network (CNN) and near-infrared spectroscopy to determine wood quality. First, the Savitzky-Golay second derivatization method is used to denoise raw data. Then kernel principal component analysis is used to reduce the dimensionality of spectral data. Then the obtained variables are sent to the proposed one-dimensional CNN model. The model introduces L2 regularization and the multi-channel convolution kernel strategy. The model is then determined by seeking the optimal convolution kernel size. Finally, the test samples are sent to the proposed CNN model to verify the performance of the model. The correct classification accuracy of the test set is 93.9%. Our model has a strong learning ability and a high robustness. The result shows that the proposed method can effectively identify different grades of Chinese zither panel wood. (C) 2019 Optical Society of America

机译：目前，用于中国古筝面板的木材等级主要是手动确定的。该方法缓慢辨别并受主观影响，这不能满足乐器市场中批量生产的要求。本文通过将卷积神经网络（CNN）和近红外光谱相结合来确定木质质量来提出一种方法。首先，savitzky-golay第二衍生方法用于代位于原始数据。然后，内核主成分分析用于降低光谱数据的维度。然后将获得的变量发送到所提出的一维CNN模型。该模型介绍了L2正则化和多通道卷积内核策略。然后通过寻求最佳卷积内核大小来确定模型。最后，将测试样本发送到所提出的CNN模型以验证模型的性能。测试集的正确分类准确性为93.9％。我们的模型具有很强的学习能力和高稳健性。结果表明，所提出的方法可以有效地识别不同等级的中国古筝板木材。（c）2019年光学学会

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来源
《Applied optics》 |2019年第18期|共6页
作者
Huang Yinglai; Meng Shiyu; Zhao Peng; Li Chao;
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作者单位

Northeast Forestry Univ Coll Informat &

Comp Engn Harbin 150040 Heilongjiang Peoples R China;

Northeast Forestry Univ Coll Informat &

Comp Engn Harbin 150040 Heilongjiang Peoples R China;

Northeast Forestry Univ Coll Informat &

Comp Engn Harbin 150040 Heilongjiang Peoples R China;

Northeast Forestry Univ Coll Informat &

Comp Engn Harbin 150040 Heilongjiang Peoples R China;

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正文语种 eng
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Wood quality of Chinese zither panel based on convolutional neural network and near-infrared spectroscopy

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