|
|
|
|
|
|
| The Method of Terahertz Spectral Classification and Identification for Engineering Plastics Based on Convolutional Neural Network |
| ZHENG Zhi-jie1, LIN Zhen-heng1, 2*, XIE Hai-he2, NIE Yong-zhong3 |
1. School of Mechanical Engineering and Automation, Fuzhou University, Fuzhou 350108, China
2. New Engineering Industry College, Putian University, Putian 351100, China
3. Xirenma United Measurement and Control (Quanzhou) Technology Co., Ltd., Quanzhou 362011, China
|
|
|
|
|
Abstract The excellent dielectric properties and metal substitutability of engineering plastics make them popular for 5G construction. The detection and characterization of several engineering plastics with similar appearances but different properties can help engineering plastics be better used in manufacturing 5G circuit boards and antenna modules. The terahertz time-domain spectroscopy detection technique (THz-TDS) was applied to spectroscopically detect several common engineering plastics, PEEK, PPS, and ABS, and the terahertz time-domain spectra of three engineering plastics were obtained. The terahertz frequency domain spectra of engineering plastics at 0.1~1.2 THz were obtained by fast Fourier transform of the terahertz time-domain spectra of the three engineering plastics. The related optical parameters were extracted to obtain the terahertz absorption spectra of engineering plastics. Analysis of the THz time-domain spectra shows that the THz time-domain spectra of different kinds of engineering plastics have differences in time delay lines and amplitudes, which can visually demonstrate the differences between various classes of plastics, indicating that THz-TDS is feasible for the classification and identification of engineering plastics. However, since the same engineering plastics exhibit similar peak positions and peaks in the terahertz band and no obvious THz characteristic absorption peaks for each material, they cannot be directly determined by fingerprint spectra. Based on this, the feasibility of applying a nonlinear instrumental convolutional neural network (CNN) to the study of engineering plastics without obvious feature absorption peaks is explored. An improved CNN classification model is proposed by optimizing the network structure and important weight parameters of CNN. The model uses the LeakyRelu activation function, adds BN layers, and utilizes the Adams gradient descent algorithm, which enables to ensure the robustness of the classifier, accelerates the network classification speed and improves the accuracy of THz spectrum recognition while solving the problem of easily falling into local optimum due to the insufficient amount of THz spectrum data, and compare this method with the traditional linear tool principal component analysis-support vector machine method (PCA-SVM). Validation experiments are conducted to verify the advantages and disadvantages of the two qualitative analysis models. The experiments show that the improved CNN classification model takes 0.15 ms to run on average, with 99.6% accuracy in the training set and 98.8% accuracy in the test set; compared with the traditional PCA-SVM classification model, its classification is significantly improved, and the classification accuracy is increased by 27.3% in the test set and 30.9% in the training set. The results show that the combination of THz-TDS and the improved CNN classification model can achieve accurate identification and classification recognition of the above three engineering plastics, which provides a new method for non-contact rapid non-destructive detection and identification of engineering plastics and can also be used as a reference for the identification and detection of other substances without THz characteristic peaks.
|
|
Received: 2022-02-24
Accepted: 2022-07-14
|
|
|
|
Corresponding Authors:
LIN Zhen-heng
E-mail: 147121176@qq.com
|
|
[1] WANG Xi-mei, ZHANG Zhen, ZHAO Zhi-hong, et al（王喜梅，张 振，赵志鸿，等）. Engineering Plastics Application（工程塑料应用）, 2016, 44(6): 114.
[2] Bartolomé E, Bozzo B, Sevilla P, et al. Cryogenics, 2017, 82: 30.
[3] Almasi D, Iqbal N, Sadeghi M, et al. International Journal of Biomaterials, 2016, 2016: 8202653.
[4] GAO Jian-kui, LI Yi-jie, ZHANG Qin-nan, et al（高建魁，李奕杰，章勤男，等）. Spectroscopy and Spectroscopy Analysis（光谱学与光谱分析）, 2021, 41(11): 3347.
[5] Zhong Shuncong. Frontiers of Mechanical Engineering, 2019, 14(3): 273.
[6] Park Hochong, Son Joo-Hiuk. Sensors, 2021, 21(4): 1186.
[7] WANG He, ZHAO Guo-zhong（王 鹤，赵国忠）. Acta Photonica Sinica（光子学报）, 2010, 39(7): 1185.
[8] Zhang T, Huang H C, Zhang Z L, et al. Infrared Physics and Technology, 2021, 118: 103878.
[9] Tanabe Tadao, Makino Yuki, Shiota Akio, et al. Optics and Photonics Journal, 2020, 10(12): 265.
[10] Palka Norbert. Optical Engineering, 2014, 53(3): 031202.
[11] Pan R, Zhao S, Shen J. Procedia Engineering, 2010, 7: 15.
[12] Yan H, Fan W H, Chen X, et al. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 2021, 258: 119825.
[13] Du H, Chen W L, Lei Y T, et al. Microchemical Journal, 2021, 168: 106440.
[14] Yang S, Li C X, Mei Y, et al. Frontiers in Nutrition, 2021, 8: 680627.
[15] Cacciari Ilaria, Corradi Giacomo. Optical Materials, 2021, 117: 111134.
[16] Dorney T D, Baraniuk R G, Mittleman D M. Journal of the Optical Society of America A—Optics Image Science & Vision, 2001, 18(7): 1562.
[17] Chen L Y, Li S B, Bai Q, et al. Remote Sensing, 2021, 13(22): 4712.
|
| [1] |
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. |
| [2] |
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. |
| [3] |
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. |
| [4] |
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. |
| [5] |
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. |
| [6] |
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. |
| [7] |
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. |
| [8] |
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. |
| [9] |
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. |
| [10] |
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. |
| [11] |
CHEN Dong-ying1, 2, ZHANG Hao1, 2*, ZHANG Zi-long1, YU Mu-xin1, CHEN Lu3. Research on the Origin Traceability of Honeysuckle Based on Improved 1D-VD-CNN and Near-Infrared Spectral Data[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(05): 1471-1477. |
| [12] |
GUO Feng1, ZHAO Dong-e1*, YANG Xue-feng1, CHU Wen-bo2, ZHANG Bin1, ZHANG Da-shun3MENG Fan-jun3. Research on Hyperspectral Image Recognition of Iron Fragments[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(04): 997-1003. |
| [13] |
LIU Xin-yu1, SHAO Wen-wu2*, ZHOU Shi-rui3. Spectral Pattern Recognition of Cardiac Tissue in Electric Shock Death and Post-Mortem Electric Shock[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(04): 1126-1133. |
| [14] |
LI Kai-yu1, ZHANG Hui2, MA Jun-cheng3, ZHANG Ling-xian1*. Segmentation Method for Crop Leaf Spot Based on Semantic Segmentation and Visible Spectral Images[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(04): 1248-1253. |
| [15] |
CHEN Qing1, TANG Bin1, 2*, LONG Zou-rong1, 2, MIAO Jun-feng1, HUANG Zi-heng1, DAI Ruo-chen1, SHI Sheng-hui1, ZHAO Ming-fu1, ZHONG Nian-bing1. Water Quality Classification Using Convolution Neural Network Based on UV-Vis Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(03): 731-736. |
|
|
|
|
