Citation: | WANG Kun, SHI Yong, LIU Chichi, XIE Yi, CAI Ping, KONG Songtao. A Review of Infrared Spectrum Modeling Based on Convolutional Neural Networks[J]. Infrared Technology , 2021, 43(8): 757-765. |
[1] |
Gente R, Busch S F, Eva-Maria Stübling, et al. Quality control of sugar beet seeds with THz time-domain spectroscopy[J]. IEEE Transactions on Terahertz ence & Technology, 2016, 6(5): 754-756. http://ieeexplore.ieee.org/document/7536209/
|
[2] |
Przybylek P. A new method for indirect measurement of water content in fibrous electro-insulating materials using near-infrared spectroscopy[J]. IEEE Transactions on Dielectrics and Electrical Insulation, 2016, 23(3): 1798-1804. DOI: 10.1109/TDEI.2016.005051
|
[3] |
Hiroaki I, Toyonori N, Eiji T. Measurement of pesticide residues in food based on diffuse reflectance IR spectroscopy[J]. IEEE Transactions on Instrumentation and Measurement, 2002, 51(5): 886-890. DOI: 10.1109/TIM.2002.807791
|
[4] |
Mignani A G, Ciaccheri L, Mencaglia A A, et al. Spectroscopy AS a "green" technique for food quality and safety applications[C]//Technical Digest of the Eighteenth Microoptics Conference of IEEE, 2013: 1-2.
|
[5] |
Nishizawa S, Morita H, Iwamoto T, et al. Terahertz time-domain spectroscopy applied to nondestructive evaluation of pharmaceutical products[C]//2011 International Conference on Infrared, Millimeter, and Terahertz Waves of IEEE, 2011: 1-2.
|
[6] |
ZOU Xiaobo, ZHAO Jiewen, Povey M J W, et al. Variables selection methods in near-infrared spectroscopy[J]. Analytica Chimica Acta, 2010, 667(1-2): 14-32. DOI: 10.1016/j.aca.2010.03.048
|
[7] |
周宣. 基于新型冠状病毒肺炎防护的医用口罩分类与使用[J]. 医疗装备, 2020(15): 10-12. DOI: 10.3969/j.issn.1002-2376.2020.15.006
ZHOU Xuan. Classification and use of medical masks based on new Coronavirus pneumonia protection[J]. Medical Equipment, 2020(15): 10-12. DOI: 10.3969/j.issn.1002-2376.2020.15.006
|
[8] |
Malek S, Melgani F, Bazi Y. One-dimensional convolutional neural networks for spectroscopic signal regression[J]. Journal of Chemometrics, 2017: e2977. DOI: 10.1002/cem.2977
|
[9] |
LIU Xuemei, ZHANG Hailiang, SUN Xudong, et al. NIR sensitive wavelength selection based on different methods[C]//2010 International Conference on Mechanic Automation and Control Engineering, 2010: 26-28.
|
[10] |
Krizhevsky A, Sutskever I, Hinton G E. Imagenet classification with deep convolutional neural networks[C]//Advances in Neural Information Processing Systems, 2012: 1097-1105.
|
[11] |
Devos O, Ruckebusch C, Durand A, et al. Support vector machines (SVM) in near infrared (NIR) spectroscopy: focus on parameters optimization and model interpretation[J]. Chemometrics and Intelligent Laboratory Systems, 2009, 96(1): 27-33. DOI: 10.1016/j.chemolab.2008.11.005
|
[12] |
Demeulemeester J, Smeets D, Barradas N P, et al. Artificial neural networks for instantaneous analysis of real-time rutherford backscattering spectra[J]. Nuclear Instruments and Methods in Physics Research, 2010, 268(10): 1676-1681. DOI: 10.1016/j.nimb.2010.02.127
|
[13] |
Lee S, Choi H, Cha K, et al. Random forest as a potential multivariate method for near-infrared (NIR) spectroscopic analysis of complex mixture samples: Gasoline and naphtha[J]. Microchemical Journal, 2013, 110: 739-748. DOI: 10.1016/j.microc.2013.08.007
|
[14] |
McCarty G W, Reeves J B, Reeves V B, et al. Mid-infrared and near‐infrared diffuse reflectance spectroscopy for soil carbon measurement[J]. Soil Science Society of America Journal, 2002, 66(2): 640-646. DOI: 10.2136/sssaj2002.6400a
|
[15] |
Gerretzen J, Szymańska E, Jansen J J, et al. Simple and effective way for data preprocessing selection based on design of experiments[J]. Analytical Chemistry, 2015, 87(24): 12096-12103. DOI: 10.1021/acs.analchem.5b02832
|
[16] |
Hubel D H, Wiesel T N. Receptive fields and functional architecture of monkey striate cortex[J]. The Journal of Physiology, 1968, 195(1): 215-243. DOI: 10.1113/jphysiol.1968.sp008455
|
[17] |
CHEN Yuanyuan, WANG Zhibin. Quantitative analysis modeling of infrared spectroscopy based on ensemble convolutional neural networks[J]. Chemometrics and Intelligent Laboratory Systems, 2018, 181: 1-10. DOI: 10.1016/j.chemolab.2018.08.001
|
[18] |
NI C, WANG D, TAO Y. Variable weighted convolutional neural network for the nitrogen content quantization of Masson pine seedling leaves with near-infrared spectroscopy[J]. Spectrochimica Acta Part A Molecular & Biomolecular Spectroscopy, 2019, 209: 32-39. http://www.ncbi.nlm.nih.gov/pubmed/30343107
|
[19] |
LeCun Y. The MNIST database of handwritten digits[EB/OL]. http://yann.lecun.com/exdb/mnist/, 1998.
|
[20] |
Krizhevsky A, Sutskever I, Hinton G E. ImageNet classification with deep convolutional neural networks[C]//International Conference on Neural Information Processing Systems, 2012: 1097-1105.
|
[21] |
CHENG G, ZHOU P, HAN J. Learning rotation-invariant convolutional neural networks for object detection in VHR optical remote sensing images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2016, 54(12): 7405-7415. DOI: 10.1109/TGRS.2016.2601622
|
[22] |
LeCun Y, Boser B E, Denker J S, et al. Handwritten digit recognition with a back-propagation network[C]//Advances in Neural Information Processing Systems, 1990: 396-404.
|
[23] |
GU J, WANG Z, Kuen J, et al. Recent advances in convolutional neural networks[J]. Pattern Recognition, 2018, 77: 354-377. DOI: 10.1016/j.patcog.2017.10.013
|
[24] |
WANG T, WU D J, Coates A, et al. End-to-end text recognition with convolutional neural networks[C]//Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012) of IEEE, 2012: 3304-3308.
|
[25] |
XU B, WANG N, CHEN T, et al. Empirical evaluation of rectified activations in convolutional network[J/OL]. arXiv preprint arXiv: 1505.00853, 2015.
|
[26] |
LeCun Y A, Bottou L, Orr G B, et al. Efficient Backprop[M]//Neural Networks: Tricks of the Trade, Springer, 2012: 9-48.
|
[27] |
Nwankpa C, Ijomah W, Gachagan A, et al. Activation functions: Comparison of trends in practice and research for deep learning[J/OL]. arXiv preprint arXiv: 1811.03378, 2018.
|
[28] |
HE K, ZHANG X, REN S, et al. Spatial pyramid pooling in deep convolutional networks for visual recognition[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 37(9): 1904-1916. DOI: 10.1109/TPAMI.2015.2389824
|
[29] |
Boureau Y L, Ponce J, LeCun Y. A theoretical analysis of feature pooling in visual recognition[C]//Proceedings of the 27th International Conference on Machine Learning (ICML-10). 2010: 111-118.
|
[30] |
Ioffe S, Szegedy C. Batch normalization: accelerating deep network training by reducing internal covariate shift[J/OL]. arXiv preprint arXiv: 1502.03167, 2015.
|
[31] |
Hinton G E, Srivastava N, Krizhevsky A, et al. Improving neural networks by preventing co-adaptation of feature detectors[J/OL]. arXiv preprint arXiv: 1207.0580, 2012.
|
[32] |
Srivastava N, Hinton G, Krizhevsky A, et al. Dropout: a simple way to prevent neural networks from overfitting[J]. The Journal of Machine Learning Research, 2014, 15(1): 1929-1958. http://dl.acm.org/citation.cfm?id=2670313
|
[33] |
Khan A, Sohail A, Zahoora U, et al. A survey of the recent architectures of deep convolutional neural networks[J]. Artificial Intelligence Review, 2020, 53(8): 5455-5516. DOI: 10.1007/s10462-020-09825-6
|
[34] |
LIN M, CHEN Q, YAN S. Network in network[J/OL]. arXiv preprint arXiv: 1312.4400, 2013.
|
[35] |
Rawat W, WANG Z. Deep convolutional neural networks for image classification: a comprehensive review[J]. Neural Computation, 2017, 29(1): 2352-2449. DOI: 10.1162/neco_a_00990
|
[36] |
Potter R K, Kopp G A, Green H C. Visible Speech, New York, 1947[J]. D. Van Nostrand Co. , 1962(8): 39.
|
[37] |
Griffin D, Lim J. Signal estimation from modified short-time Fourier transform[J]. IEEE Transactions on Acoustics, Speech, and Signal Processing, 1984, 32(2): 236-243. DOI: 10.1109/TASSP.1984.1164317
|
[38] |
Padarian J, Minasny B, McBratney A B. Using deep learning to predict soil properties from regional spectral data[J]. Geoderma Regional, 2019, 16: e00198. DOI: 10.1016/j.geodrs.2018.e00198
|
[39] |
Blackman R B, Tukey J W. The measurement of power spectra from the point of view of communications engineering[J]. Bell System Technical Journal, 1958, 37(1): 185-282. DOI: 10.1002/j.1538-7305.1958.tb03874.x
|
[40] |
Ng W, Minasny B, Montazerolghaem M, et al. Convolutional neural network for simultaneous prediction of several soil properties using visible/near-infrared, mid-infrared, and their combined spectra[J]. Geoderma, 2019, 352: 251-267. DOI: 10.1016/j.geoderma.2019.06.016
|
[41] |
WANG Q, BO Z, MA H, et al. A method for rapidly evaluating reliability and predicting remaining useful life using two-dimensional convolutional neural network with signal conversion[J]. Journal of Mechanical Science and Technology, 2019, 33(6): 2561-2571. DOI: 10.1007/s12206-019-0504-x
|
[42] |
WEN L, LI X, GAO L, et al. A new convolutional neural network-based data-driven fault diagnosis method[J]. IEEE Transactions on Industrial Electronics, 2017, 65(7): 5990-5998. http://ieeexplore.ieee.org/document/8114247
|
[43] |
谢丽娟. 转基因番茄的可见/近红外光谱快速无损检测方法[D]. 杭州: 浙江大学, 2009.
XIE Lijuan. Rapid non-destructive detection of Transgenic tomatoes by visible/near-infrared Spectroscopy[D]. Hangzhou: Zhejiang University, 2009.
|
[44] |
王璨, 武新慧, 李恋卿, 等. 卷积神经网络用于近红外光谱预测土壤含水率[J]. 光谱学与光谱分析, 2018, 38(1): 42-47. https://www.cnki.com.cn/Article/CJFDTOTAL-GUAN201801008.htm
WANG Can, WU Xinhui, LI Xiangqing, et al. Application of convolutional neural network in near infrared spectroscopy to predict soil moisture content[J]. Spectroscopy and Spectral Analysis, 2018, 38(1): 42-47. https://www.cnki.com.cn/Article/CJFDTOTAL-GUAN201801008.htm
|
[45] |
温馨. 基于深度学习的水果糖度可见/近红外光谱无损检测方法研究[D]. 北京: 北京交通大学, 2018.
WEN Xin. A Nondestructive Testing Method forvisible/near-infrared spectra of fruit Sugar Based on Deep learning[D]. Beijing: Beijing Jiaotong University, 2018.
|
[46] |
Kiranyaz S, Ince T, Abdeljaber O, et al. 1-d convolutional neural networks for signal processing applications[C]//2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)of IEEE, 2019: 8360-8364.
|
[47] |
CHEN Y Y, WANG Z B. End-to-end quantitative analysis modeling of near‐infrared spectroscopy based on convolutional neural network[J]. Journal of Chemometrics, 2019, 33(5): e3122. DOI: 10.1002/cem.3122
|
[48] |
LIU J, Osadchy M, Ashton L, et al. Deep convolutional neural networks for Raman spectrum recognition: a unified solution[J]. Analyst, 2017, 142(21): 4067-4074. DOI: 10.1039/C7AN01371J
|
[49] |
鲁梦瑶, 杨凯, 宋鹏飞, 等. 基于卷积神经网络的烟叶近红外光谱分类建模方法研究[J]. 光谱学与光谱分析, 2018, 38(12): 78-82. https://www.cnki.com.cn/Article/CJFDTOTAL-GUAN201812014.htm
LU M Y, YANG K, SONG P F, et al. The study of classification modeling method for near infrared spectroscopy of tobacco leaves based on convolution neural network[J]. Spectroscopy and Spectral Analysis, 2018, 38(12): 78-82. https://www.cnki.com.cn/Article/CJFDTOTAL-GUAN201812014.htm
|
[50] |
Ruder S. An overview of multi-task learning in deep neural networks[J/OL]. arXiv preprint arXiv: 1706.05098, 2017.
|
[51] |
ZHANG Y, YANG Q. A survey on multi-task learning[J/OL]. arXiv preprint arXiv: 1707.08114, 2017.
|
[52] |
Ramsundar B, Kearnes S, Riley P, et al. Massively multitask networks for drug discovery[J/OL]. arXiv preprint arXiv: 1502.02072, 2015.
|
[53] |
DU Jian, HU Bingliang, LIU Yongzheng, et al. Study on quality identification of macadamia nut based on convolutional neural networks and spectral features[J]. Spectroscopy and Spectral Analysis, 2018, 38(5): 1514-1519. http://en.cnki.com.cn/Article_en/CJFDTotal-GUAN201805036.htm
|
[54] |
Kingma D P, Ba J Adam: a method for stochastic optimization[J/OL]. arXiv preprint arXiv: 1412.6980, 2014.
|
[55] |
Acquarelli J, van Laarhoven T, Gerretzen J, et al. Convolutional neural networks for vibrational spectroscopic data analysis[J]. Analytica Chimica Acta, 2017, 954: 22-31. DOI: 10.1016/j.aca.2016.12.010
|
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