激光与光电子学进展, 2020, 57 (15): 153005, 网络出版: 2020-08-04   

基于人工神经网络的水彩笔油墨红外光谱模式识别 下载: 926次

Infrared Spectral Pattern Recognition of Watercolor Pen Ink Based on Artificial Neural Network
作者单位
1 中国人民公安大学刑事科学技术学院, 北京 100038
2 北京石油化工学院化学工程学院, 北京 102617
引用该论文

王晓宾, 马枭, 王新承. 基于人工神经网络的水彩笔油墨红外光谱模式识别[J]. 激光与光电子学进展, 2020, 57(15): 153005.

Xiaobin Wang, Xiao Ma, Xincheng Wang. Infrared Spectral Pattern Recognition of Watercolor Pen Ink Based on Artificial Neural Network[J]. Laser & Optoelectronics Progress, 2020, 57(15): 153005.

参考文献

[1] 何欣龙, 陈利波, 王继芬, 等. 基于K近邻算法的塑钢窗拉曼光谱分析[J]. 激光与光电子学进展, 2018, 55(5): 053001.

    He X L, Chen L B, Wang J F, et al. Raman spectroscopy analysis of plastic steel window based on K nearest neighbors algorithm[J]. Laser & Optoelectronics Progress, 2018, 55(5): 053001.

[2] 马枭, 姜红, 杨佳琦. X射线荧光光谱结合多元统计分析塑料打包带(绳)[J]. 激光与光电子学进展, 2019, 56(22): 223005.

    Ma X, Jiang H, Yang J Q. Examination of plastic pack belts (ropes) via X-ray fluorescence spectrometry combined with multivariate statistical analysis[J]. Laser & Optoelectronics Progress, 2019, 56(22): 223005.

[3] 何欣龙, 王继芬, 张倩, 等. 基于多分类模型的记号笔墨水红外光谱分析[J]. 化学通报, 2019, 82(2): 169-174.

    He X L, Wang J F, Zhang Q, et al. Infrared spectroscopy analysis of marker ink based on multi-classification model[J]. Chemistry, 2019, 82(2): 169-174.

[4] 李军, 赵鹏程. 圆珠笔油墨字迹色痕检测方法的研究进展[J]. 山西警察学院学报, 2017, 25(3): 95-99.

    Li J, Zhao P C. Research progress of detection methods on ballpoint writing inks[J]. Journal of Shanxi Police College, 2017, 25(3): 95-99.

[5] 史晓凡, 李心倩, 许英健, 等. 高效液相色谱法鉴定蓝色圆珠笔油墨字迹的书写时间[J]. 光谱学与光谱分析, 2006, 26(9): 1765-1768.

    Shi X F, Li X Q, Xu Y J, et al. Determination of writing age of blue ballpoint pen inks by high performance liquid chromatography[J]. Spectroscopy and Spectral Analysis, 2006, 26(9): 1765-1768.

[6] Honorato R S, de Juan A N. Use of Raman spectroscopy and chemometrics to distinguish blue ballpoint pen inks[J]. Forensic Science International, 2015, 249: 73-82.

[7] 田高友, 袁洪福, 刘慧颖, 等. 小波变换用于近红外光谱数据压缩[J]. 分析测试学报, 2005, 24(1): 17-20, 24.

    Tian G Y, Yuan H F, Liu H Y, et al. Application of wavelet transform to compressing near infrared spectra data[J]. Journal of Instrumental Analysis, 2005, 24(1): 17-20, 24.

[8] Shao X G, Zhuang Y D. Determination of chlorogenic acid in plant samples by using near-infrared spectrum with wavelet transform preprocessing[J]. Analytical Sciences, 2004, 20(3): 451-454.

[9] Trygg J, Kettaneh-Wold N, Wallbäcks L. 2D wavelet analysis and compression of on-line industrial process data[J]. Journal of Chemometrics, 2001, 15(4): 299-319.

[10] 罗斯特, 李增勇, 张明, 等. 基于小波变换的体内外酒精含量近红外光谱检测与分析[J]. 光谱学与光谱分析, 2012, 32(6): 1541-1546.

    Luo S T, Li Z Y, Zhang M, et al. Detection and analysis of alcohol near-infrared spectrum in vitro and vivo based on wavelet transform[J]. Spectroscopy and Spectral Analysis, 2012, 32(6): 1541-1546.

[11] 于竹林, 刘洁. 基于小波变换的航空润滑油酸值红外光谱分析[J]. 分析试验室, 2017, 36(1): 47-50.

    Yu Z L, Liu J. Predicting acid number of lubricating oil with infrared spectroscopy treated by wavelet transformation[J]. Chinese Journal of Analysis Laboratory, 2017, 36(1): 47-50.

[12] 马殿旭, 刘刚, 于海超, 等. 基于离散小波变换对不同种类瓜籽的FTIR鉴别研究[J]. 光散射学报, 2015, 27(4): 390-395.

    Ma D X, Liu G, Yu H C, et al. Determination of different species of melon seeds by Fourier transform infrared spectroscopy combined with discrete wavelet transform[J]. The Journal of Light Scattering, 2015, 27(4): 390-395.

[13] JoshiA, Rajshekhar, ChandranS, et al. Arrhythmia classification using local Hölder exponents and support vector machine[M] ∥Pal S K, Bandyopadhyay S, Biswas S, et al. Computer Vision-ECCV 2005. Lecture Notes in Computer Science. Cham: Springer, 2005, 3776: 242- 247.

[14] Li C F, Liner C L. Singularity exponent from wavelet-based multiscale analysis: a new seismic attribute[J]. Chinese Journal of Geophysics, 2005, 48(4): 953-959.

[15] Scafetta N, Griffin L, West B J. Hölder exponent spectra for human gait[J]. Physica A: Statistical Mechanics and Its Applications, 2003, 328(3/4): 561-583.

[16] 何亚, 王继芬. 基于特征波段-Fisher-K近邻的木器漆拉曼光谱的快速无损鉴别[J]. 激光与光电子学进展, 2020, 57(1): 013001.

    He Y, Wang J F. Rapid nondestructive identification of wood lacquer using Raman spectroscopy based on characteristic band-Fisher-K nearest neighbor[J]. Laser & Optoelectronics Progress, 2020, 57(1): 013001.

[17] Sato T. Application of an artificial neural network to the identification of amino acids from near infrared spectral data[J]. Journal of Near Infrared Spectroscopy, 1993, 1(4): 199-208.

[18] Dziuba B. Identification of Propionibacteria to the species level using Fourier transform infrared spectroscopy and artificial neural networks[J]. Polish Journal of Veterinary Sciences, 2013, 16(2): 351-357.

[19] Fidêncio P H, Ruisánchez I, Poppi R J. Application of artificial neural networks to the classification of soils from São Paulo state using near-infrared spectroscopy[J]. The Analyst, 2001, 126(12): 2194-2200.

[20] Argyri A A, Panagou E Z, Tarantilis P A, et al. Rapid qualitative and quantitative detection of beef fillets spoilage based on Fourier transform infrared spectroscopy data and artificial neural networks[J]. Sensors and Actuators B-chemical, 2010, 145(1): 146-154.

[21] Mayfield H T, Eastwood D L, Burggraf L W. Infrared spectral classification with artificial neural networks and classical pattern recognition[J]. Proceedings of SPIE, 2000, 4036: 54-65.

[22] 叶树彬, 徐亮, 李亚凯, 等. 基于人工神经网络的傅里叶变换中红外光谱法对食用油油烟种类识别研究[J]. 光谱学与光谱分析, 2017, 37(3): 749-754.

    Ye S B, Xu L, Li Y K, et al. Study on recognition of cooking oil fume by Fourier transform infrared spectroscopy based on artificial neural network[J]. Spectroscopy and Spectral Analysis, 2017, 37(3): 749-754.

[23] 全宇, 王忠庆, 何苗. 基于交叉熵的神经网络在病理图像分析中的应用[J]. 中国医科大学学报, 2009, 38(6): 446-448.

    Quan Y, Wang Z Q, He M. Application of neural network based on cross-entropy method in pathological image analysis[J]. Journal of China Medical University, 2009, 38(6): 446-448.

[24] 朱琳, 陈佩杰. 应用ROC曲线确定活动计数在青春期少年运动强度诊断中的最佳临界值[J]. 体育科学, 2012, 32(11): 70-75.

    Zhu L, Chen P J. Determination of best cut off value of activity count in diagnosis exercise intensity of adolescents by receiver operating characteristic (ROC) curve analysis[J]. China Sport Science, 2012, 32(11): 70-75.

王晓宾, 马枭, 王新承. 基于人工神经网络的水彩笔油墨红外光谱模式识别[J]. 激光与光电子学进展, 2020, 57(15): 153005. Xiaobin Wang, Xiao Ma, Xincheng Wang. Infrared Spectral Pattern Recognition of Watercolor Pen Ink Based on Artificial Neural Network[J]. Laser & Optoelectronics Progress, 2020, 57(15): 153005.

本文已被 4 篇论文引用
被引统计数据来源于中国光学期刊网
引用该论文: TXT   |   EndNote

相关论文

加载中...

关于本站 Cookie 的使用提示

中国光学期刊网使用基于 cookie 的技术来更好地为您提供各项服务,点击此处了解我们的隐私策略。 如您需继续使用本网站,请您授权我们使用本地 cookie 来保存部分信息。
全站搜索
您最值得信赖的光电行业旗舰网络服务平台!