多角度光散射颗粒的粒径解析和属性识别
发布时间：2018-08-03 15:20
【摘要】：通过提取光散射信号中颗粒粒径和属性的非线性特征向量,利用广义神经网络(GRNN)同时解析颗粒粒径和识别属性。采用经验模态分解(EMD)方法分解颗粒物的光散射信号,提取三维能量分布,计算3种相同粒径不同属性颗粒的样本熵,发现样本熵能够反映颗粒的属性;为了消除粒径和属性对散射的影响,对散射信号进行Hilbert变换,提取时频域特征,与样本熵结合组成高维特征集,通过局部线性嵌入(LLE)算法将特征集归为6个特征向量,作为广义神经网络的输入层,解析粒径和识别属性;采用粒径为0.11μm的二氧化硅颗粒、2μm和4μm的聚苯乙烯小球进行实验,结果表明,粒径解析和属性识别的正确率均在90%以上。
[Abstract]:By extracting the nonlinear eigenvector of particle size and attribute in the light scattering signal, the generalized neural network (GRNN) is used to analyze the particle size and the recognition attribute simultaneously. The empirical mode decomposition (EMD) method is used to decompose the light scattering signals of particles, extract the three-dimensional energy distribution, calculate the sample entropy of three kinds of particles with the same particle size and different attributes, and find that the sample entropy can reflect the properties of the particles. In order to eliminate the influence of particle size and attribute on scattering, the scattering signal is transformed into Hilbert transform, time and frequency domain features are extracted, and the high Viterbi feature set is formed by combining with sample entropy. The feature set is classified into six feature vectors by means of locally linear embedding (LLE) algorithm. As the input layer of the generalized neural network, the particle size and recognition properties are analyzed, and the experimental results show that the accuracy of particle size resolution and attribute recognition are above 90%. The experiments are carried out with silica particles of 0.11 渭 m and polystyrene pellets of 4 渭 m.
【作者单位】： 哈尔滨理工大学测控技术与通信工程学院;中兴仪器(深圳)有限公司;
【基金】：国家科技重大专项(2016YFF0103000)
【分类号】：O436.2
，
本文编号：2162154
[Abstract]:By extracting the nonlinear eigenvector of particle size and attribute in the light scattering signal, the generalized neural network (GRNN) is used to analyze the particle size and the recognition attribute simultaneously. The empirical mode decomposition (EMD) method is used to decompose the light scattering signals of particles, extract the three-dimensional energy distribution, calculate the sample entropy of three kinds of particles with the same particle size and different attributes, and find that the sample entropy can reflect the properties of the particles. In order to eliminate the influence of particle size and attribute on scattering, the scattering signal is transformed into Hilbert transform, time and frequency domain features are extracted, and the high Viterbi feature set is formed by combining with sample entropy. The feature set is classified into six feature vectors by means of locally linear embedding (LLE) algorithm. As the input layer of the generalized neural network, the particle size and recognition properties are analyzed, and the experimental results show that the accuracy of particle size resolution and attribute recognition are above 90%. The experiments are carried out with silica particles of 0.11 渭 m and polystyrene pellets of 4 渭 m.
【作者单位】： 哈尔滨理工大学测控技术与通信工程学院;中兴仪器(深圳)有限公司;
【基金】：国家科技重大专项(2016YFF0103000)
【分类号】：O436.2
，
本文编号：2162154
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